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The Relationship of Weather with Electricity Prices: a Case Study of Albania

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The Relationship of Weather with Electricity Prices: a Case Study of Albania The relationship of weather with electricity prices: A case study of Albania Renewable energy vulnerability to weather conditions and its relationship with electricity prices in the Albanian Energy Market. BACHELOR/MASTER [Bachelor] THESIS WITHIN: Economics NUMBER OF CREDITS: 15 credits PROGRAMME OF STUDY: International Economics AUTHOR: Evgjenia Greku and Zhuohan Xie JÖNKÖPING June 2020 Bachelor Thesis in Economics Title: The relationship of weather with electricity prices: A case study of Albania. Authors: Evgjenia Greku and Zhuohan Xie Tutor: Anna Nordén Date: 2020-06-09 Electricity price, hydropower, renewable energy, state monopoly, day-ahead market, climate change, air temperature, rainfall, river water flow Abstract Electricity markets may become more sensitive to weather conditions because of higher penetration of renewable energy sources and climatic changes. Albania is 100% reliant on hydropower for its domestic energy generation, making this country compelling to investigate as it is highly sensitive to changing weather conditions. We use an ARMA-GARCH model to investigate whether weather and economic factors had a relationship with monthly hydroelectricity prices in the Albanian Energy Market in the period 2013-2018. We find that electricity price is affected by variations in weather and is not utterly robust to extreme hydrological changes. Generally, our dependent variable appears to be particularly influenced by air pressure followed by temperature and rainfall. We also perceive that there is a relationship between economic factors and hydroelectricity prices, where residual supply appears to have a significant negative relationship with our dependent variable. However, we were originally anticipating a higher dependency of electricity prices on weather conditions, due to the inflated hydro-power reliance for electricity production in the Albanian Energy Market. This effect is offset by several factors, where the state monopolized behaviour of the energy sector occupies a predominant influence on our results. i List of Abbreviations ARCH Autoregressive Conditional Heteroscedasticity ARMA Autoregressive and Moving Average Model CEIC Census and Economic Information Centre EUR Euro EUROSTAT European Statistical System GARCH Generalized Autoregressive Conditional Heteroscedasticity GDP Gross Domestic Product GWH Gigawatt hours IEA International Energy Agency INSTAT Albanian Institute of Statistics KESH Albanian Power Corporation MASL Meters Above Sea Level MWH Megawatt hours MB Millibar UNECE United Nations Economic Commission for Europe VIF Variance Inflation Factors ii Table of Contents 1. Introduction ...................................................................................... 1 1.1 Research Problem……………………………………………………………….2 1.2 Purpose…………………………………………………………………………..3 2. Background ...................................................................................... 4 2.1 Weather ............................................................................................................... 5 2.2 Electricity Energy Market ................................................................................... 5 2.3 Resource Exploitation and River Flows .............................................................. 6 2.4 The day-ahead market ......................................................................................... 7 3. Literature Review ............................................................................ 9 3.1 Theoretical Framework ....................................................................................... 9 3.2 Previous Empirical Studies ............................................................................... 10 4. Hypothesis ....................................................................................... 14 5. Method ............................................................................................ 16 5.1 Econometric model ........................................................................................... 16 5.2 Variable Description ......................................................................................... 18 Dependent Variable ........................................................................................... 19 Independent Variables ....................................................................................... 20 5.3 Data analysis method ........................................................................................ 23 6. Results ............................................................................................. 24 6.1 Statistics ............................................................................................................ 24 6.2 Findings ............................................................................................................. 27 7. Conclusion ...................................................................................... 31 7.1 Discussion ......................................................................................................... 31 7.2 Concluding Remarks ......................................................................................... 33 8. Reference list .................................................................................. 35 iii Figures Figure 1: Average Level of Exploitation and River Flows of Fierza Hydropower Plant During 2018. Source: KESH, 2018 ............................................................................ 6 Figure 2: Day-ahead price formation in practice. Source: Nord Pool Consulting, 201610 Figure 3: Simulated Spot Electricity Prices, 2013-2018. Source: Nord Pool, 2016. 19 Tables Table 1: Expected Signs of Independent Variables ................................................... 15 Table 2: Descriptive Statistics .................................................................................... 26 Table 3: Correlation Matrix ........................................................................................ 26 Table 4: Effects of explanatory variables on the log of the average monthly electricity price, 2013-2018 .................................................................................................... 27 Table 5: Variance Inflation Factors ............................................................................ 39 Table 6: Correlogram ................................................................................................. 39 Table 7: Group Unit Root Test ................................................................................... 40 Table 8: Heteroskedasticity Test, ARCH Test ........................................................... 40 Table 9: First Regression Output ............................................................................... 41 Table 10: Second Regression Output ......................................................................... 41 Appendix Appendix (EViews Outputs) .................................................................................... 39 iv 1. Introduction ______________________________________________________________________ In this chapter we present and motivate the overarching area of the thesis and explain the focus and purpose of our study. ______________________________________________________________________ In the last decade, many countries have initiated the adoption of energy from renewable resources, after careful consideration of climate change implications and scarce energy assets. In our case, Albania is characterized as “blessed” with freshwater resources and is ranked third after Italy in the European continent (Eurostat, 2019). However, being 100% dependent on water resources for electricity production, is showing extreme reliability risk towards changing weather conditions and insecure energy production forecasts (World Bank, 2016). Albania is a country with heterogeneous terrain and weather conditions, thus a dependency on hydropower for the dominant proportion of its power needs is particularly vulnerable to changes in climate. Hence, assessing the reliance of electricity prices on weather conditions, is of great importance because the utilization of renewable energy resources in the case of Albania is inevitable. We will study and carefully consider the impact of air pressure, rainfall, and temperature on hydro energy demand and supply which subsequently alternates electricity price trends. This analysis will be implemented in order to show the degree of vulnerability to weather and emphasize the crucial need for diversification of the present energy resource mix that Albania possesses. Hydropower, which exploits river water flows in Albania as the fundamental input into electricity generation, is the leading renewable source for electricity generation globally, supplying 71% of all renewable electricity (Kumar et al., 2011). Even so, extreme weather conditions amend the annual mean and seasonality of runoff, all of which influence the availability and stability of hydroelectricity production and concurrently increase the storage value of hydropower reservoirs. In consequence, uncertainty associated with climate change poses great risks and challenges for hydropower planning and management (Hamududu et al., 2012). Notwithstanding the fact that renewable energy is associated with reduced marginal costs and consequently lower electricity prices, the consideration of weather conditions is essential, especially when investigating this type of energy market that is 100% based on renewable
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