AIR POLLUTION MITIGATION AND CDM POTENTIAL OF WIND POWER IN PAKISTAN
A thesis submitted by
Abdullah Mengal
In accordance with the requirements for the degree of Doctor of Philosophy
in
Mechanical Engineering
Department of Mechanical Engineering
Faculty of Engineering
Mehran University of Engineering & Technology Jamshoro December 2017
i AIR POLLUTION MITIGATION AND CDM POTENTIAL OF WIND POWER IN PAKISTAN
A thesis submitted by Abdullah Mengal
In accordance with the requirements for the degree of
Doctor of Philosophy
Supervisor: Prof. Dr. Khanji Harijan Department of Mechanical Engineering Mehran University of Engineering & Technology Jamshoro
Co-Supervisor: Prof. Dr. Mohammad Aslam Uqaili Department of Electrical Engineering, Mehran University of Engineering & Technology Jamshoro
Department of Mechanical Engineering Mehran University of Engineering & Technology Jamshoro December 2017
ii IN THE NAME OF ALLAH, THE MOST GRACIOUS, THE MOST MERCIFUL, WHO’S HELP WE SEEK
Dedicated
to
My dearest parents, beloved wife, and children whose wholehearted prayers and encouragement made me able to achieve this goal.
iii MEHRAN UNIVERSITY OF ENGINEERING & TECHNOLOGY JAMSHORO
This thesis, written by Mr. Abdullah Mengal under the direction of his supervisors, and approved by all the members of the thesis committee, has been presented to and accepted by the Dean, Faculty of Engineering, in fulfillment of the requirements for the degree of Doctor of Philosophy in Mechanical Engineering.
______(Supervisor) (Co-supervisor)
______(Internal Examiner) (External Examiner)
______Director Co-director Dean Postgraduate Studies Postgraduate Studies Faculty of Engineering
iv ACKNOWLEDGMENT
All praises to Almighty Allah for the power and his blessing who give me the ability and courage for completing this work on time. In addition, I cannot forget to give salutation to the ideal man of the world and most respectable personality Prophet MUHAMMAD (Peace
Be upon Him) being a constant source of knowledge and guidance.
I would like to express my sincere appreciation to my supervisors Prof. Dr. Khanji Harijan and Prof. Dr. Mohammad Aslam Uqaili for their continuous guidance, support, and motivation for my studies and research work. Without their enthusiastic support and supervision, this research work was not possible.
Besides my supervisors, I would like to owe special thanks to my research fellows Nayyar
Hussain Mirjat and Dr. Gordhan Das Walasai for their valuable support and guidance in learning modelling tools, data analysis and reviewing my research articles and thesis. I pay special thanks to Prof. Dr. Syed Feroz Shah for his contribution in helping me about mathematical modelling solutions. I am also very thankful to all the staff members of
Directorate of Postgraduate Studies of Mehran University of Engineering & Technology for their cooperation. My special thanks and appreciations are to my colleagues and hostel fellows Muhammad Asif Memon and Muhammad Bakhash Alvi.
I pay my exceptional gratitude to my parents for their wholehearted prayers and salutary advices that have made me successful in everywhere in my life. May Allah give them long life with full of satisfaction and happiness. My deepest appreciation goes to my beloved
v wife and children who sacrificed too much during my studies. I extend tributes to my uncle
(late), whom I passed my childhood and got an early education under his supervision.
Finally, I am very thankful to authorities of Balochistan University of Engineering
&Technology, Khuzdar and Mehran University of Engineering &Technology, Jamshoro for providing me financial support and opportunity to accomplish this research work.
vi TABLE OF CONTENTS
Acknowledgment vi List of Notations xiii List of Abbreviations xvi List of Tables xix List of Figures xxi Abstract xxiii Chapter 1 INTRODUCTION 1 1.1 INTRODUCTION 1 1.2 ELECTRICITY CRISIS IN PAKISTAN 2 1.3 EMISSIONS FROM ELECTRICITY GENERATION 4 1.3.1 GHG emissions and their environmental impacts 6 1.3.2 Air pollutant emissions and their environmental impacts 9 1.4 MITIGATION OF GHGS AND OTHER AIR POLLUTANT EMISSIONS 11 1.5 CLEAN DEVELOPMENT MECHANISM (CDM) 13 1.5.1 Global status of CDM projects 15 1.5.2 CDM projects status in Pakistan 19 1.6 PROBLEM STATEMENT 20 1.7 RESEARCH AIM AND OBJECTIVES 22 1.8 METHODOLOGY 23 1.9 STRUCTURE OF THE STUDY 26 Chapter 2 LITERATURE REVIEW 29 2.1 INTRODUCTION 29 2.2 ELECTRICITY GENERATION IN PAKISTAN 29 2.3 ELECTRICITY DEMAND FORECASTING 32 2.4 GREENHOUSE GAS AND OTHER AIR POLLUTANT EMISSIONS 37
vii 2.5 MODELLING ELECTRICITY DEMAND AND EMISSIONS USING LEAP 39 2.6 MITIGATION OF GHGS AND OTHER AIR POLLUTANT EMISSIONS 43 2.7 DIFFUSION OF WIND POWER 46 2.8 EMISSIONS MITIGATION THROUGH WIND POWER 48 2.9 WIND POWER DEVELOPMENT IN PAKISTAN 50 2.10 COST OF ELECTRICITY GENERATION FROM WIND ENERGY 52 2.11 CDM POTENTIAL OF WIND POWER 55 2.12 CONCLUSIONS 58 Chapter 3 ELECTRICITY GENERATION POLICIES AND PLANS 60 3.1 INTRODUCTION 60 3.2 ELECTRICITY DEMAND AND SUPPLY SITUATION IN PAKISTAN 60 3.3 NATIONAL POWER POLICY 2013 66 3.3.1 Goals 67 3.3.2 Targets 68 3.3.3 Impacts of implementation 69 3.4 POWER GENERATION POLICY 2015 69 3.5 ELECTRICITY GENERATION PLANS OF PAKISTAN 71 3.5.1 Hydroelectric power 71 3.5.2 Coal power 72 3.5.3 Oil and natural gas power 74 3.5.4 Nuclear power 76 3.5.5 Renewable power 76 3.6 CONCLUSIONS 79 Chapter 4 MODELING AND FORECASTING POWER GENERATION AND EMISSIONS 81 4.1 INTRODUCTION 81 4.2 ENERGY SCENARIO MODELLING TOOLS 81 4.2.1 MARKAL 82 viii 4.2.2 MESSAGE 83 4.2.3 LEAP 84 4.3 LEAP ENERGY MODEL FRAMEWORK FOR PAKISTAN 86 4.3.1 Electricity demand forecast 86 4.3.2 Basic assumptions for the LEAP energy modeling 87 4.3.3 Reference or baseline Scenario 89 4.3.4 Alternative scenarios 90 4.4 RESULTS AND DISCUSSION 94 4.4.1 Reference or baseline scenario 94 4.4.2 Alternative scenarios 97 4.4.3 GHGs and other air pollutant emissions 103 4.5 CONCLUSIONS 112 Chapter 5 MODELING AND FORECASTING WIND POWER GENERATION 114 5.1 INTRODUCTION 114 5.2 GLOBAL STATUS OF WIND POWER 114 5.3 WIND POWER DEVELOPMENT IN PAKISTAN 116 5.3.1 Wind power potential 117 5.3.2 Existing wind power status 119 5.4 FUTURE DIFFUSION OF WIND POWER IN PAKISTAN 120 5.4.1 Development of mathematical model for wind power diffusion 122 5.4.2 Scenario development 126 5.5 RESULTS AND DISCUSSION 129 5.6 CONCLUSIONS 133 Chapter 6 ESTIMATION OF ELECTRICITY GENERATION COST 134 6.1 INTRODUCTION 134 6.2 WIND POWER GENERATION COSTS 134 6.3 COST ESTIMATION AND COMPARISON OF CONVENTIONAL ELECTRICITY GENERATION SYSTEMS WITH WIND ENERGY 135
ix 6.3.1 Life cycle cost of electricity generation 135 6.3.2 Levelized cost of electricity generation 136 6.4 COST ESTIMATION AND COMPARISON OF ELECTRICITY GENERATION SYSTEMS WITH (CCS) TECHNOLOGY 139 6.4.1 Pre-combustion capturing method 140 6.4.2 Post-combustion capturing method 140 6.4.3 Oxy-fuel combustion capture method 141 6.4.4 Levelized cost of electricity generation with CCS technology 141
6.4.5 Cost of CO2 avoidance 141 6.5 DATA AND ASSUMPTIONS 142 6.6 RESULTS AND DISCUSSION 146 6.6.1 Cost estimation 146 6.6.2 Cost comparison 150 6.7 CONCLUSIONS 152 Chapter 7 MODELING AND FORECASTING AIR POLLUTION MITIGATION POTENTIAL OF WIND POWER 154 7.1 INTRODUCTION 154 7.2 AIR POLLUTION MITIGATION 154 7.2.1 Air pollution mitigation through wind power 155 7.3 DEVELOPMENT OF MATHEMATICAL MODEL FOR AIR POLLUTION MITIGATION BY WIND POWER 156 7.3.1 Baseline grid emission factor estimation for GHG emissions 158 7.3.2 Baseline grid emission factors estimation for air pollutant emissions 170 7.4 RESULTS AND DISCUSSION 173 7.4.1 GHG emissions 173 7.4.2 Air pollutant emissions other than GHGs 174 7.5 CONCLUSIONS 182 Chapter 8 MODELLING AND FORECASTING CDM POTENTIAL OF WIND POWER 183
x 8.1 INTRODUCTION 183 8.2 CDM POTENTIAL OF WIND POWER IN PAKISTAN 183 8.2.1 Methodology 184 8.3 RESULTS AND DISCUSSION 185 8.3.1 Emission trading revenue 186 8.3.2 Sensitivity analysis 187 8.4 CONCLUSIONS 190 Chapter 9 CONCLUSIONS AND RECOMMENDATIONS 191 9.1 INTRODUCTION 191 9.2 CONCLUSIONS 192 9.3 RECOMMENDATIONS 201 9.4 FUTURE WORK 203 REFERENCES 205 APPENDIX 216
xi LIST OF NOTATIONS
A = Rotor swept area of wind turbine AUE wind = Annual useful energy supplied the wind turbine C = Scale Parameter C(t) = Cost occurred in year CE = Externality cost CEFe = CO2 eq baseline grid emission factor of electricity generation CF = Plant Capacity factor CK = Sum of the capital costs of plant CL = Plant construction life CO&M = Operating, and maintenance cost Cp = Coefficient of performance of the rotor of wind DR = Depreciation rate EF = Emission factor of fuel EFCO2, m, i, y = Average CO2 emission factor of fuel i used in power plant m in year y EFCO2,i,y = Net amount of electricity generated in year y EFEL, m,y = CO2 eq emission factor for power plants m in year y EFgrid OM simple, y = Simple grid OM emission factor in year y EFgrid, BM, y = BM CO2 eq emission factor in the year y EFgrid, CM, y = CM emission factor in year y eFuel = Escalation rate of fuel EGm,y = Net amount of electricity generated by the power plants m in year y EGwind = Electricity generation output from wind turbine eO&M = Escalation rate of operating and maintenance F(v) = Weibull probability distribution function FC = Fuel cost FCi, y = Quantity of fossil fuel type i utilized for the electricity generation system FOM = Fixed O&M cost HR = Heat rate HY = Hours per year k = Shape Parameter M = Exploitable wind power potential N(t) = Cumulative installed capacity of wind power NCVi,y = Net calorific value of fossil fuel type i in year y P(v) = Power produce from the wind PL = Plant life PLFwind = Plant load factor
xii PVC = Present value cost Pwind = Capacity of wind power plant p = Constant of integration q = Diffusion rate of wind power r = Discount rate t = Time t’ = Point of inflection TPC = Total plant cost V = Wind speed VCi = Speed at which turbine starts power production VCO = Speed at which the turbine stop to power production VOM = Variable O&M cost WBM = Weight of the BM emission factor WOM = Weight of the OM emission factor m, y = Average net energy efficiency of power plant m in year y g = Availability factor of wind turbine ra = Air density $ = US dollar ¢ = US cent
Subscripts
a = Air Ci = Cut-in CO = Cut-out e = Exponential/ equivalent i = Type of fossil fuel m = Type of power plant O&M = Operating, and maintenance y = Relevant year
Formulas
CO2 = Carbon dioxide
CH4 = Methane
N2O = Nitrous oxide CO = Carbon monoxide
NOx = Nitrogen oxide
SO2 = Sulphur dioxide
xiii LIST OF ABBREVIATIONS
AEDB = Alternative Energy Development Board ANN = Artificial Neural Network ARIMA = Autoregressive Integrated Moving Average BAU = Business as Usual scenario BM = Build Margin CCNG = Combined Cycle Natural Gas CCS = Carbon Capture and Storage CDM = Clean Development Mechanism
CERS = Certified Emissions Reductions CM = Combine Margin CNG = Compressed Natural Gas CSFO = Conventional Steam Fuel Oil DISCOs = Distribution Companies DNA = Designated National Authority EB = Executive Board ESP = Electrostatic Precipitators FBC = Fluidized Bad Combustion FFC = Fauji Fertilizer Company FGD = Flue-gas desulfurization GA = Genetic Algorithm GAINS = Greenhouse Gas and Air Pollution Integrations and Synergies GDP = Gross Domestic Product GENCOs = Generation Companies GHGs = Greenhouse Gases GIS = Geographic Information System GTDO = Gas Turbine Diesel Oil GWP = Global Warming Potential HD = High Diffusion scenario HDI = Human Development Index ICC = Imported Coal Combustion IEA = International Energy Agency IGCC = Integrated Gasification Combined Cycle IIASA = International Institute for Applied System Analysis IPCC = Inter governmental Panel on Climate Change
xiv IPI = Iran Pakistan and India IPPs = Independent power producers KANUPP = Karachi Nuclear Power Plant K-Electric = Karachi Electric LC/MR = Low Cost/Must-Run LCC = Local Coal Combustion LCCOE = Life Cycle Cost of Electricity Generation LCOE = Levelized Cost of Electricity Generation LEAP = Long-range Energy Alternatives Planning LNG = Liquefied Natural Gas LOI = Letters of Intent LS SVMs = Least Squares Support Vector Machines MARKAL = MARKet ALlocation MD = Moderate Diffusion scenario MESSAGE = Model for Energy Supply Strategy Alternatives and Their General Environmental Impact MLR = Multiple Linear Regressions MRH = More Hydro Energy MRHN = More Hydro Nuclear Energy MRR = More Renewable Energy NEPRA = National Electric Power Regulatory Authority NREL = National Renewable Energy Laboratory NTDC = National Transmission Dispatch Company OM = Operating Margin PAEC = Pakistan Atomic Energy Commission PCRET = Pakistan Council of Renewable Energy Technologies PDD = Project Design Document PEPCO = Pakistan Electric Power Company PM = Particulate Matter PMD = Meteorological Department PSO = Particle Swam Optimization REF = Reference Scenario SAARC = South Asian for Regional Cooperation SCR = Catalytic Reduction SECMC = Sindh Engro Coal Mining Company SNCR = Selective Non-Catalytic Reduction SSRL = Sino Sindh Resources Limited
xv STAR = Smooth Transition Auto Regressive SVMs = Support Vector Machines T&D = Transmission and Distribution TAPI = Turkmenistan Afghanistan Pakistan and India TED = Technology Environment Database TOE = Tone of Oil Equivalent UNFCCC = United Nations Framework Convention on Climate Change VOC = Volatile Organic Compound WAPDA = Water & Power Development Authority WE = Wind Energy
xvi LIST OF TABLES
Table 1.1: Status of CERs issued to CDM projects until December 2014 16 Table 1.2: Status of CDM projects under process till December 2014 16 Table 1.3: Status of CDM projects in Pakistan till December 2014 20 Table 3.1: Proposed hydropower plants 73 Table 3.2: Proposed coal power plants 74 Table 3.3: Proposed natural gas power plants 75 Table 3.4: Proposed nuclear power plants 76 Table 3.5: Proposed renewable (other than hydro) power plants 78 Table 4.1: Assumptions for modelling 89 Table 4.2: Base year electricity generation and model input data 90 Table 4.3: Share of various energy sources in electricity generation 97 Table 4.4: GHGs and other air pollutant emissions in all scenarios 103 Table 5.1: Status of wind power projects in Pakistan 120 Table 5.2: Demographic and economic indicators of India and Pakistan (2013) 126 Table 5.3: Cumulative installed capacity of wind power in India 128 Table 5.4: Wind power technology diffusion modelling parameters 129 Table 5.5: Forecasted installed capacity and electricity generation of wind power 131 Table 6.1: Cost and performance data for power plants without CCS 142 Table 6.2: Cost and performance data for electricity generation plants with CCS 143 Table 6.3: LCOE economic parameters 143 Table 6.4: Damage costs of emissions from electricity generation technologies 145 Table 6.5: Emission factors of fuels used in electricity generation technologies 145 Table 6.6: External costs of emissions of electricity generation plants 146
Table 6.7: CO2 avoided costs of electricity generation plants 152 Table 7.1: Electricity generation during most recent years in Pakistan 161 Table 7.2: Fuel consumption for electricity generation (TOE) 163 Table 7.3: Net calorific value of fuels used for electricity generation 163 Table 7.4: GHG emission factors (kg/TJ) of fuels used for electricity generation 163
xvii Table 7.5: Estimated emission factors of fuels in CO2 eq 164
Table 7.6: Results of OM emission factors (ton CO2 eq/MWh) 164 Table 7.7: Electricity generation data of five most recently commissioned plants 166 Table 7.8: Annual electricity generation in 2013 166 Table 7.9: Power plants generating 20% of total electricity generation 167 Table 7.10: Emission factors of power plants 169 Table 7.11: Result of baseline grid emission factor in 2013 171 Table 7.12: Emission factors of air pollutant emissions (kg/TJ) of fuels 171 Table 7.13: OM grid emission factors (kg/MWh) of air pollutant emissions 172 Table 7.14: BM emission factors (kg/MWh) of air pollutant emissions 172 Table 7.15: CM emission factors (kg/MWh) of air pollutant emissions 173 Table 7.16: Annual GHG emissions mitigation potential by wind power plants 174
Table 7.17: Uncontrolled SO2 emissions mitigation potential by wind power plants 175 Table 7.18: Uncontrolled CO emissions mitigation potential by wind power plants 175
Table 7.19: Uncontrolled NOx emissions mitigation potential by wind power plants176 Table 7.20: Uncontrolled PM emissions mitigation potential by wind power plants 176 Table 7.21: Uncontrolled VOC emissions mitigation potential by wind power plants177 Table 7.22: Air pollutant emissions control devices and their removal efficiencies 179
Table 7.23: Controlled SO2 emissions mitigation potential by wind power plants 179 Table 7.24: Controlled CO emissions mitigation potential by wind power plants 180
Table 7.25: Controlled NOx emissions mitigation potential by wind power plants 180 Table 7.26: Controlled PM emissions mitigation potential by wind power plants 181 Table 7.27: Controlled VOC emissions mitigation potential by wind power plants 181 Table 8.1: Forecasted annual CER generation from wind power 186
xviii LIST OF FIGURES
Fig. 1.1: Global electricity generation by sources ...... 5
Fig. 1.2: Global CO2 emissions from fossil fuels combustion ...... 6 Fig. 1.3: Global CDM registered projects by resource ...... 17 Fig. 1.4: Global CDM registered projects by country ...... 18 Fig. 3.1: Electricity demand and generation status ...... 62 Fig. 3.2: Electricity installed capacity during 2008-2013 ...... 64 Fig. 3.3: Electricity generation during 2012-13 ...... 65
Fig. 3.4: CO2 emissions growth in Pakistan ...... 66 Fig. 4.1: Comparison of electricity demand forecast by LEAP model and NTDC..... 87 Fig. 4.2: Electricity generation system model framework for Pakistan ...... 93 Fig. 4.3: Primary energy resources conversion and emissions under all scenarios .... 94 Fig. 4.4: Electricity installed capacity under reference scenario...... 96 Fig. 4.5: Total electricity generation under reference scenario ...... 97 Fig. 4.6: Electricity installed capacity under MRR scenario ...... 98 Fig. 4.7: Total electricity generation under MRR scenario ...... 99 Fig. 4.8: Electricity installed capacity under MRH scenario ...... 100 Fig. 4.9: Total electricity generation under MRH scenario ...... 101 Fig. 4.10: Electricity installed capacity under MRHN scenario ...... 102 Fig. 4.11: Total electricity generation by source under MRHN scenario ...... 102
Fig. 4.12: Annual CO2 emissions in all scenarios ...... 104
Fig. 4.13: Annual SO2 emissions in all scenarios...... 106 Fig. 4.14: Annual NOx emissions in all scenarios ...... 106
Fig. 4.15: Annual CH4 emissions in all scenarios ...... 108 Fig. 4.16: Annual PM emissions in all scenarios ...... 109
Fig. 4.17: Annual N2 O emissions in all scenarios ...... 110 Fig. 4.18: Annual CO emissions in all scenarios ...... 111 Fig. 4.19: Annual VOC emissions in all scenarios...... 112 Fig. 5.1: Global cumulative installed capacity of wind power ...... 116
xix Fig. 5.2: Wind map of Pakistan ...... 118 Fig. 5.3: Technology diffusion curve ...... 121 Fig. 5.4: Comparison of HDI curves of India and Pakistan ...... 127 Fig. 5.5: Comparison of actual and estimated cumulative wind power installed capacity in India ...... 129 Fig. 5.6: Forecasted cumulative installed capacity of wind power in Pakistan under all scenarios ...... 130 Fig. 5.7: Forecasted annual wind power capacity addition in Pakistan under all scenarios ...... 131 Fig. 5.8: Annual electricity generation from wind power in Pakistan ...... 133 Fig. 6.1: LCOE without CCS technology ...... 147 Fig. 6.2: LCOE with CCS technology ...... 149 Fig. 6.3: LCOE with CCS technology including efficiency reduction ...... 150 Fig. 6.4: LCOE comparison of power plants ...... 151 Fig. 7.1: Annual global investment in renewable energy projects ...... 156 Fig. 8.1: CDM revenue potential of wind power under BAU scenario ...... 188 Fig. 8.2: CDM revenue potential of wind power under MD scenario ...... 189 Fig. 8.3: CDM revenue potential of wind power under HD scenario ...... 189
xx ABSTRACT
Pakistan is facing electricity crisis since last many years due to the huge gap between supply and demand. The demand for electricity is rapidly increasing due to the increasing population and modern lifestyle. Dealing with this situation government of Pakistan has planned to generate electricity from locally available low-cost energy sources and imported coal. Generation of electricity from coal shall increase the “greenhouse gases (GHGs)” and other air pollutant emissions in the already fossil fuel dominated power generation system of the country. These emissions affect the local as well as the global environment.
Renewable energy sources, particularly wind energy is growing very fast globally for power generation to mitigate these emissions. Wind energy plants in addition to producing electricity could earn revenue as per the CDM agreement of Kyoto Protocol. This study focusing the future power generation asses the associated emissions, mitigation measures and CDM potential of wind power in Pakistan. Long-range Energy Alternatives Planning
(LEAP) model is used to forecast the electricity demand, generation and emissions under various scenarios for the modelling period 2013-2035. Wind power diffusion model is developed and used to forecast electricity generation from wind power plants in Pakistan.
For the economic analysis of wind power, Levelized Cost of Electricity (LCOE) generation of various power plants with and without CCS technology is estimated and compared with the LCOE of wind power plants. In addition, mathematical models are developed and used to forecast the mitigation potential of GHGs and other air pollutant emissions through wind
xxi power plants. Subsequently, CDM potential of wind power plants in Pakistan is forecasted using these mathematical models.
The electricity demand in the country is projected to rise from 139 TWh in 2013 (base year) to 442 TWh in 2035 (end year) at 5.4% annual average growth rate. In order to meet this growing demand electricity generation is likely to increase from 96 TWh in 2013 to 442
TWh in 2035 in all scenarios. The simulation results of LEAP model illustrate a significant increase in emissions of GHGs and other air pollutants from the base year to the end year of this study.
The modelling of wind power diffusion in the country shows that about 53% to 90% of the total technical potential of wind power in Pakistan could be utilized under various scenarios up to 2035. As such, anticipated installed capacities of wind power could generate about 81
TWh to 137 TWh of electricity. The economic assessment of various power plants signifies that electricity generation cost of wind power plants is competitive with all types of fossil fuel based power plants even when their external costs are not considered. The estimation of emissions exhibits that there is the enormous potential for mitigation of GHGs and other air pollutant emissions through wind power for Pakistan. It is also estimated that exploitation of wind power in the country could generate 0.30 million to 0.67 million cumulative CERs through CDM in 2013 which shall increase and reach 47 to 80 million cumulative CER sin 2035 under various scenarios. As such, at the carbon price (2013) in the international market, these CERs could earn about 300 to 500 million US $ during the same period under emission trading. This signifies that Pakistan has enormous wind energy potential at its different locations which could be harnessed to produce a considerable xxii amount of power. Generation of power from wind energy will not only help in overcoming the existing power shortage in the country but it would also be a significant achievement towards the mitigation of emissions for the conservation of the environment. Finally, the wind power projects being emissions free source of energy can also be an attractive form of earning revenue through CDM in developing countries like Pakistan.
xxiii CHAPTER 1 INTRODUCTION
1.1 INTRODUCTION
Energy is the prime mover of social and economic development for any country. Therefore, its steady, uninterrupted supply for transportation, industrial and domestic use must be ensured. Population increment, urbanization, industrialization and technological development have greatly increased the demand and energy consumption. Transformation of energy in useful forms to compete for the rapidly growing demand due to increasing consumption is a gigantic challenge, especially for the developing countries. Fossil fuels like (oil, coal and natural gas) are the major sources of meeting the energy demand globally which make about 82% of world energy needs (IEA, 2014a).
Pakistan is a developing country having a progressive gross domestic product (GDP) of
232 billion US$, with the annual growth rate 4.4% during 2013. On the basis of population, the country is ranked sixth largest in the world with 182 million population in 2013 (SBP,
2013b, WB, 2013a). Since 2006 Pakistan is suffering from acute energy crises particularly that of electricity due to increased demand, insufficient primary energy supplies, the poor performance of energy sector, and inappropriate fuel mix for power generation. Following sections of this chapter provides insight into the electricity crisis of Pakistan, assessment of emissions from electricity generation, mitigation of GHGs and other pollutants, CDM
1 2
mechanism appraisal, problem statement of this study, research aim and objectives, summary methodology and structure of this thesis.
1.2 ELECTRICITY CRISIS IN PAKISTAN
Pakistan has abundant primary energy resources; however, during past two decades due to the extensive growth in population and low supply of energy, i.e. the per capita supply of energy remained very low. During the calendar year, 2012-13 per capita energy supply was
0.36 TOE which is one of the lowest in the world (Anwar, 2016, GOP, 2013b). Due to this low supply of energy majority of the population has remained lack of access to basic energy needs like electricity or natural gas. About 61% of the country’s population lives in rural areas and use biomass and kerosene for cooking, heating, and lighting (WB, 2016a).
The natural gas demand has grown beyond the transmission/supply capacity and large users, mainly industries, power plants, cement industries and transport sector (CNG stations) are curtailed especially during winter months to ensure supplies to domestic, commercial and key industrial use, such as fertilizer (Uqaili et al., 2005a). The prevailing energy crisis in the country has forced thousands of industries to shut down operations, which has directly affected industrial production along with the livelihoods of thousands of families. It has been a major drag on the economy and a serious hurdle to the social development of the country with an estimated cost of 10% of the GDP during the last more than 5 years. Pakistan’s energy crisis, if not dealt with at both the operating and strategic levels in the immediate future, it might become a national security threat (Nawaz et al.,
2014, Rauf et al., 2015). 3
Electric power which is the secondary source of energy considered to be the lifeline of any economy and playing a pivotal role in the socio-economic development of a country. Poor governance, lack of planning and management, inefficient power generation system and high distribution losses have created immense challenges and crisis in the power sector of
Pakistan. Load shedding has become the frequent trend in every sector of the country.
Many industries have been forced to shut down entirely or slow down their production, while residential consumers in urban as well as rural areas are facing a load shedding duration of about12 hours on daily basis during the months of summer (Ali and
Nitivattananon, 2012, Coutinho and Butt, 2014). Power crisis is expected to worsen in coming years due to the increase in demand.
The present growing demand for electricity cannot be met from existing generating plants capacity. Further, generating electricity from costly imported fuel has also plunged the country into the existing power crisis owing to enormous oil import bills and issues of circular debt thereof. Dealing with this challenge, the present government announced a new power policy in 2013 (Hussain et al., 2016). The goal of this power policy is to build a power generation capacity that could meet Pakistan’s electricity needs in a sustainable way.
For achieving this goal on long-term basis government has made a plan to ensure the generation of electricity for domestic, commercial and industrial use by focusing on shifting country’s power generation mix towards low-cost sources such as coal (local and imported), hydro, nuclear and gas. Under these new plans power plants based on local coal available at Thar in Sindh province and many other power plants on imported coal will be constructed in different phases with the collaboration of Government of the People's 4
Republic of China. In addition to the construction of new coal-based power plants existing oil-based power plants in public sector will be converted to coal fuels. Generation of electricity from coal is a big environmental concern in terms of emissions (GOP, 2013a).
1.3 EMISSIONS FROM ELECTRICITY GENERATION
The demand for energy throughout the world is increasing extensively owing to its increasing consumption. The global energy demand rose by 3.3% annually over the past many years (Farooq et al., 2013). Electricity is one of the main forms of energy used for the social and economic development of any country. According to the estimations of the
IEA, the electricity consumption in 2013 was about 1674 million TOEs contributing about
18 % of the total global energy consumption, which may further increase in future. The rapidly increasing consumption of electricity due to the increasing population, industrial growth and technological developments has increased its demand. In order to meet this growing demand for electricity; the countries are harnessing both conventional and non- conventional electricity generation sources. Despite much development in new clean electricity generation sources such as nuclear and renewables, the fossil fuel based electricity generation sources dominate throughout the world. In 2013 about 67% (15719
TWh) of electricity was generated from different fossil fuels (coal, oil natural gas worldwide in which about 41% (9631 TWh) was only from coal as illustrated in Fig. 1.1
(IEA, 2014b).
The utilization of the fossil fuels for the power generation to meet its increasing demand plays a key role in the growth of greenhouse gases (GHGs) and other air pollutant 5
emissions which are the key environmental issues of today’s developing era. The main components of these emissions like carbon dioxide (CO2), methane (CH4) and nitrous oxide
(N2O) are considered as GHGs while other air pollutant emissions are sulphur dioxide
(SO2), carbon monoxide (CO), oxides of nitrogen (NOx), particulate matter (PM) and volatile organic compound (VOC).
Other 6%
Hydro Natural Gas 22% 16% Oil 4% Nuclear 11%
Coal 41%
Fig. 1.1: Global electricity generation by sources (IEA, 2014)
Although the air pollutants such as SO2, NOX, and PM are some way controlled by use of low sulfur fuels, scrubbers and de-NOX burners, catalytic converters and Flue-gas desulfurization (FGD) but CO2 and other GHGs are continuously increasing worldwide into the atmosphere. Since the industrial revolution, the annual CO 2 emissions from the combustion of fossil fuels have considerably increased from nearly zero to almost 32 billion ton in 2012 as shown in Fig. 1.2. The major share of these emissions is from power sector where electricity is generated from oil, natural gas, and coal. Coal power plants release more CO2 emissions than other fossil fuel power plants (IEA, 2014a). 6
35
30 ) s 25 e n n o t
n 20 o i l l i B
( 15 s n o i s i 10 m e 2 O
C 5
0 1971 1975 1980 1985 1990 1995 2000 2005 2010 2011 2012 Year
Fig. 1.2: Global CO2 emissions from fossil fuels combustion (IEA, 2014)
1.3.1 GHG emissions and their environmental impacts
The combustion of fossil fuels for power generation and other industrial processes have greatly increased the concentration of GHGs in the atmosphere particularly CO2. The increasing concentration of these gases traps the heat reflected from the surface of the earth cause the global warming which ultimately results in the climate change effects (Harijan et al., 2011, Sonibare, 2010).
1.3.1.1 Global warming
Global warming is the international phenomenon which can be illustrated that average temperature of Earth is increasing. Scientists all over the world have consensus on global warming that the average temperature of the Earth has risen between 0.4 to 0.8 °C since 7
past 100 years and this will increase up to 5.8 °C by the year 2100. The increase of GHGs,
deforestation, agriculture and other human activities are the main causes of the global
warming. Global warming has changed the natural order of environment in many ways like
climate change, rising of sea level due to the melting of glaciers and life threat to all living
organisms (Berrang-Ford et al., 2011, IPCC, 2013).
There are worldwide efforts to reduce GHG emissions to control the global warming.
According to the fourth assessment report of Intergovernmental Panel on Climate Change
(IPCC) global CO2 eq emissions needs to be reduced about 50% by 2050 compared to the year 2000 to stabilize the average warming temperature of the world which should not
exceed to 2° C relative to the pre-industrial levels (IPCC, 2013, McGlade and Ekins, 2015).
1.3.1.2 Climate change
Climate change is described as variation in weather conditions extended for a long time. It is one of the most crucial issues that humanity is facing today due to the anthropogenic emissions by combustion of fossil fuels for economic activities. The concentration of CO2 which is the main GHG has increased from 280 ppm (parts per million) from pre-industrial level to the 396 ppm in 2013. The concentration of Other GHGs like CH4 and N2O has also considerably increased during the same period (IEA, 2014a). The existence of these gases in the atmosphere trap the heat reflected from the surface of the earth causing the greenhouse effect. This heat increases the temperature of the atmosphere which disturbs the natural climate system resulting extreme and unpredictable weather conditions causing climate change. 8
Climate change has affected every region throughout the world. In some regions, severe
weather conditions are seen such as heavy rain and floods are becoming more common
while other regions are experiencing worst heat waves and droughts. These climate change
impacts cause hundreds of billions of dollars economic loss every year. There are many
indications that the future will be negatively impacted if the existing environmental
degradation continues. Therefore, it is essential that these emissions should be under
control (Berrang-Ford et al., 2011, IPCC, 2007, WB, 2014a).
Although Pakistan makes a minute contribution to overall global GHG emissions, but it is
among the countries which are severely vulnerable due to climate change. It has a lack of
technical and financial capability to cope up these adverse effects. The heavy floods of
2010 from monsoon rains killed about 2000 people and displaced twenty million from their
homes in many regions of the country. Physical infrastructure and public services like
roads, bridges, railway lines, schools and hospitals destroyed during these floods.
According to the government estimate, the economic losses were about $45 billion (GOP,
2012, Rasul, 2012). A severe heat wave shocked the various parts of the country in June
2015, which caused more than 1200 deaths, particularly in Karachi. According to the
reports, most of the country was remained under the grip of a heat wave from June 18
to June 24, with high temperatures recorded 49 °C in southern parts of the country
(DAWN, 2015, GOP, 2015).
1.3.2 Air pollutant emissions and their environmental impacts
The air pollutants other than GHGs such as SO2 and NOx during the combustion of fossil fuels for power generation and other industrial processes are discharged into the 9
atmosphere. These emissions chemically combine with the atmospheric moisture, oxygen, and other compounds to form harmful amounts of sulfuric acid and nitric acid. When it rains these acidic compounds combine with water droplets in the clouds turning them into acidic and fall on the earth in the form of acid rain. Acid rain causes great damage to crops, animals, soil, trees, buildings and other infrastructure. This situation acidifies the water bodies in the lakes, rivers, and ponds, making the water insecure for fish and other wildlife.
Eutrophication is also an effect caused by air pollutant emissions in which water bodies receive excessive amounts of nutrients in the form of nitrogen and phosphorus exist in some air pollutants. This process stimulates the growth of algae and other wild plants in water bodies causing the depletion of oxygen which in turn is a life threat to fish and other animals. Although the Eutrophication is a natural phenomenon on the brinks of lakes and rivers, but the human activities largely stimulate it by increasing the rate of accumulation of nutrients into ecosystems. The discharge of large amount of nitrogen oxides from power generation plants, vehicles, and other energy generation sources contribute the amount of nitrogen and phosphorous entering into the ecosystems.
The air pollutants emissions are discharged into the atmosphere by the combustion of fuels which form smog. The atmospheric air pollutants such as VOCs, SO2, and NOx discharge from the power plants, vehicles, and other industrial processes chemically react with each other under the sunlight to form ground level ozone and fine particles. The formation of ground level ozone, fine particles, and other gaseous air pollutants cause smog which hampers the visibility and disturbs the air traffic as well as ground traffic thereby increasing 10
the probability of accidents. Smog formation is most severe under the stagnant weather
situations, e.g. during the winter months when wind speeds are low.
The air pollutant emissions affect the human health directly or indirectly which is very
distressing. According to the Global Burden of Air pollution (GBA) reports about 5.5
million deaths were occurred worldwide in 2013 because of the direct or indirect effects of
ambient (population growth, urbanization, and industrialization) and household
(unavailability and poor access to the clean fuels in rural areas) air pollution. More than
64% of these deaths were recorded in Asian countries (IHME, 2013). Pakistan ranked 6th
largest country in the world due to the number of deaths caused by the exposure of PM 2.5
fine particle emissions. The annual mean concentration of PM 2.5 particles have exceeded
limited values of 10 μg/m3as established by World Health Organization (WHO) and it has reached 50 to 100 μg/m3 in some big cities of the country (Mir et al., 2016).
The effects of air pollutants may be short-term effects or long-term effects depending upon
the length of time of exposure to these emissions. Short-term effects include respiratory
infections, irritation of eyes, nose, and throat, bronchitis and pneumonia. Short-term effects
of air pollutants also cause asthma and emphysema. Long-term effects can be lung cancer,
heart diseases, brain damage and kidney failure. Higher temperatures and more frequent
and severe extreme weather events may increase the risk of deaths from dehydration and
heat strokes as well as injuries from intense local weather changes. Because of increasing
effects of climate change on the human health and environment degradation, the solution
to the reduction of GHGs and other air pollutant emissions was felt globally. 11
1.4 MITIGATION OF GHGS AND OTHER AIR POLLUTANT EMISSIONS
To handle the problem of climate change due to the increase of GHG emissions, the United
Nations made an international environmental agreement about the climate change called
“United Nations Framework Convention on Climate Change (UNFCC)” at its Earth
Summit in Rio de Janeiro in 1992. About more than 168 countries from all over the world express concern over the environmental degradation particularly Ozone depletion and the
GHG effect. The main purpose of this agreement is to stabilize GHG concentration level well below to prevent hazardous anthropogenic interference with the environment. Since the agreement, most of the industrialized and European countries have agreed to reduce
GHG emissions of five percent average against 1990 level and limiting the global average temperature rise to a maximum of 2 °Cover pre-industrial levels (Dong et al., 2012,
UNFCCC, 2006, Zhou, 2015).
Air pollutant emissions like GHGs are also a big challenge at a local and regional level for human health and infrastructure. These are also closely related to the GHG emissions discharged simultaneously from the combustion of fossil fuels, in spite of their different properties. Therefore, global as well as country level policies have been made for air quality standards in industry, power and transport sectors to reduce these air pollutant emissions by minimizing their environmental effects (Mir et al., 2016).
There are many approaches for reducing the anthropogenic emission in the atmosphere like employing greater energy efficiency by developing latest technologies, energy conservation practices and capturing and storing carbon from fossil fuels based power 12
generation and industrial processes. Another way is to switch from fossil-fuelled energy sources to alternative energy sources such as renewables (Hydro, solar, wind, and biomass) and nuclear. Although the nuclear energy is emissions free source but, its high cost, waste material disposal, radiation risk and international restrictions have made its utilization limited. Renewables are one of the most important and useful energy sources for the mitigation of GHGs and other air pollutant emissions (Harijan, 2008). They are reliable and abundantly available every region of the world. The generation costs of renewable energy sources particularly wind energy are potentially decreasing along with technological improvements and becoming cost-effective energy supply than conventional fossil-fueled sources. Wind turbines produce more energy than solar and other renewable sources at the same rated capacity due to its better capacity factor. (Akorede et al., 2012,
Liu et al., 2011, Mahesh and Jasmin, 2013).
Wind energy is an indigenous power source, available virtually in every part of the world.
Like another renewable source, wind energy is a clean and environmentally friendly energy source which can play a significant role in the mitigation of emissions. It can be used for meeting power demand in the direct, grid-connected modes as well as individual and remote area applications. Power generation cost from wind energy is now either the same or cheaper than conventional fossil fuel based power generation technologies, even taking into account the externalities of fossil fuels occurred during the construction, transportation, and installation of the wind turbine (Pechak et al., 2011).
In Pakistan huge potential of wind power is available, but it has not been utilized significantly so far. Lack of financial resources is the main barrier to exploiting the wind 13
power potential in Pakistan and converting this potential into useful projects. Pakistan is
already in a whirlpool of budget deficit due to its poor economy and is incapable of
supporting heavy investment in wind power projects. So the only option that remains for
the country to rely on the foreign investment or financial support for sustainable
development through carbon trading mechanism.
1.5 CLEAN DEVELOPMENT MECHANISM (CDM)
Reduction of GHG emissions is the main aim of United Nations Framework Convention
on Climate Change (UNFCCC). In 1997 an agreement in this respect was also made
between all the countries of the world under the umbrella of United Nation in a convention
held in Kyoto city of Japan, called Kyoto protocol. Three flexible mechanisms were
included in Kyoto protocol viz. Emissions Trading, Joint Implementation and Clean
Development Mechanism (CDM). Among these mechanisms, CDM is quite competent to
increase sustainable development in developing countries (non-Annex I countries) and
assist developed countries (Annex I countries) to achieve their emission reduction targets
in a cost-effective manner (Pechak et al., 2011).
The clean development mechanism (CDM) is a carbon emission trading mechanism started by UNFCCC under the Kyoto Protocol for the low-cost mitigation of GHG emissions from the atmosphere. It provides an opportunity for developed countries to fulfil their emissions reduction commitment and in the same way helps the developing countries in carrying out sustainable development. Through CDM projects the developing countries generate saleable Certified Emission Reduction (CERs) or carbon credits and sell these CERs to the 14
industrialized countries in international carbon market through the emission trading (ET).
The earned credits from the developed countries (donors) are invested in clean energy
generation technologies in developing countries (hosts) for sustainable development. Each
tradable CER or carbon credit is equivalent to the quantity of reduction of one ton CO2 eq
emissions of GHGs releasing to the environment during the combustion of fossil fuels.
Many types of projects from the large capacity to small of renewable energy sources,
efficient combustion technologies, fuel switching and industrial waste gases which
destroying the atmosphere can be eligible for the CDM (IISD, 2009, Parmal Singh
Solanki1, 2013, UNFCCC, 2008).
The Kyoto protocol commitment covers the stabilization of concentration of six
greenhouse gases: carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), hydrofluorocarbons (HFCs), sulfur hexafluoride (SF6) and perfluorocarbons (PFCs) in the
atmosphere to counter with the climate changes. It permits industrialized countries the
option of making a target for the reduction of these emissions of GHGs. Many activities
on the earth have destroyed the forestry which is the big source of stabilization of CO2 in the environment; reforestation is also the part of the Kyoto protocol.
1.5.1 Global status of CDM projects
CDM is promoting the carbon trading of emissions reduction based projects in many
developing countries since 2000. In 2004 the PDD of 82 projects were submitted to the
Executive Board (EB) of UNFCCC and subsequently got registered. This was the first
batch of CDM projects, after which the CDM rapidly developed and a large number of
projects were registered during September 2005 to May 2006 (Liu, 2006, UNEP, 2007). 15
Up to the end of 2008 more than 4000 CDM projects were submitted to the EB in UNFCCC for validation, out of which about 1000 were registered and became eligible for the issuance of CERs. The majority of these projects were from China (52% of total projects), India was second largest 16% whereas Brazil got 7% of total projects. By the end of 2012, about 4650 projects were registered as CDM projects by EB of UNFCCC from which 650 million
CERs generated. China, India, Korea and Brazil were the most host countries (Schroeder,
2009, UNFCCC, 2012).
CDM project activities are increasing continuously in developing countries throughout the world. By the end of 2014, total 7809 PPDs of different projects were submitted to the
CDM EB for the registration, out of which 7589 were registered whereas 212 projects were on validation process and 8 projects were requested for the registration. The issued CERs to total 2573 projects by the end of 2014 were 1.88 billion at the end of first commitment period and it is expected to be about 4 billion CERs from all the crediting periods Table
1.1 (UNFCCC, 2016). The possible CERs from all registered projects are 2.15 billion at the end of first commitment period while 8.15 billion CERs are expected from all the crediting periods. The projects under validation process would generate CERs 3.6 million under first commitment period and 408 million from the all crediting periods Table 1.2
(UNFCCC, 2016).
Table 1.1: Status of CERs issued to CDM projects until December 2014 No. of CDM CERs at first CERs at the end CERs at the end CERs at the end projects CERs committed period of current of 2015 of 2020 issued (31-12-2012) crediting period 2573 1882 million 3087 million 3959 million 4007 million 16
Table 1.2: Status of CDM projects under process till December 2014 CERs at the CERs at first CDM project CERs at the CERs at the end of current Status committed period processes end of 2015 end of 2020 crediting (31-12-2012) period Applied 7809 2199 million 4794 million 7986 million 8560 million Registered 7589 2196 million 4724 million 7701 million 8148 million Requesting for 8 0 0.359 million 2 million 3.13 million registration Validation 212 3.6 million 70 million 283 million 408 million
1.5.1.1 Distribution of registered projects by resource
The projects that were submitted by the different project developers for registration to the
UNFCCC EB consist on different kinds and sources; however, the basic purpose of each project activity was to the reduction of GHG emissions as compared the baseline emissions.
The various kinds of projects that were registered by the end of 2014 with UNFCCC were included in Mining and mineral production, Metal production and process, Agricultural,
Energy, Waste Handling, and Transportation. The distribution status of these projects is shown in Fig. 1.3.
It can be viewed that the largest share of these projects 73% is from the energy generation
(renewable and non-renewable) because energy generation from fossil fuel sources produces a large amount of emissions and renewables are the best mitigation sources of these emissions. The second largest share 12.25% is from waste handling and disposal. The idea of handling of waste and its disposal is to protect the environment from hazardous effects of wastes. The best management of waste can be carried out by landfills which have a great potential all over the world. There is also a big share of agricultural based projects as compared to the shares of other resources of metal production, transportation, and 17
chemical industry. Agricultural wastes are the main source of methane emissions which is a component of GHGs having 24 times greater GWP than the CO2. These agricultural wastes can be used as the alternative to fossil fuels for power generation and simultaneously avoid the emissions of methane into the environment (UNEP, 2009,
UNFCCC, 2014b).
0.30% 0.95% 0.24% 0.30% 0.76% 1.26% 2.27% 0.32% Metal production Transpotation 12.25% Fugitive emiss. (halon/SF6) Mining/mineral production 1.28% Chemical industrial 2.65% Agriculture 4.15% Waste handling and disposal Energy distribution. Affrestation/reforestation Energy demand 73.26% Fugitive emiss. (solid/oil/gas) Manufacturing industry Energy generation. (ren/non-ren)
Fig. 1.3: Global CDM registered projects by resource (UNFCCC, 2014)
1.5.1.2 Distribution of registered projects by country
From the Fig. 1.4 it is clear that the China has the largest share of registration with CDM projects 59.8% as leading CERs generation country among all the developing countries.
Most of the China's projects are from renewable energy resources such as the wind, solar, hydro and biomass. India is also among the signatory countries of the Kyoto Protocol. In 18
terms of CDM registered projects and their associated CERs generations, India’s status is second largest across the world with a share of 13.21%. In the distribution of project types, wind energy is highest from all other sources in India followed by biomass, hydro and heat recovery. Brazil and Korea are also very interestingly in good positions with respect to
CERs generation having their shares 6.49% and 8.44% respectively (UNFCCC, 2014b).
1.58% 10.45%
China 6.49% India 8.44% Republic of Korea Brazil 59.83% 13.21% Mexico Others
Fig. 1.4: Global CDM registered projects by country (UNFCCC, 2014)
1.5.2 CDM projects status in Pakistan
Pakistan endorsed the Kyoto protocol in 1997 immediately after its adoption worldwide and signed it on 11 January 2005, became entitled to take the advantage of CDM projects. A CDM cell was established under the ministry of Climate change for providing awareness of CDM projects, implementation policy and technical support for the development of CDM in the country and also to ensure the easy functioning of the carbon trading. Ministry of Climate change has been assigned the task of Designated 19
National Authority (DNA) for the CDM projects evaluation and to assist the government about the technical issues of CDM projects (Ahmed and Salman, 2012).
The CDM cell in Pakistan allows submission of projects in the potential areas of energy
(renewable energy, energy conservation, energy efficiency and cogeneration), solid waste management (landfills, waste management, and recycling), and industrial processes (GHG emission reductions from various industries) and transportation
(alternative or biofuels, efficient engines and mass transit systems). The project promoters develop CDM projects from one of the above major areas and submit it to
CDM cell. The registered projects that have been awarded CERs in Pakistan mostly are from the energy sector (GOP, 2006a).
The existing development of the CDM projects in Pakistan is very pitiable and the registration rate of projects is very low as compared other developing countries. Lack of government interest for the enhancement of CDM activities and unawareness of projects developers about the significance of the CDM are the main causes of the sluggish development of CDM in the country. So for only 102 projects have become eligible for receiving registration with UNFCCC EB, out of which only 37 projects are being granted with carbon credits. 67 projects out of all applied 161 projects are in validation process to the DNA as described in Table 1.3. Mostly registered projects are from the energy areas which reduce a significant amount of GHG emissions. The emissions reduction from all carbon crediting projects is estimated to be more than 41 million tons (GOP, 2014b). 20
Table 1.3: Status of CDM projects in Pakistan till December 2014 Status of projects Number of projects Projects applied for registration 161 Registered 102 Issuance of CERs 37 Validation process 67 GHG emissions reduction in million tons 4.15
1.6 PROBLEM STATEMENT
Pakistan is facing the electricity crisis since last many years due to the huge gap between supply and demand. The demand for electricity is rapidly increasing due to the increasing population and modern lifestyle. The existing power generation system of the country is unable to cater power to meet the growing demand. Dealing with the situation the Government of Pakistan (GoP) has planned to generate electricity from low-cost energy sources of local and imported coal. Generation of electricity from coal will increase the GHGs and other air pollutant emissions in the already fossil fuel dominated power generation system of the country. The GHGs and other air pollutant emissions are major concerns to the local as well as the global environment in the form of climate change and air pollution. Pakistan has highly affected by the adverse effects of the climate change and air pollution. Although its contribution to the global emissions is minimum but it is among the most vulnerable affected countries by the climate change.
Globally nations are trying to find and implement alternative power generating sources that consist of less carbon and air pollutants emitting activities to build up a safe environment. In this respect wind power is one of the attractive options for power 21
generation that is rapidly growing renewable energy source free from emissions during the power generation process. Pakistan has many excellent wind power potential sites at the coastline and some other parts of the country which could be fully utilized to meet the growing power demand. Even with this abundant wind power potential available in the country, it has not been exploited significantly only a limited number of power plants have been installed and connected to the national grid so far.
At the global level, the climate change mitigation task is undertaken by Kyoto protocol of UNFCCC. The clean development mechanism (CDM) is one of the emission trading mechanisms under the Kyoto Protocol which offers the financial assistance to the developing countries to achieve sustainable development in carbon emission reduction projects. In the same way, it assists the developed countries one of the least cost options for their emission reductions targets. The types of projects which could be analyzed by
CDM are energy efficiency, energy conservation and management, fuel switching, cogeneration process and renewable energy source utilization. Pakistan has ratified the
Kyoto Protocol in 1997 and implemented it in 2005 but since that, it has not extensively succeeded to get benefits from the CDM. Pakistan is well behind from its neighboring countries India and China in case of CDM based projects. India and China are the leading developing countries in the world in case of CDM registered projects.
As such, Pakistan needs to plan power generation program which should cause minimum emissions. In this context, renewable energy, particularly wind energy potential of Pakistan can be exploited to add new generation capacity. 22
In the perspective of above-mentioned problems in Pakistan a comprehensive study for tackling the power crisis in a sustainable and environmentally acceptable way in the country is very essential. This study, therefore, shall explore the air pollutant emissions mitigation and CDM potential of wind power in Pakistan.
1.7 RESEARCH AIM AND OBJECTIVES
The aim of this research study is to explore the CDM potential and mitigation of emissions through wind power generation to defeat the on-going electricity crises in Pakistan which would greatly assist energy experts, planners and policymakers. In order to accomplish this aim following objectives are set in this study.
1. Modelling and forecasting power generation and associated emissions for
Pakistan.
2. Modelling and forecasting wind power generation in Pakistan.
3. Estimating and comparing electricity generation cost from wind power plants
with the thermal power plants in Pakistan.
4. Modelling and forecasting air pollution mitigation potential of wind power in
Pakistan.
5. Modelling and forecasting CDM potential of wind power in Pakistan.
1.8 METHODOLOGY
This study consists of multidisciplinary topics pertaining energy system, therefore, it requires various, energy and environmental tools, several methodological frameworks, 23
mathematical and economic models for the achievement of all its objectives as briefly summarized below
Modelling and forecasting power generation and associated emissions.
For modelling and forecasting of the future power generation along with associated emissions for Pakistan, the current power generation system, existing power demand, power generation capacity, power generation technologies, power generation sources and the available potential of various energy sources of Pakistan was extensively analysed.
Various studies pertaining to energy and environmental modelling, emission estimations from power generation systems, the impact of emissions on the environment and emissions free power generation sources like renewables that have been carried out on the international level as well as national level were comprehensively reviewed. The future power expansion plans and policies announced by the government of Pakistan were also reviewed. The basic purpose of the review of the literature was to select an appropriate energy and environmental modelling tool that could estimate and forecast future power generation and emissions for the country.
On account of the literature reviewed a scenario based integrated energy and environmental accounting modelling tool LEAP was selected. Through the LEAP model electricity demand, its generation and associated emissions from the combustion of fossil fuels were forecasted for Pakistan from the base year 2013 to 2035.
Modelling and forecasting wind power generation in Pakistan. 24
Future growth pattern of a technology can be forecasted through modelling process. There are various mathematical models available in the literature being used for forecasting the diffusion of technologies. Among these technologies diffusion models, the “logistic model” or “Pear model” and its variations are a leading group due to their better functions than other diffusion models in case of estimation fitness and diffusion forecast of technologies particularly renewable energy technologies. In this study, the logistic diffusion model was used for diffusion of wind power in Pakistan because it is very effective for the renewable technology diffusion strategies than the other technology diffusion models. The parameters of the logistic diffusion model were estimated through the historical data of wind power installed capacity. Due to the unavailability of past historical data for wind power in Pakistan, the analogous approach was used by taking the historical wind power data of India having Geographical and economic similarities with
Pakistan. From the estimated parameters of the logistic diffusion model, the wind power installed capacity in Pakistan was forecasted throughout the study period. Electricity generation from wind power plants was estimated by using the data of capacity factor and wind energy potential in the country as available in various literatures.
Estimation and comparison of electricity generation cost from wind power plants with the thermal power plants.
The suitability of any energy source for power generation depends on its economic and environmental feasibility. In this context, the per unit electricity generation cost from wind power plants was estimated. The cost of per unit electricity generation from the wind power 25
plants was estimated through the Levelized capital as well as operation and maintenance costs considering the existing discount rate, fuel costs and escalation rate of fuel cost in the country. To assess the competitiveness of electricity generation cost from the wind power plants to the conventional power plants, the Levelized costs of electricity generation from various conventional power plants based on fossil fuels was also estimated and compared with wind power plants. The Levelized cost of electricity generation from wind power plants was also compared with carbon capture and storage (CCS) based conventional fossil fuel power plants for the assessment of cost competitiveness of emissions reduction processes.
Modelling and forecasting air pollution mitigation potential of wind power.
For the sustainability and economic development, the reduction of GHGs and other air pollutant emissions through the use of renewable energy sources are globally considered as one of the best practices. For the assessment of mitigation of emissions through wind power plants in Pakistan mathematical formulation is developed, the parameters for this model were electricity generation from wind power plants and existing baseline emission factor of the grid which supply electricity to the country. The baseline grid emission factor in this study is determined through approved baseline methodology for grid-connected electricity generation from renewable energy ACM0002 (version 16) of UNFCCC.
Modelling and forecasting CDM potential of wind power.
The reduction of emissions is estimated by baseline emissions minus the emissions produced by wind power plants. Since wind power plants are renewable energy sources, 26
free from emission and their emissions are considered as zero during power generation process. Therefore, the baseline emissions are equal to emissions reduction. According to the CDM law, each ton of GHG (CO2 eq) emissions reduction is equal one CER. The total tons of GHG emissions reduction is equal to the number of CERs generated by wind power plant. In this study CDM potential of wind power plants in Pakistan was forecasted by reduction of emissions by wind power plants. The revenue of CERs depends upon the per ton price of the carbon. Due to the uncertainty of future carbon price sensitivity analysis of carbon price was carried out.
1.9 STRUCTURE OF THE STUDY
This study is structured into nine chapters of this thesis. A brief of each chapter is given as under:
Chapter 2 covers the comprehensive review of all the previous studies that have been carried out by various researchers at national as well as international levels related to the objectives of this study. The topics covered in the review pertains to the energy crisis in
Pakistan, electricity demand forecast using energy modelling tools, GHGs, and other air pollutant emissions estimation and their mitigations and emissions mitigation through wind power. The cost of electricity generation from wind energy and CDM potential of wind power were also reviewed from literature and discussed.
In Chapter 3 the overall electricity generation system of Pakistan is discussed. This includes electricity demand and supply situation in Pakistan and a detailed description of future plans of government pertaining power generation. Besides these topics latest power 27
policies announced by the government of Pakistan, their goals and targets are also explained in this chapter.
Development of energy modelling framework and future power generation and emissions assessment for Pakistan is described in Chapter 4. Integrated energy and environmental assessment modelling tool LEAP is used for forecasting electricity demand and projection of electricity generation and its associated emissions from diversified fuel mix starting from the base year to end year of the study period. A baseline or reference scenario along with three alternative scenarios which are: More Renewable Energy (MRR), More Hydro
Energy (MRH) and More Hydro Nuclear Energy (MRHN) developed to analyse different power generation options for Pakistan. Developed modelling framework, all scenarios, and associated emissions are also discussed in this Chapter.
In Chapter 5 global status of wind power, wind power potential and its development in
Pakistan are analyzed and discussed in detail. Future wind power technology diffusion in
Pakistan was forecasted using “logistic diffusion model” under three scenarios which are:
Business as Usual scenario (BU), Moderate Diffusion scenario (MD) and High Diffusion scenario (HD).
Levelized cost of electricity generation (LCOE) of wind power plants was estimated.
LCOE of conventional fossil fuel power plants with external costs of emissions was also estimated and compared with LCOE of the wind power plants. Further, the cost estimation and comparison of conventional fossil fuel electricity generation plants with and without
Carbon Capture and Storage (CCS) technology to the wind power plants is undertaken and discussed in Chapter 6. 28
Chapter 7 identifies the GHGs and other air pollutant emissions emitted from the power sector of Pakistan, mathematical models for the mitigation of GHGs and air pollutant emissions are developed to estimate the baseline grid emission factor for mitigation of these emissions through wind power are described in this chapter.
Chapter 8 focuses on the Clean Development Mechanism (CDM), methodological aspects of CDM project development, undertake the development of a model for CDM potential of wind power plants in Pakistan and, an estimate of the CDM potential of wind power plants in Pakistan.
Chapter 9 presents the conclusion of the whole study and recommendations for the implementation of policies for the development of wind power in Pakistan. Besides this future work based on this study is also discussed. Chapt er 2 CHAPTER 2 LITERATURE REVIEW
2.1 INTRODUCTION
In this chapter, the literature related to the past studies pertaining Pakistan’s energy crises, energy planning, modelling of electricity demand and generation, GHGs and other air pollutant emissions from the power sector, estimation methodologies, mitigation of emissions through renewables, diffusion of wind power in Pakistan, wind power development, CDM potential of renewables and CDM potential of wind energy has been reviewed. The purpose of this review of the literature has been to find out the best modelling techniques for utilizing the indigenous resources like wind power to overcome the electricity crisis in sustainable and emissions free manner in the country.
2.2 ELECTRICITY GENERATION IN PAKISTAN
Pakistan is currently facing a severe power crisis. During the past many years power demand has remarkably increased with strong economic and population growth in the country, even with this increasing demand no steps have been taken for the expansion of power generation capacity. The widening gap between the supply and demand for electricity has created a big shortage of power which has badly affected the economic and social development. The reckless and irresponsible power generation planning and policies over the previous three decades are the major causes of the existing power crisis. These policies directly hampered the progress of the abundant and inexpensive domestic energy
29 30
resources and highly rely on expensively imported sources of energy. It would become a serious challenge for the credibility and integrity of the country if this power crisis not sincerely considered at apriority level. The present overall energy disaster in Pakistan has been reviewed by Kessides (2013). He has discussed all the important issues related to energy sector particularly power which are responsible for directly or indirectly in the crisis of energy. According to the observations the lack of adequate planning, insufficient installed capacity in power generation, circular debt, the inefficiency of existing public- sector power plants, high transmission, and distribution losses and shifting from cheap hydro to expensive oil based generation are the major causes of the power and energy shortage. This study suggests some key solutions to defeat the power crisis like an effective energy policy should be made and immediately implemented, indigenous cheap resources should be utilized, inefficient public power plants may replace to new advanced power plants and necessary steps should be taken for the reduction transmission and distribution losses.
The dilemma of electricity shortage due to the growing gap between supply and demand has included Pakistan in the list of energy deficient countries where a large number of the population has no access to electricity. A comprehensive analysis of energy potential and its role in the economic development in Pakistan have been presented by Mahmood et al.
(2014). They have discussed the main issues related to energy sector like energy potential estimation, lack of proper management system, low share of renewable resources and security of imported energy. They have warned that the existing reserves of oil and gas are limited in the country and if the current rate of consumption of these resources continued 31
than they will be depleted within 13 and 16 years respectively. The overwhelming reliance on the imported fuels would be a security risk, however, the energy import option cannot be ignored due to energy scarcity. Mega import of energy projects like gas pipelines of
Turkmenistan, Afghanistan, Pakistan, and India (TAPI) and Iran, Pakistan and India (IPI) and Liquefied Natural Gas (LNG) from Qatar could be a remedy for energy deficiency. On the long term basis, the utilization of heavy deposits of coal in Thar will make the country energy independent.
The situation of energy and its development in Pakistan has been reviewed by Rauf et al.
(2015).They have discussed nearly all the main sections of the energy sector in the country like existing energy reserves and their effective utilization, consumption, and supply of energy, current power crisis and its causes, role of various government and private institutions for the betterment of energy and different energy policies implemented by the government. It is concluded that the government, as well as private organizations, should make efforts for the exploitation of existing energy resources particularly utilizing all the renewable energy sources to defeat the power crisis in the country. Foreign investors should be facilitated by the government for the promotion of renewable energy technologies to get a maximum share from these resources.
Mirza et al. (2007) have suggested that the exploitation of indigenous renewables resources could diminish the problem of energy crisis. This concludes that Indigenous reserves of oil and gas are limited and the country heavily depends on imported oil which has increased the generation cost of energy, on the other hand, generation of energy from the conventional resources pollutes the environment. The best solution to these problems is to 32
utilize the renewables such as Hydel, solar, wind and biomass energy for meeting growing electricity demand in Pakistan and subsequent contribution in air pollution abatement.
2.3 ELECTRICITY DEMAND FORECASTING
Long term electricity demand forecasting is the key point for energy investment planning which plays an imperative role for governments of developing countries. In order to meet the increasing demand for electricity to the economic development of a country, an exact and reliable forecasting of electricity consumption in all sectors is of great importance.
Electricity demand forecasting models are developed according to the specific requirements of a particular country. Some conventional models are mostly used for forecasting the electricity demand such as time series; regression models, econometric models as well as flexible computing techniques such as artificial intelligence, fuzzy logic, and genetic algorithm are also used for forecasting the electricity demand.Kandananond
(2011) has forecasted the electricity demand for Thailand using three different techniques such as autoregressive integrated moving average (ARIMA), artificial neural network
(ANN) and multiple linear regression (MLR). The past data related gross domestic product
(GDP), population growth, stock index, electricity consumption and income from export of industrial product in Thailand have been used for predicting the future electricity demand. The comparison of results of all the technique illustrates that the ANN model has a minimum error than ARIMA and MLR.
Torrini et al. (2016) have estimated the long-range future electricity consumption in
Brazilian commercial, industrial and residential sectors by means of fuzzy logic 33
methodology including GDP and population as variables. For the validation of the results, two alternative methods Holt Two-Parameter and EPE (official projection) have been selected in the study. The results obtained from the fuzzy model were quite better than the
Holt Two-Parameter and very similar to the official projections showing just 1.5% error.
Regression models have been used for forecasting the electricity consumption in Italian domestic and non-domestic sectors by Bianco et al. (2009). They have used historical data of the population growth, electricity consumption, gross domestic product (GDP) and per capita GDP as input parameters in these models. The comparison of results with the results forecasted through more complex models like MARKAL-Time by the government shows that they are almost similar.
Forecasting of energy consumption can be done successfully by the Grey Modelling techniques which need very limited input data. Electrical consumption in Turkey has been forecasted from 2013 to 2025 by Hamzacebi and Es (2014) utilizing the optimized Grey
Modelling system. Results were obtained through direct and iterative ways and were verified with other modelling techniques which display supremacy of Grey Modelling over others.
Planning for expansion of power structure to meet the electricity demand is necessary to enhance the economic development. The sector based long-term electricity demand for
Bangladesh has been projected till 2035 by Mondal (2010). Three scenarios low GDP, normal GDP, and high GDP growth have been developed in this study. The model results show that demand for electricity increased several times in 2035 as compared to the base 34
year. Technological development, economic growth, social changes and modern lifestyle are the main factors cause to increase the electricity demand.
Electricity demand of industrial zone in Iran has been predicted by Behrang et al. (2011) with the help of Particle Swam optimization (PSO) and genetic algorithm (GA) models.
Gross domestic product (GDP), population growth, the number of customers, cost and electricity productions were main parameters used in this linear and exponential modelling.
According to the experimental results, although PSO and GA both are good models but in the case of data requirements, PSO is much better than GA.
Kaytez et al. (2015) have employed some most advanced techniques such as Support
Vector Machines (SVMs) and Least Squares Support Vector Machines (LS SVMs) for predicting the accurate electricity consumption in Turkey. Using the historical data from
1970 to 2009 they considered the total installed capacity of electricity, gross electricity generation and population growth as independent variables in these models. For verifying the accuracy of models, they compared the results with forecasted electricity consumption results performed through conventional artificial neural networks (ANNs) and regression analysis. The results specify that suggested SVM and LS SVM models are accurate and very fast forecasting methods.
Hussain et al. (2016) Hussain et al. (2016) have predicted the total and sector wise electricity consumption in Pakistan by Auto Integrated Moving Average (ARIMA) and
Holt-Winter models using time series data. This study implies that Holt-Winter model is suitable and gives best results compared to ARIMA model for forecasting total and component-wise electricity consumption. Throughout the forecasted period the electricity 35
generation remained less to meet the increasing demand, particularly in the industrial sector, therefore, it has been recommended that least cost indigenous renewable energy resources should be utilized to meet the demand.
Electricity demand has been forecasted for next fifteen years for Pakistan using multiple regression model and Univariate time series models. In this study, the main variables like electricity demand, GDP, population and per capita income related to electricity demand have been selected.
Using the Smooth Transition Autoregressive (STAR) model, Nawaz et al. (2014) have forecasted the demand for electricity in Pakistan from 1971 to 2012 containing 41 years period. The forecasted results of this study illustrate that if the cost of electricity generation is considered as the changing variable then electricity follows the curvilinear path. The results further illustrate that assuming the 3% GDP growth rate the demand for electricity would be double in 2020 than the current electricity demand in 2012 whereas at 6% GDP growth rate the electricity demand of the country would be threefold as compared to the existing demand. It has been suggested that implanting the following measures, the demand for electricity would be met very easily like increasing the efficiency of generation systems, taking up an integrated institutional approach, promoting the culture of energy conservation and altering the energy mix from the conventional thermal to the renewable sources.
Choudhary et al. (2008) have applied the Time Series models to review the electricity projections made through the Medium-Term Development Framework (MTDF) by the
Government of Pakistan. They observed that these projections have been overestimated 36
and mostly targets were not attained due to many reasons. They have proposed that for diversification of electricity generation and achievement of targets for new plannings should be made at priority basis.
In 2011 a comprehensive energy model for Pakistan was developed by International
Resources Group (IRG) USA with the technical and financial support of Asian
Development Bank (ADB) and Ministry of planning and development, Government of
Pakistan. The basic task for the development of integrated energy model (Pak- IEM) was to build up long term policy scenarios for the energy sector in Pakistan to meet the challenges of energy shortage in the country. According to this energy model, in the support of the current economic growth related to the 5.7% of average GDP requires four times enhancement in electricity generation up to 2030 which should be about 82 GW of new capacity addition to the existing system of 22 GW. Under the existing consumption scenario of resources, the reserves of the conventional natural gas will exhaust soon which will further increase the imports of energy from 27% to the 48% of the total energy supply.
In case of an increase in imports, an alternative scenario shows the inclusion of coal to the energy mix of the country by 30% of total energy supply up to 2030 (IRG, 2011).
2.4 GREENHOUSE GAS AND OTHER AIR POLLUTANT EMISSIONS
Various studies have been carried out for the estimation of GHGs and other air pollutant emissions from the power sector in many countries around the world. Sonibare (2010) has studied the emission of uncontrolled air pollutants from all the existing and proposed thermal power plants of Nigeria. He has estimated the emission ranges of air pollutant 37
emissions such as CO, NOx, PM, SO2, and VOCs from the thermal power plants based on
fossil fuels. This study has concluded that the present augmenting share of thermal power
plants in the power generation system of Nigeria will promote the massive discharge of
above mentioned air pollutants, which will be unequally distributed around the country
causing environmental and health impacts.
Mazandarani et al. (2011) have carried out a study about the new thermal power plants that
have been planned to build by the Iranian government for the assurance of economic
growth in the country. This research study has investigated the composition of future power
plants and predicted the consumption of fuels and their associated emissions until 2025. It
has been estimated through this study that if the power plant composition does not change
then the CO2 emission by 2025 will increase another 2.1times and if the composition changes then it will increase 1.6 times for new power plants. In the same manner, other air pollutant emissions such as SO2, NOx, and CO will also increase. In the conclusion, it has been suggested that the share of coal and fuel oil should be reduced while the share of natural gas with combined cycle should be increased to minimize the emissions.
Ali and Nitivattananon (2012) have performed a study on one of the concentrated population city Lahore Metropolitan Administration (LMA) to forecast energy consumption, CO2 emissions and land cover/land use by urbanization. Five sectors
(Commercial, Residential, Transportation, Industry, and Agricultural) have been selected.
The results of this study illustrate that industrial and residential sectors are vibrant consumers of energy and CO2 emitters among all other sectors of the city. Therefore, there is a need that policymakers recognize such vulnerable situation of energy use and GHG 38
emissions to take proper and timely actions to cope with the threats of climate change
which has a great impact on all over the country as well as on the city.
Air pollutant emissions have been estimated in the Shandong province of China by Xiong
et al. (2016). Due to the large consumption of coal in power generation system, Shandong
is considered as a high source of air pollutant emissions in China. In accordance with
estimations carried out in this study, the total NOx, SO2 PM and mercury emissions remain
706 kt, 754kt, 64kt, and 10kt respectively in 2012. A significant reduction in emission
could be possible under the high-efficiency control technology scenarios up to 2030 than
the baseline scenario by 2012. This study recommends that efficiencies of all emissions
controlling technologies should be improved to make further emissions reductions.
Overall emissions of NOx, SO2, and PM from all energy sectors have been assessed by the
combination of Greenhouse gas and Air pollution Integrations and Synergies (GAINS) and
Pak-IEM models in Pakistan by Mir et al. (2016). The baseline scenario shows that the
emissions of NOx, SO2, and PM increase by 2.2, 2.4 and 2.5 times from 2007 to 2030.
They have warned that if the effective air pollution control strategies are not undertaken in
future than Pakistan will be unable to defeat the effects of air pollution on the human health
and environment.
The studies discussed above have been carried out by various researchers in different
countries throughout the world to assess the air pollutant emissions such as CO, NOx, PM,
SO2, VOCs, and GHGs like CO2 emissions from thermal power plants based on fossil fuels.
No, any study has been carried out in Pakistan to assess the GHGs other than CO2 and air pollutant emissions from power sector of Pakistan. Only a single study has been carried 39
out by Mir et al. (2016) using Air Pollution Integrations and Synergies (GAINS) and Pak-
IEM models. They have only assessed few air pollutant emissions such as NOx, SO2, and
PM from all energy sectors in Pakistan. In this study, GHG emissions other than CO2 and air pollutant emissions such as CO, NOx, PM, SO2, and VOCs have been estimated from thermal power plants in Pakistan up to 2035.
2.5 MODELLING ELECTRICITY DEMAND AND EMISSIONS USING LEAP
Many researchers have performed modelling for electricity generation systems and their
associated emissions through an integrated energy and environmental modelling tool
LEAP. Kale and Pohekar (2014) have forecasted electricity demand and supply for the
Indian state of Maharashtra up to 2030 using LEAP model. Three Scenarios, Business as
usual (BAU), Energy Conservation (EC) and Renewable Energy (REN) have been
developed in this study. According to the results, the values of GHG emissions for BAU
and EC scenarios in the year 2030 are 245.2% and 152.4% respectively more than the base
year while these are 46.2% less for REN scenario in the same year. This study is concluded
that there is no other option to add new coal based power generation capacity in future.
Maharashtra has a golden opportunity to develop electricity generation using
environmentally friendly and economically viable electricity infrastructures through
renewables.
Future electricity generation planning in Lebanon has been assessed by Dagher and Ruble
(2011). They have developed baseline scenario (BS) and two alternative scenarios such as
renewable energy scenario (RES) and the natural gas scenario (NGS) by using Long-range 40
Energy Alternatives Planning (LEAP) modelling software. In this study environmental, technological and economic analysis for all scenarios has been considered. The results illustrate that both alternative scenarios are cost-effective and environmentally acceptable than the baseline scenario. Although all scenarios assist in diversifying electricity generation mix of Lebanon but, dependency on imported fuel oil can efficiently reduce only through RES scenario.
Jun et al. (2010) have analyzed the economic and environmental influence of renewable energies in the electricity generation sector of South Korea through energy and environmental modelling tool LEAP. Various renewable sources like solar energy, wind energy, and landfill gas waste incineration energy are considered for analysis. In the conclusion, it has been suggested that government should implement a strong mechanism or energy policy for the induction of renewable energies into the power sector.
Long term planning for the supply of energy and its demand is very essential for the economic development of a country. Energy modelling techniques play a really important role in the long term planning. Applying LEAP model Huang et al. (2011) have forecasted the long term energy demand and supply for Taiwan. They have compared the results of future energy supply and its associated emissions of GHGs estimated under the baseline scenario with alternative energy efficiency improvement scenarios. The results of this study demonstrate that GHG emissions extensively reduce due to the reduction in demand for energy under efficiency improvements scenarios.
Current and future electricity demand for urban and rural residential levels in Pakistan have been evaluated by Urooj et al. (2013) using scenario-based LEAP model. They have 41
developed three scenarios such as Business-as-usual for the reference case and two alternative renewables scenarios of wind energy and solar energy for the comparison of results with reference scenario. They have used the data of 2010 as the base year and simulated it up to the next 30 years. The results of this study specify that due to the growing rate of population in urban as well as in the rural area, the demand for electricity significantly increases.
Using the LEAP model, Environmental and economic effects of various electricity generation scenarios have been studied by Park et al. (2013) for Korea. The simulation results of this study signify that although the electricity generation cost of renewable-based scenario is higher but greenhouse gas emissions are about 80% lower in this scenario than the other scenarios in spite of exclusion of nuclear energy. The increased cost of renewable energy scenario can be compensated by the other benefits like a decrease in GHG emissions, deterioration of environment and dependence on imports of energy from other countries. This paper demonstrates that implementation of sustainable development is not a difficult task it can be achieved very easily with the help of political determination and contribution by the general public.
McPherson and Karney (2014) have carried out a study of the existing as well as the future power generation scenarios and their effects on the generation costs, environmental impacts and diversified power generation for Panama. Modelling through the LEAP model, four different scenarios have been developed and evaluated in this study. The study concludes that with the utilization of renewables for the diversification of electricity 42
generation system of Panama will decrease the power generation costs and also minimize the vulnerability of environment due to hazardous emissions.
Emissions reduction in the electricity sector of Turkey has been evaluated through scenario-based modelling software LEAP by Özer et al. (2013). Under the different power policies of Turkey, business as usual (BAU) and emissions mitigation scenarios have been developed for the simulation of results from 2006 as a base year until 2030 end year of the study period. In BAU scenario the carbon dioxide CO2 emissions increase considerably while in emissions Mitigation scenario the CO2 emissions from electricity generation rise only 5.8% per year during 2006 to 2030 period. In the conclusion, it has been suggested that due to the modifications in electricity generation structure varying amounts of CO2 reduction could be achieved.
Gul and Qureshi (2012) have developed a model for diversified electricity generation system for Pakistan through the LEAP. They have simulated the electrical power supply model to get economically feasible fuel mix which has minimum environmental impacts.
In the conclusion, they have recommended that Pakistan has the opportunity for making its power sector more economically and environmentally viable to develop hydro and other indigenous renewable energy resources instead of planning power generation from coal.
2.6 MITIGATION OF GHGS AND OTHER AIR POLLUTANT EMISSIONS
The affordable, reliable and environmentally acceptable availability of electricity for the commercial, industrial and residential use of a country plays a significant role in its economic prosperity and sustainable development. Therefore, the reduction of GHGs and 43
other air pollutant emissions from electricity generation sector is essential to minimize the
impacts of air pollution on the humans and natural environment. Generation of electricity
from renewable energy sources is now considered an alternative energy source to reduce
atmospheric pollution. Many studies have been carried out in this context. Tsilingiridis et
al. (2011) have conducted research for the reduction of air pollutant emissions using
renewable energy sources for power generation in Cyprus. According to this study, the
power generation is the major contributor to emissions like CO2, SO2, NOx, and N2O in the total emissions of Cyprus. For the mitigation of these emissions, the Government of Cyprus has planned to produce 211 MW of electricity from RES in future which will reduce a significant amount of these air pollutant emissions. It is concluded in the study that although the contribution of RES is very low in the energy mix of Cyprus, but the targets put by the government are ambitious.
Boudri et al. (2002) have studied the potential of renewable energies in China and India and their cost-effectiveness in air pollution abatement in Asia. They have compared the costs and environmental integrated assessment of the fossil fuels with renewable energies using the RAINS-ASIA model for the period of 1990-2020. They have performed an optimization analysis, aiming to minimize the abatement costs. In the RAINS-ASIA optimization model, two scenarios BAU (Business-as-Usual) and POL (Policy) have been developed. It is concluded that in POL- scenario emissions are lower than in the BAU- scenario because of increasing share of renewable energy sources. CO2 emissions are 18% lower in the POL scenario than in the BAU-scenario, as estimated for India, similarly, these are around 7% lower in POL scenario than in the BAU-scenario for China up to 2020. The 44
SO2emissions from India and China are 20% and 5% lower in the POL scenario than in the
BAU-scenario, respectively for 2020.
The Macedonian existing lignite base power system has been reviewed for the mitigation of GHG emissions potential by Taseska et al. (2011) using WASP model. In this study, one business as usual and two mitigation scenarios have been developed for the period of
2008 to 2025. In the first mitigation scenario, some lignite coal fuel based power plants are replaced by natural gas combined cycle power plants while in the second mitigation scenario the share of latest renewable energy sources is increased along with the decrease
in consumption of electricity for large industrial users. The results show that emissions
reduce from 78% to the 41% in the first scenario whereas in the second scenario they reduce
from 78% to 14% during the study period of 2008 to 2025.
Mahesh and Jasmin (2013) have analysed the potential of renewable energy and its
investment for the mitigation of CO2 emissions in India. According to the analysis of this study, the share of investment in the renewable energy based projects increases more than
35% and it reaches around $12 billion in the energy mix of the country in 2013 as compared to 2011. The study concludes that India has huge potential to the mitigation of CO2 emissions through investment in renewable energy sources which is the best way for CO2 emissions mitigation.
Anandarajah and Gambhir (2014) have developed models for the reduction of CO2 emissions through renewable energy sources for the implementation of climate change policy in India. The analysis of the study shows that renewable energy sources can play an important role in carbon free energy system. The results of the study demonstrate that there 45
are a lot of challenges available in the way of the development of renewables in the country which should be settled for the enhancement of renewable energy sources for the implementation of climate change policy.
Renewable energy potential and its environmental, social and economic impacts on the power structure of Pakistan have been analyzed by Farooq et al. (2013). They have used the bottom-up long term energy system modelling tool based on MARKAL. They have analysed that under renewable implementation policy the fossil fuel consumption would reduce more than 90% in 2050 and GHG emissions would also decrease from 490 million tons to 27 million tons. However, the cost of electricity generation extensively increases by the implementation of policy for power generation from renewable sources.
Yousuf et al. (2014) have estimated the GHG emissions in Pakistan considering the grid- connected power plants. They have evaluated the baseline grid emission factor of the country according to the electricity generation data of 2009 for the mitigation of GHG emissions by alternative electricity generation sources like wind, solar and hydro. They have determined the opportunities to earn the Certified Emissions Reductions (CERs) which are financial support to emissions mitigation projects.
The studies reviewed above have been performed to evaluate the significance of renewable energy sources for the mitigation of emissions produced from conventional fuel sources.
In Pakistan, the studies so far have been undertaken to explore the potential of all renewable energy sources and their social, economic and environmental impacts on the power sector.
None of the studies have been carried out to assess the potential of wind power for the reduction of air pollutant emissions from the thermal power plants. In the current study, 46
the potential of mitigation of air pollutant emissions through wind power has been forecasted.
2.7 DIFFUSION OF WIND POWER
Global economic development is highly related to the supply of energy which has been accompanied its transition from one major fuel source to another. Currently, fossil fuel based sources are the dominant energy sources throughout the world. But the growing environmental concern, depletion of fossil fuel sources and increasing prices are the main factors to the next transition in the form of renewable energy sources. Various research activities have been undertaken for the diffusion of renewable technologies throughout the world. Ali and Semwal (2014) have analyzed the significance of renewable energy sources in India. They have stated that due to the high energy consumption, increasing share of coal consumption for meeting high energy demand, extensive dependency on imported fuels and instability of oil prices in the global market, the importance of renewable energy sources in the country have largely increased. They have indicated that wind energy has developed rapidly in India than other renewable sources and ranked the country fourth position throughout the world in case of wind power installed capacity. In the conclusion of this paper, it has been suggested that for the mitigation of GHG emissions the renewable energy sources should be further boosted up in India in future.
Kim et al. (2014) have made a research plan for the development of renewable energy sources based on the forecasted diffusion of renewable energy in the Korea by investigating the experiences of other developed countries having similarity index with Korean market. 47
Due to the lack of historical data on renewable energy developments in Korea, the analogous approach is adopted by taking the German market as the reference case on the basis of similarity. The logistic function is used to analyze the effect of the investment on the development of renewable energies. This study concludes that for the reduction of heavy reliance on the imported fuels a reasonable investment for the development of renewable energy sources should be made.
Daim et al. (2012) have forecasted the diffusion of various renewable energy sources like biomass, geothermal, wind and solar through logistic growth curve using the historical data of these renewable energy sources in the USA. The results of this study indicate that if current research, development and demonstration rates persist then these technologies will not attain expected development rates, therefore, the government of the USA and other state governments should make some policies to increase the development of renewables in the country.
Mabel et al. (2008) have predicted the growth of wind energy in five states of India. In this study, the logistic diffusion model is used to forecast the wind energy. The results indicate that the wind energy technology is presently making an important contribution to the power generation system of the country. This study concludes that government policies are mostly favourable for the achievement of the wind power potential in the India.
Davies and Diaz-Rainey (2011) have assessed the international diffusion of wind energy under different scenarios. They evaluate the effect of diffusion of wind energy under the governmental policies and observed that pattern of diffusion of wind technology takes a 48
different shape (BASS curve) when the policies are considered. Without any country policy
conventional logistic diffusion takes with similar diffusion speed and path.
2.8 EMISSIONS MITIGATION THROUGH WIND POWER
Wind power is considered as a feasible alternative source for power generation to fossil
fuels which exhaust the natural resources very rapidly and are also responsible for adverse
impacts on environment leading to climate change. Therefore, the global installed capacity
of wind power is increasing very fast to mitigate the emissions discharged from the
combustion of fossil fuels which ultimately reduce the environmental impacts. Several
studies are carried out in many countries to estimate the potential of mitigation of emissions
through the wind. Saidur et al. (2011) have studied the environmental impact of wind
energy and its global energy policy. They describe that wind energy is a clean,
environmentally friendly and cheaper source of energy. Wind energy being an emission
free source of energy can be an effective way of reduction of air pollutants emissions
especially CO2, NO2 and SO2 which are produced during the combustion of fossil fuels for energy generation. Unlike the conventional energy sources, the consumption of water reduces during the generation of energy from wind energy source. On the other hand, wind energy has some negative impacts such as the killing of wild animals and birds during the collision of a wind turbine, noise problems and interference of communication systems.
With the technological improvement, these impacts are becoming very less.
Combined margin baseline emission factors of air pollutant emissions in China’s provincial power grids have been estimated by Cai et al. (2013) for the annually updated status of air 49
pollutant emissions. The estimation of baseline emission factor is very useful for the
researchers and planners in developing a comprehensive strategy for mitigation of air
pollutant emissions from the power sector and other industrial processes. They suggest that
the wind energy is the best technology to attain the largest amount of co-abatement in many
parts of the China by making air pollution control in the much better way along with
earning a financial support by the approval of CDM processes.
Holttinen et al. (2015) have estimated the CO2 emissions reduction by wind energy generation methods using different methodologies. The results of this estimation show that renewable energy sources replacing natural gas have a lower displacement impacts as compared to carbon-intensive coal for which renewable energy source replaces more displacement impacts. In the conclusion, it has been recommended that if an appreciable amount of wind resources are utilized by demand-side management (DSM) then greater
CO2 displacement would be possible. Solanki et al. (2013) have estimated the mitigation of CO2 emissions in power sector of Oman. In this paper, they have evaluated the baseline electric grid emission factor of Oman and suggested that the electricity generation through wind energy projects is the best option for the reduction of CO2 emissions. They also analyse the financial opportunities for CDM projects through wind energy, its challenges, and barriers facing to the implementation.
Ma et al. (2013) have analyzed the co-benefits by wind power plants such as GHGs and other air pollutant emissions mitigation and water savings as consumed during the electricity generation from fossil fuel power plants in Xinjiang Uygar region of China.
They have suggested that due to the inadequate economic resources government could not 50
tackle the problem of mitigation of GHGs and other air pollutant emissions and supply of water for the conventional power plants. Development of wind power not only saves expenses but at the same time it deals with environmental issues also. This study concludes that economic co-benefits like mitigation of GHGs and other air pollutant emissions along with saving of water consumption in conventional power plants could be achieved by using wind energy for electricity generation.
2.9 WIND POWER DEVELOPMENT IN PAKISTAN
For outstanding power generation from the wind, the study pertaining to the geographical distribution of wind speeds, wind parameters, environmental impact assessment, and wind speed data for certain time is very necessary. In Pakistan context, different studies have been undertaken by various researchers and governmental organizations for the wind energy resource assessment and its future dissemination in the country. Harijan et al. (2011) have forecasted the diffusion of wind power in Pakistan using analogous approach and logistic technology diffusion model. They have presented market penetration forecasts of wind power under different policy scenarios.
According to the results of this study, about 42, 58 and 73% of the total technical potential of wind power in the country under Standard Scenario (SS), Moderate Scenario (MS), and
Optimistic Scenario (OS) scenarios respectively could be utilized by the year 2030. They have concluded and suggested that the utilization of the maximum potential of wind energy for power generation would reduce the pressure on fuel imports, mitigate the air pollutant emissions from the environment and improve the socio-economic conditions of the people of Pakistan. 51
Bhutto et al. (2013) have reviewed the past, present, and future of wind energy use in Pakistan and also discussed the difficulties and barriers which are faced for its development. This study recommends that although the immense potential of wind energy in the country exists but efforts are needed to effectively utilize this low-cost renewable energy source Bhutto.
Aman et al. (2013) have estimated the potential of wind energy to overcome the power deficiency in Karachi, the economic heart of Pakistan. In this paper, four years data of wind speed at different heights is collected from the Metrological Department (MD) and analyzed through statistical software which shows that there is the immense potential of wind energy available in the city. They have suggested that with the inclusion of small wind turbines at a domestic level a massive amount of power could be saved and supplied to industrial usage to solve power shortage in Karachi. The problem of the cost of the wind turbine could be settled when the turbines are manufactured by local industry.
Farooqui (2014) has studied the future possibilities of renewable energy sources penetration in the total energy mix of Pakistan. He indicates that the energy consumption in Pakistan is very low as compared to the rest of world because the energy mix in Pakistan mostly depends on imported petroleum products. Although the country is rich in all the renewable energy resources but these resources have not been exploited so far. According to the estimations of this study,
Pakistan has the prospect potential of 30 GW installed capability from hydropower and 50 GW of wind power up to 2030. 52
2.10 COST OF ELECTRICITY GENERATION FROM WIND ENERGY
Wind power generation costs can be competitive with conventional power plants when the fuel prices in international market and environmental impacts due to air pollutant emissions are considered. Although the wind power has no any fuel cost but its capital costs are still high however due to growing diffusion and market competition capital costs of wind power are rapidly decreasing. Total generation cost comparison of wind power plant with conventional fossil-fueled power plants has been evaluated by Mousavi et al. (2012)in Iran using Levelized cost estimation approach. According to their observations, the high subsidies for fossil fuel based power generation in Iran are the main hindrances for the development of renewable energy sources especially wind power.
Wind energy potential site examinations and electricity generation costs studies in central
Turkey have been done by Gökçek and Genç (2009) using time-series method. They economically evaluate the different capacity wind energy conversion systems and investigate the effects of escalation ratio, maintenance costs and annual wind speed on the overall electricity generation cost. The results of this study illustrate that the electricity generation cost of all wind energy capacity systems at various locations in Turkey is between 29 to 35 cents/kWh.
Blanco (2009) has compared the existing wind energy generation costs of onshore and offshore wind turbines based on the survey carried out among the manufacturers and developers of a wind turbine in European countries. This study also analysed the cost reduction potential of wind energy through the use of learning curves and also highlights 53
the economics of wind energy under various policies. According to the results the costs of electricity generation has considerably increased during the past many years due to the rise in the price of associated raw materials but in the long-term, it is expected that costs will reduce. Generation costs of Wind turbines installed on offshore are more than the onshore due to their distance from shore, grid connectivity and constructions in seawater.
Techno-economical assessment and potential of wind energy in many locations of the
Algeria have been examined considering the wind data at the height of 10 m by Diaf and
Notton (2013). They have estimated per kWh of electricity generation with the help of power curves using the S82/1500 Suzlon wind turbines. The results of this study illustrate that some locations have best wind potential for exploiting wind power and their electricity generation costs vary between 0.0179 US dollars per kWh to 0.0518 US dollars per kWh.
In the conclusion, it has been analysed that the wind energy is more feasible than oil for electricity generation in the southern areas of the country.
Harijan et al. (2009) have estimated the per unit electricity generation cost of wind energy at various sites in the coastal areas of Balochistan and Sindh provinces of Pakistan using the net present value analysis technique. It is observed that the electricity generation cost from wind energy in coastal areas of Pakistan varies between 4.2 US cents/kWh to 21.0
US cents/kWh at various locations. From the estimations of this study, it has been observed that most of the wind energy sites at the coastal areas of Pakistan particularly in Sindh province are cost competitive to conventional thermal power plants connected to the grid even not including the externalities. 54
Few researchers in Pakistan have estimated the Levelized cost of electricity generation of
wind power in the country. Most of them estimated the cost of electricity generation from
wind power for the particular location in the country, not for the overall country
considerations. Moreover, none of them have compared the estimated cost from wind
power with other fossil fuel based electricity generation sources. The current study
estimated the Levelized cost of electricity generation from wind power for throughout the
country and also compared the estimated cost of electricity from wind power with fossil
fuel based electricity generation sources such as Gas Turbine Diesel oil Electricity
Generation, Local Coal Combustion Electricity Generation, Imported Coal Combustion
Electricity Generation, Integrated Gasification Combined Cycle Electricity Generation and
Combined Cycle Natural Gas Electricity Generation sources.
2.11 CDM POTENTIAL OF WIND POWER
CDM is a market-oriented mechanism developed in the Kyoto Protocol through which
sustainable development projects in developing countries could earn saleable carbon
credits equivalent to the amount of CO2 lessen or avoid. The qualifying ratio of the projects from wind energy under the CDM is more than the other technologies that could earn credits to avoid the combustion of fossil fuels for electricity generation. In developing countries like China and India researches have been performed to estimate the CDM potentials of various renewables most likely in wind energy technology to earn credit from industrialized countries for sustainable development. Pechak et al. (2011) have evaluated the experiences achieved through the implementation of CDM, in order to investigate its 55
role in the development of wind energy markets in developing countries. They have mentioned the role of United Nations International agreement “the Kyoto Protocol” in which 37 industrialized countries recognized as Annex-I countries have been bound to reduce their GHG emissions by getting credits through CDM and assist the developing non-Annex-I countries for their sustainable projects. They have concluded that due to the lack of necessary data of wind energy in the developing countries, the project developers face difficulties in the development of projects. They carry out studies for the requirements of necessary data on their own expenses which further delays the implementation of the projects.
Nautiyal and Varun (2012) have investigated the existing status as well as the development of renewable energy sources in India. They have specified that Kyoto protocol is the key agreement between the countries to counter the climate change problems. It allows the developing countries to sell emission credits known as CER. They have defined and showed the seven steps of CDM cycle (Development of project, project validation,
Registration, Monitoring of project, project verification, Certification, Issuance of CER).
In the conclusion discussions, they have identified some barriers associated with the CDM projects and suggested that there should be some more effective policies to overcome these barriers and make CDM more effective to accomplish the objectives related to environmental protection.
Purohit and Michaelowa (2007) have carried out the research on the Potential of wind power projects under the CDM in India. They have estimated the diffusion of Wind power projects in India using the logistic diffusion model for time variation of cumulative installed capacity 56
of wind power. Two scenarios for the diffusion of wind power such as business as usual or
standard scenario (SS) and optimistic scenario (OS) have been presented. According to the
results of this research study, there is an immense theoretical potential of reduction of CO2
emissions through the utilization of wind power in India. But on the basis of the historical
trend of diffusion of wind power in the country, the authors have projected that despite the
very high favourable assumptions; the diffusion of wind power is not expected to reach the
maximum utilization potential in next fifteen years.
Jain and Kushare (2007) have estimated the cost-effectiveness of renewable energy sources
such as wind turbine and solar PV systems considering carbon emissions trading under
CDM. The results of this paper exhibit that the total gross potential of solar and wind energy
in India could attractabout1 billion $ revenue per year through carbon trading at the existing
carbon price of 3 $/ton of CO2 in the global market. This carbon credits in addition to the generation of revenue also help to protect the environment by emissions reduction due to the implementation of renewable energy sources.
Solanki et al. (2013) have estimated the mitigation of CO2 emissions in power sector of
Oman. They have evaluated the electric grid emission factor of Oman and suggested that the electricity generation through wind energy projects is the best option for the reduction of
CO2 emissions. They have estimated that a 25 MW proposed wind power plant replaces about 45552 tons of CO2 emissions annually from grid-connected electricity supply which could earn US$ 61.49 million for the 10 years credit period. Quantification of baseline emission factors of air pollutant emissions of regional power grids in China has been carried out by Cai et al. (2013). According to the estimations of this study, the average emission 57
factor of the three main air pollutant emissions of NOx, SO2, and PM2.5in six power grids of
China as per the electricity generation data of 2010 is 1.9 kg/MWh, 1.8 kg/MWh and 0.3 kg/MWh respectively. They have stated that although some air pollutant emissions control technologies such as low nitrogen oxide burner (LNB) could replace a significant amount of emissions but wind power being an emission free source of power generation could replace the total amount of air pollutant emissions in China. The projects of wind power along with controlling air pollution can earn revenue through CDM approval process.
CDM implemented by Kyoto Protocol is a very supportive mechanism for the achievement of emissions mitigation targets of GHGs and other air pollutant emissions. Additionality process for the CDM projects plays the vital role in the eligibility of CDM projects. The
Additionality determination of small scale hydropower projects has remained an issue in the parties under the UNFCCC. The case study for the determination of “Additionality” activities of Shiba hydropower CDM project in China has been performed by Yunna and Quanzhi
(2011). Through the establishment of baseline and Additionality analysis, it has been verified that Shiba hydropower project is CDM based project meeting all the requirements of
UNFCCC which could generate about 40,697 tons of CO2 emissions reductions.
Biomass gasification projects replace GHGs and other air pollutant emissions, which could be a big contribution towards sustainable development in rural areas. Therefore, being an emissions reductions energy sources they could generate CERs through the registration under
CDM. In this regard, efforts have been made by Purohit (2009) in India to analyse the economic potential of biomass gasification projects through CDM. The results of this analysis indicate that there is an immense potential for CO2 mitigation by the use of biomass 58
gasification projects in India. The study estimates that a 31 GW of biomass gasification potential is available from 74 million tons of agricultural residues in the country which could generate more than 67 TWh of electricity annually. The CERs from the mentioned biomass gasification potential theoretically could reach 58 million tons annually in India. He has done these projections under the diffusion of biomass gasification projects based on past running programs by the government of India.
2.12 CONCLUSIONS
It is evident from the literature review undertaken in this chapter that generation of power from conventional fossil fuels is contributing large amounts of GHGs and other air pollutant emissions which have serious adverse effects on the global, regional and local environment. In this context, renewable energy sources particularly wind energy is getting greatest ever attention for electricity generation and to mitigate these emissions. However, in Pakistan’s context, so far no study has been undertaken which estimate the GHGs and other air pollutant emissions from the existing as well as the planned power generation system of the country and mitigation these emissions through wind energy. As such, this study using energy and environmental modelling tool “LEAP” estimates the GHGs and other air pollutant emissions from the existing as well as the planned power generation system of Pakistan from the base year 2013 to the end year 2035. Further, this study covers the emissions mitigation through wind energy for electricity generation and estimates its
CDM potential in Pakistan which is exceptionally rare in the existing literature. Chapt er 3 CHAPTER 3 ELECTRICITY GENERATION POLICIES AND PLANS
3.1 INTRODUCTION
Sustainable generation and thus the supply of electricity is considered as the key determinant for the social and economic prosperity of a country. From heavy industry to a small enterprise, large commercial activities to household activities largely depend on the constant supply of the electricity. The increasing population and technological developments have extensively increased the electricity demand. This chapter deals with the overall electricity generation structure of Pakistan. Electricity generation sources, shares of different fuels and their consumption for electricity generation, emissions from electricity generation, existing power generation policies, their goals, and targets are broadly discussed in this chapter.
3.2 ELECTRICITY DEMAND AND SUPPLY SITUATION IN PAKISTAN
Pakistan inherited power generating capability of only 60 MW at the time of independence in 1947 for the population of 31.5 million. Soon after the independence, the government of
Pakistan in 1952 obtained the maximum shareholdings of KESC and created the WAPDA in 1958, the objective was to develop the power sector of the country for entering into the phase of development. Later on, the power capacity of the country was expanded to 119
MW in the year 1959 (Chaudhry, 2010). Power sector development got momentum following 1970 with the installation of various thermal and hydro units. The total installed
59 60
capacity was 636 in 1970 which was increased to 9094 MW in 1990.The number of electrified villages in the country was 609 at the time of establishment of the WAPDA which was increased to 1882 villages by the year of 1965 (Javaid et al., 2011, Kessides,
2013).
However, due to the increasing industrialization, urbanization, and electrification of rural areas, the electricity consumption in the country escalated very fast, which led to the 9-10 percent growth in demand of electricity per annum (Rauf et al., 2015). The generation of power was incapable competing with the increasing demand in early years of the 1990s which created an excessive shortage of power, especially for the industrial and commercial consumers. This shortage of power imposed the load shedding of 1500 to 2000 MW. The major cause of this capacity shortage was the lack of budgets for the costly oil fuel reliance existing power system as well as the investment for new installations into the power system. The economic growth of Pakistan declined to 4-5 percent annually as compared to the previous years of 6 percent annually during the period of the 1990s (Kessides, 2013,
PIDE, 2012).
Dealing with crucial issue government decided to develop ‘Policy Framework for
Motivation of Private Sector Power Generation system in 1994. The inclusion of private sector into power development and expansion plan although proved to be a generation of surplus power, however, this policy led to the dramatic expansion of thermal power projects resulted in costly generation system instead of very cheap hydro/thermal power generation mix in the country. 61
The annual growth in the power demand was continuously increasing and it had reached
more than 7 percent annually during many years. Although the increasing demand was very
slow and it was about 3 to 4 percent annually up to 2003-2004, however it sharply increased
in subsequent years and reached 10 percent annually in 2007-2008. During 2003-2004
periods generation of power was about equal to the demand but unfortunately, this status
was not continuous in the following periods. The supply of electricity remained lagging
behind the demand in the country (Rauf et al., 2015). The status of demand and generation of power from the year 2003-04 to the 2013-14 has been given in the Fig. 3.1 which shows that the gap between generation and demand has crossed the 7000 MW (NTDC, 2014).
Demand Generation 30000
25000 )
W 20000 M ( y t
i 15000 c i r t c e l 10000 E
5000
0
Years
Fig. 3.1: Electricity demand and generation status (NTDC, 2014)
A major cause of the existing power shortage issue is a lack of long term planning for power expansion. According to the Integrated Energy Plan 2022, it will be required to the 62
power sector of Pakistan to induct 3000-4000 MW of power annually into the national grid
up to 2022 when the total power demand will reach the 55000 MW (Ullah, 2013).
Unfortunately, since many years neither adequate new power installations have been made
nor have measures been taken for improvements of the existing power generation system.
The efficiency of the existing public sector thermal power generation plants (GENCOs) is
very poor and most of the plants are working at their de-rated capacity. Largely relying on
oil based thermal power generation has contracted the exploitation of national resources
like least cost power generation projects from hydro wind and solar power (Kessides, 2013,
Rauf et al., 2015).
Electricity generation sector in Pakistan is largely in the grip of fossil fuels. Electricity is mostly generated from imported oil, indigenous natural gas and a minimum amount of local coal. Massive potential of hydro, other renewables and nuclear energy resources are available in the country but these resources have not been significantly exploited yet.
Further, despite the increasing demand for power in the country, no significant increase in supply side has been carried out. Total installed capacity increased at a meager escalation of 14.86% such as from 19420 MW in 2008 to 22812 MW in 2013. The major increase is
85% in thermal installed capacity by IPPs at elevated tariff, which are generating electricity from very expensive imported furnace oil and natural gas. The total installed capacity from
2008 to 2013 in the national grid has been shown in Fig. 3.2 (HDIP, 2013). 63
25000 ) W
M 20000 ( y t i c a 15000 p a c d e l
l 10000 a t s n i
y 5000 t i c i r t c
e 0 l
E 2008 2009 2010 2011 2012 2013 Years
Hydel Thermal (WAPDA) Thermal (K-Electric) Thermal (IPPS) Nuclear
Fig. 3.2: Electricity installed capacity during 2008-2013 (HDIP, 2013)
According to the data from “Hydrocarbon Development Institute of Pakistan”, the total electricity generated in the country during the financial year 2012-13 was 96,122 GWh.
The contribution from fossil fuels was the huge chunk of the generation having 64.2%
(61711 GWh) of the total generation in which the shares of natural gas and furnace oil were
56% (34604 GWh) and 44% (26914 GWh) respectively out of the fossil fuel fired plants
(61711 GWh). The share of electricity generation from each source has been shown in the
Fig. 3.3 (HDIP, 2013).
Like global trend power sector in Pakistan is also dominated by fossil fuels based thermal electricity generation. Most of the power plants run on the imported furnace oil and domestic natural gas. In addition to this, a small share of electricity is also generated from coal. The generation of power by large scale through fossil fuel based power plants has 64
promoted the environmental emissions such as GHG and other air pollutants. The CO2 emissions in Pakistan have increased from 14 million tons in 1960 to 163 million tons in
2011 as shown in Fig. 3.4.
Oil Hydro 36% 31%
Coal 0.1% Gas Nuclear 28% 5%
Fig. 3.3: Electricity generation during 2012-13 (HDIP, 2013)
The main sources of these emissions are power generation, transportation, and cement manufacturing industries, in which power generation contributes more than 40% of these emissions (WB, 2016b).
Although Pakistan’s contribution to the global GHG emissions is miniscule which is not playing a key role in global warming but its emissions based on various sectors come to be the main source of GHG and other air pollutant emissions. According to the Global Economy rankings, the share of Pakistan in total global GHG emissions is merely 0.8 percent and it is ranked as 135th in the list of global emitters on a per capita basis. However, due to geophysical conditions, climatic extremes, it is ranked as the third position in the Global
Climate Vulnerability Index (PAK-INDC, 2016). 65
180
160 ) s
e 140 n o t
n 120 o i l l i 100 M ( s
n 80 o i s s i 60 m e 2
O 40 C 20
0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Years
Fig. 3.4: CO2 emissions growth in Pakistan
Air pollutant emissions and GHGs are also rapidly growing which cause an environmental problem in Pakistan. According to the reports of the Pakistan Environmental Protection
Agency (Pak-EPA), air pollution levels for the many big cities of Pakistani have been recorded seven times higher than those established by the international organizations. Highly inefficient power generation system, inadequate air pollution control technologies, accelerated growth in number of vehicles, increasing industrial activity and open burning of solid waste are some of the key factors for declining ambient air quality in Pakistan. In order to resolve the problem of electricity shortage in the country on a permanent basis and focusing on converting the energy mix toward the low-cost sources, the government has developed different National Power Policies. 66
3.3 NATIONAL POWER POLICY 2013
For the mitigation of prevailing power crisis in the country, Ministry of Water & Power government of Pakistan announced an ambitious power policy in 2013. The aim of this power policy was to develop a strategy to meet the present and future power requirement of the country. This bold step not only will deal with main challenges that are presently confronted to the power sector of Pakistan but it will also put the country on the rapid economic as well as the social development. The outcomes of this power policy will also provide much relief to all citizens of the country with changing their standard of living. In this power policy worthwhile incentives have been offered for the local as well as for foreign investors for the expansion of power generation capacity with a concentration on the generation of very inexpensive electricity from the indigenous available resources.
3.3.1 Goals
For the achievement of long term solution of existing power crisis along with defeating its challenges in the country, Government of Pakistan placed the following goals in the power policy.
· Develop a power generation and expansion capacity of Pakistan in such a way that
it could meet power requirements of the country in a sustainable manner.
· Utilize the available indigenous resources like coal (Thar coal) and hydro for power
generation to ensure the inexpensive and affordable power generation for domestic,
commercial and industrial consumers of the country.
· Build a culture of energy conservation and responsibilities for its proper usage. 67
· Reduce the pilferage and adulteration in the supply of fuel for the power generation
plants.
· Make advancement in power transmission network for minimization of
transmission losses.
· Develop a world standard efficiency in power generation system
· Reduce inefficiencies in the power distribution network.
· Reduce financial losses in power generation system.
· Ensure good governance in all federal as well as provincial level departments and
regulating bodies involved in the administrative system of power generation sector.
3.3.2 Targets
The government of Pakistan put the main targets for achieving the goals of demand and supply gap, efficiency, affordability, financial capability and good governance in power system. The achievements of these targets will measure the success of the policy in addition to the nation’s ability to overcome the main problem faced by the power sector. The following are the main targets of the power policy.
· Reduce the current demand and supply gap of power from 5000-6000 to 0 by 2017.
· Reduce the present generation cost from 12¢ /kWh to 10¢/ kWh by the year 2017.
· Reduce the transmission and distribution network losses of 23-25% to16% by 2017.
· Increase the recovery of electricity bills from85% to 95% by the year 2017.
· Reduce the time duration for decision-making process at the Ministry, associated
departments of power generation and regulations from long duration to short. 68
3.3.3 Impacts of implementation
The achievements of all the goals of this policy would bring economic improvements in the power sector of the country. By the end of the five-year term of this government it is expected that the gap between demand and supply would not only eliminate but in addition to this, the country would have also an abundant supply of power. This additional power could be sold to the nearby regional countries which will alter the Pakistan from the energy deficient country to the regional power exporter country. The implementation results of the power policy will also reduce power generation cost significantly which would decrease power bills of the end consumer. The efficient renovations in transmission and distribution system will reduce the line losses.
Although the above improvements and expansions in power sector are carried out and furthermore the generation cost decreases with the implementation of main goals of this policy but electricity generation from local coal (lignite) as well as imported coal
(bituminous) will highly affect the environment due to hazardous emissions. The environmental concern and the effects of these emissions on it are the main focus of this study.
3.4 POWER GENERATION POLICY 2015
The government of Pakistan is working on multi direction approach consist of development of power projects based on indigenous as well imported resources both in public and private sector to overcome the power crisis in the country. The expansion of power also requires the heavy transmission network, therefore, the Government of 69
Pakistan has decided to expand the national transmission system through the private sector.
Provision of all facilities along with the safe environment for the competitive concession and handsome return on investment for the private investors is necessary.
In order to develop the infrastructure of the power sector and its expansion in Pakistan, the supporting of local as well as foreign investors should be the main factor in all policies announced by the government. For power generation in Pakistan fuels like imported coal and other fuels have their different supply chain which needs to be addressed. Other problems such as the delay of payment to the power generation companies with liquidity issues can be addressed by comprehensive power generation policy.
Keeping in view all above issues government has announced a new power generating policy in 2015 by offering very attractive incentives for the elimination of the supply and demand gap of power in a minimum time with the very simplified process. This power generation policy will bring affordable electricity for the socio-economic development of the country. In order to achieve the targets of the power policies discussed above the government has made a plan to increase the electricity generation by including indigenous low-cost energy sources in power generation mix.
3.5 ELECTRICITY GENERATION PLANS OF PAKISTAN
The government of Pakistan has determined to overcome the existing power crisis through the increase of installed capacity in the energy mix of the country with the 70
development of various power projects based on coal, hydro and renewable energy sources. As a result of these efforts, it is expected that about 16 to 20 GW of power generation capacity would be added to national grid during the next four to five years, which would reduce the load shading duration. A brief assessment of these future power projects in light of current national power policy is discussed as under:
3.5.1 Hydroelectric power
There is a huge potential of hydropower in the country which could be utilized to produce low-cost electricity. The government has made medium and long-term plans for hydropower capacity expansion under the current power policy. Six projects with the total capacity of 384 MW have been completed and added to the national grid during
2015. The Gulpur and Patrind hydropower projects of smaller capacities are anticipated to be completed by the end of 2017, which will add 247 MW to the national grid.
Further, around 969 MW of power addition is expected from the Neelum-Jhelum project by November 2016. A number of hydropower projects are anticipated to come online in 2017 including the fourth and fifth Tarbela expansions, which have the potential of generating electricity to the tune of 1910 MW. The detail engineering design for projects at Patan 2,800 MW, Dasu of 2,160 MW, and Thakot 2,800 MW has been undertaken or will be undertaken under the current power policy. Some other long- term projects are Bunji 7,100 MW, Kohala 1,100 MW, and Diamer-Bahasha 4,500
MW. Completion of these projects may secure the country from energy crises. Brief detail of these projects is listed in Table 3.1 (GOP, 2013a, WAPDA, 2011). 71
3.5.2 Coal power
Pakistan is blessed with the 6th largest reserves of coal in the world. These deposits have been found in all four provinces of the country; as well as in Azad Jammu and
Kashmir. The total reserves have been estimated to be 186 billion tons, out of which major reserves of 185 billion tons are in the province of Sindh (Uqaili et al., 2005b).
Realizing the benefit of coal based power generation, the government has decided to install coal power plants at different locations using both public and private investment.
Initially, 10 power units, each of 660 MW, total 6600 MW of installed capacity on imported coal energy will be established at Gaddani Energy Park in Balochistan.
Furthermore, many power plants are proposed to be installed in various districts of the
Punjab province based on imported coal.
Sindh Engro Coal Mining Company (SECMC) the joint venture of Sindh Government and Engro Power Company is also developing a coal mine and power plants in block II of Thar coal mines. SECMC is expected to complete the construction of a 660 MW power plant in the first phase by 2018, while in the second phase another 660 MW power plant would be commissioned by 2019. In Block III of Thar coal mines, 5000
MW power plants are expected to be installed by various companies, whereas 7,500
MW of power by Sino Sindh Resources (Pvt.) Limited (SSRL) China will be established in Block I of coal mines in different Phases. Three power plants each of
1320 MW are proposed to be in installed at Jamshoro, Lakhra, and port Qasim by
PEPCO and K-Electric. Table 3.2 below summarizes coal based power plants under current power plans by the government of Pakistan (Coutinho, 2014, TCEB, 2015). 72
Table 3.1: Proposed hydropower plants Capacity Executing Expected date of Name of power plant (MW) agency completion Hydro Neelum Jhelum 969 WAPDA 2016 Tarbela 4th,5th extension 1910 WAPDA 2017 Patrind Hydropower 147 IPP 2017 Akhori dam project 600 WAPDA 2018 Sehra Hydropower project 130 IPP 2019 Dasu Hydropower project 4320 WAPDA 2019 Diamer Basha Dam 4500 WAPDA 2020 Suki Kinari Hydropower 870 IPP 2020 Karot Hydropower project 720 IPP 2020 Bunji Hydropower 7100 WAPDA 2022 Azad Pattan Hydropower 640 IPP 2022 Lower Palas Hydropower 665 IPP 2022 Lower Spat Gah Hydropower 496 1PP 2022 Kohala Hydropower project 1100 IPP 2023 Mahl Hydropower project 590 IPP 2023 Thakot Hydropower 2800 WAPDA 2024 Patan Hydropower 2800 WAPDA 2025 Munda Dam project 740 WAPDA - Mohmand Dam Hydropower 800 WAPDA - Shyok Dam project 690 WAPDA - Chakoti-Hattan Hydro project 500 IPP - 73
Table 3.2: Proposed coal power plants Capacity Executing Expected date of Name of power plant (MW) agency completion Imported coal Coal power plants at Punjab 2x660 PEPCO 2018 Coal power plants at Punjab 5280 IPP Different phases Coal power plants at Jamshoro 2x660 PEPCO 2018 Coal power plants at Gaddani 2x660 PEPCO 2018 Coal power plants at Gaddani 8x660 IPP Different phases Coal power at Port Qasim 2x660 IPP 3-4 Years Conversion of Jamshoro power plant 850 PEPCO Different phases from oil to coal Conversion of Muzaffargarh power 1350 PEPCO Different phases plant from oil to coal Conversion of Guddu power plant 640 PEPCO Different phases from oil to coal Conversion of K-Electric power plants 1260 K-Electric 2018 from oil to coal Conversion of HUBCO power plant 1292 IPP Different phases from oil to coal Local coal Sino Sindh Resources (Pvt.) Limited 7500 IPP Different phases (SSRL) China Thar Power Company 5000 IPP Different phases Ltd. (THARCO) SECMC Oracle Coalfields UK 1400 IPP Different phases GENCOS 1320 PEPCO 3-5 Years Sindh/ETON Japan power 3960 IPP Different phases
3.5.3 Oil and natural gas power
The steep increase in furnace oil prices in the international market has rendered the electricity generation mix highly unsustainable and costly in Pakistan. This generation mix is economically unviable also when externality costs of pollutants like greenhouse gas emissions are considered. In this regard, the government has planned not to install oil fuel- based power plants in future, therefore, in this study, the existing oil fuel power plants are 74
gradually decreased whereas no new power plant is considered for the modelling of future electricity generation.
Natural Gas is a clean, safe, efficient and environment-friendly fuel. It contributes about 45% of the total primary energy supply mix in the country. Pakistan has a widespread gas network of pipelines to cater the requirement of more than 8.4 million consumers across the country by providing about 4 billion Cubic Feet per day natural gas (GOP, 2014a). The GOP is implementing a multi-pronged approach which includes importing piped natural gas from neighbouring countries like Iran and Turkmenistan or LNG from Qatar and to develop indigenous energy resources to meet its energy needs, especially for power generation
(Farooq and Kumar, 2013). In this context two power plants based on natural gas Uch-II and
Guddu having installed capacity of 404 MW and 747 MW respectively have been completed and added to the national grid in 2014.Four power plants having total capacity of 4883 MW on Re-gasified Liquefied Natural Gas (RLNG) fuel based are under process on different locations in Punjab province which will be expected to complete up to 2019. Table 3.3 below summarizes the natural gas-based power plants which have started commercial operation or expected to start commercial operation in future (PPIB, 2016).
Table 3.3: Proposed natural gas power plants Capacity Executing Expected date Name of power plant (MW) agency of completion Uch-II power plant 404 IPP 2014 Guddu power plant 747 PEPCO 2014 RLNG based power plant Bhikki Punjab 1180 IPP 2017 RLNG based power plant Balloki 1223 IPP 2018 Punjab RLNG based power plant Haveli Punjab 1230 IPP 2018 RLNG based power plant Jhang Punjab 1250 IPP 2019 75
3.5.4 Nuclear power
The Pakistan Atomic Energy Commission (PAEC) is responsible for planning, construction, and operation of nuclear power plants in the country. PAEC is presently operating three plants i.e. Karachi Nuclear Power Plant (KANUPP), along with Chashma Nuclear Power
Plant Unit-1 (C-1) and Unit-2 (C-2). The construction of two more power plants at Chashma,
C-3, and C-4, of 340 MW each are in progress and are expected will be commissioned by the end of 2017.
The ground breaking ceremony of two Karachi Coastal Nuclear Power Plants (K-2) and (K-
3) of 1,100 MW each was held in November 2013 and it is expected that they would be completed in 2020. The 2,200 MW nuclear power projects are proposed at the coastal belt of Balochistan near Hub as well. A summary of nuclear power plants under the current power policy is given in Table 3.4 (PAEC, 2014).
Table 3.4: Proposed nuclear power plants Expected Capacity Executing Name of power plant date of (MW) agency completion Chashma Nuclear power plant unit-3 (C-3) 680 PAEC 2016 – 2017 and unit-4 (C-4) Karachi Nuclear power plants (K-2) 2200 PAEC 2020 and (K-3) Chashma Nuclear power plant unit-5 1000 PAEC Proposed Coastal Nuclear power plant Hub 2200 PAEC Proposed Balochistan 76
3.5.5 Renewable power
Pakistan has immense potential of renewable energy (RE) resources and, if exploited
effectively, they can play a significant role in the energy security and independence. In
2003, the Alternative Energy Development Board (AEDB) was established to work as a
central agency for the development, promotion, and facilitation of renewable energy
technologies and also to make appropriate policies and plans for their utilization. The
Government of Pakistan has given a task to AEDB to ensure that 15% of total power
generation is from renewable energy by 2030 (Farooqui, 2014). Solar, wind, and biomass
are the main RE resources in the country, the potential of each resource is discussed as
under:
3.5.5.1 Solar energy
Pakistan being in a sunny region can acquire the advantage of solar energy technologies appropriately. The mean solar radiation in the country is recorded as 200-250 watt per m2 per day. This potential is feasible for both PV and solar thermal applications. Realizing this potential, the Government has taken steps to harness power from solar. A 1,000 MW of solar PV plant has been undertaken at “Quaid-e-Azam Solar Power Park” in district
Bahawalpur, Punjab province on 9th May 2014. Another 500 MW solar based power plant is also on cards to be set up by a Canadian Company at “Quaid-e-Azam Solar Power Park” which will be completed in two years (DAWN, 2014). 77
3.5.5.2 Wind energy
The wind corridor in Pakistan that is 60 km wide starts from Gharo to Keti Bander along the coastal area and 180 km long up to the Hyderabad in Sindh province is blessed with wind energy. In addition to that resource, there are other wind sites available in the coastal belt of Balochistan and some Northern regions of the country. The latest wind energy potential in Pakistan was estimated by the National Renewable Energy Laboratory (NREL)
USA in collaboration with USAID.
Table 3.5: Proposed renewable (other than hydro) power plants Capacity Executing Expected date Name of power plant (MW) agency of completion Solar PV power park Punjab 1000 AEDB Different phases Canadian Solar power 500 AEDB 2016 Company Zurlu Wind Energy Sindh 56 AEDB 2014 Fuji Fertilizer Energy Sindh 50 AEDB 2014 Capacity of wind power to be 2726 AEDB 2016 commissioned Bagasse based power plants 83 AEDB -
According to this estimation, the total potential of wind power is 346 GW, out of this
about 60 GW to 70 GW is technically exploitable but in spite of this potential, wind
power has not been utilized in Pakistan on large scale (GOP, 2011, Siddique and Wazir,
2016). Only two power plants “Fuji Fertilizer Energy Company Limited (FFCEL)” and
Zurlu Wind Energy” with cumulative /installed capacity of 106 MW have been
connected to the National grid. Other wind power plants i.e. 50 MW Foundation Wind
energy I, 50 MW Foundation Wind energy II, 50 MW Sapphire Power, 50 MW Metro 78
and 50 MW China Three Gorges are at final stages. In addition to these, Letters of
Intent have also been issued by AEDB to various power generation companies for the
installation of 450 MW of wind power projects. Another 2,276 MW of wind power
projects are currently in the feasibility process. The brief of renewable energy projects
already undertaken or to be undertaken as per governments plans are given in Table 3.5
(AEDB, 2015a, Bhutto et al., 2013, GOP, 2013a).
3.5.5.3 Biomass energy
Pakistan is basically an agricultural country, its livestock and agricultural activities produce massive amounts of biomass energy resources in the form of crop residues such as bagasse, cotton straw, rice husk and animal wastes, dung. Pakistan is the world’s fifth largest producer of sugarcane, with an average production of about 50 million tons annually. This amount of sugarcane crushing in 80 sugar mills of the country produces 10 million tons of bagasse that could be an immense source of energy for power generation (Amjid et al., 2011). Pakistan has the potential to produce 3,000
MW of electricity from bagasse and 5,000 MW from livestock. The national power policy 2013 stipulates to produce 83 MW of electricity from bagasse. Letters of Intent
(LOIs) have been issued to different bagasse based power plants by the Alternative
Energy Development Board (AEDB). These power plants will be installed at Jhang and
Faisalabad in Punjab, at Mirpurkhas, in Sindh and at Mardan in Khyber Pakhtunkhwa provinces (Farooqui, 2014, Zuberi et al., 2013). 79
3.6 CONCLUSIONS
The power sector of Pakistan is currently under serious crises. The electricity demand has exceeded the supply which has created a huge gap between supply and demand.
Generation of electricity from imported oil is also a big challenge to the economy as well as an environmental concern. The severity of this power shortage demand and supply gap has pushed the government of Pakistan to develop comprehensive power policy which should cater the existing challenges. Therefore, an ambitious power policy was announced in 2013. The bold approach of this power policy was meant not only to address the main challenges that are currently faced by the power sector but also to set
Pakistan on a path of rapid economic and social development. As such, serious efforts are now essential to exploit the available energy resources in order to overcome the gap between electricity demand and supply along with lowering the cost generation. Chapt er 4 CHAPTER 4 MODELING AND FORECASTING POWER GENERATION AND EMISSIONS
4.1 INTRODUCTION
In this chapter, various energy and environmental scenario modelling tools are briefly analyzed to select one of these for modelling the diversified future expansion of power generation along with its environmental assessment. Subsequently, LEAP energy and environmental modelling tool is used to forecast electricity demand and project supply side scenarios for the study period 2013-2035. In the supply side, reference scenario has been modelled to resent government’s power expansion plan and policies for the study period which then compared with three other alternative scenarios also developed using LEAP mode.
4.2 ENERGY SCENARIO MODELLING TOOLS
Energy planning plays a significant role in decisionmaking for the development of national governments and international organizations. The energy planning was initiated since the industrialization age but it was very limited and mostly focused on the environmental effects, energy supply, and costs issues. After oil crisis in 1970, it progressed and energy policy decisions were taken at a national and international level on the basis of energy planning (Pohekar and Ramachandran, 2004). The effects of GHG emissions on the environment due to the combustion of fossil fuels for the energy needs were observed by 80 81
scientists and researchers during past many years. Therefore, now the energy planners and policymakers are performing energy planning in a feasible and sustainable way according to the guidelines of IPCC (Pękala et al., 2010).
Energy security, environmental impacts, social acceptability, political influence, economic viability, risk, and uncertainties are the main challenges faced during the long term and sustainable planning for energy. In order to deal with these and other particular problems of energy planning, several computers supported modelling tools have been developed for the long term and sustainable planning which are being used throughout the world by various nations. These modelling tools are different from each other according to their application, methodology, geographical usage, analytical approach, data requirements and economic parameters. Some of the most common modelling tools are MARKAL, LEAP, and ENPEP (Gul and Qureshi, 2012, Mondal, 2010). Each of these tools is discussed here.
4.2.1 MARKAL
MARKAL refers as (MARKet ALlocation) is energy/economic modelling tool, widely used for the energy and its economic and environmental analyses at a global, national and regional level over the period of many decades. It was developed by group members of
International Energy Agency (IEA) in 1974 and is being used in 70 countries by 250 organizations (Connolly et al., 2010). MARKAL is an optimization bottom-up dynamic modelling tool which is employed to select the least-cost energy generation source from energy generation and transformation technologies. The user enters the data of cost, conversion technologies and energy demand to the model, as a result, the integrated approach in the model matches the supply side technologies with demand and identifies 82
which technology should be preferred. The new low energy and carbon-intensive
technologies allow the user to analyze the impact of these technologies on the total
generation costs, change of fuels and intensity of GHGs and other air pollutant emissions.
Despite the complex Mathematical equations and theory integrated to the MARKAL, it is
a user-friendly modelling tool because the user can effectively work with the model without
command on these computational techniques. The complex computational task is
accomplished by window based data handling tool ANSWER (Farooq et al., 2013, Mondal
et al., 2014).
There are various constraints in the application of MARKAL which make the least-cost
solution more realistic. These constraints are related to the constant supply of energy, the
satisfaction of energy demand and environmental concerns, energy policy, the balance of
energy, energy consumption capacity, replacement of capacity with new installations and
carbon tax in case of carbon emissions (Bhattacharyya and Timilsina, 2010, Connolly et
al., 2010).
4.2.2 MESSAGE
MESSAGE stands for (Model for Energy Supply Strategy Alternatives and their General
Environmental Impact) is a scenario based modelling tool which was built up by the
International Institute for Applied Systems Analysis (IIASA) in 1980 in Austria (Connolly
et al., 2010). It is a dynamic linear programming model which reduces the overall cost of energy supply over the given time period. MESSAGE presents a flexible framework for the absolute estimation of main energy challenges and is widely being used for the socioeconomically and technological strategies to defeat these energy challenges. It is an 83
engineering system development modelling tool which is employed for evaluating the climate change policies, medium to long term energy planning and developing energy system scenarios for global or national regions. It simulates all the thermal power generation, renewable energy systems, transport technologies, and energy generation costs analysis. The basic objective of the MESSAGE modelling tool is the assessment of global and regional emission mitigation strategies and also the determination of their cost- effective reduction measures (IHS, 2011).
4.2.3 LEAP
Long-range Energy Alternatives Planning (LEAP) is an integrated energy and environmental modelling tool which was developed at Stockholm Environment Institute in
1980. It is an accounting framework based computer software tool used by thousands of institutes in 190 countries all over the globe to analyze the energy consumption, production and resource extraction in all areas of energy and environmental engineering
(COMMEND, 2015). LEAP is a bottom-up model mainly used to compute energy supply and demand in both energy and non-energy sectors and also estimates the emissions related to these sectors from global to the regional level. It also allows top-down macroeconomic modelling simulation of electricity generation and expansion planning over the medium to long term. The input data requirements in the LEAP modelling tool are very less which is the key advantage of this modelling tool. The Technology Environment Database (TED) available in the LEAP model is a unique feature that is used for the assessment of environmental impacts due to the emissions (Kale and Pohekar, 2014, McPherson and
Karney, 2014). 84
LEAP includes many other modules such as key assumptions, demand, transformation, and resources. Data types like total population, urban and rural population, households, GDP and other related data are the main inputs in key assumption module. For modelling the final energy consumption in all sectors like domestic, commercial, industrial and agricultural the Demand analysis module is used while in transformation analysis module the conversion and transportation of energy resource from its extraction point to the final consumption end is carried out (Özer et al., 2013, Park et al., 2013).
LEAP has a scenario manager which is used to express individual policy measures.
Scenarios are self-description storylines of how an energy system might progress over time.
Using LEAP model, policy analyst builds and assess alternative scenarios for the comparison of energy requirements, social costs and environmental impacts of the policy- neutral business-as-usual scenarios (COMMEND, 2015).
The above illustrated and other energy modelling tools are employed for energy planning and environmental impact assessment studies, however, each of these has their own constraints and limitations. MARKAL/TIMES is a bottom-up optimization model mainly used for least cost medium to long-term energy planning and policy analysis. It requires high computer skills and large input data. The initial cost of the MARKAL/TIMES software is very high and the model development process is a long-term and continuous process. MESSAGE like a MARKAL is also a bottom-up optimization model rather than accounting model mainly used for energy supply analysis. LEAP is low cost (freely available for academicians), accounting based long-term energy planning modelling tool.
The technology and environment database (TED) built in LEAP discriminates it from all 85
other energy and environment models. Therefore, in this study, LEAP modelling tool is used for the assessment and analyses of future power generation and its associated emissions in Pakistan
4.3 LEAP ENERGY MODEL FRAMEWORK FOR PAKISTAN
LEAP energy model framework developed for Pakistan under this study focuses on electricity demand, generation plan, and emissions estimation for the modelling period
2013-2035. Reference scenario is initially developed based on government’s current electricity generation expansion plan and policy followed with three alternative policy scenarios which are evaluated by comparing their obtained values with that of the reference one. The analysis of environmental emissions from electricity generation sources in reference and alternative scenarios gives the best options for the mitigation of these emissions.
4.3.1 Electricity demand forecast
Electricity demand is an important element that should be estimated for the planning of expansion of power generation systems. The National Transmission and Dispatch
Company (NTDC) using a multiple regression analysis has forecasted electricity demand in the country, which increases at 5.4% annual growth rate from the 2013 to 2035 (NTDC,
2013). The main variables considered during the electricity demand forecast by the NTDC were the contribution of various sectors in GDP, electricity tariff and consumption in various sectors and the population of the country. This electricity demand, with the help of the LEAP model, was also developed using the annual growth rate of 5.4% which 86
illustrates that electricity demand is likely to rise from 139 TWh in 2013 (base year) to 442
TWh in 2035 (end year). Electricity demand forecasted through LEAP model and NTDC multiple regression analysis are compared in Fig. 4.1. This shows that demand forecast estimated by the LEAP model is mostly parallel to that of the estimated by NTDC.
500 ) h
W 450
G NTDC LEAP
d 400 n a
s 350 u o
h 300 T (
d 250 n a
m 200 e
D 150 y t i c
i 100 r t c
e 50 l E 0 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 Years
Fig. 4.1: Comparison of electricity demand forecast by LEAP model and NTDC
4.3.2 Basic assumptions for the LEAP energy modeling
The basic assumptions for the key parameters related to the modelling framework of
Pakistan's power generation systems are described in this section. The details of these assumptions are given in Table 4.1. The overall transmission and distribution (T&D) losses are assumed to reduce up to 6.4% in 2017 and about 5.7% up to 2035 as described by the
NTDC. For developing future power generation system, the currently working oil and natural gas based combined cycle, simple gas turbines are assumed to continue with 87
existing efficiency and availability but those power plants which will be converted from oil fuel to coal fuel of new technology will be operational on the new efficiency and availability.
Reserve margin describes as the fraction of additional power generation capacity which is available to meet the peak load in case the sudden increase in demand can happen. In other words, to say, the reserve margin is the difference between power generation capacity and maximum annual demand. Keeping in view the situation of the power system in Pakistan the reserve margin is assumed as 12% during the modelling period. The load duration curve
(LDC) illustrates the variation in power demand of any system with respect to time from the highest values to the lowest values during a specified period. In this study, the LDC is selected on the hourly basis throughout the year. The GHGs and other environmental air pollutant emissions from all the fuels used for the power generation system are assessed by their associated emission factors available in the TED (Technology and Environment
Database) in the LEAP model. For all fuels, the Tier 1 default emission factors published by IPCC (Intergovernmental Panel on Climate Change) are selected. The dispatch rule for all the power generation plants is followed as merit order according to the government of
Pakistan’s regulating body. The capacity credit is the fraction of availability of the power plant to the standard availability of thermal power plant. For the modeling, through LEAP the capacity credit is assumed on the basis of previous studies. 88
Table 4.1: Assumptions for modelling Parameters Assumptions overall transmission and Assumed to reduce up to 6.4% in 2017 and about distribution (T&D) losses 5.7% up to 2035 as published by NTDC. Emission factors of fuels Tier 1 default emission factors published by IPCC (Intergovernmental Panel on Climate Change) available in LEAP database TED Reserve Margin Assumed 12% according to the previous studies Dispatch rule Followed by merit order according to the government of Pakistan’s regulating body Load duration curve (LDC) Calculated according to the published data in the “Pakistan energy yearbook 2013” Capacity credit Capacity credit is assumed on the basis of previous studies Plant development For presently operational oil and gas steam turbines existing technologies whereas for coal, combined cycle gas turbines, solar, wind and biomass new mature technologies are assumed.
4.3.3 Reference or baseline Scenario
Reference or Baseline electricity generation scenario for Pakistan is developed under the existing power policy announced by the Government for the development of power generation system. To meet the above forecasted demand for electricity, additional installed capacity shall be required to be added to the supply system in different phases from the base year 2013 to 2035 in Pakistan. The new installed capacity in the reference scenario is projected to be based on different power generation sources like imported coal
(20 GW), local coal (20 GW), hydro (36 GW), natural gas (8 GW), nuclear (8 GW) and renewables (wind, solar, biomass) other than hydro (15 GW) which have been explained in the previous section of “Future power development in Pakistan”. The base year electricity generation data is given in Table 4.2, which is used as input into the LEAP scenario modelling (Mariam Gul, 2012, GOP, 2013a, HDIP, 2013, IRG, 2011). 89
Table 4.2: Base year electricity generation and model input data Efficiency Output Capacity Maximum Fuel (%) (GWh) (GW) availability (%) Hydro 80 29857 68 60 Coal 45 61 0.01223 75 Natural Gas 50 27116 6.59 70 Nuclear 34 4553 0.75 85 Oil 40 34534 8.2 50 Other Renewable 34 0 0 34
4.3.4 Alternative scenarios
Three alternative scenarios are developed by the scenario manager in LEAP model to
comparing results with the main reference scenario over the period 2013 to 2035. The total
planned power installed capacity in these scenarios is increased in such a way that that
overall generation of electricity remains same in all the alternative scenarios as in reference
scenario but only the shares of fuels in each alternative scenario change. All the developed
scenarios with their respective fuel shares are expressed below.
4.3.4.1 More Renewable Energy (MRR) scenario
According to the government’s existing power policy, much of the electricity will be generated from the imported and indigenous coal due to soaring oil prices. This will produce a massive amount of GHGs and other air pollutant emissions which are regional as well as global environmental threat. Pakistan has enormous renewable energy resources potentials which are untapped so far on the large scale. According to the literature, the exploitable potential of solar, wind and biomass is 169 GW, 65 GW, and 15 GW respectively in the country (Perwez et al., 2015). In this scenario, the share of renewables
(other than hydro) is increased, while the share of coal is decreased. The increment in 90
renewable energy is from the wind, solar and biomass resources in which the major share
is from wind power. The basic idea of increasing the share of renewables in this scenario
is to encourage the renewable energy for the energy mix of the country and find out the
best option for emission free environment. The exploitation of major renewable energy
resources would also reduce the heavy dependency of the country on costly imported fuels.
4.3.4.2 More Hydro Energy (MRH) scenario
Hydropower is the most important and global renewable energy generation source capable of meeting the base load electricity requirements as well as the peak and unexpected electricity demand in power generation system. It constitutes the 16% of the global electricity generation with the capacity of 3900 TWh at the 2014 (IEA, 2014d). The electricity generation cost from hydropower is very cheap because no any fuel is consumed during generation of electricity which makes it a competitive source of renewable energy.
The hydroelectric power plants do not produce any industrial waste and emissions during the power generation process like other fossil fuel power plants produce during the power generation process. If the “life cycle assessment” of hydropower plants is performed then they like other renewable power generation sources exhibit emissions. Pakistan is blessed with about 100 GW hydropower potential out of which 59 GW has been identified
(WAPDA, 2011). In this scenario, the share of the hydro resource is increased by reducing the share of coal as identified in baseline or reference scenario. 91
4.3.4.3 More Hydro Nuclear Energy (MRHN) scenario
Nuclear is also among the group of energy resources and technologies that is available today to deal with environmental impact challenges due to the GHGs and other air pollutant emissions from combustion of fossil fuels. Nuclear together with the hydropower emits about the negligible amount of CO2 and other GHG emissions. The emissions considered
for their total life cycle are fewer than 15 g CO2 eq/kWh (IAEA, 2014). It is a baseload electricity generation option which is particularly suitable for large-scale, uninterrupted electricity supply to defeat the demand. Including the nuclear power in the energy supply mix can help to solve the issue of high electricity generation cost and security because abundant uranium sources are available all over the world whereas the cost of uranium is only a small portion of the total cost occurred in electricity generation. Keeping in view the cost and environment benefits of nuclear and hydropower in this scenario the shares of nuclear and hydropower are increased by cutting the share of coal.
Through the LEAP modelling tool, the simulation of power generation and emissions estimation for different fuels like imported coal (bituminous), local coal (lignite), natural gas, oil, hydro, nuclear and renewables (wind, solar, biomass) other than hydro is carried out for reference as well as all other alternative scenarios from the base year 2013 to end year 2035. 92
Fig. 4.2: Electricity generation system model framework for Pakistan
LEAP energy model framework development and conversion of fuels resources to electricity generation to meet the existing demand for Pakistan is shown in Fig. 4.2 which illustrates that how energy resources are extracted and converted to final energy to meet the demand. Fig. 4.3 also shows conversion flow diagram of the primary energy resources
(fuels) under various scenario options into power generation to meet the electricity demand along with the discharge of associated environmental emissions. 93
Fig. 4.3: Primary energy resources conversion and emissions under all scenarios
4.4 RESULTS AND DISCUSSION
4.4.1 Reference or baseline scenario
The results obtained from the simulation of the model framework in reference or baseline scenario for the existing as well as for the planned installed capacity of electricity generation are shown in Fig. 4.4. According to the results about 107 GW of new installed capacity is required from various available sources up to 2035 in order to meet the growing electricity demand. This new installed capacity raises the overall capacity of reference scenario by 124 GW in 2035. The maximum shares are from the low-cost electricity generation sources of coal and hydro having capacities of 40 GW and 36 GW respectively.
In the new power policy, it has been decided by the government that more than 60% of the 94
existing low-efficiency high fuel cost oil fired power plants will be converted to low-cost coal fuels fired power plants. Therefore, instead of an increase in new installations from the oil based power plants the existing share of oil reduces from 8.6 GW to 3 GW in this scenario. For the diversification of electricity generation system government has planned to install some new power plants based on natural gas. In order to achieve this hence another 8 GW of new installed capacity from natural gas has been added to the overall installations. The supply of gas for new power plants has been planned to be imported through LNG and gas pipeline. 15 GW of renewable energy sources other than hydro have also been included in the total installed capacity up to the study time horizon since various wind, solar and biomass power generation plants are under process and are expected to be added to national grid up to 2035.
Under the reference or baseline scenario, the electricity generation increases to meet the growing electricity demand by consuming shares of all fuels. The total electricity generation increases from 96 TWh in 2013 to 442 TWh in 2035 as it has been shown in
Fig. 4.5 in which the major shares are from hydro and coal. The share of oil drastically decreases from 36% to only 2% since there is no new installation from the oil based source during the study period. The reduction in the share of oil is replaced by inexpensive hydro and coal power plants. The overall installations from hydro and natural gas power sources will increase when some huge hydropower projects are commissioned and planned power plants based on imported gas are installed, therefore, as a whole the electricity generation share of hydro increases from 31% to 37% in the base year to end year whereas the share of natural gas significantly decreases from 28.2% to 13% during the same period. 95
Fig. 4.4: Electricity installed capacity under reference scenario
Nuclear is also a reasonably low-cost electricity generation source, in this scenario its share increases from 5% in the base year to 9% to the end year. The share of other renewables in
2013 is 0% but its share generally increases in 2015 by 1.2% and reaches to 6% in 2035.
Electricity generation from coal fired power plants is cost competitive than oil fired power plants thus the share of coal in electricity generation mix is considerably increases from
0.1% to 34% during the considered modelling time period.
As per reference or baseline scenario, the electricity generation shares from various energy sources for the modelling period are shown in Table 4.3. 96
Fig. 4.5: Total electricity generation under reference scenario Table 4.3: Share of various energy sources in electricity generation
Fuel source Percentage share (2013) Percentage share (2035) Oil 36 2 Natural gas 28.2 13 Nuclear 5 9 Coal 0.1 34 Hydro 31 37 Renewable 0.0 6
4.4.2 Alternative scenarios
4.4.2.1 MRR Scenario
Renewable energy sources such as solar, wind, and biomass are expected to play a significant role in the mitigation of emissions to counter the climate change and sustainability issues. Therefore, in this scenario the share of installed capacity of other renewable increases from 15 GW to 30 GW while the installed capacity of coal reduces 97
from 40 GW to 20 GW compared to the REF scenario as Fig. 4.6 shows. The installed capacities of other sources like hydro, nuclear and oil remain same as in the reference or baseline scenario. The inclusion of large share of renewable sources can reduce the reliance on coal but it needs flexible power sources such as the natural gas power to compensate electricity demand due to their low capacity factor. The new installed capacity from natural gas is 20 GW in this scenario which enhances the overall installed capacity from all sources to 132 GW to meet the power demand.
Fig. 4.6: Electricity installed capacity under MRR scenario
Overall electricity generation output in this scenario is same as in the reference or baseline scenario but only the generation share of other renewable energy sources significantly increases due to the increment in its installed capacity. The output share of other renewable energy sources is 0% in 2013 and it increases about 11% of total output in 2035. The electricity generation share of coal reduces from 34% as in the reference or baseline 98
scenario to 16% in this scenario in 2035 because of reduction of its installed capacity as shown in Fig. 4.7. The immense penetration of renewable energy sources in the total electricity generation system brings superb environmental and economic impacts because these are indigenously available and emissions free resources.
Fig. 4.7: Total electricity generation under MRR scenario
4.4.2.2 MRH Scenario
In this hydro Reliance Scenario, the installed capacity of hydropower increases from 36
GW as in the reference or baseline scenario to 40 GW up to 2035. The installed capacity shares of all other sources remain same as in the reference or baseline scenario except the installed capacity of coal reduces from 40 GW as in reference or baseline scenario to 34
GW in this scenario up to the end year of study period as shown in Fig. 4.8. Increasing the installed capacity of hydropower presents a viable option for electricity generation from 99
more cost-effective and green power generation sources. The total installed capacity becomes about 123 GW with the increasing share of the hydro in this scenario.
Fig. 4.8: Electricity installed capacity under MRH scenario
In this scenario, the total electricity generation also remains same as in the reference or baseline scenario except for the shares of hydro and coal power only change. The electricity generation share of hydropower increases from 37% as in reference or baseline scenario to
42% in the current scenario up to 2035 as shown in Fig. 4.9. The increase in the share of hydropower depicts the low-cost, secure and sustainable electricity generation option. The share of coal reduces from 34% as in the reference or baseline scenario to 28% in this scenario up to 2035 to balance the increase of hydro. The electricity generation shares of all sources other than hydro and coal power do not change. 100
Fig. 4.9: Total electricity generation under MRH scenario
4.4.2.3 MRHN Scenario
Nuclear power is added with hydropower in this scenario to offer another cost-effective, sustainable, technologically superior and long-term energy supply option of electricity generation source. The installed capacities of nuclear and hydropower increase from 8 GW and 36 GW as in reference or baseline scenario to 10 GW and 40 GW in this hydro nuclear reliance scenario respectively up to 2035. In the response to an increase in installed capacities of these fuels the installed capacity of coal reduces from 40 GW as in reference or baseline scenario to 31 GW in the existing scenario as shown in Fig. 4.10. The overall power installed capacity in this scenario becomes 123 GW in 2035 to meet its increasing demand. Share of nuclear and hydropower in total electricity generation is illustrated in
Fig. 4.11. 101
Fig. 4.10: Electricity installed capacity under MRHN scenario
Fig. 4.11: Total electricity generation by source under MRHN scenario 102
The generation shares of nuclear and hydropower increase 2% and 5% from reference
scenario to this scenario respectively in 2035 while the share of coal reduces 8% in this
scenario as compared to the reference or baseline scenario. The reduction in electricity
generation share from coal is an optimistic scenario to mitigate the effects of emissions
which are damaging the environment directly or indirectly. The electricity generation
shares of all other sources remain same as in the reference or baseline scenario.
4.4.3 GHGs and other air pollutant emissions
From the LEAP model simulation results it is evident that reference or baseline scenario
based on existing and planned power system in Pakistan is dominant with thermal power
generation sources produce main GHGs and other air pollutant emissions of CO2, CO,
NOX, N2O, SO2, CH4, PM, and VOC from the fuels such as coal, oil, and natural gas. The estimated values of these emissions from the base year 2013 to the end year in all scenarios are shown in Table 4.4. Each of these tabulated components of emissions is further explained with respect to the reference and other three alternative scenarios as under:
Table 4.4: GHGs and other air pollutant emissions in all scenarios 2035 Emissions Units 2013 REF MRR MRH MRHN CO2 million tons 34 143 103 126 117 CO kilotons 9 48 63 45 43 NOx kilo tons 92 157 167 146 140 N2O kilo tons 0.2 82 41 70 63 SO2 kilo tons 253 553 372 487 454 CH4 kilo tons 1 1.8 2.3 1.7 1.6 PM kilo tons 7 96 49 82 75 VOC kilo tons 2.4 5.7 13 5.7 5.7 103
4.4.3.1 CO2 emissions
CO2 is a main GHG emissions component which releases in the process of electricity generation due to combustion of fossil fuels. It is projected that CO2 emissions increase from 34 million tons in the base year 2013 to 143 million tons in the final year of study period 2035 under the REF scenario and from 34 million tons to 103 million tons in MRR scenario respectively. In the case of MRH scenario, the CO2 emissions vary from 34 million tons in the base year to 126 million tons by the end year 2035 whereas in the case of MRHN scenario CO2 emissions vary as of between 34 million tons to 117 million tons from the base year to the final year respectively, which are illustrated in Fig. 4.12.
Fig. 4.12: Annual CO2 emissions in all scenarios
The increase of CO2 emissions is 118% less in the MRR scenario as compared to the REF scenario. This is a significant reduction in emissions due to the addition of massive share of renewable energy sources, particularly wind energy, which is an emission free source of 104
energy. In the REF scenario, the sudden increase in CO2 emissions depicts that the upcoming power plants are mostly from coal based which discharge a huge amount of these emissions. In MRHN and MRH scenarios, the emissions are 76% and 51% less than the
REF scenario respectively because of more additions from the hydro and nuclear energy of environmentally friendly sources.
4.4.3.2 SO2 emissions
Using the coal as a fuel for electricity generation in all scenarios produces a lot of emissions but due to highly efficient critical pressure boilers and advanced FBC combustion technology as considered in this study the hazardous emissions like SO2 reduce to a minimum level. However, despite this substantial amount of emissions also discharge from the combustion of coal for power generation. In addition to this SO2 emits from the existing oil based power plants where the emission control systems have not been installed.
Therefore, SO2 emissions increase only 300 kilo ton (kt) during the whole study period under the REF scenario which is the highest increase as compared to the other alternative scenarios because of highest share of coal in this scenario. The amount of SO2 emissions in other alternative scenario increase as 234 kt in MRH scenario, 201kt in MRHN and only
120 kt in MRR scenario from 2013 to 2035. The SO2 emissions under REF as well as all alternative scenarios are expressed in Fig. 4.13.
4.4.3.3 NOx emissions
NOX emissions from existing and planned thermal power plants in the reference and all alternative scenarios during the modelling period are presented in the Fig. 4.14. 105
Fig. 4.13: Annual SO2 emissions in all scenarios.
Fig. 4.14: Annual NOx emissions in all scenarios
The results illustrate that the NOX emissions increase 72% in REF scenario from 2013 till
2035 while at the same time period the increase is estimated to be 59%, 53% and 83% in
MRH, MRHN and MRR alternative scenarios respectively. The thermal power plants 106
particularly based on coal, as proposed during the study period, would be a major source
of increased NOX emissions in the reference scenario. NOX emissions are more in MRR scenario than the other scenarios due to the large installed capacity of natural gas to compensate the low capacity factor renewable power plants. Power plants based on natural gas produce more NOX emissions than coal based power plants.
4.4.3.4 CH4 emissions
Methane like CO2 is also another component of GHG emissions. It is projected in this study that methane (CH4) emissions discharge from the existing and planned biomass, natural gas and oil based power generation plants. The CH4 emissions increase from 1 kt to 2.3 kt base year to final year in MRR scenario which is a 137% increase compared to the base year. These emissions increase 80% in REF scenario, 71% in MRH scenario and 65% in
MRHN scenario from the base year 2013 to the final year of study period as these are illustrated in Fig. 4.15. The increasing share of CH4 emissions is very high in MRR scenario as compared to other alternative scenarios because of high shares of biomass and natural gas fuels in this scenario for power generation. Biomass fuels like crops residues; bagasse, rice husk and corn straw discharge a large amount of CH4 emissions during combustion.
CH4 is the main component of natural gas fuels; therefore, power plants based on natural gas also discharge unburnt CH4.
4.4.3.5 PM emissions
PM emissions produced during the combustion of fossil fuels mostly from all types of coal fuels for power generation but due to the latest advanced combustion technologies of 107
FBC, critical pressure boilers and emissions control equipment; these emissions are reduced considerably as considered in this study. However, despite this substantial amount of emissions also discharge from these power plants. Further, the existing oil based power plants in the country have no as such control system for the reduction of PM emissions, produce a significant amount of this types of emissions. The PM emissions in the base year are almost same in all scenarios due to an equal amount of oil and coal power plants but in the final year these emissions increase and reach 96 kt, 82 kt, 75 kt and 49 kt in REF, MRH,
MRHN and MRR scenarios respectively as Fig. 4.16
Fig. 4.15: Annual CH4 emissions in all scenarios demonstrates. The maximum emissions increase is in the fossil fuel dominant REF scenario whereas the minimum increase is in the renewable energy source based MRR scenario. 108
4.4.3.6 N2O emissions
N2O is also a GHG which mostly discharges from the combustion of coal and the very minimum amount of such emissions discharges from oil and natural gas fuel based power plants. N2O emissions under reference as well as all alternative scenarios as estimated in
this study are shown in Fig. 4.17. N2O emissions are estimated to increase from 0.2 kt in
2013 to 82 kt in 2035 under REF scenario, 0.2 kt to 70 kt under MRH, 0.2 kt to 63 kt under
MRHN and 0.2 kt to 41 kt under the MRR scenarios respectively for the same period.
These emissions are highest in REF scenario owing to the highest share of coal in this scenario. In all alternative scenarios, the emissions of N2O are comparatively less than REF scenario. These emissions are lowest in the MRR scenario in which a large amount of electricity is generated from renewable energy sources they do not produce any N2O emissions.
Fig. 4.16: Annual PM emissions in all scenarios 109
4.4.3.7 CO emissions
CO emissions from all scenarios are shown in Fig. 4.18. The results show that the quantity of CO emissions grows 6 times higher in 2035 than in 2013 under REF scenario whereas
7, 5 and 5 times under MRR, MRH and MRHN scenarios respectively. It appears that increasing share of CO emissions is almost same in all scenarios except MRR scenario in which more emissions discharge than the other scenarios during the study period.
Combustion of biomass fuels such as bagasse produces more CO emissions than the coal or other fuels. In MRR scenario installed capacity of bagasse and natural gas is higher than the other scenarios; therefore, CO emissions are more in this scenario.
Fig. 4.17: Annual N2 O emissions in all scenarios 110
4.4.3.8 VOCs emissions
Volatile Organic compound (VOCs) like Propane, Benzene, Xylene and other compounds of gasoline have low boiling point evaporates readily at room temperature.
These types of emissions discharge from the combustion of biomass, natural gas and fuel oil for the energy generation. The projected emissions of VOCs from power generation system of Pakistan in all scenarios are described in Fig. 4.19. The range of increase in these emissions from base year to the final year of this study period is estimated to be 2.4 to 5.7 kt in REF, MRH and MRHN scenarios whereas in the MRR scenario it is estimated to be
2.4 to 13 kt for the period 2013-2035.
Fig. 4.18: Annual CO emissions in all scenarios
Since the range of emissions is same in all scenarios except in MRR scenario in which it is moderately high whereas the the major source of VOCs is from the combustion of bagasse 111
for power generation. The increasing installed capacity of other renewables in MRR scenario increases the share of bagasse which produces more VOCs. Another source of increasing VOC emissions is the natural gas which has more installed capacity in MRR scenario.
Fig. 4.19: Annual VOC emissions in all scenarios
4.5 CONCLUSIONS
In this chapter, the future power generation options for Pakistan were analyzed alongside estimation of the associated emissions. In order to overcome the existing electricity crisis in the country due to the increasing demand and limited supply, Government of Pakistan has planned to install various power projects based on coal, hydro and renewable sources instead of costly imported oil as per power policy 2013. Modelling of the planned electricity generation capacity of the country based on REF scenario and three alternative 112
scenarios namely MRR, MRH and MRHN were undertaken using integrated energy and environmental model LEAP for the study period 2013-2035. A massive amount of GHGs and other air pollutant emissions were forecasted from the base year to the end year mainly from the coal and other fossil-fueled power plants. REF scenario having high shares of coal fuel produces more emission than other alternative scenarios. MRR scenario with highest shares of other renewables mostly wind power produces the lowest amount of emissions. Chapt er 5 CHAPTER 5 MODELING AND FORECASTING WIND POWER GENERATION
5.1 INTRODUCTION
Fossil fuels like coal, oil, and gas have had been the main sources of electricity generation since the industrial revolution. However, due to the environmental and climate change issues, these sources are of the great concerns to the modern world. Moreover, because of their hasty consumption, these sources are depleting very fast. As such, owing to these the issues of the fossil fuels utilization for the electricity generation, globally the renewable energy resources are preferred now a day. The renewable energy resources such as wind energy are not only environmentally acceptable but also a non-depletable source of energy.
This chapter covers the introduction of wind energy, electricity generation from wind energy, the global status of wind power development, wind power potential and development in Pakistan. Further, mathematical models are also developed and discussed in this chapter for diffusion of wind power in Pakistan. Finally, different scenarios for the electricity generation from wind power for Pakistan is forecasted and discussed.
5.2 GLOBAL STATUS OF WIND POWER
The available global wind energy potential on onshore and offshore at 100 m hub height is about 1700 TW which can be attained when all winds at all speeds are used to power wind turbines. More than half of this exists in areas of the world that could be developed by
113 114
practically (Bhutto et al., 2013). Huge amounts of electricity can be generated by both community and grid-connected scales if this enormous potential of wind is exploited.
Wind energy is emerging as one of the largest utilizing source of energy among the other renewable technologies globally. The total worldwide installed capacity of wind power reached 432 GW by the end of 2015. China is the leading country in the world has installed capacity of 145 GW making 33.6% of total global installed capacity in which 30.5 GW was installed during 2015.The United States has the second largest position in terms of wind installed capacity with 74.5 GW which is the 17.2% of the total world installed capacity. India has made its fourth place with an installed capacity of 25 GW 5.8% of the world total just behind the Germany having installed capacity of 45 GW with 10.4% of the world total. In Asia, the China and India are the top countries than Japan (2.8 GW) and other Asian countries like South Korea and Taiwan. The annual installed capacity of wind power is increasing with a 26% annual growth, showing the big global interest in generating the electricity from wind energy. The global annual cumulative installed capacity of wind power from 2000 to 2015 has been shown in Fig. 5.1. The new rapid development in power generation from wind energy is due to its decreasing cost, uncertainty in prices of conventional fuels, environmental issues, policies and subsidies by governments, public awareness about global warming and climate change and energy security. On the basis of this increasing development and awareness about the advantage of wind energy, it is expected that it would be the cheapest form of energy generation in the world in coming decades. 115
500 )
W 450 G (
y 400 t i c
a 350 p a 300 C d
e 250 l l a t
s 200 n I 150 e v i t 100 a l u 50 m m
o 0 C
Year
Fig. 5.1: Global cumulative installed capacity of wind power
According to the assessment carried out by various researchers that up to 2030 more than
20% of the electricity demands of the world could be met by wind energy. These economic
and environmental advantages of wind energy persuade the countries to include the wind
power as clean source of energy in their energy mix (GWEC, 2015, Siddique and Wazir,
2016).
5.3 WIND POWER DEVELOPMENT IN PAKISTAN
Pakistan is located amid the latitude 24 ̊̊ and 37 ̊ North and longitude 62̊ and 75 ̊ East bordering with Iran in the west, India in the east, Afghanistan in the northwest, Arabian
Sea in the south and China in the north. The total covered area of the country is 803,950
Km2 out of which 59% consists on mountains and plateaus whereas plains and deserts make
41% of total area. The coastline of Pakistan extending from Indian border towards east to 116
the Iranian border to the west is about 1050 Km long. The maximum length of the coastline
800 km is situated in province of Balochistan while about 250 km area falls in Sindh province. This coastline is blessed with huge wind energy potential. In addition to the coastal areas in the country, some isolated areas of Balochistan, Khyber Pakhtunkhwa,
Punjab provinces and northern areas have also the significant wind potential to generate power (Bhutto et al., 2013, Mirza et al., 2007).
5.3.1 Wind power potential
For the successful power generation from wind energy, the study of the fundamental parameters related to wind speed, characteristics of the wind and geographical distribution of wind speed is necessary to be carried out. Knowing the significance of wind power various studies for the assessment of wind power potential in Pakistan have been carried out at different times. Pakistan Meteorological Department (PMD) with the financial support of Ministry of Science &Technology (MoST) carried out the study for the assessment of wind power potential in coastal areas of Sindh and Balochistan in 2002.
Three years wind speed data were collected at 44 installed stations at height of 10m and
30m and it was extrapolated for power potential at 50m. The wind power potential was found to be 43 GW in the coastal region of Sindh province with a covered area of 9700 km2 which is better than the wind potential in coastal regions of Balochistan province.
After the completion of wind power potential assessment of the coastal areas of Pakistan in the first phase, PMD in the next phase performed the survey for the estimation of wind power potential in the Northern areas of Pakistan by the collection of wind speed data from
42 wind stations. Wind power potential revealed from these areas was very low having 117
10% to 18% capacity factor which is incapable for the grid-connected power generation but only suitable for small scale individual power generation (Farooqui, 2014, Siddique and Wazir, 2016).
The current wind power potential assessment and associated wind map of Pakistan was developed by National Renewable Energy Laboratory (NREL) USA in collaboration with
USAID in 2007 with the help of computerized mapping method of Geographic Information
System (GIS) software. According to this report, there is 346 GW gross wind power potential in the country, out of which 132 GW is technically exploitable and it exists in 3% of total area under the class 4+ category as shown in Fig. 5.2 (NREL, 2007).
Fig. 5.2: Wind map of Pakistan (NREL (2007) 118
The major wind power potential in the range of 60 GW to 70 GW is available in southern parts of Sindh and northwestern parts of Balochistan which are suitable for centralized grid connection electricity generations (Elliott, 2011, Siddique and Wazir, 2016).
5.3.2 Existing wind power status
Wind power was not developed so greatly in Pakistan despite its huge potential available in the country. Only individual wind turbines on small scale were installed by Pakistan
Council of Renewable Energy Technologies (PCRET) in different parts of the country up to 2012. Reducing the heavy reliance on fossil fuels and exploiting of alternative energy sources like wind, solar and biomass was the aim of the government, therefore, a separate organization AEDB was established in 2003 for this purpose. AEDB has been given the power to build up policies and plans for the development of renewable energy sources, provide technical support to the project developers and evaluate the projects related to renewable sources. Ministry of water and power with the collaboration of AEDB formed and implemented the first renewable energy policy 2006 in the country. This policy attracted the national and international investors for the investment in renewable projects
(GOP, 2006b). Later on, this renewable energy policy was amended by new policy
Alternative and Renewable Energy Policy 2011 (GOP, 2011).
The incentives announced in renewable energy policies have attracted many investors in the fields of renewable energy. About 56 national and international wind power project developing companies have applied for the letter of intent to AEDB for the installation of wind power plants in the wind power potential areas of the country. AEDB has issued LOI to the 48 wind power developing companies and has also allocated project land to 26 power 119
companies of 50 MW each for wind power generation projects. NEPRA has issued
Electricity generation licenses to 19 companies and also has settled tariff with
21companies. Three wind power generation companies FFC wind Energy limited Pakistan
(49.5 MW), Zorlu wind Energy limited Pakistan (56.4 MW) and Three Gorges wind power limited Pakistan (49.5 MW) have started their commercial operation and are supplying electricity to the national grid. The current status of all the wind power projects in Pakistan is described in Table 5.1 (AEDB, 2015b).
Table 5.1: Status of wind power projects in Pakistan No. of power Status of wind power projects projects Applied for the letter of intent (LOI) 56 Obtained the letter of intent (LOI) 48 Obtained the project land 26 Obtained the electricity generation license from the NEPRA 19 Power projects acquired tariff from NEPRA 21 Power projects acquired the letter of support (LOS) from the AEDB 18 Power projects signed the energy purchase agreement (EPA) with 9 CPPA Power projects signed the implementation agreement (IA) with 6 AEDB Power projects attained the financial closer 5 Power projects attained the commercial operation 3
5.4 FUTURE DIFFUSION OF WIND POWER IN PAKISTAN
The diffusion is termed as the increase of innovation in a society over the time. When this innovation is technological then it is called as technology diffusion. The technology diffusion process over the time follows the S-shape curves. The S-shaped curves express the growth of one variable in terms of another variable. In their growth pattern, they show initially the slow growth, then sudden rapid increase in growth up to a certain point, after this again slow 120
in growth to the upper limit to the point of dissemination (Rao and Kishore, 2010) as shown in Fig. 5.3.
Early adopters or innovators are the first groups of the society that adopts the innovations or new technologies during the initial phase of their diffusion process. These are the adopters of new innovations who do not influence by the previous adopters in decision making. The second group who adopts the new innovations, influenced by the early adopters of innovations or new technologies are labeled as imitators. Finally, the market arrives at maturity, when most of the adopters of the society have adopted the innovations or new technologies. This group is called as laggard who adopts the innovations or new technologies when these become outdated and have been replaced by new innovations or technologies
(Harijan et al., 2011, Meade and Islam, 2006).
Fig. 5.3: Technology diffusion curve 121
5.4.1 Development of mathematical model for wind power diffusion
Future growth pattern of a technology is forecasted through modelling process. Various
mathematical models are available for forecasting the diffusion trends of a technology.
Mathematical models such as Bass model, Gompertz model, Logistic model, and Pearl
model are mostly used for the diffusion of various technologies (Kim et al., 2014). The
literature shows that “logistic model” offers better utilization potential and fitness
estimation than the other technology diffusion models (Purohit and Kandpal, 2005). The
“logistic model” is used in forecasting from the biological growth phenomenon to the
computer diffusion and economic development processes. In this study, the “logistic
model” is used for forecasting the diffusion of wind power in Pakistan (Harijan et al.,
2011).
M N(t) = (5.1) 1 + e Where N(t)represents the cumulative capacity of wind power installed in the country in
time t, M is the exploitable wind power potential in the country, q diffusion rate of wind
power in the country, and p is the constant of integration.
The maximum time at the inflection point t’ or at half of the time the logistic model can be
obtained by taking the second derivative of the equation (1) with respect to time t, while
equating it to zero. This gives the value:
p t = (5.2) q 122
Eq. (2) illustrates that parameters p and q of the diffusion model determine the inflection point or half of the adoption time. Putting this in Eq. (1) it can be written as:
( ) = (5.3) 1 + ( ) The values of the parameters of the logistic diffusion model are required for forecasting the future penetration of a new technology. These values of parameters are estimated by the past market penetration data of the same technology, usually for more than four years duration. If the past time series data of wind power technology is available then the coefficients of regression p and q are estimated by the linear regression form of the Eq. (1)
M N(t) ln = p qt (5.4) N(t) − − In the case when required past time series adoption data of the new technology are not available or the available data are very limited then other data acquiring approaches like experts opinions, a collection of data through a survey or by the analogous data approach can be employed for the estimation of parameters of technology diffusion model. The analogous approach involves the use of same technology adoption data of another region or country where this technology already exists and has been adopted a long time ago and various social, geographical, cultural and economic similarities should exist between both regions and countries. For obtaining the best results of analogous approach for technology diffusion the time gap should be at least 10 years between the economic index of both regions and countries (Kim et al., 2014). 123
From the literature, it is apparent that the past time series data related to the diffusion of wind power in Pakistan are very limited and not sufficient for the estimation of parameters of the technology diffusion model, therefore for their estimation a strategy of analogous approach is adopted. In the analogous approach, the country or region where most of the qualities of social and geographical attributes are comparable to Pakistan is selected for acquiring the past time series data of wind power. In this study, the adjacent country to the
Pakistan, India is selected for analogous approach since like Pakistan India has the large rural population and it is also geographically similar to the Pakistan, moreover, wind power has developed in India a long time ago. Both India and Pakistan are neighboring countries and also, they are the members of the South Asian for Regional Cooperation (SAARC) countries having many things common. Both the countries have ambitious renewable energy policies for the development of renewable energy sources in their countries.
Different renewable policies have been announced by both countries to harness maximum renewable energy sources to supply energy to their national grids as well as rural areas through renewable energy. Although in India each state government has its own renewable policy but the overall Indian government has made a progressive target of 175 GW, from various renewable energy sources such as wind, solar, small hydro, and biomass up to
2022. For the promotion and development of renewable energy resources in India, the
Ministry of New and Renewable Energy (MNRE) is carrying out a flourishing role which offers very lucrative incentives, like capital cost and interest subsidy, concessional finance, fiscal incentives, and viability gap funding. 124
In Pakistan, the facilitation, promotion, and development of renewable resources are implemented through Alternative Energy Development Board (AEDB). The AEDB has been given the task to produce 5% total power from renewable energy by 2030. The government of Pakistan announced its first Renewable Energy Policy in 2006. The main objective of this policy is to explore the existing renewable energy resources of the country and attract investment in electricity generation projects. This policy offers very profitable incentives to investors for investing in renewable energy based projects to put Pakistan in the renewable energy mix countries of the world.
In the comparison of the features of India and Pakistan for the analogous approach different indicators like, population, per capita GDP, literacy rate, per capita electricity consumption and the rural population of both countries have been considered. The demographic and economic indicators of both countries is described in Table 5.2 (GOP, 2014a, MNRE,
2013, UNDP, 2013, WB, 2013b, WB, 2013c). The similarity of indicators of both the neighbouring countries confirms that the technological diffusion can be matched between these countries.
The Human Development Index (HDI) is a mechanism used for the measurement of the rank of countries in levels of social and economic development related to three criteria: life expectancy, education, and the per capita national income. When the life expectancy in a particular country is high, it has high literacy rate and its per capita income is also high then it gets higher scores in HDI. 125
Table 5.2: Demographic and economic indicators of India and Pakistan (2013)
Indicators Pakistan India GDP per capita $1,255 $1,550 Composition of GDP Agricultural sector 21.20% 17.40% Industry sector 21.00% 25.80% Services sector 57.80% 56.90% Primary energy supply per capita 0.355 TOE 0.50 TOE Electricity consumption per capita 430.25 kWh 778 kWh Electricity access Total population 69% 75.30% Rural population 57% 67% Imported oil as % of total oil consumption 64% 76% Population below poverty line 21% 22% Rural population 63% 68% Literacy rate 58% 74% Human Development Index (HDI) 0.537 0.586
The comparison of HDI curves of both countries has been shown in Fig. 5.4 (UNDP, 2013), which are almost parallel during the given period. The HDI curves indicate that in 2000 the Indian HDI is approximately equal to the HDI of Pakistan in 2010. Since there is a time gap of ten years between two countries so it confirms that India could be as an analogous country for forecasting the wind power diffusion in Pakistan.
5.4.2 Scenario development
Scenarios are the approaches of promoting possible futures positions based on different assumptions, facts, and trends, events and systems where more understanding is required for particular plan or strategy. Considering the facts and future trends of diffusion of wind power technology in Pakistan; three different scenarios have been developed in this study. 126
India Pakistan 0.7
0.6
0.5
0.4 I D H 0.3
0.2
0.1
0 1980 1990 2000 2005 2007 2010 2011 2012 2013 Years
Fig. 5.4: Comparison of HDI curves of India and Pakistan (UNDP, 2013)
These scenarios are Business as Usual (BAU) scenario, Moderate Diffusion (MD) scenario, and High Diffusion (HD) scenario. The BAU scenario illustrates that new cumulative installations of wind power in future will continue with existing policies and events with no new policy or interference. In the HD scenario, it has been assumed that in the past, if the diffusion of wind power technology had been taken place by the market influence instead of subsidies, then the cumulative installations of wind power technology would be triple than the existing level of installations. In the MD scenario, it has been assumed that in the past, if the diffusion of wind power technology had been taken place by the influence of market as well as subsidies, then the cumulative installations of wind power technology would be double than the existing level of installations. 127
The past time series data of Indian wind power installed capacity have been taken from the literature. The installed capacity of wind power is given in Table 5.3 (Bhattacharya and
Jana, 2009, Mahesh and Jasmin, 2013).
Table 5.3: Cumulative installed capacity of wind power in India Cumulative installed Annual capacity Year capacity (MW) addition (MW) 1993 79 40 1994 185 106 1995 576 391 1996 820 244 1997 940 120 1998 992 52 1999 1095 103 2000 1167 72 2001 1456 289 2002 1702 246 2003 2125 423 2004 3000 875 2005 4430 1430 2006 6270 1840 2007 7845 1575 2008 9655 1810 2009 10930 1275 2010 13070 2140 2011 14990 1920 2012 16180 1190
Wind power technology diffusion model parameters have been estimated with the help of statistical software Mini-Tab 17.0 through the regression analysis of past time series data of wind power installed capacity in India. The values of these parameters are shown in
Table 5.4. For the validation of results, the values of wind power diffusion technology model parameters have been used in Eq. (5.3) and the cumulative installed capacity of wind power in India has been estimated considering the maximum technical potential in India as 128
45 GW (Ali and Semwal, 2014). The estimated results of the cumulative installed capacity of wind power in India effectively match the actual cumulative installed capacity of wind power as Fig. 5.5 illustrates.
Table 5.4: Wind power technology diffusion modelling parameters
Scenarios Parameters BAU MD HD q 0.267 0.294 0.369 t’ 23 19 15
18000
) 16000 W M
( 14000 y t
i Actual c
a 12000 p
a Estimated c 10000 d e l l a
t 8000 s n i e
v 6000 i t a l
u 4000 m o
C 2000
0 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 Year
Fig. 5.5: Comparison of actual and estimated cumulative wind power installed capacity in India
5.5 RESULTS AND DISCUSSION
Wind power installed capacity in Pakistan under different scenarios has been forecasted by using the estimated wind power technology diffusion model parameters of India in the Eq. 129
(5.3). The results of the forecasted cumulative installed capacity of wind power under
BAU, MD, and HD scenarios are demonstrated in Fig. 5.6, which is like a S-shape graphs.
The S-shape expansion of the graph shows that initially due to the resistance to adopting a new technology the growth is very slow. But after some time when the technology becomes familiar, the growth increases exponentially up to a certain time limit then again it becomes slow while approaching to the maximum expansion limit when everyone has adapted to this new technology. The forecasted annual capacity addition of wind power in Pakistan is described in Fig. 5.7. It shows that the annual capacity addition in wind power increases up to a certain point called inflection point then the growth in annual capacity addition decreases as the installation capacity reaches its upper limit. The correspondence years for these inflection points are 2026, 2030 and 2034 in scenarios HD, MD and BAU respectively.
70 BAU Scenario 60 HD Scenario )
W 50 MD Scenario G ( y t i
c 40 a p a c 30 d e l l a
t 20 s n I 10
0 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 Year
Fig. 5.6: Forecasted cumulative installed capacity of wind power in Pakistan under all scenarios 130
7000
6000 BAU Scenario
) HD Scenario
W 5000 M
( MD Scenario n o
i 4000 t i d d a
y 3000 t i c a p
a 2000 C
1000
0 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 Years
Fig. 5.7: Forecasted annual wind power capacity addition in Pakistan under all scenarios
The annual electricity generation from the forecasted cumulative installed capacity of wind power has been estimated considering the value of average annual capacity factor of the wind turbine as 25% on the basis of the data available in existing literature (Bhutto et al.,
2013, Harijan et al., 2011). The forecasted values of the cumulative installed capacity of wind power and its associated electricity generation under different scenarios have been summarized in Table 5.5. The forecasted results show that up to 2030 about 17 GW, 33
GW, and 53 GW wind power could be installed in BAU, MD and HD scenarios respectively and it is expected that they could reach to 37 GW, 53 GW, and 63 GW in 2035 in all the scenarios respectively. 131
Table 5.5: Forecasted installed capacity and electricity generation of wind power
Cumulative installed capacity Annual electricity generation (GW) (TWh) Year Scenarios BAU MD HD BAU MD HD 2013 0.24 0.44 0.53 0.52 0.95 1.17 2014 0.31 0.58 0.77 0.68 1.28 1.68 2016 0.53 1.04 1.58 1.16 2.28 3.47 2018 0.89 1.85 3.23 1.96 4.06 7.07 2020 1.51 3.26 6.40 3.31 7.15 14.02 2022 2.54 5.65 12.09 5.55 12.37 26.48 2024 4.21 9.51 21.02 9.22 20.82 46.04 2026 6.87 15.33 32.50 15.04 33.56 71.18 2028 10.90 23.21 43.98 23.87 50.83 96.31 2030 16.63 32.50 52.91 36.41 71.18 115.87 2032 24.02 41.79 58.60 52.61 91.52 128.33 2034 32.50 49.67 61.77 71.18 108.79 135.28 2035 36.81 52.85 62.73 80.62 115.74 137.39
The above mentioned wind power installed capacities in 2030 are about 24%, 47% and
76% of the total technical wind power potential of Pakistan while these are 53%, 76% and
90% of the total technical wind power potential in above all the scenarios respectively in
2035. These wind power installed capacities will generate electricity about 36 TWh, 71
TWh and 116 TWh in 2030 while in the same way about 81 TWh, 116 TWh, and 137 TWh in 2035 under the BAU, MD and HD scenarios respectively. The electricity generation curves of all the scenarios up to 2035 are presented in Fig. 5.8. 132
160
140 ) h
W 120 BAU MD HD T ( n
o 100 i t a r e 80 n e g y t
i 60 c i r t
c 40 e l E 20
0 2013 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2035 Year
Fig. 5.8: Annual electricity generation from wind power in Pakistan
5.6 CONCLUSIONS
In this chapter “Logistic Model” has been used to forecast the electricity generation from the wind energy under three policy scenarios using analogues approach. The forecasting results suggest that about 53%, 76% and 90% of the total technical potential of wind energy in Pakistan could be used for installing wind power plants under BAU, MD and HD scenarios respectively up to 2035. As such, these installed capacities of wind power could generate about 81 TWh, 116 TWh and 137 TWh of electricity under BAU, MD and HD scenarios respectively which are much greater than the existing electricity generation from conventional power plants in the country. Chapt er 6 CHAPTER 6 ESTIMATION OF ELECTRICITY GENERATION COST
6.1 INTRODUCTION
The economic viability of any power generation technology is attributed to its safe, clean, reliable operation and affordability. In this context, the electricity generation cost of renewable energy sources particularly wind energy which was previously very high but in recent years this has remarkably decreased due to the improvements following continuous research and developments. As such, the electricity generation cost of wind energy is now competitive or even less than fossil fuels electricity generations costs in the high wind potential areas. In this chapter, the electricity generation cost of wind energy has been estimated for Pakistan and compared with other conventional fossil fuel based electricity generation plants of the country.
6.2 WIND POWER GENERATION COSTS
Wind power is the mature and rapidly growing technology among the other renewable energy sources. The power generation cost of wind energy largely depends on wind resources available in particular area, the height of the hub rotor mechanism, the cost of the wind turbine and its concern equipment, availability of power transmission line near to the power source and accessibility wind power potential locations. Other than these factors wind power generation cost also have significant effects on the system load profile,
133 134
electricity generation mix, operating procedures of the system, electricity market and cost
of land acquiring for the wind power project. A wind turbine in terms of power generation
cost is the largest component of wind power generation system which makes about 70% of
overall system cost. Therefore, wind power generation cost largely effects by its cost
(Kooten and Timilsina, 2009, Sadati et al., 2015).
6.3 COST ESTIMATION AND COMPARISON OF CONVENTIONAL ELECTRICITY GENERATION SYSTEMS WITH WIND ENERGY
For the cost analysis of electricity generation systems, four main costs pertinent to power
generation system must be considered. These costs include capital cost, fuel cost,
operation, and maintenance (O&M) cost and external costs. Capital cost consists of the
initial expenses for planning & design, equipment, land, engineering systems, and
installations. This cost is the amount that can occur at the initial stage of the project. The
operation and maintenance are the costs that occur in the repair, operation, inspection, and
salaries of labor during yearly operations (Harijan et al., 2009, Mousavi et al., 2012).
There are two main approaches for the estimation of the cost of electricity generation from the power plants, which are described as follows.
6.3.1 Life cycle cost of electricity generation
Life cycle cost of electricity (LCCOE) generation is an economic technique that involves
the assessment of expected costs incurred during the whole life (from construction to the
retirement and dispose of) of electricity generation plant. It also includes the costs incurred
in research and design, construction, operation and maintenance, retirement and disposal 135
(El-Kordy et al., 2002). The total costs are discounted from the date of commissioning to
the whole life of the plant into present value. As such, present value can be calculated as
(Mousavi et al., 2012):
PV = C(t) (1 + r) (6.1) Where C(t) is the cost occurred in year t, n is the economic life of the plant, L is the
construction period and r is the discount rate.
LCCOE of the power plants can be obtained by the summation of present values of all costs
like capital cost (CK), fuel cost (CF), Operating and maintenance cost (CO&M) and externality cost (CE)
LCC = C + C + C & + C (6.2)
Although LCCOE helps for decisions prior to accruing or developing assets related to the
power projects but one of the drawbacks is that it is not appropriate for the comparison of
the costs of different electricity generation plants producing electricity from various
sources having a different life time.
6.3.2 Levelized cost of electricity generation
Levelized cost of electricity generation (LCOE) is generally used to measure the electricity
generation costs of power plants and compare these costs with other electricity generation
plants over their economic life. It is the uniform revenue of each year to recover all the
expenses over a specified life time of the electricity generation plants. LCOE is basically 136
the ratio of the total cost to build and operate a power plant throughout its life to the total output of the power plant in the same period represented in cost/kWh. Capital costs, fuel costs, operation and maintenance (O&M) costs, financing costs and rate of utilization of the plant are the main inputs for its estimation which are converted to the present value
(PV) cost. Multiplying the PV by a factor called capital recovery factor (CRF) transfer it into the Levelized cost.
The LCOE is calculated using the equations given below (Mousavi et al., 2012):
C & × (1 + e & ) C × (1 + e ) LCOE = C + + (1 + r) (1 + r) (6.3) r(1 + r) × + C (1 + r) 1 − DR × TPC(1 + r) C = (6.4) HY × CF FOM C (6.5) & HY × CF C = FC × HR (6.6)
Where,
Ck Sum of the capital costs of plant
CO&M Operating and maintenance cost
CFuel Fuel cost
TPC Total plant cost ($/kW) 137
r Discount rate (%)
CL Plant construction life (Years)
HY Hours per year
CF Plant Capacity factor (%)
FOM Fixed O&M cost ($/kW year)
VOM Variable O&M cost ($/kWh)
FC Fuel cost ($/MMBtu)
eO&M Escalation rate of O&M cost (%)
eFuel Escalation rate of fuel
PL Plant life
DR Depreciation rate
6.3.2.1 External cost
External costs due to the emissions from the electricity generation or other industrial process are not the actual generation costs but these are the costs which indirectly bear by the society due to the effects on the environment. The effects of air pollution on the health of humans and other animals, crops, buildings, oceans, forests, climate and visual interference are the main sources of external cost. Electricity generation from fossil fuels like coal, oil, and gas accounts the largest source of these external costs (El-Kordy et al.,
2002). CO2 and other GHG emissions are associated with global warming and climate 138
change effects costs while other air pollutants like NOx, SO2, and VOC lead to the health
and regional environmental impacts costs. SO2 emissions are the main air pollutants for the formation of atmospheric acid rain which damages the buildings, crops, and forests
(Tsilingiridis et al., 2011). The external costs associated with the emissions from thermal power plants is calculated by the following equation and added to the LCOEs of electricity generation plants from which these emissions discharge (Mousavi et al., 2012).
C = EF × HR × EDC (6.7)
Where
CE External cost (¢/kWh)
EF Emission factor of fuel (g/Btu)
HR Heat rate (Btu/kWh)
EDC Environmental damage cost (¢/g)
6.4 COST ESTIMATION AND COMPARISON OF ELECTRICITY GENERATION SYSTEMS WITH (CCS) TECHNOLOGY
There are worldwide efforts to reduce CO2 emissions by improving energy efficiency and
use of alternative energy sources. However, besides these options Carbon Capture and
Storage (CCS) technology is also a significant solution to mitigate CO2 emissions caused
by burning of fossil fuels. In this technology; CO2 emissions in the flue gases which discharge from the thermal power plants and other industrial processes by the combustion of fossil fuels are separated, then compressed and transported to the places of stably stored, 139
such as underground geological formations. CO2 capturing from coal and other fossil fuel- fired power plants can be achieved by mainly using three methods such as post- combustion, pre-combustion and oxy-combustion.
6.4.1 Pre-combustion capturing method
In the pre-combustion capturing process CO2 emissions are removed before the actual
combustion of fuel. In this process, fuels are first oxidized under high pressure and
temperature to form synthesis or syngas which is a mixture of hydrogen (H2) and carbon
monoxide (CO). The syngas then undergoes further water-gas shift reaction to form H2 and
CO2 rich mixture. The CO2 is finally separated by physical or chemical absorption process,
from the H2. The produced H2 is combusted in power plants and other industrial
applications as fuel which generates zero emissions. This type of CO2 capturing process is
mainly used in new power plants and comparatively expensive to be installed on the
existing power plants.
6.4.2 Post-combustion capturing method
The post-combustion capturing method is used to separate the CO2 emissions from thermal power plant’s flue gases, or combustion exhausts. For separation of CO2 emissions from the other constituents of flue gas, a chemical solvent aqueous amines or ammonia is generally used which extracts emissions by absorption process. CO2 gas is then removed from the solvent by application of heat regeneration method. This type of capturing method is employed on new as well as on already existing plants. 140
6.4.3 Oxy-fuel combustion capture method
Oxy-fuel combustion capturing method involves the burning of fossil fuel in the presence
of pure oxygen instead of air. Pure oxygen (O2) is separated from the air through a heat
exchanger and is combusted with a fuel source. The composition of flue gases mainly
consists of CO2 gas and water vapours. CO2 gas is easily separated from flue gases, where
it is compressed and then transported to the sequestrated places. This method of capturing
CO2 gas theoretically viable but it involves a large amount of oxygen consumption which elevates its cost considerably (Davison, 2007, Lilliestam et al., 2012).
6.4.4 Levelized cost of electricity generation with CCS technology
LCOE of power plants with CCS technology is employed as estimation and comparison of
electricity generation costs of different thermal power plants with the CO2 emissions capturing technologies over the power plants total economic life. It is equal to the sum of discounted costs in present value in each year up to the total life of power plant divided by its total electricity generation (Finkenrath, 2012).
6.4.5 Cost of CO2 avoidance
When electricity generation systems with CCS are considered for estimation of the
economic costs then not only the LCOE is considered but the cost of avoiding the CO2
emissions needs to be taken into account as well. The cost of CO2 emissions avoided is the ratio of the difference in LCOEs to the differences in specific CO2 emissions having with
CCS and without CCS (Wang et al., 2013): 141
LCOE LCOE C avoided = (6.8) − − 6.5 DATA AND ASSUMPTIONS
In order to compare the LCOE of different fuel sources for electricity generation with CCS and without CCS technology to the wind energy, different types of power plants are selected in this study. All the relevant data for the estimation of electricity generation costs of these plants are given in the Tables 6.1 and 6.2 (EIA, 2013, IRG, 2011). The description and names with an abbreviation of the power plants are given as:
CSFO Conventional steam fuel oil GTDO Gas turbine diesel oil LCC Local Coal Combustion ICC Imported Coal Combustion IGCC Integrated Gasification Combined Cycle CCNG Combined Cycle Natural Gas WE Wind Energy Table 6.1: Cost and performance data for power plants without CCS Electricity generation sources without CCS Cost parameter CSFO GTDO LCC ICC IGCC CCNG WE Plant capacity (MW) 100 100 100 100 100 100 100 Total plant cost ($/kW) 1100 550 3246 3246 4400 1023 2213 Variable O&M cost 2.4 5.5 4.47 4.47 7.22 3.27 0 ($/MWh) Fixed O&M cost 12.25 16.3 37.8 37.8 62.25 15.37 39.55 ($/kW/year) Fuel cost ($/MMBtu) 15.11 16.18 7.3 10.81 7.3 5.61 0 Heat rate (Btu/kWh) 10400 13100 8800 8800 8700 6430 0 Plant life (years) 30 30 30 30 20 30 20 Plant capacity factor (%) 65 82 85 85 75 87 2 Construction life (years) 5 5 4 4 4 4 1 142
LCOE estimation of fuel oil and diesel oil based power plant with CCS technology is not considered due to the unavailability of data and furthermore, there is no new oil based power plant installation plan in future by the government even the existing oil based power plants will be converted to coal.
Table 6.2: Cost and performance data for electricity generation plants with CCS Electricity generation sources with CCS Cost Parameter LCC ICC IGCC CCNG Plant capacity (MW) 100 100 100 100 Total plant cost ($ /kW) 5227 522.7 6599 2095 Variable O&M cost ($/MWh) 9.51 9.51 8.45 6.78 Fixed O&M cost ($/kW/year) 80.53 80.53 72.83 31.79 Fuel cost ($/MMBtu) 7.3 10.81 7.3 5.61 Heat rate (Btu/kWh) 12000 12000 10700 7525 Plant life (years) 30 30 20 30 Capacity factor (%) 85 85 75 87 Construction life (years) 4 4 4 4
Economic parameters used for cost estimation are the discount rate, escalation rate of
O&M cost, and escalation of fuel cost. Data related to these economic parameters are taken from the reports of Ministry of Economic Affairs & Statistics and State Bank government of Pakistan and are presented in Table 6.3 (GOP, 2013b, SBP, 2013a).
Table 6.3: LCOE economic parameters Factor Value (%) Discount rate 8 Escalation of rate of O&M 2 Escalation of rate of fuel cost 3
The construction period of the electricity generation plant plays an important role in the cost estimation because all expenses before the generation are part of the capital cost and when the construction period increases then ultimately costs will increase as 143
well. Further, the depreciation rate (DR) is calculated using the straight-line method
which is the ratio of the difference between capital cost and salvage value to the total
life of the power plant. Straight-line depreciation method is applied here based on the
assumption that power plant equipment depreciate uniformly over the entire life of the
equipment. In the calculation of DR, it is also assumed that the salvage would be zero
at the retirement of the plant.
In this study, the cost estimation with CCS technology only includes the cost of CO2
capturing while the costs pertaining to its compression, transportation, and storage are
not considered. Further, for the IGCC plants pre-combustion capturing process and for
other power plants post-combustion capturing process is considered. In the case of wind
energy, only onshore wind power generation is considered.
Previous studies have signified that cost of electricity generation remarkably increases
while overall efficiency of the plant decreases when it is equipped with CCS
technology. The main reason for the same is that capital and operational costs of all
components related to capturing unit are high and they consume more energy during
the CO2 capturing process. As such, additional amount of fuel will be required when the electricity generation plant consumes more energy due to CCS technology. High capital cost and additional fuel cost increases the LCOE of the plants having CCS technology. In this study, it is also assumed that overall efficiency reduction will remain about 10% (IEA, 2013, Rubin and Zhai, 2012, IEA, 2014d).
As already has been described in previous sections that the main GHGs and other air pollutants discharge from thermal power generation plants during the combustion of 144
fossil fuels are CO2, CH4, N2O, SO2, NOx, CO, PM, and VOC. For the estimation of external cost due to these emissions, the data for the per unit damage cost and emission factors for different fuels are required. Therefore, the required data for the uncontrolled emission factors of all fuels, environmental damage cost of associated emissions and heat rate of the selected electricity generation plants are taken from the literate are presented in Tables 6.4 and 6.5 (Mousavi et al., 2012, Roth and Ambs, 2004).
Table 6.4: Damage costs of emissions from electricity generation technologies Emissions Damage cost ($/kg) Emissions Damage cost ($/kg) CO2 0.02 PM 6.92 CH4 0.34 CO 1.06 N2O 0.15 SO2 2.94 NOx 0.97 VOC 5.26
Table 6.5: Emission factors of fuels used in electricity generation technologies Emission factor (kg/TJ) Fuels CO2 CH4 N2O NOx PM CO SO2 VOC Fuel oil 77 400 3 0.6 528.23 27.56 43 1578 - Diesel oil 74 100 3 0.6 877 17.57 183.45 434.3 0.176 Natural gas 56 100 1 0.1 669.23 3.46 68.98 0.253 18 Coal (local) 101 000 1 1. 5 96.75 2350 4.033 915 - Coal 96 100 1 1. 5 96.75 2129 4.033 806 - imported)
It is also assumed that all GHGs and other air pollutant emissions are totally removed
by CCS technology as well as by other advanced air pollution control devices,
therefore, the external costs due to the emissions are not considered in the cost
estimation of electricity generation with CCS technology. 145
6.6 RESULTS AND DISCUSSION
6.6.1 Cost estimation
External costs of emissions are estimated using the values of emission factors, heat rates
and the environmental damage costs from Tables 6.1-6.5 in Eq. (6.7). The estimated results
described in Table 6.6 show that coal fuel based power plants have more external costs due
to the releasing of more environmentally damaging emissions of SO2 and PM. The external
costs of oil fuel based power plants are although better than coal fuel based but they also
have significant external costs. The natural gas fuel power plants have least external cost
due to the free from SO2 and PM emissions. Wind power plants do not consume any fuel for electricity generation, have zero external costs.
Table 6.6: External costs of emissions of electricity generation plants Electricity generation plants External cost (¢/kWh) CSFO 8.68 GTDO 4.97 LCC 11.11 ICC 10.3 IGCC 9.52 CCNG 1.174 WE 0
Using the technical, economic and external costs parameters data of all power plants from
Tables 6.1, 6.3 and 6.4 respectively in Eqs: (6.3) - (6.7); the LCOE generation of selected
power plants is estimated. The results are described in Fig. 6.1. The results indicate that
electricity generation cost from the GTDO power plants is about 32.13 ¢/kWh which is the
most expensive electricity generation option using diesel oil as fuel. 146
Generation cost External cost 35 )
h 30 W k / ¢
( 25 S C
C 20 t u o h t
i 15 w E
O 10 C L 5
0 CSFO GTDO LCC ICC IGCC CCNG WE Electricity generation plants
Fig. 6.1: LCOE without CCS technology
This is because the cost of fuel used in this type of power plant is high as compared to other power plants and the external costs of associated emissions such as SO2 and NOx are also more. CSFO power plants using the fuel oil for electricity generation are next expensive to the GTDO power plants. CSFO power plants based on fuel oil emit more hazardous emissions of PM and SO2 which has a great effect on the environment and human health consequently increasing their damage costs. Further, costly imported fuel oil used in these power plants is also a major factor to increase their electricity generation cost. The electricity generation cost of these types of power plants is about 29.46 ¢/kWh. The LCOE generations of LCC and ICC power plants are 22.44 ¢/kWh and 24.72 ¢/kWh respectively using local and imported coal as fuels. The electricity generation cost of ICC power plants 147
is more than the LCC power plants due to the imported fuel. The least cost of electricity generation is 5.13 ¢/kWh from the WE power plants utilizing the energy of blowing winds.
The reason of their least-cost power generation is that the capital costs of wind power generation technology have significantly reduced since last many years and these types of power plants do not consume any fuel, therefore, have no fuel costs as well as external costs. The electricity generation cost of the NGCC plants based on domestic natural gas is
6.94 ¢/kWh which is relatively close to the WE plants. Finally, the cost of generating electricity from IGCC power plants based on the local coal is determined to be 24.73 ¢/kWh which is more than LCC power plants utilizing the same local coal as fuel because of the high capital cost of these power plants.
Using the technical data of power plants having CCS technology from Table 6.2 and economic parameters data from Table 6.3 in Eqs: (6.3) - (6.6) the LCOE generations with
CCS technology are estimated without considering the external costs of emissions. The results are illustrated in Fig. 6.2. Although the CCS is one of the key CO2 reduction technology but the main barrier to its development is very high capital and operating costs.
The highest electricity generation cost of 22.08 ¢/kWh is from ICC power plants with CCS technology utilizing imported coal (bituminous) as fuel which is owing to the fact that cost of imported coal is high as compared to the domestic coal used in other power plants. IGCC plants with CCS technology having 19.85 ¢/kWh cost are less expensive than ICC power plants with CCS technology although having a high capital cost since the fuel used in these power plants is local coal (lignite) which is cheaper than the imported coal. Finally, it is determined that least cost generation with CCS technology is 7.99 ¢/kWh from NGCC 148
plants using domestic natural gas as fuel for electricity generation. It is well established
and discussed in previous sections of this study that CCS technology requires additional
energy consumption during capturing process which decreases the overall efficiency of the
power plant known as the energy or efficiency penalty.
25
20 ) h W k / ¢
( 15 S C C h t i
w 10 E O C L 5
0 LCC ICC IGCC CCNG WE Electricity generation plants
Fig. 6.2: LCOE with CCS technology
This efficiency penalty is different for different electricity generation power plants. Due to the additional consumption of energy for CCS process, it requires more fuel for the plant to generate the same amount of electricity as for it without CCS. Fuel cost is an important factor of the LCOE generation, as such; when it increases the overall economic cost will definitely increase. In this study the energy penalty is assumed to be 10% for all electricity generating sources, therefore, the fuel costs for all generating sources will increase by 10%.
The LCOE generation due to additional fuel costs is shown in Fig. 6.3. 149
30
25 ) h W
k 20 / ¢ ( S C
C 15 h t i w
E 10 O C L 5
0 LCC ICC IGCC CCNG WE Electricity generation plants
Fig. 6.3: LCOE with CCS technology including efficiency reduction
6.6.2 Cost comparison
The comparison of estimated LCOE generation with CCS and without CCS technology for various power plants as estimated in this study is shown in Fig. 6.4. From the results, it is obvious that LCOE generation significantly increases when electricity generating plants are considered with CCS technology than the plants without CCS technology but oil fuel based power plants are more expensive even having no CCS technology because of costly imported fuels used in these power plants. The increase in cost is around 44% when electricity is generated by power plants having CCS capturing units. The highest generation cost of 24.29 ¢/kWh is from the ICC power plants when the efficiency reduction due to the CCS technology is considered. 150
LCOE without CCS LCOE with CCS LCOE With CCS (Efficiency reduction) 30
25
) 20 h W k / ¢
( 15 E O C
L 10
5
0 CSFO GTDO LCC ICC IGCC CCNG WE Electricity generation plants
Fig. 6.4: LCOE comparison of power plants
The increasing electricity generation cost in ICC power plants is owing to the imported coal as fuel used in these power plants as compared to the other power plants with CCS technology in which indigenous (lignite) coal is used as fuel. The increase in cost is about 2.24 ¢/kWh due to efficiency reduction which is the highest in all electricity generation plants. The electricity generation cost of CCNG plants only increases 0.81
¢/kWh due to efficiency reduction, therefore, its total cost is lowest than the other fossil fuel power plants. WE power plants are non-fossil fuel and emission free electricity generation plants, mitigation of CO2 is done without the CCS technology, as such; they are cost competitive in case of CO2 reduction.
Cost estimation of CO2 avoided is a useful standard criterion for the economic comparison of CO2 capture technology to the electricity generation plants without 151
same. The estimated results of CO2 avoided costs from coal, as well as natural gas electricity generation, are presented in Table 6.7. The CO2 avoided costs from natural gas are 40% and 10% higher than the local coal and imported coal respectively since the emission difference of reference plant to the capturing plant is less as compared to the coal wherein difference is more.
Table 6.7: CO2 avoided costs of electricity generation plants Electricity generation CO capture CO avoided cost Fuel 2 2 technology technology ($/ton CO2) Post- Gas Combined cycle 100 combustion Pulverized fuel Post- Local coal 60 combustion combustion Pulverized fuel Post- Imported coal 90 combustion combustion Local coal Gasification Technology Pre-combustion 73
The cost of CO2 avoided is also more in imported coal and Local coal (IGCC) plants than the simple local coal based plants due to the high difference in the generation costs of reference and capturing plants.
6.7 CONCLUSIONS
The finding of this chapter suggests that electricity generation cost of wind power plants is competitive or even less than all types of conventional power plants although the external costs due to emissions from these plants not considered. In this context, CCS is one of the significant CO2 reduction technologies for sustainable electricity generations from the conventional power plant. However, it is the very expensive option of CO2 reduction even though the costs pertaining its compression, 152
transportation, and sequestration to the geological places not considered. It is, therefore, recommended that renewable energy sources like wind energy, which is abundantly available in Pakistan and cheaper than conventional electricity generation plants should be considered as most suitable and sustainable option for the CO2 reduction in Pakistan. Chapt er 7 CHAPTER 7 MODELING AND FORECASTING AIR POLLUTION MITIGATION POTENTIAL OF WIND POWER
7.1 INTRODUCTION
Air pollutant emissions from power generation and other industrial processes are not only
adversely affecting the environment but at the same time are sources of health-related
problems of the all living organism. Therefore, mitigation of air pollutant emissions
through various methods to address the environmental and other concerns is attaining
greatest ever importance. In this chapter mathematical models are developed to analyze the
reduction of GHGs and other air pollutant emissions through wind energy based power
plants. The result analysis of the developed models provides important insight and benefits
of harnessing wind energy for reduction of GHGs and other air pollutant emissions.
7.2 AIR POLLUTION MITIGATION
There are worldwide efforts to reduce CO2 and other air pollutant emissions to protect the
environment from the impact of these emissions. The SO2 and other solid air pollutants like
PM are generally controlled by the mechanical components such as scrubbers, cyclones, electrostatic precipitators Flue-gas desulfurization (FGD) (Damgaard et al., 2010). NOX and VOC emissions can be reduced by modifications in the combustion process and through the use of catalytic reduction in the way of flue gases of power plants. The mitigation of CO2 and other GHG emissions is generally carried out by efficiency
153 154
improvements in fossil fuel combustion, using low-carbon fuels, installations of carbon
capture and storage (CCS) technology on power plants and utilization of renewable and
nuclear resources for power generation. The above first three options are the best solutions
for CO2 and other GHG emissions reduction but they have some limitations. The reduction of emission through efficiency improvement is limited. The CCS technology is a very expensive emissions mitigation option whereas the availability of low-carbon fuels is very limited (Liu et al., 2011, Mao et al., 2014).
7.2.1 Air pollution mitigation through wind power
The reduction of GHGs and other air pollutant emissions through the use of renewable
energy sources particularly wind power is globally considered as one of the best practices
for the sustainability and economic development. Large-scale renewable energy based
projects vastly play a significant role in the energy supply balance of a country along with
the control of air pollutant emissions. Since 1970s oil crisis; the research in the
development, application, and assessment of renewable energy systems such as hydro,
wind, solar and biomass is growing very fast throughout the world. The world investments
in renewable energy projects have reached a record point of 286 billion US dollar at the
end of 2015 which makes 2.4 trillion US dollars since last twelve years. The annual global
investment of renewable energy projects from 2004 to 2015 has been shown in Fig. 7.1
which illustrates that the investment in renewable energy sources mainly in solar and wind
has increased six times in last twelve years. This is equivalent to 1.4 Gigatons of CO2 and 155
other air pollutant emissions reductions due to the installed capacity of these renewable
energy sources (UNEP, 2016).
350 n o
i 300 l l i b
S 250 U $ t n
e 200 m t s e
v 150 n i e l
b 100 a w e n
e 50 R 0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Year
Fig. 7.1: Annual global investment in renewable energy projects (UNEP, 2016)
7.3 DEVELOPMENT OF MATHEMATICAL MODEL FOR AIR POLLUTION MITIGATION BY WIND POWER
The power produced by wind turbine basically depends on the parameters of its location
and design. The parameters related to the location of a wind turbine are wind speed, the
density of air and wind volume whereas the design parameters consist of the coefficient of
the performance of wind rotor, swept area of the rotor, cut-in, cut-out and rated wind speed
range of rotor. Thus the annual useful energy supplied by the wind turbine AUEwind can be determined as: (Pallav Purohit, 2007) 156
( ) AUE = 8760 g P v F(v)dv (7.1) Where g represents the availability factor of wind turbine, P(v) is the power produced by
the wind turbine at wind speed v in meter/second (m/s), F(v) represents the Weibull
probability distribution function, VCi stands the speed at which turbine starts power
production and VCO the speed at which the turbine stop to power production. The power output of the wind turbine can be estimated at wind speed v as:
1 P(v) = C Av (7.2) 2 ρ Where Cp is the coefficient of performance of the rotor of a wind turbine, ra represents the air density, A is the rotor swept area and v represents the wind speed.
The variation in wind speed at any site is normally illustrated with the help of probability distribution function F(v). The expression of the Weibull probability distribution function is given as:
k v ( ) F(v) = e (7.3) c c Where the k is the shape parameter while c represents the scale parameter,
Substituting the values of the wind turbine power output P(v) and Weibull probability
distribution F(v) from Eqs:(7.2) and (7.3) into the Eq: (7.1) the annual useful energy
supplied by the wind turbine can be estimated as (Pallav Purohit, 2007) 157
GCE = (8760 × PLF × P )CEF (7.4)
Where the PLFwind is the plant load factor of the wind power plant, Pwind represents the installed capacity of wind power plant in MW; CEFe is the CO2 baseline grid emission factor of electricity generation system. The expression in the brackets on the righthand side of the Eq: (7.4) is the annual amount of electricity saved by wind power plant. In this study, the amount of electricity saved by the wind power plants is the annual electricity generated through wind power plants in Pakistan which have been estimated in Chapter 5 Table 5.5.
7.3.1 Baseline grid emission factor estimation for GHG emissions
The baseline grid emission factor is used to determine the baseline emissions from the grids
that supply electricity to any country. The volume of emissions that have been released by
grid-connected power plants in absence of the proposed renewable power generation
project is described as baseline emissions. The difference between baseline emissions and
project’s emission is the reduction in emissions. In this study, the baseline grid emission
factor is determined through approved baseline methodology for grid-connected electricity
generation from renewable energy ACM0002 (version 16) of UNFCCC. According to this
methodological tool baseline grid emission factor is estimated by combined margin (CM)
emission factor which is the weighted average of the operating margin (OM) and the build
margin (BM) emission factors for the displacement of electricity generated by the power
plants in electricity generation systems (UNFCCC, 2014a). 158
7.3.1.1 Operating margin (OM)
The OM emission factor refers to the weighted average of emissions released by existing power plants in a grid system excluding low cost/must-run (LC/MR) sources whose current electricity generation would affect the proposed new renewable project. It is estimated by the total emissions releasing from thermal power plants to the net electricity generation by these power plants.
7.3.1.2 Build margin (BM)
The BM emission factor refers to the weighted average of emissions releasing by the power plants whose construction and future operation would affect the new renewable power projects. The BM is based on the power plants which are commissioned during the last five to ten years period.
For the estimation of baseline grid emission factor, following six steps are undertaken as stated in the methodological tool ACM0002 (version 16) of (UNFCCC, 2014a).
Step 1: Identify the relevant electricity generation systems
Step 2: Choose an OM emission factor method from different options
Step 3: Calculate the OM emission factor according to the selected method
Step 4: Identify the power plants that will be included in the BM determination
Step 5: Calculate BM emission factor
Step 6: Calculate the CM emission factor
These steps in the context of a national grid of Pakistan are further explained in detail as in
the following: 159
1. Identify the relevant electricity generation systems
The electricity is supplied to the national grid in Pakistan by two electricity supply companies, PEPCO and K-Electric. K-Electric company is responsible for operating and managing electricity system in Karachi metropolitan and its adjoining areas whereas in rest of the Pakistan the operation and management of electricity is undertaken by PEPCO as it has been explained in previous chapters. NTDC also supplies 700 MW of electricity to the
K-Electric from its Hubco-Jamshoro transmission line. The whole area of Pakistan is considered as a single grid where supplied electricity is being generated from diversified fuels such as fuel oil, high-speed diesel, natural gas, hydropower, coal and nuclear (GOP,
2014a, NEPRA, 2013). All off-grid power plants are not included in this study.
2. Choose an OM emission factor method from different options
Before the selection of OM emission factor method, the determination of low-cost/must- run LC/MR and Non-LC/MR power plants and their associated electricity generation data of recent five years must be estimated first. Hydro and nuclear powers are considered as the LC/MR generation resources in Pakistan. Therefore, the required data for total electricity generation and LC/MR electricity generation sources are collected from the energy yearbook 2013 (HDIP, 2013).
The average of total LC/MR electricity generation data of recent five years is 34.6% which constitutes less than the threshold limit of 50% of total electricity generation in recent five years as shown in Table 7.1. The emission factor tool presents four different methods for the estimation of OM emission factor which are illustrated as: 160
(a) Simple OM emission factor
(b) Simple adjusted OM emission factor
(c) Dispatch data analysis OM emission factor
(d) Average OM emission factor
Due to the inadequate availability of data in Pakistan the option (b) and (c) are not considered. Option (d) is only selected when the LC/MR electricity generation sources constitute the more than 50% of the total electricity generation. Therefore the option (a) is selected for the estimation of OM emission factor for Pakistan since the LC/MR electricity generation sources in Pakistan constitute less than 50% of the total electricity generation sources.
1. Calculate the OM emission factor according to the selected method
The simple OM emission factor is estimated by the weighted average emissions per unit electricity generation usually (ton of emissions/MWh) of all electricity generating power plants, providing electricity to the national grid system, not considering the LC/MR power plants.
Table 7.1: Electricity generation during most recent years in Pakistan
2013 2012 2011 2010 2009 Fuel GWh % GWh % GWh % GWh % GWh % Hydro 29857 31.06 28517 30 31811 33.7 28093 29.46 27784 30.33 Nuclear 4553 4.74 5265 5.54 3420 3.62 2894 3.03 1618 1.77 Total LC/MR 34410 35.8 33782 35.52 35231 37.33 30987 32.49 29402 32.09 Coal 61 0.06 96 0.1 88 0.09 116 0.12 113 0.12 Oil 34534 36 33562 35.29 33186 35.16 36175 37.93 32423 35.39 Natural gas 27116 28.21 27650 29.1 25879 27.42 28079 29.44 29678 32.39 161
The calculation of OM grid emission factor is carried out on the basis of data on fuel
consumption and net electricity generation of each power plant. The Equation used for the
calculation of simple OM emission factor is as under:
( , × , × , , ) , , = (7.5) ∑ Where
EFgrid OM simple, y, Simple grid OM emission factor in year y (ton emissions/MWh)
FCi,y Quantity of fossil fuel type i utilized for the electricity generation
system in year y (unit mass)
NCVi,y Net calorific value of fossil fuel type i in year y (GJ/ Unit mass)
EFCO2,i,y Emission factor of fossil fuel type i in year y (ton of emissions/GJ)
EGy Net amount of electricity produced and supplied to the grid by all
power plants in the grid system, not considering LC/MR plants/units,
in year y (MWh)
i Type of fossil fuel utilized in power plant for electricity generation
system in year y
y The relevant year
The data pertaining to the fuel consumption (FCi, y) and net calorific value (NCVi, y) of all fuels are taken from the Energy yearbook 2013 for the calculation of OM. The values of fuel consumption of recent three years and Net Calorific Value are listed in Tables 7.2 and
7.3 respectively (HDIP, 2013). 162
Table 7.2: Fuel consumption for electricity generation (TOE)
Fuel Type 2011 2012 2013 Coal (lignite) 43169 46800 28204 Furnace oil 7827500 7206839 7342755 Diesel oil 105160 203072 218584 Natural gas 6493766 6732876 7084177 Table 7.3: Net calorific value of fuels used for electricity generation
Fuel type Net calorific value (GJ/ton) Coal (lignite) 19.8 Furnace oil 42.3 Diesel oil 43 Natural gas 48
The values of emission factors (EFCO2, i, y) of all the fuels used for electricity generation are taken from the greenhouse gas inventory guidelines of IPCC. The values of these emission factors are illustrated in Table 7.4 (IPCC, 2006).
Table 7.4: GHG emission factors (kg/TJ) of fuels used for electricity generation
Fuel type CO2 CH4 N2O Coal (lignite) 101000 1 1.5 Furnace oil 77400 3 0.6 Diesel oil 74100 3 0.6 Natural gas 56100 1 0.1
It is pertinent to mention that Global Warming Potential (GWP) is the measurement of impacts of the greenhouse gases in the atmosphere. Each greenhouse gas has different intensity of effect on the atmosphere which measures how much heat one ton of a gas will entrap into the atmosphere over a given period of time comparative to CO2 over the same time. The main greenhouse gases released from the combustion of fossil fuels as mentioned 163
already in this study are CO2, CH4, and N2O and their relevant GWP at 100 years are 1, 21 and 310 respectively (UNFCCC, 2014c).
Further, carbon dioxide equivalent (CO2 eq) is a term describing different GHGs in a common unit which would have the equivalent global warming impact. The CO2 eq emission factor is estimated using emission factors of all fuels given in Table 7.4 and the
GWP of each respective GHG. The estimated results of emission factors are given in Table
7.5.
Table 7.5: Estimated emission factors of fuels in CO2 eq
Fuel type CO2 eq (ton/TJ) Coal (lignite) 101.49 Furnace oil 77.649 Diesel oil 74.29 Natural gas 56.152
Using the data from the Tables (7.1) - (7.5) in Eq: (7.5) the OM grid emission factor (EFgrid,
OM simple, y) is estimated. The results of OM grid emission factor based on the data of recent years i.e. 2013, 2012 and 2011 and their average value of a grid emission factor are listed in Table 7.6.
Table 7.6: Results of OM emission factors (ton CO2 eq/MWh) 2013 2012 2011 Average Result 0.665 0.649 0.693 0.668
2. Identify the power plants that will be included in the BM emission factor
determination 164
The BM grid emission factor is estimated on the basis of the average effect of emission
factors of a sample group of power generation plants. This sample group of power
generation plants that have been employed for the estimation of BM emission factor
consists either:
· The group of five power plants which have been most newly built and are not
registered with CDM executive board; or
· The group of power capacity addition plants in the total electricity generation
system that consists of 20% of total electricity generation system (in MWh) which
have been most newly built excluding those power plants which are registered with
CDM executive board.
According to the above, first criteria, the group of five power generation plants (SET5-
Units) is identified which started supplying electricity generation to the national grid and
are mostly new, excluding those power plants which are registered as CDM project
activities. The annual electricity generation (AEGSET-5-units, in MWh) of these power
plants are estimated and described in Table 7.7. Further, according to the above second criteria; annual electricity generation of the total system is referred as (AEGtotal, in MWh).
From the total electricity generation system, a group of power plants excluding the power plants that registered with CDM executive board is identified which supply electricity to the national grid system and are mostly new and consists of 20% of (AEGtotal in MWh).
Then, the annual electricity generation (AEGSET-20% in MWh) of this group of power 165
plants (SET≥20%) is estimated. The estimated results of total annual electricity
generation (AEG total, in MWh) and (AEGSET-20% in MWh) are presented in Table 7.8.
Table 7.7: Electricity generation data of five most recently commissioned plants
Generation Commissioning Name of power plant Fuel (GWh) date 2013 Hub Power Limited 22-04-2011 FO 820 Chashma II 10-5-2011 Nuclear 1652 Foundation Power (Daharki) Limited 16-05-2011 Natural gas 1380.8 Halmore Power Company Limited 16-06-2011 Natural gas 374 Jinnah Hydropower 10-1-2012 Hydro 202.01 Total 4428.81
Table 7.8: Annual electricity generation in 2013 Electricity generation Units 2013 Total generation [AEG total ] GWh 96122 20% of [AEG total ] GWh 19224
According to the procedure adopted above, from the two groups of power plants, the one
with the highest output of electricity generation is selected for estimation of BM grid
emission factor. It is found that SET≥20% consists highest annual electricity generation system than SET5-plants, therefore it is selected as (SET sample) for BM emission factor estimation in this study. Since no any power plant included in SET≥20% has been registered in CDM executive board, furthermore none of the power plant is supplying electricity more than ten years. The commissioning date and electricity generation of each power plant included in SET≥20% are presented in Table 7.9 (NEPRA, 2013, PPIB,
2016). 166
Table 7.9: Power plants generating 20% of total electricity generation
Electricity Commissioning Name of power plants Fuel generation date (GWh) 2013 Ghazi Barotha Hydropower 5-7-2003 Hydro 7164.66 Korangi CCP 1-11-2008 Gas 795.00 Attoc Generation limited 17-03-2009 FO 1255 Atlas Power Limited 18-12-2009 FO 1383 Engro Energy Limited 27-03-2010 Gas 1691.5 Saif Power Limited 27-04-2010 Gas 742.4 Orient Power Company Ltd. 24-05-2010 Gas 345.9 Nishat Power Limited 9-6-2010 FO 1309.9 Nishat Chonia Power Limited 21-07-2010 FO 1250.3 Sapphire Electric Company Ltd 4-10-2010 Gas 877 Khan Khawar Hydropower 10-10-2010 Hydro 296.65 Liberty Power Tech Ltd. 13-01-2011 FO 1468.2 Hub Power Company Limited 22-04-2011 FO 820 Chashma II 10-5-2011 Nuclear 1652 Foundation Power Company 16-05-2011 Gas 1380.8 (Daharki) Limited Halmore Power Generation Company 16-06-2011 Gas 374 Limited Jinnah Hydropower 10-1-2012 Hydro 202.01 Total 23008.32 3. Calculate BM emission factor
The BM emission factor is the generation-weighted average emission factor (ton CO2 eq/MWh) of the group of power plants “m” which has been selected for its estimation and is supplying electricity most recent years for which the electricity generation data is available. The BM emission factor is calculated as:
EG , × EF , , EF , , = (7.6) EG , ∑ ∑ Where 167
EFgrid, BM, y BM CO2 eq emission factor in the year “y” (ton CO2
eq/GWh)
EGm,y Net amount of electricity generated and supplied to the
national grid by the power plants “m” in year “y” (GWh)
EFEL, m,y CO2 eq emission factor for power plants “m” in year “y”
(ton CO2 eq/GWh)
If for the power plants “m” only the electricity generation data and the types of fuel used is available; the CO2 eq emission factor (EFEL,m,y) for each power plant “m” in year “y”
(ton CO2 eq/GWh) can be estimated by the emission factor of the fuel used and the efficiency of the power plant, using the following equation:
EF , , , × 3.6 EF , , = (7.7) , η Where
EFEL, m,y CO2 eq emission factor for power plants “m” in year “y” (ton
CO2 eq/GWh)
EFm,i,y Average CO2 emission factor of type of fuel i used in power
plant “m” in year “y” (ton CO2 eq/GJ)
m ,y Average net energy efficiency of power plant “m” in year “y”
(%)
According to the available literature the specific fuel consumption data of each electricity
generation plant in Pakistan is not available therefore, taking the average emission factor 168
of each fuel used for power generation from the IPCC guidelines 2006 and average net power plant efficiency from the energy yearbook 2013 the CO2 eq emission factor for each power plant that has been identified for BM emission factor is estimated by Eq: 7.7.
The estimated results are shown in Table 7.10.
Using the data of the net amount of electricity generation (EGm, y 2013) in GWh and the
CO2 eq emission factor of the fuel used by each power plant from the Table 7.10 in Eq.
(7.6) the BM grid emission factor for 2013 is estimated which is found to be 0.315 ton
CO2 eq/MWh.
Table 7.10: Emission factors of power plants
EF , , EFCO ,m,i,y EL m y 2 (ton CO EG Name of power plants Fuel Technology (ton CO Efficiency 2 m,y 2 eq/MWh) (2013) eq/GJ Ghazi Barotha Hydropower Hydro Hydro turbine - - - 7164.66 Korangi CCP Gas Combined cycle 0.06 0.60 0.34 795.00 Attoc Generation limited FO Diesel engines 0.08 0.40 0.71 1255 Atlas Power Limited FO Combined cycle 0.08 0.46 0.61 1383 Engro Energy Limited Gas Combined cycle 0.06 0.60 0.34 1691.5 Saif Power Limited Gas Combined cycle 0.06 0.60 0.34 742.4 Orient Power Company Ltd. Gas Combined cycle 0.06 0.60 0.34 345.9 Nishat Power Limited FO Diesel engines 0.08 0.40 0.71 1309.9 Nishat Chonia Power FO Diesel engines 0.08 0.40 0.71 1250.3 Limited Sapphire Electric Company Gas Combined cycle 0.06 0.60 0.34 877 Ltd Khan Khawar Hydropower Hydro Hydro turbine - - - 296.65 Liberty Power Tech Ltd. FO Diesel engines 0.08 0.40 0.71 1468.2 Hub Power Company FO Diesel engines 0.08 0.40 0.71 820 Limited Chashma II Nuclear Nuclear reactor - - - 1652 Foundation Power Company Gas Combined cycle 0.06 0.60 0.34 1380.8 (Daharki) Limited Halmore Power Generation Gas Combined cycle 0.06 0.60 0.34 374 Company Limited Jinnah Hydropower Hydro Hydro turbine - - - 202.01 Total EGm ,y (2013) GWh 23008.32 169
4. Calculate the CM grid emission factor
As already it has been discussed in this study that the CM grid emission factor is the
weighted average of the OM and the BM grid emission factors for the supply of electricity
generated by the power plants in electricity generation system. The CM emission factor is
calculated as:
EF , , = EF , , × W + EF , , × W (7.8)
Where
EFgrid,CM,y CM emission factor in year y (ton CO2 eq/MWh)
EFgrid,OM,y OM emission factor in year y (ton CO2 eq/MWh)
EFgrid,BM,y BM emission factor in year y (ton CO2 eq/MWh)
WOM Weight of the OM emission factor
WBM Weight of the BM emission factor
As per the guidelines of ACM0002 by the UNFCCC for wind power projects, the weighted effects of OM and BM grid emissions factors are 0.75 and 0.25 respectively, therefore, CM grid emission (baseline emission factor) can be estimated by the Eq.7.8.
The estimated results of OM, BM and CM emission factors are presented in Table 7.11.
7.3.2 Baseline grid emission factors estimation for air pollutant emissions
The air pollutant emissions other than GHG emissions considered in this study are SO2,
NOx, CO, PM, and VOC which have already been discussed in previous chapters.
Methodology for quantifying the baseline grid emission factors for the air pollutant 170
emissions is same as that of GHG baseline grid emission factors. The CM emission factor,
the weighted average of OM and BM emission factors can be estimated for all air pollutant
emissions.
Table 7.11: Result of baseline grid emission factor in 2013 Emission factor Unit Result OM (EF grid, OM, y ) (ton CO2 eq/MWh) 0.669 BM (EF grid,BM, y ) (ton CO2 eq/MWh) 0.315 CM (EF grid, CM, y ) (ton CO2 eq/MWh) 0.581
The data related to the emission factors of air pollutant emissions (EFair pollutant, i, y) is acquired from the United States environmental protection agency. The values of all emission factors of air pollutant emissions are illustrated in Table 7.12 (EPA, 2011).
Table 7.12: Emission factors of air pollutant emissions (kg/TJ) of fuels
Type of fuel NOx CO SO2 PM VOC Coal 96.75 4.033 806 2129 - Furnace oil 528.23 43 1578 27.56 - Diesel oil 877 183.45 434.3 17.57 0.1763 Natural gas 669.23 68.98 0.21 3.46 18
Using the data from the Tables 7.2, 7.3 and 7.12 in Eq. (7.5), the OM grid emission factor
(EFgrid, OM simple, y) for each air pollutant emissions is estimated in the same way as it is used for calculation of OM emission factor for GHG emissions. The average results of OM emission factors of recent years 2013, 2012 and 2011 for all air pollutants are listed in
Table 7.13. 171
Table 7.13: OM grid emission factors (kg/MWh) of air pollutant emissions
Results Pollutants 2013 2012 2011 Average SO2 8.241 8.15 9.132 8.51 CO 0.582 0.563 0.578 0.574 NOx 6.08 5.91 6.18 6.051 PM 0.204 0.231 0.244 0.227 VOC 0.086 0.082 0.082 0.084
The BM emission factor signifies that prospective group of power plants whose
construction and future electricity generation process would be affected by the new
electricity generation plants. The BM grid emission factors for all air pollutant emissions
can be calculated in a similar way as it has been used for the GHGs calculations. The same
electricity generation data (AEGSET-20% in MWh) of power plants and NCV data of all fuels is used accept the (EF, m, i, y) emission factors data. The (EF, m, i, y) emission factors data of all air pollutant emissions is given in Table 7.12. Using the data from Tables 7.3, 7.9 and
7.12 in Eq. (7.6) the BM grid emission factors for all air pollutant emissions are estimated and presented in Table 7.14.
For the wind and solar power generation technologies as already has been stated during the estimation of GHGs baseline grid emission factor, the CM baseline grid emission is the weighted average of 0.75 and 0.25 for OM and BM emission factors respectively.
Table 7.14: BM emission factors (kg/MWh) of air pollutant emissions
Pollutants Results (2013) SO2 4.56 CO 0.236 NOx 2.61 PM 0.085 VOC 0.029 172
Therefore, using the data of OM and BM grid emission factors from the Tables 7.13 and
7.14 in Eq. (7.8), the CM baseline emission factors for air pollutant emissions SO2, CO,
NOx, PM, and VOC are estimated. The estimated results are presented in Table 7.15.
Table 7.15: CM emission factors (kg/MWh) of air pollutant emissions
Pollutants Results (2013) SO2 7.52 CO 0.49 NOx 5.20 PM 0.191 VOC 0.070
7.4 RESULTS AND DISCUSSION
7.4.1 GHG emissions
Using the estimated value of baseline grid emission factor for GHGs and forecasted
electricity generation data from wind power plants under various scenarios from the Tables
5.5 and 7.11 in Eq. (7.4), the GHG emissions mitigation through wind power plants in
Pakistan is estimated. The estimated results from the base year 2013 to the end year 2035
in BAU, MD and HD scenarios for this study are presented in Table 7.16.
The estimated results of this study indicate that there is an immense potential for mitigation
of GHG emissions in Pakistan through the application of wind power. The CO2 eq emissions mitigation potential in 2013 under the BAU, MD and HD scenarios is 0.30 million tons, 0.56 million tons, and 0.68 million tons respectively and it is expected to increase 46.84 million tons, 67.24 million tons and 79.82 million tons in BAU, MD and
HD scenarios respective in 2035. 173
Table 7.16: Annual GHG emissions mitigation potential by wind power plants
Annual electricity Baseline EF CO2 eq emissions mitigation year generation (TWh) ton CO2 (million tons) BAU MD HD eq/MWh BAU MD HD 2013 0.52 0.95 1.17 0.58 0.30 0.56 0.68 2014 0.68 1.28 1.68 0.58 0.40 0.74 0.98 2016 1.16 2.28 3.47 0.58 0.67 1.33 2.02 2018 1.96 4.06 7.07 0.58 1.14 2.36 4.11 2020 3.31 7.15 14.02 0.58 1.92 4.15 8.15 2022 5.55 12.37 26.48 0.58 3.23 7.19 15.39 2024 9.22 20.82 46.04 0.58 5.36 12.10 26.75 2026 15.04 33.56 71.18 0.58 8.74 19.50 41.35 2028 23.87 50.83 96.31 0.58 13.87 29.53 55.96 2030 36.41 71.18 115.87 0.58 21.16 41.35 67.32 2032 52.61 91.52 128.33 0.58 30.57 53.17 74.56 2034 71.18 108.79 135.28 0.58 41.35 63.21 78.60 2035 80.62 115.74 137.39 0.58 46.84 67.24 79.82
The CO2 eq mitigation potential in HD scenario is more than the BAU and MD scenarios because the diffusion of wind power is very high in this scenario. Due to the high diffusion of wind power more electricity generation is forecasted in HD scenario as compared to the other scenarios. The MD scenario in which the diffusion of wind power is forecasted by the past influence of market and subsidies for the diffusion of wind power has mitigation potential of emissions more than BAU scenario. In BAU scenario the new installations of wind power in future continue with existing policies and events with no new policy or interference has the minimum CO2 eq emissions mitigation potential due to the low wind power diffusion as compared to the MD and HD scenarios.
7.4.2 Air pollutant emissions other than GHGs
The mitigation potential of uncontrolled air pollutant emissions SO2, CO, NOx, PM and VOC by wind power plants is estimated using the estimated baseline grid emission factors of these 174
pollutants and the forecasted wind power electricity generation from Tables 7.15 and 5.5 respectively in Eq. 7.4. The results of emissions mitigation of all air pollutants through wind power plants in Pakistan are given in Tables 7.17-7.21.
Table 7.17: Uncontrolled SO2 emissions mitigation potential by wind power plants
Annual electricity generation Baseline EF SO2 emissions mitigation (kt) year (TWh) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 7.52 3.92 7.18 8.76 2014 0.68 1.28 1.68 7.52 5.11 9.61 12.63 2016 1.16 2.28 3.47 7.52 8.69 17.18 26.08 2018 1.96 4.06 7.07 7.52 14.73 30.54 53.14 2020 3.31 7.15 14.02 7.52 24.89 53.75 105.44 2022 5.55 12.37 26.48 7.52 41.77 93.03 199.14 2024 9.22 20.82 46.04 7.52 69.33 156.60 346.24 2026 15.04 33.56 71.18 7.52 113.09 252.39 535.24 2028 23.87 50.83 96.31 7.52 179.52 382.26 724.24 2030 36.41 71.18 115.87 7.52 273.81 535.24 871.33 2032 52.61 91.52 128.33 7.52 395.63 688.21 965.03 2034 71.18 108.79 135.28 7.52 535.24 818.08 1017.33 2035 80.62 115.74 137.39 7.52 606.27 870.36 1033.16
Table 7.18: Uncontrolled CO emissions mitigation potential by wind power plants
Annual electricity generation CO emissions mitigation Baseline EF year (TWh) (kt) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 0.49 0.26 0.47 0.57 2014 0.68 1.28 1.68 0.49 0.33 0.63 0.82 2016 1.16 2.28 3.47 0.49 0.57 1.12 1.70 2018 1.96 4.06 7.07 0.49 0.96 1.99 3.46 2020 3.31 7.15 14.02 0.49 1.62 3.50 6.87 2022 5.55 12.37 26.48 0.49 2.72 6.06 12.98 2024 9.22 20.82 46.04 0.49 4.52 10.20 22.56 2026 15.04 33.56 71.18 0.49 7.37 16.45 34.88 2028 23.87 50.83 96.31 0.49 11.70 24.91 47.19 2030 36.41 71.18 115.87 0.49 17.84 34.88 56.78 2032 52.61 91.52 128.33 0.49 25.78 44.84 62.88 2034 71.18 108.79 135.28 0.49 34.88 53.31 66.29 2035 80.62 115.74 137.39 0.49 39.50 56.71 67.32 175
Table 7.19: Uncontrolled NOx emissions mitigation potential by wind power plants
Annual electricity generation NO emissions mitigation Baseline EF x year (TWh) (kt) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 5.20 2.71 4.96 6.06 2014 0.68 1.28 1.68 5.20 3.53 6.65 8.73 2016 1.16 2.28 3.47 5.20 6.01 11.88 18.03 2018 1.96 4.06 7.07 5.20 10.19 21.12 36.75 2020 3.31 7.15 14.02 5.20 17.21 37.17 72.91 2022 5.55 12.37 26.48 5.20 28.88 64.33 137.71 2024 9.22 20.82 46.04 5.20 47.94 108.29 239.42 2026 15.04 33.56 71.18 5.20 78.20 174.52 370.11 2028 23.87 50.83 96.31 5.20 124.14 264.33 500.80 2030 36.41 71.18 115.87 5.20 189.34 370.11 602.52 2032 52.61 91.52 128.33 5.20 273.57 475.89 667.31 2034 71.18 108.79 135.28 5.20 370.11 565.70 703.47 2035 80.62 115.74 137.39 5.20 419.23 601.84 714.42
Table 7.20: Uncontrolled PM emissions mitigation potential by wind power plants
Annual electricity generation PM emissions mitigation Baseline EF year (TWh) (kt) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 0.19 0.10 0.18 0.22 2014 0.68 1.28 1.68 0.19 0.13 0.24 0.32 2016 1.16 2.28 3.47 0.19 0.22 0.44 0.66 2018 1.96 4.06 7.07 0.19 0.37 0.78 1.35 2020 3.31 7.15 14.02 0.19 0.63 1.37 2.68 2022 5.55 12.37 26.48 0.19 1.06 2.36 5.06 2024 9.22 20.82 46.04 0.19 1.76 3.98 8.79 2026 15.04 33.56 71.18 0.19 2.87 6.41 13.59 2028 23.87 50.83 96.31 0.19 4.56 9.71 18.40 2030 36.41 71.18 115.87 0.19 6.95 13.59 22.13 2032 52.61 91.52 128.33 0.19 10.05 17.48 24.51 2034 71.18 108.79 135.28 0.19 13.59 20.78 25.84 2035 80.62 115.74 137.39 0.19 15.40 22.11 26.24 176
Table 7.21: Uncontrolled VOC emissions mitigation potential by wind power plants
Annual electricity generation VOC emissions mitigation Baseline EF year (TWh) (kt) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 0.07 0.04 0.07 0.08 2014 0.68 1.28 1.68 0.07 0.05 0.09 0.12 2016 1.16 2.28 3.47 0.07 0.08 0.16 0.24 2018 1.96 4.06 7.07 0.07 0.14 0.28 0.50 2020 3.31 7.15 14.02 0.07 0.23 0.50 0.98 2022 5.55 12.37 26.48 0.07 0.39 0.87 1.85 2024 9.22 20.82 46.04 0.07 0.65 1.46 3.22 2026 15.04 33.56 71.18 0.07 1.05 2.35 4.98 2028 23.87 50.83 96.31 0.07 1.67 3.56 6.74 2030 36.41 71.18 115.87 0.07 2.55 4.98 8.11 2032 52.61 91.52 128.33 0.07 3.68 6.41 8.98 2034 71.18 108.79 135.28 0.07 4.98 7.62 9.47 2035 80.62 115.74 137.39 0.07 5.64 8.10 9.62
From the results which have been described in above Tables, it can be seen that a significant
amount of air pollutant emissions could be mitigated by the application of wind power to
the national grid of Pakistan. The Table 7.17 shows that SO2 emissions mitigation is 3.92
kt in 2013 under BAU scenario which increases and reaches to 606.27 kt in 2035, while in
the same way, it increases from 7.18 kt and 8.76 kt in 2013 to 870.36 kt and 1033.16 kt in
MD and HD scenarios respectively in 2035. The mitigation of CO emissions has been
shown in Table 7.18 in which the mitigation of CO emissions are 39.50 kt, 56.71 kt and
67.32 kt in 2035 in BAU, MD and HD scenarios respectively. The Table 7.19 illustrates
the mitigation of NOx emissions in all the scenarios from 2013 to 2035. The mitigation potential of NOX emissions increases from 2.71 kt to 419.23 kt in BU scenario, from 4.96 kt to 601.84 kt in MD scenario and from 6.06 kt to 714.42 kt in HD scenario. Tables 7.20 and 7.21 give the values of PM and VOC emissions mitigation respectively under all three scenarios for the entire study period. The baseline emissions intensity (kg/MWh) of SO2 is 177
highest than all other air pollutants, therefore, its associated emissions are also highest as
compared to other air pollutants. The reason for the highest SO2 emissions intensity of the
national grid of Pakistan is that more than 35% of electricity is generated from fuel oil
which releases more SO2 emissions per unit of electricity generated. CO and NOX emissions mostly release from the combustion of natural gas. About 30% of electricity in
Pakistan is generated through the natural gas therefore per unit CO and NOX emissions release from national grid of Pakistan are also high. VOC and PM emissions mostly release from the combustion of coal for energy generation. The generation of electricity in Pakistan from coal is negligible consequently; the intensity of VOC and PM emissions is very low in the national grid.
The air pollutant emissions that have been considered above in an uncontrolled way can be controlled through many technological methods and controlling devices. Primarily these emissions can be reduced by modifications in combustion systems and switching to cleaner fuels and process. In the secondary measures, the emissions are controlled by emissions reduction devices used in the flue gas passages of the power plants.
Controlling of SO2 emissions is carried out by desulfurization technologies such as dry/semi-dry desulfurization (Dry/Semi-dry FGD), Wet Desulfurization (Wet-FGD) and furnace limestone. NOx emissions are controlled by selective noncatalytic reduction
(SNCR), selective catalytic reduction (SCR) and low NOx burners whereas for controlling the PM emissions from thermal power plants the devices used are electrostatic precipitators
(ESP), mechanical collectors, scrubbers and fabric filters. Thermal oxidizers and catalytic thermal oxidizers are normally used for reducing the CO and VOC emissions (Moretti et 178
al., 2012, Xiong et al., 2016). The emissions removal efficiencies of all these devices are described in the Table 7.22 (Cai et al., 2013, Xiong et al., 2016).
Table 7.22: Air pollutant emissions control devices and their removal efficiencies
Pollutant Control devices Removal efficiency (%) SCR NOx SNCR 50-90 LNB Wet-FGD SO2 Dry/Semi-dry FGD 50-92 Furnace limestone ESP PM Fabric filters 95-98 Scrubber Thermal oxidizers CO 90-95 Catalytic thermal oxidizers Thermal oxidizers VOC 90-95 Catalytic thermal oxidizers
Table 7.23: Controlled SO2 emissions mitigation potential by wind power plants
Annual electricity Baseline EF SO2emissions mitigation (kt) year generation (TWh) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 2.18 1.13 2.08 2.54 2014 0.68 1.28 1.68 2.18 1.48 2.78 3.65 2016 1.16 2.28 3.47 2.18 2.51 4.97 7.54 2018 1.96 4.06 7.07 2.18 4.26 8.83 15.37 2020 3.31 7.15 14.02 2.18 7.20 15.55 30.50 2022 5.55 12.37 26.48 2.18 12.08 26.91 57.60 2024 9.22 20.82 46.04 2.18 20.05 45.29 100.14 2026 15.04 33.56 71.18 2.18 32.71 73.00 154.81 2028 23.87 50.83 96.31 2.18 51.92 110.56 209.47 2030 36.41 71.18 115.87 2.18 79.19 154.81 252.01 2032 52.61 91.52 128.33 2.18 114.43 199.05 279.11 2034 71.18 108.79 135.28 2.18 154.81 236.61 294.24 2035 80.62 115.74 137.39 2.18 175.35 251.73 298.82 179
Considering the average values of removal efficiencies of all emissions control devices that have been described above, the emissions mitigation of air pollutant emissions through wind power in the control way is estimated and presented in the following Tables 7.23-7.27.
Table 7.24: Controlled CO emissions mitigation potential by wind power plants
Annual electricity generation CO emissions mitigation Baseline EF year (TWh) (kt) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 0.04 0.02 0.04 0.04 2014 0.68 1.28 1.68 0.04 0.03 0.05 0.06 2016 1.16 2.28 3.47 0.04 0.04 0.08 0.13 2018 1.96 4.06 7.07 0.04 0.07 0.15 0.26 2020 3.31 7.15 14.02 0.04 0.12 0.26 0.52 2022 5.55 12.37 26.48 0.04 0.20 0.46 0.97 2024 9.22 20.82 46.04 0.04 0.34 0.77 1.69 2026 15.04 33.56 71.18 0.04 0.55 1.23 2.62 2028 23.87 50.83 96.31 0.04 0.88 1.87 3.54 2030 36.41 71.18 115.87 0.04 1.34 2.62 4.26 2032 52.61 91.52 128.33 0.04 1.93 3.36 4.72 2034 71.18 108.79 135.28 0.04 2.62 4.00 4.97 2035 80.62 115.74 137.39 0.04 2.96 4.25 5.05
Table 7.25: Controlled NOx emissions mitigation potential by wind power plants
Annual electricity NO emissions mitigation Baseline EF x year generation (TWh) (kt) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 1.56 0.81 1.49 1.82 2014 0.68 1.28 1.68 1.56 1.06 1.99 2.62 2016 1.16 2.28 3.47 1.56 1.80 3.56 5.41 2018 1.96 4.06 7.07 1.56 3.06 6.34 11.02 2020 3.31 7.15 14.02 1.56 5.16 11.15 21.87 2022 5.55 12.37 26.48 1.56 8.66 19.30 41.31 2024 9.22 20.82 46.04 1.56 14.38 32.49 71.83 2026 15.04 33.56 71.18 1.56 23.46 52.36 111.03 2028 23.87 50.83 96.31 1.56 37.24 79.30 150.24 2030 36.41 71.18 115.87 1.56 56.80 111.03 180.76 2032 52.61 91.52 128.33 1.56 82.07 142.77 200.19 2034 71.18 108.79 135.28 1.56 111.03 169.71 211.04 2035 80.62 115.74 137.39 1.56 125.77 180.55 214.33 180
Table 7.26: Controlled PM emissions mitigation potential by wind power plants
Annual electricity generation PM emissions mitigation Baseline EF year (TWh) (t) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 0.01 3.48 6.38 7.79 2014 0.68 1.28 1.68 0.01 4.54 8.54 11.23 2016 1.16 2.28 3.47 0.01 7.72 15.27 23.19 2018 1.96 4.06 7.07 0.01 13.10 27.15 47.24 2020 3.31 7.15 14.02 0.01 22.12 47.78 93.73 2022 5.55 12.37 26.48 0.01 37.13 82.70 177.03 2024 9.22 20.82 46.04 0.01 61.63 139.21 307.79 2026 15.04 33.56 71.18 0.01 100.53 224.36 475.81 2028 23.87 50.83 96.31 0.01 159.59 339.81 643.82 2030 36.41 71.18 115.87 0.01 243.41 475.81 774.58 2032 52.61 91.52 128.33 0.01 351.70 611.80 857.88 2034 71.18 108.79 135.28 0.01 475.81 727.25 904.37 2035 80.62 115.74 137.39 0.01 538.95 773.71 918.44
Table 7.27: Controlled VOC emissions mitigation potential by wind power plants
Annual electricity generation VOC emissions mitigation Baseline EF year (TWh) (t) kg/MWh BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 0.01 2.73 5.01 6.12 2014 0.68 1.28 1.68 0.01 3.57 6.71 8.82 2016 1.16 2.28 3.47 0.01 6.06 11.99 18.21 2018 1.96 4.06 7.07 0.01 10.29 21.32 37.10 2020 3.31 7.15 14.02 0.01 17.37 37.53 73.61 2022 5.55 12.37 26.48 0.01 29.16 64.95 139.03 2024 9.22 20.82 46.04 0.01 48.40 109.33 241.72 2026 15.04 33.56 71.18 0.01 78.95 176.20 373.67 2028 23.87 50.83 96.31 0.01 125.33 266.87 505.62 2030 36.41 71.18 115.87 0.01 191.16 373.67 608.31 2032 52.61 91.52 128.33 0.01 276.20 480.47 673.73 2034 71.18 108.79 135.28 0.01 373.67 571.14 710.24 2035 80.62 115.74 137.39 0.01 423.26 607.63 721.29
The above results show that despite considering the most advanced emissions control devices for thermal power plants the considerable amount of convention air pollutants can 181
be mitigated through the utilization of wind power in the national grid of Pakistan up to
2035 under the BAU, MD and HD scenarios.
7.5 CONCLUSIONS
In this chapter, mathematical models for the mitigation of GHGs and other air pollutant emissions in an uncontrolled and controlled ways by wind power plants were developed.
As such, the modelling parameters of baseline CM grid emission factors for GHGs and other air pollutant emissions were estimated. With the help of estimated parameters and electricity generation data from wind power plants, through modelling approach, the mitigation of GHGs and other air pollutant emissions by the wind power plants were forecasted from the base year 2013 to the end year 2035. The analysis of results of these models reveals that there is the enormous theoretical potential of mitigation of GHGs and other air pollutant emissions with wind power in the national grid of Pakistan. It was also viewed, from the estimated mitigation potential of air pollutant emissions, that significant amounts of air pollutants could be mitigated by wind power plants even if advanced controlled devices are considered. Chapt er 8 CHAPTER 8 MODELLING AND FORECASTING CDM POTENTIAL OF WIND POWER
8.1 INTRODUCTION
This chapter focuses the assessment of CDM potential, as per Kyoto protocol, for the low- cost mitigation of GHGs and other air pollutants. Mathematical models are developed for the estimation of CDM potential of wind energy in Pakistan through the methodological tool developed by United Nations Framework Convention on climate change (UNFCCC).
CDM potential of wind energy in Pakistan is forecasted for the specified study period.
Sensitivity analysis is carried out for the estimation of revenue from the CDM potential.
8.2 CDM POTENTIAL OF WIND POWER IN PAKISTAN
Project developers of the Alternative and Renewable energy (ARE) in Pakistan are trying for their projects registered with CDM EB Board and earn financial benefits from carbon credits. So for 18 renewable energy projects have been registered and approved annual
CERs from these projects are 1.3 million whereas, 29 projects are under way for registration from which 1.6 million CERs are expected annually. The majority of these registered renewable energy projects are from wind power. Out of 18 renewable energy projects 8 wind power projects of 50 MW each has been registered. The cumulative amount
182 183
of CERs generation from these wind power projects is 0.71 million annually and other 18
wind power projects are under registration process (AEDB, 2012).
8.2.1 Methodology
Estimating the CDM potential of renewable energy, the imperative step is to determine the
baseline emissions or reference emissions. For the estimation of CDM potential of wind
power projects in a grid-connected electricity generation system a consolidated and
approved baseline methodology is accessible by UNFCCC. In this study, the ACM0002
version 16.0 of grid-connected electricity generation for renewable energy (UNFCCC,
2014a) sources is used. The baseline emissions (ton CO2 eq/MWh) are estimated with the
help of following expression.
Baseline CO eq Emissions = EG × EF (8.1)
Where EGwind is the electricity generation output from wind power plant in MWh. EFBaseline is the baseline grid emission factor in (ton CO2 eq/MWh). The baseline grid emission factor for GHGs considered in this study is (0.578 ton CO2 eq/MWh) which has been estimated in Chapter 7 by considering the weighted average of all existing electricity generation sources connected to national grid OM emission factor and the weighted average of most recently built electricity generation sources connected to national grid BM emission factor.
For the wind power plants, the weighted average of OM emission factor is 0.75 whereas for the BM emission factor it is 0.25. The summation of both weighted margin emission factors is termed as CM emission factor which is the actual emission coefficient or baseline 184
Grid EFBaseline. In this study, electricity generation output from wind power plants in
Pakistan has been forecasted using the logistic diffusion model. The electricity generation output forecasted in Chapter 5 Table 5.5 is used in this chapter.
8.3 RESULTS AND DISCUSSION
Using the electricity generation data of wind power plants from the Table 5.5 and baseline
grid emission factor 0.578 ton CO2 eq/MWh in the Eq. (8.1), the baseline emissions of wind
power plants in Pakistan have been forecasted.
The reduction in emissions is estimated by baseline emission minus project activity
emissions. Since wind power is a renewable energy, emission free electricity generation
source. The emissions from project activity are considered as zero. Therefore, the baseline
emissions are equal to emissions reduction. As already has been stated that each ton of
GHGs (CO2 eq) emissions reduction is equal to one CER, the total tons of reduction of
GHG emissions is equal to the number of CERs generated. The forecasted electricity generation output and associated CERs generations from emissions reduction through wind power plants are presented in Table 8.1.
The results illustrate that CER generation through the mitigation of emissions by wind power plants is 0.3 million in BAU scenario, 0.55 million in MD scenario and 0.67 million in HD scenario during the base year 2013 whereas in 2035 about 46.6 million, 66.9 million and 79.4 million CERs are generated under BAU, MD, and HD scenarios respectively.
This indicates that the highest CERs are generated in HD scenario while the next highest generation of CERs is in MD scenario. 185
Table 8.1: Forecasted annual CER generation from wind power
Forecasted annual electricity Forecasted annual CERs generation (TWh) generation (million) Years Scenario BAU MD HD BAU MD HD 2013 0.52 0.95 1.17 0.30 0.55 0.67 2014 0.68 1.28 1.68 0.39 0.74 0.97 2016 1.16 2.28 3.47 0.67 1.32 2.00 2018 1.96 4.06 7.07 1.13 2.35 4.08 2020 3.31 7.15 14.02 1.91 4.13 8.10 2022 5.55 12.37 26.48 3.21 7.15 15.31 2024 9.22 20.82 46.04 5.33 12.04 26.61 2026 15.04 33.56 71.18 8.69 19.40 41.14 2028 23.87 50.83 96.31 13.80 29.38 55.67 2030 36.41 71.18 115.87 21.05 41.14 66.97 2032 52.61 91.52 128.33 30.41 52.90 74.17 2034 71.18 108.79 135.28 41.14 62.88 78.19 2035 80.62 115.74 137.39 46.60 66.90 79.41
8.3.1 Emission trading revenue
The generated CERs from the emissions reduction activities are sold in international carbon market through emission trading process to earn revenue for the sustainable projects in developing countries. CDM registered projects simultaneously produce conventional power as well as financial attractiveness by the trading of carbon credits or CERs for the reduction of emissions. The revenue from the CERs is greatly influenced by numerous factors such as the amount of CERs generated by the emission reduction activity, the price of CER or carbon credit in the international market and the transaction costs incurred during the registration process of the emission reduction activity. Several counties, states, organizations, and corporations have implemented such emission trading mechanism for the climate change mitigation (WB, 2014b, Yunna and Quanzhi, 2011). 186
At local level governments have allowed the companies or organizations certain limits of releasing emissions into the atmosphere at a certain time. If any company or organization desires to release more emissions from its limit then it must purchase emission credits from the other organization or company which is willing to sell its extra emission credits.
Currently, there are seventeen emission trading systems (ETSs) working around the four continents which include thirty five countries, twelve provinces or states and seven cities.
European Union Emission Trading Scheme (EU-ETS) is the largest emission market dealing with GHG emissions mitigation causing climate changes. Other regional emissions trading schemes are New Zealand emissions trading scheme, Quebec emission trading scheme, Australian emissions trading scheme, Japan emission trading scheme and recently emission trading scheme has been developed by China. United States has developed national emission market to combat the acid rain impact due to the emissions of SOx and
NOx (ICAP, 205, WB, 2015).
8.3.2 Sensitivity analysis
The revenue from the CERs or carbon credits is highly sensitive to the current and future carbon price in international market. The price of carbon in the international market has remarkably fluctuated last few years which creates immense uncertainty for the future carbon prices. Due to this fluctuating carbon prices, sensitivity analysis is carried out in this study. In the sensitivity analysis, five scenarios are considered throughout the study period about the future carbon price which includes as current price (no change), 10% increase, 20% increase, 10% decrease, and 20% decrease. The carbon price in the 187
international market (2013) is 6.5 US$/ton of CO2 (WB, 2014b). The results of this sensitivity analysis under the BAU, MD and HD scenarios are shown in Figs. 8.1-8.3.
400 ) $
S 350 U n o i
l 300 l i M ( 250 l No change a i t
n 10% increse
e 200 t o
p 20% increase
e 150 u
n 10%decrease e
v 100 e
r 20%decrease
M 50 D C 0 2013 2016 2020 2024 2028 2032 2035 Years
Fig. 8.1: CDM revenue potential of wind power under BAU scenario
From the Figs. 4.5 and 4.6 it can be seen that at higher carbon prices more revenue can be generated through CDM potential of wind power plants and when the per ton carbon price decreases lower revenue can be obtained through CDM potential of wind power plants.
Further, in the high diffusion of wind power scenarios such as HD and MD scenarios more
CERs are generated, therefore, they earn more revenues than the BAU scenario which generates fewer CERs consequently has less revenue generation. 188
600
500
$ 400
S No change U
n 300 10% increse o i l l i 20% increase
M 200 10%decrease
100 20%decrease
0 2013 2016 2020 2024 2028 2032 2035 Years
Fig. 8.2: CDM revenue potential of wind power under MD scenario
700
600
500 $
S No change
U 400 n 10% increse o i l l 300 i 20% increase M 200 10%decrease 20%decrease 100
0 2013 2016 2020 2024 2028 2032 2035 Years
Fig. 8.3: CDM revenue potential of wind power under HD scenario 189
8.4 CONCLUSIONS
CDM potential of wind power plants was assessed in this chapter. Utilizing the baseline
CM grid emission factors for GHG emissions (CO2 eq) and electricity generation output data from wind power plants, as these were estimated in previous chapters, the CDM potential of wind power in Pakistan was forecasted up to 2035 using relevant mathematical models. From the forecasted CERs, the revenue of CDM potential of wind power plants was estimated at the current carbon price in the international market. As such, under the current carbon price of 6.5 US$/ton of CO2 eq, the revenue generation from CERs trading, with assumptions of no changing in carbon price during the study period, have been estimated to be 302.89 million $, 434.83 million $ and 516.16 million $ in BAU, MD and
HD scenarios respectively in 2035. The price of carbon in the international market extremely fluctuates, therefore, a sensitivity analysis was carried out in the study. In the sensitivity analysis, five scenarios were considered which include as current price (no change), 10% increase, 20% increase, 10% decrease and 20% decrease which provided a significant insight into the results. Chapt er 9 CHAPTER 9 CONCLUSIONS AND RECOMMENDATIONS
9.1 INTRODUCTION
The basic purpose of this research study is to estimate future electricity demand and associated emissions, mitigation of the emission through wind power and CDM potential of wind power in Pakistan. Dealing with challenges of increasing power demand and associated elevation generation cost in the country,the government has made a long-term plan to ensure the future power generations from low-cost local coal (Thar), imported coal and natural gas. The increasing share of coal and natural gas will increase the GHGs and other air pollutant emissions in the already thermal dominated power generation mix of the country. On the other hand, Pakistan has a huge potential of wind power which could be exploited to meet the challenges of high power demand and elevated generation cost without releasing emission into the atmosphere. Wind power as a renewable and emissions free energy source is also a financially supportive through CDM.
In this study future, power generation and emissions are projected through modelling under different scenarios from the base year 2013 to the end year 2035. Electricity generation from wind power plants has also been forecasted in different scenarios through the development of mathematical models, emissions mitigation. Further, CDM potential of wind power has also been projected during the specified study period.
190 191
Based on the finding of this study, following conclusions and recommendations are summarized in the following section.
9.2 CONCLUSIONS
Future power generation
· Electricity demand in the country is estimated to increase at an annual growth rate
of 5.4%, as such, it is likely to rise from 139 TWh in 2013 (base year) to 442 TWh
in 2035 (end year). In order, to meet the forecasted demand of electricity, the new
power plants shall be required in different phases of the study period. The new
installed capacity in the reference or baseline scenario is anticipated to be based on
different resources like imported coal (20 GW), local coal (20 GW), hydro (36
GW), natural gas (8 GW), nuclear (8 GW) and renewables (wind, solar, biomass)
(15 GW). The capacity of oil based plants substantially decreases from 8.6 GW to
3 GW since there is no new planned installation from the oil based source during
the study period. Under this scenario, the electricity generation increases to meet
the growing electricity demand by consuming relevant shares of all fuels. As such,
the total electricity generation increases from 96 TWh in 2013 to 442 TWh in 2035.
· In the MRR scenario, the share of installed capacity of other renewable increases
from 15 GW to 30 GW while the installed capacity of coal reduces from 40 GW to
20 GW (10 GW each for the local as well as imported coal) compared to the REF
scenario. The installed capacities of other sources like hydro, nuclear and oil remain
same as in the REF scenario. The inclusion of large share of renewables sources in 192
MRR scenario can reduce the reliance on coal but it needs flexible power sources
such as natural gas to compensate electricity demand due to their low capacity
factor. The new installed capacity of the natural gas is 20 GW which enhance the
overall installed capacity from all sources to 132 GW to meet the power demand.
Overall electricity generation output in MRR scenario remains also same as in the
REF scenario but only the generation shares of other renewables and a natural gas
increase due to the increment in their installed capacities and the generation share
of coal decreases.
· In the MRH Scenario, the installed capacity of hydropower increases from 36 GW
as in the reference or baseline scenario to 40 GW up to 2035. The installed
capacities from other resources remain same as in the reference or baseline scenario
except that the installed capacity of coal decreases from 40 GW to 34 GW up to the
end year. In this scenario, the total electricity generation also remains same as in
the reference or baseline scenario except for the change in shares of the hydro and
coal power. The electricity generation share of hydropower increases from 37% as
in reference or baseline scenario to 42% in this scenario. The generation share of
coal reduces from 34% as in the reference or baseline scenario to 28% in this
scenario up to 2035 to balance the increase of hydro.
· In the MRHN scenario, the installed capacities of nuclear and hydropower increase
from 8 GW and 36 GW as in reference or baseline scenario to 10 GW and 40 GW
respectively in this hydro nuclear reliance scenario. In the response to increasing
installed capacities of these fuels, the capacity of coal reduces from 40 GW as in 193
reference or baseline scenario to 31GW in the present scenario. Electricity
generation output shares from nuclear and hydropower are more in this scenario.
The generation shares of nuclear and hydropower increase 2% and 5% respectively
from reference or baseline scenario to this scenario in 2035 while the share of coal
reduces 8% in this scenario as compared to the reference or baseline scenario.
Emissions from power generation
· CO2 is a main GHG emissions component which releases in the process of
electricity generation due to combustion of fossil fuels. It is projected that CO2
emissions increases from 34 million tons in the base year 2013 to 143 million tons
in the final year of study period 2035 under the REF scenario and from 34 million
tons to 103 million tons in MRR scenario respectively. In the case of MRH
scenario, the CO2 emissions vary from 34 million tons in the base year to 126
million tons by the end year 2035 whereas in the case of MRHN scenario CO2
emissions vary as of between 34 million tons to 117 million tons from the base year
to the final year respectively.
· NOX emissions increase 72% in REF scenario from 2013 till 2035 while at the same
time period the increase is estimated to be 59%, 53% and 83% in MRH, MRHN,
and MRR alternative scenarios respectively. The thermal power plants particularly
based on coal, as proposed during the study period, would be a major source of
increased NOX emissions in the reference scenario.
· Methane (CH4) emits from the existing and planned biomass, natural gas and oil
based power generation plants. The CH4 emissions increase from 1 kt to 2.3 kt base 194
year to final year in MRR scenario which is a 137% increase compared to the base
year. These emissions also increase 80% in REF scenario, 71% in MRH scenario
and 65% in MRHN scenario from the year 2013 to the final year of the study period.
The increasing share of CH4 is very high in MRR scenario as compared to other
alternative scenarios.
· N2O emissions are estimated to increase from 0.2 kt in 2013 to 82 kt in 2035 under
REF scenario, 0.2 kt to 70 kt under MRH, 0.2 kt to 63 kt under MRHN and 0.2 kt
to 41 kt under the MRR scenarios respectively for the same period.
· The range of increase in VOCs emissions from base year to the final year of this
study period is estimated to be 2.4 kt to 5.7 kt in REF, 2.4 kt to 5.7 kt in MRH and
2.4 kt to 5.7 kt in MRHN scenarios whereas in the MRR scenario it is 2.4 kt to 13
kt for the period 2013-2035.
· CO emissions are estimated to grow from 9 kt to 48 kt under REF scenario whereas
from 9 kt to 63 kt under MRR scenario, from 9 kt to 45 kt under MRH scenario and
from 9 kt to 43 kt under MRHN scenarios during the study period 2013 to 2035.
· PM and SO2 emissions produce during the combustion of coal, however, under the
assumption that latest advanced technologies and emissions control equipment
shall be used during the study period, these emissions reduce considerably.
However, despite this substantial amount of emissions also discharge from these
power plants. Further, the existing oil based power plants in the country have no as
such control system for the reduction of PM and SO2 emissions, produce a
significant amount of this types of emissions. 195
· It is found that REF scenario having high shares of coal fuel produces more
emission than other alternative scenarios. MRR scenario with highest shares of
other renewables mostly wind power produces the lowest amount of emissions.
Electricity generation from wind power plants
· It is estimated that up to 2030 about 17 GW, 33 GW and 53 GW of wind power
could be installed in the country under BAU, MD and HD scenarios respectively.
Further, it is expected that with this trend continuing to the installed capacity of
wind power shall reach 37 GW, 53 GW and 63 GW in 2035 under BAU, MD and
HD scenarios respectively. These anticipated wind power installed capacities in
2030 form, about 24%, 47% and 76% of the total technical wind power potential
of Pakistan under above mentioned three scenarios and would form about 53%,
76% and 90% of the total technical wind power potential in 2035 under all the
scenarios.
· It is estimated that wind power installed capacities would generate about 36 TWh,
71 TWh and 116 TWh electricity in 2030 under the BAU, MD and HD scenarios
respectively while in the same way about 81 TWh, 116 TWh and 137 TWh of
electricity generation could be possible in 2035 under BAU, MD and HD scenarios
respectively.
Cost of electricity generation from wind power plants and comparison
· Electricity generation from the GTDO power plants inclusive of 4.97 ¢/kWh
external cost is estimated to be 32.13 ¢/kWh. It is, therefore, found to be the most 196
expensive electricity generation option using diesel oil as fuel. CSFO power plants
utilizing the fuel oil for electricity generation are next expensive to the GTDO
power plants. The generation cost of these types of power plants inclusive of 8.68
¢/kWh external cost is about 29.46 ¢/kWh. Finally, the generation cost of LCC and
ICC power plants inclusive of the external cost of environment is 22.44 ¢/kWh and
24.72 ¢/kWh for local and imported coal fuels respectively
· The least cost of electricity generation is found to be 5.13 ¢/kWh for the WE power
plants. The electricity generation cost of the NGCC plants based on domestic
natural gas including 1.17 ¢/kWh of the external cost is 6.94 ¢/kWh which is
relatively close to the WE plant. Finally, the costs of generating electricity from
IGCC including 9.52 ¢/kWh of the external cost of the environment based on the
local coal fuel has been determined to be 24.73¢/kWh.
· LCOE significantly increases when electricity generating plants are considered
with CCS technology than the plants without CCS technology. The highest
electricity generation cost of 24.29 ¢/kWh is from ICC power plants with CCS
technology utilizing imported coal (bituminous) as fuel. IGCC plants with CCS
technology with 21.84 ¢/kWh generation cost are less expensive than ICC power
plants with CCS technology utilizing local coal (lignite) as fuel. LCC plants with
CCS technology based on local coal (lignite) as fuel have generation cost of 18.67
¢/kWh. Finally, the least cost of electricity generation with CCS technology was
determined to be 8.79 ¢/kWh for NGCC plants using domestic natural gas as fuel.
WE power plants are non-fossil fuel and emission free electricity generation plants, 197
as such, CO2 mitigation is achieved in this case without the CCS technology, as
such; they are cheapest option in case of CO2 reduction.
Mitigation potential of GHGs and other air pollutant emissions through wind power
· The estimated results of this study indicate that there is an immense potential for
mitigation of GHG emissions in Pakistan through the application of wind power.
The CO2 eq emissions mitigation potential in 2013 under the BAU, MD and HD
scenarios are 0.30 million tons, 0.56 million tons, and 0.68 million tons respectively
and it is expected to increase 46.84 million tons, 67.24 million tons and 79.82
million tons in BAU, MD and HD scenarios respective in 2035.
· A significant amount of air pollutant emissions in an uncontrolled way could be
also mitigated by application of wind power to the national grid of Pakistan. SO2
emissions mitigation potential has been estimated to be 3.916 kt in 2013 under BAU
scenario with substantial increase reaches to 606.269 kt in 2035. Further, for the
same period, SO2 emissions mitigation potential has been estimated to increase
from 7.179 kt and 8.764 kt to 870.355 kt and 1033.158 kt in MD and HD scenarios
respectively in 2035.
· The mitigation potential of CO emissions has been estimated to be 39.504 kt,
56.712 kt and 67.320 kt in 2035 in BAU, MD and HD scenarios respectively. NOx
emissions mitigation potential has also been estimated to increase from 2.708 kt to
419.228 kt in BAU scenario, from 4.964 kt to 601.841 kt in MD scenario and from
6.060 kt to 714.418 kt in HD scenario for the study period. 198
· The mitigation potential of VOC emissions has been estimated as 0.036, 0.067 and
0.082 kt in 2013 under BAU, MD and HD scenarios respectively which are
expected increase and reach approximately 5.643, 8.102, 9.617 kt in 2035 under the
same scenarios. The mitigation potential of PM emissions has been estimated to
increase from 0.09, 0.18, 0.22 kt in 2013 to 6.95, 13.59 and 22.13 in 2035 under
the BAU, MD and HD scenarios respectively.
· It is found that despite considering the most advanced emissions control devices
and technologies for controlling the air pollutant emissions from thermal power
plants, a considerable amount of these emissions yet discharges which can be
mitigated through the utilization of wind power in the national grid of Pakistan up
to 2035.
CDM potential of wind power plants
· The CERs generation has been estimated to be 0.3 million in BAU scenario, 0.55
million in MD scenario and 0.67 million in HD scenario during the base year. In
the year 2035 the CERs generation has been estimated to be 46.6 million, 66.9
million, and 79.4 million under BU, MD and HD scenarios respectively. This
indicates that the highest CERs are generated in HD scenario while the next highest
generation of CERs shall be in MD scenario.
· Under the carbon price (2013) of 6.5 US$/ton of CO2 eq, the revenue generation
from CERs trading is estimated to be 1.96 million US$, 3.55 million US$ and 4.38
million US$ in BAU, MD and HD scenarios respectively in 2013. Further, 199
assuming no change in carbon price during the study period, the total revenue
earned will become 302.9 million US$, 438 million US$ and 516 million US$ in
2035 under BAU, MD and HD scenarios respectively.
· Further, in the case of 10% increase in the price of carbon in the international
market the generation of revenue from CERs has been estimated to be 333 million
US$, 478 million US$ and 567 million US$ under BAU, MD and HD scenarios
respectively in 2035. Similarly, a 10% decrease in carbon price shall slightly reduce
the generation of revenue from CERs and estimated to be 272 million US$, 391
million US$ and 464 million US$ under BAU, MD and HD scenarios respectively
in 2035.
· And in the case of a 20% increase in the price of carbon in the international market
the generation of revenue from CERs has been estimated to be 363 million US$,
522 million US$ and 619 million US$ under BAU, MD and HD scenarios
respectively in 2035. Similarly, a 20% decrease in carbon price the generation of
revenue from CERs will reduce and estimated to be 242 million US$, 348 million
US$, and 413 million US$ under BAU, MD, and HD scenarios respectively in
2035.
9.3 RECOMMENDATIONS
Pakistan has enormous wind energy potential at its different locations which could be harnessed to produce a considerable amount of power. Generation of power from wind energy will not only overcome the existing power shortage in the country but it would also 200
cause the mitigation of air pollutant emissions for the protection of the environment. Wind power projects with international mechanisms of CDM are very attractive investments in developing countries like Pakistan for the reduction of GHG emissions. This shall also help in minimizing the effects of global warming and climate change. Based on this study, following measures are recommended for the development of wind power generation system in Pakistan.
· The government should develop policies wherein effective financial incentives
should be offered for foreign investors to invest in wind power generation as well
as wind turbine manufacturing industries for the development and transfer of wind
energy technology in the country. The financial incentives may also include
exemption from government taxes, interest free loans, and subsidies on capital
investment.
· MoWP should guarantee the purchase of power from the wind power generation
companies with lucrative tariffs to be offered by the power regulator NEPRA.
· The main infrastructure like electricity transmission lines and other communication
systems should be developed in the wind potential areas which will promote the
setting up of wind power generation projects in these areas.
· The R&D programmers in the field of wind energy development should be initiated
in the academic as well as research institutes to support the development of
technology in the country. In this context, sufficient funds should be ensured for
R&D efforts to cause transfer of the laboratory-based products to commercial based
products. 201
· Programs for the training of local manpower regarding installation and
manufacturing of wind turbine should be started. For this purpose, reputed
international firms should be hired.
· Information and awareness about the significance of the wind energy technology
should be conveyed through a media campaign to every class of the society.
· Government through the Ministry of the environment should facilitate and provide
awareness for both public and private sectors about for acquiring the financial
benefits from the international organization of UNFCCC through climate change
mitigation mechanism of CDM.
· In the power policies announced by the government, the CDM potential,
development of CDM projects and its implementation in power sector as an
environmental safety should be included on a priority basis.
· Necessary legislation regarding mitigation of the air pollutant emissions should be
done and such legislation must be implemented to ensure minimum emissions from
all industrial and power sectors. The heavy financial penalties should be imposed
on those industries and power generation companies which release more emission.
9.4 FUTURE WORK
Following recommendations for future work are suggested based on this study:
· Due to the unavailability of the historical cumulative installed capacity of wind
power in the country, this study through analogous approach has used the historical
cumulative installed capacity of wind power for the estimation of parameters of 202
logistic diffusion model. The wind power installed capacity in Pakistan is now
growing, therefore, in future for the diffusion of wind power in the country, the data
of cumulative capacity of wind power in Pakistan should be used.
· In this study, the air pollution mitigation and CDM potential of wind power in
Pakistan was estimated. In future other renewable sources such as solar, biomass,
fuel cell, hydrogen, and geothermal power can be used to determine the air pollution
mitigation and CDM potential in Pakistan.
· This study has forecasted the diffusion of wind power considering the on-shore
wind energy potential in the country while the utilization of offshore wind energy
potential is also rapidly increasing throughout the world. Therefore, in future wind
power diffusion can be forecasted considering the off-shore wind energy potential
of Pakistan.
· The China Pakistan Economic Corridor (CPEC) is a bilateral trade and economic
cooperation between China and Pakistan. Multi-billion $ investments are planned
for the development of energy infrastructure in Pakistan through these investments.
CPEC energy projects include various hydro, coal and renewable based power
plants shall be constructed and included in the power generation sector of Pakistan
in different phases. These new power plants would resolve the chronic issue of
power shortage in the country. Future power generation and emissions forecast,
mitigation of emissions could be based on these new CPEC based power plants. 203
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APPENDIX 1 JOURNAL PUBLICATIONS
The following papers of author have been published in the research journals
1. Mengal, A., Uqaili, M.A., Harijan, K., and Memon, A.G. (2014), “Competitiveness of Wind Power with the Conventional Thermal Power Plants using Oil and Natural Gas as Fuel in Pakistan”, Energy Procedia, Volume 52, pp. 59-67. 2. Mengal, A., Uqaili, M.A., Harijan, K., Mirjat, N.H., and Shah, S.M.A. (2017), “Cost estimation and comparison of carbon capture and storage (CCS) technology with wind energy”, Mehran University Research Journal of Engineering & Technology, Volume 36, pp. 337-384. 3. Mengal, A., Uqaili, M.A., Harijan, K., Mirjat, N.H., and Valasai, G.D. (2017), “Mitigation of GHG Emissions with a 50 MW Wind Power Plant: A Case Study of Pakistan”. Sindh University Research Journal, Volume49, pp. 49-54.