AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES
USAID ENERGY PROGRAM
16 May 2018 This publication was produced for review by the United States Agency for International Development. It was prepared by Deloitte Consulting LLP. The author’s views expressed in this publication do not necessarily reflect the views of the United States Agency for International Development or the United States Government.
AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES
USAID ENERGY PROGRAM CONTRACT NUMBER: AID-OAA-I-13-00018 DELOITTE CONSULTING LLP USAID | GEORGIA USAID CONTRACTING OFFICER’S REPRESENTATIVE: NICHOLAS OKRESHIDZE AUTHOR(S): DAVID MUJIRISHVILI, VALERIY VLATCHKOV LANGUAGE: ENGLISH
16 MAY 2018
DISCLAIMER: This publication was produced for review by the United States Agency for International Development. It was prepared by Deloitte Consulting LLP. The author’s views expressed in this publication do not necessarily reflect the views of the United States Agency for International Development or the United States Government.
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES i
DATA
Reviewed by: Jake Delphia
Practice Area: Renewable Energy Development
Key Words: Wind Forecast, Wind Power Forecast
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES ii
ABSTRACT The objective of USAID Energy Program is to support Georgia’s efforts to facilitate increased investment in power generation capacity as a means to increase national energy security, facilitate economic growth, and enhance national security. The program will have a significant impact on energy market reform efforts of the Government of Georgia (GoG) to comply with the country’s obligations under the Energy Community Treaty (EnCT). The investment objective will be achieved through the provision of technical assistance to a variety of stakeholders in the energy sector. The ultimate goal of this program is to enhance Georgia’s energy security through improved legal and regulatory framework and increased investments in the energy sector. The ultimate expected outcome of this program is an energy legal and regulatory framework that complies with European requirements and encourages competitive energy trade and private sector investments. The purpose of Task 4 of USAID Energy Program is to support integration of Variable Renewable Energy (VRE) into the power system. Wind and solar forecasting is a dynamic research and development area, with new software models and findings emerging rapidly. The aim of this report is to provide policy guidance and recommendations to policy makers in Georgia when considering variable renewable energy integration and electricity market arrangements.
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ACRONYMS
AEE Asociacion Empresarial Eolica AEMO Australian Energy Market Operator AEOLIS Forecasting Services AESO Alberta Electric System Operator AGC Automatic Generation Control AGL Above Ground Level AnEn Analog Ensemble ANKF Analog-space Kalman Filter ANN Artificial Neural Networks API Application Programming Interface ARMA Auto-Regressive Moving Average ARX Auto-Regressive with Exogenous Input ASCII American Standard Code for Information Interchange AWEFS Australian Wind Energy Forecasting System BPA Bonneville Power Administration BWF Botievo Wind Farm CAISO California Independent System Operator CB Capacity Building CC Cloud Cover CCD Charged Coupled Device CIRA Cooperative Institute for Research in the Atmosphere CPV Concentration Photovoltaics DER Distributed Energy Resources DICast Dynamic Integrated Forecast DIF Diffuse Irradiance DIRD Direct Irradiance DMOS Dynamic Model Output Statistics DNI Direct Normal Irradiance DNVGL Global Quality Assurance and Risk Management Company DOE US Department of Energy DR Demand Response DTU Denmark Technical University DUID Dispatchable Unit Identifier DWD Germany's National Meteorological Service EC Energy Community ECMWF European Centre for Medium Range Weather Forecasting EIR Eligible Intermittent Resources EnCT Energy Community Treaty ENFOR Forecasting and Optimization Solutions for the Energy Sector ENTSO-E European Network of Transmission System Operator – Electricity E-RTFDDA Ensemble - Real-Time Four-Dimension Data Assimilation EU European Union EUMETSAT European Organization for the Exploitation of Meteorological Satellites FTP File Transfer Protocol
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES iv
GDPS Global Deterministic Prediction System GEDF Georgian Energy Development Fund GEM Global Environmental Mesoscale GENCO Generation Company GFS Global Forecast System GHI Global Horizontal Irradiance GHIRD Global Horizontal Irradiance GIS Geographic Information Systems GNERC Georgian National Energy and Water Supply Regulatory Commission GOES Geostationary Operational Environmental Satellite GoG Government of Georgia GPS Global Positioning System GSE Georgian State Electrosystem GSF Global Forecast System GTS Global Telecommunication System GUI Graphical User Interface GW Gigawatt HPP Hydro Power Plant HRRR High Resolution Rapid Refresh ID Identifier IMM Informatics and Mathematical Modelling ISO Independent System Operator LiDAR Light Detection and Ranging MAD Mean Absolute Deviation MAE Mean Absolute Error MAPE Mean Absolute Percentage Error MCP Measure-Correlate-Predict MM5 Mesoscale Model 5 MMR Multivariate Minimum Residual MOS Model Output Statistics MoU Memorandum of Understanding MSE Mean Square Error MW Megawatt MWh Megawatt Hour NAM North American Model NCAR National Centre for Atmospheric Research NCEP US National Centers for Environmental Prediction NEA National Environmental Agency NMAE Normalized Mean Absolute Error NOAA National Oceanographic and Atmospheric Administration NREL US National Renewable Energy Laboratory NRMSE Normalized Root Mean Square Error NVE NIRAS Norwegian Water Resources and Energy Directorate and Danish Consortium NWP Numerical Weather Prediction PDF Probability Density Function POA Plane of Array
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES v
PSU Pennsylvania State University PV Photovoltaic QC Quality Control QR Quantile Regression QWF Qartli Wind Farm RAP Rapid Refresh RFP Request for Proposals RMSE Roof Mean Square Error RTFDDA Real-Time Four-Dimension Data Assimilation RTO Regional Transmission Operator RUC Rapid Update Cycle SCADA Supervisory Control and Data Acquisition SCE Southern California Edison SoDAR Sonic Detection and Ranging SOWIE Software SPF Solar Power Forecast SSH Secure Shell Protocol TPP Thermal Power Plant TSI Total Sky Imager TSO Transmission System Operator TWh Terawatt Hour TYNDP Ten Year Network Development Plan UCAR University Corporation for Atmospheric Research Independent Advisory, Testing, Inspection and Certification Body for a Broad UL AWST Range of Industries USAID United States Agency for International Development VDRAS Variation Doppler Radar Assimilation VRE Variable Renewable Energy WG Working Group WGS World Geodetic System WMO World Meteorological Organization WPF Wind Power Prediction WPPT Wind Power Prediction Tool WRF Weather Research and Forecasting WRG Wind Resource Grid XML Extensible Markup Language
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES vi
CONTENTS
EXECUTIVE SUMMARY ...... 9
BACKGROUND ...... 10
METHODOLOGY ...... 13
REGULATORY INSTRUMENTS FOR SUPPORTING VRE INTEGRATION IN GEORGIA ...... 14
WIND POWER FORECASTING TOOLS ...... 16 Time Horizons and Type of Wind Power Forecasting ...... 16 Wind Power Forecast Methods ...... 17 Physical Wind Power Prediction Models ...... 20 Statistical Models ...... 21 Hybrid (Physical + Statistical) Models ...... 23
NATIONAL ENVIRONMENTAL AGENCY (NEA) FORECASTING OF WIND SPEED ...... 24
SOLAR POWER FORECASTING METHODS...... 28 Physical Method ...... 30 Numerical Weather Prediction ...... 30 The Satellite and Cloud Imagery ...... 31 Total Sky Imagery (TSI) Based Forecasting ...... 33 Statistical Methods ...... 35 Hybrid Methods ...... 35 Potential Users of Solar Power Forecasting Output ...... 35 Data Requirement for Solar Forecasting ...... 36
SURVEY OF THE SERVICE PROVIDERS (CONFIDENTIAL) ...... 38 Services and Tools of Responded VRE Forecast Providers ...... 40 National Center for Atmospheric Research ...... 40 UL AWST ...... 48 VAISALA Wind Forecasting System ...... 51 VAISALA Solar Power Forecasting System ...... 52 ENFOR - WindFor™ ...... 53 ENFOR-SOLARFOR™ ...... 54 DNVGL Forecaster ...... 56 Meteologica ...... 57
EVALUATION AND COMPARISON OF FORECASTING MODELS ...... 58
BENCHMARKING FORECASTING TOOLS AND SELECTION OF VENDORS ...... 60
RECOMMENDATIONS ...... 62
APPENDIX ...... 65
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Generator and Meteorological parameters ...... 65 Static Data Requirement for Energy Conversion Model – Australian Wind Energy Forecasting System of AEMO ...... 65 Surveyed forecasting service and tool providers ...... 66
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EXECUTIVE SUMMARY The objective of USAID Energy Program is to support Georgia’s efforts to facilitate increased investment in power generation capacity as a means to increase national energy security, facilitate economic growth, and enhance national security. The program will have a significant impact on energy market reform efforts of the GoG to comply with the country’s obligations under the EnCT. The investment objective will be achieved through the provision of technical assistance to a variety of stakeholders in the energy sector. The goal of this program is to enhance Georgia’s energy security through improved legal and regulatory framework and increased investments in the energy sector. The ultimate expected outcome of this program is an energy legal and regulatory framework that complies with European requirements and encourages competitive energy trade and private sector investments. The purpose of Task 4 of USAID Energy Program is to support integration of non-hydro renewable energy into the power system. With the increasing penetration of wind power and solar power, VRE1 forecasting is quickly becoming an important topic for the electric power industry. System operators, generating companies, and regulators all support efforts to develop better, more reliable and accurate forecasting models. Wind farm owners and operators also benefit from better wind power prediction to support competitive participation in electricity markets against more stable and dispatchable energy sources. In general, VRE forecasting can be used for a number of purposes, such as: generation and transmission maintenance planning, determination of operating reserve requirements, unit commitment, economic dispatch, energy storage optimization (e.g., pumped hydro storage), and energy trading. The objective of this report is to review and at some extent provide description of VRE forecasting tools and methodologies and their application to power systems operations. This report provides detailed descriptions of the methodologies underlying state-of-the-art VRE forecasting models. The report includes: • Regulatory instruments for supporting variable renewable energy integration in Georgia focusing on key regulatory issues associated with the deployment of variable renewable energy sources, especially wind and solar power. Drawing upon research and experiences from various international contexts, it identification of key issues and ideas that have emerged as VRE deployment has grown and presents a framework for understanding regulatory issues within the larger context of power system evolution. Finally, in order to help the Georgian energy regulator anticipate issues that may arise in the future, the chapter related to the regulatory instruments aims to provide a forward look at regulatory lessons learned in cases of penetrations of VRE in other electricity markets; • A review of Numerical Weather Prediction (NWP) systems (meteorological systems for weather predictions) and description of its utilization for VRE forecasting. Predictability is the key to managing wind power’s variability. The larger the area, the better the overall prediction of aggregated wind power, with a beneficial effect on the amount of balancing reserves required, especially when gate-closure times in the power market take the possible accuracy levels of wind power forecasting into account. Also, the description of how NWP characteristics may affect the performance of the wind and solar forecasting models; • A review of the Wind Power Forecasting (WPF) methods, description of various time horizons for wind forecasting, data requirement for forecasting system; • A review of the Solar Power Forecasting (SPF) forecasting methods, description of various time horizons for wind forecasting, data requirement for forecasting system; • The survey of forecasting service and tolls providers which describes the key futures of services and tools under the disposal of third parties applicable to Georgia; • Approach for benchmarking of forecasting tools and selection of vendors together with the description of the complexity surrounding the purchase of forecasting services, tools or development of forecasting systems; • Recommendations for initial stage of forecasting system development.
1 Variable Renewable Energy (VRE) are generating facilities where electric energy is produced from a source that is renewable, cannot be stored by the facility owner or operator and has inherent variability that is beyond the control of the facility owner or operator.
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BACKGROUND Georgia is in the process of implementing reforms in the power generation sector that targets the generation of electricity from the renewable energy sources. In this process, the driving force is European Union (EU) Directive on Renewable Energy - 28/2009/Energy Community (EC) that determines certain obligation for Georgia to increase renewable energy share in final energy consumption. Georgia, as a country with considerable potential of all types of renewable energy resources, has promising perspectives for complying with the mentioned directive and ensure the further development in this sector. To move toward the approximation of legislation to the requirement prescribed in mentioned directive, the draft law "on Enhancing Energy Production from Renewable Energy Sources" is under development through the support provided by the Norwegian Water Resources and Energy Directorate and Danish Company "Nires" consortium "NVE-NIRAS". Georgia currently has a winter peak in electricity demand, although summer consumption is growing as a consequence of increasing air conditioning in the summer months. The summer load is relatively flat since the air conditioning units tend to run all day, unlike the peak morning and evening peaks of traditional residential consumers. Winter peak load coincides with a reduction of hydro capacity as the mountains retain precipitation as snow or ice, resulting in low river flows and empty reservoirs, and the consequent deployment of thermal plant and relatively high cost imports. Conversely, in the spring and early summer season, there is too much water available, so the thermal plants reduce output, and water is often spilled, bypassing the turbines of Hydro Power Plants (HPPs). Through the different activities, GoG is undertaking the efforts to attract investment to the energy sector to support Georgia’s renewable energy potential utilization and meet growing demand. HPP projects firmly occupy the first place in terms of quantity, priority and importance. Nevertheless, VRE (Wind and Solar) is also considered to be an intrinsic part of Georgia’s energy future. To support the increase of renewable energy’s share in final energy consumption and meet the growing demand on electricity, the GoG has been periodically signing Memorandum of Understanding (MoUs) with potential investors. The proposed projects under the MoUs could be classified to the three main categories: • Construction and Licensing Stage; • Under the Feasibility Study Stage with the obligation for construction; • Under the Technical Economic Study Stage. If assumed that the projects from all the above listed categories2 could be commenced for the operation in the 2030-2040 period, then Georgia’s electricity generation installed capacity might exceed 11GW as indicated in the graph provided below. Graph 1: Share of Technology in Installed Generation Capacity 2030-2040
VRE, 1664.6
TPP , 1155
HPP , 8237.17
VRE – Variable Renewable Energy; TPP – Thermal Power Plant; HPP – Hydro Power Plant.
2 Source: Former Ministry of Energy Webpage List of MoUs 27 December 2017 http://www.energy.gov.ge/projects/pdf/pages/Mimdinare%20Ganakhlebadi%20Sainvestitsio%20Proektebi%201810%20geo.pdf
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Respectively, the 33 TWh which theoretically can be generated under the mentioned scenario, could result in power export theoretical potential up to 15 TWh per year with fully met domestic demand up to 18.5 TWh. The contribution to the total power generation from the VRE projects theoretically can be estimated up to 6 TWH which is up to 20% of theoretically projected power generation as shown in the graph provided below. Graph 2: Theoretical Scenario – GWh Power Generation 2030
3300 3800 2800 3300 2300 2800 1800 2300
1300 1800 1300 800 800 300 300 -200 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec -200 -700 -700 -1200 -1200
-1700 PV MOU-Study WF Existing WF MOU-Study -1700 Biomass MOU-Study HPP Existing HPP MOU/Construction/Licensing HPP MOU/Study/C_Obligation TPP existing TPP Under Construction Import/Export Supply/Consumption VRE generation is variable, with limited predictability, and the resource is site specific. Thus, in this scenario, it is not easy to assimilate high volumes of variable generation. A 15%3 penetration of VRE power (measured as a percentage of annual generation) may be easily integrated in one power system while causing significant challenges in another, depending upon a range of factors including resource distribution, market rules, system size, grid reliability, level of interconnection, and system operation protocols. More specifically wind or solar and the resulting power produced by VRE plants are neither constant nor schedulable. One of the fundamental difficulties faced by power system operators is the unpredictability and variability of VRE generation. This has both technical and commercial implications for the efficient planning and operation of power systems. The additional uncertainty and variability caused by an increasing penetration of VRE generation raises the question of whether current requirements for operating reserves are adequate. The need for regulation services (operating reserves) may increase due to the short-term variations in VRE power generation. The old Thermal Power Plants (TPPs) have limited flexibility in their production, and the imports tend to be based on long term contracts. However, there is always some water in the reservoirs which could be preserved by the deployment of VRE. An increase in fast starting reserves (plants with high ramp-up speed) may be necessary to be able to counter large-level penetration and uncertainty in wind and solar power generation for the forecasting horizon and the existence of a forecasting tool could support the process of operating reserve capacity determination. Consequently, the operating reserve requirement could depend on the forecasted VRE generation. Moreover, the unit commitment decisions are obviously of major importance to maintain reliability and cost efficiency in the power system. The generation from VRE power plants and the information in forecasts has to be efficiently integrated into the unit commitment problem to deal with system reliability and safety4. Clearly, from a systems operation viewpoint, a large volume of variable supply causes difficulties in establishing the appropriate reserves at a reasonable cost so as to be able to comply with their primary mission: to maintain the system frequency, reliability and to be able to meet peak demand.
3 Source: Overview of VRE Regulatory Issues Mackay Miller and Sadie Cox National Renewable Energy Laboratory 4 Source: Wind Power Forecasting and Electricity Market Operations by Audun Botterud*, Jianhui Wang Decision and Information Sciences Division Argonne National Laboratory Accessed 5/03/2018 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.455.8151&rep=rep1&type=pdf
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VRE characteristics such as dependency of the resource to the location, its variability5 and uncertainty6, has led to several challenges in terms of integrating VRE7 into the grid in case of Georgia. The Ten-Year Network Development Plan (TYNDP) 2017-20278 adopted by the Georgian State Electrosystem (GSE) proposes limits of the transmission system of new VRE capacity integration in a time and spatial scale as its provided below on the graphs:
Figure 1: VRE Zones Graph 3: VRE Zones Power Potential
2500 Installed capacity mW Annual output (mln kWh) 2000 2000
1500
1000 600 450 500 500 500 200 150 200 200 130 150 50110 50120 100 50 50 0
Graph 4: Permissible Capacity of VRE Integration by Zones and Years
10 Year Development Plan - Total Permissable Capacities by Years 1 Poti 2 Chorokhi 3 Kutaisi 4 Mountain- Sabueti I 5 Mountain-Sabueti II 6 Gori-Kaspi 7 Paravani 8 Samgori 9 Rustavi 450 400 350 300 250 200 150 100 50 0 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Moreover, TYNDP sets specific requirements regarding transmission system and generation facilities (transmission system upgrade, new hydropower generation capacities and generation facilities upgrade with the Automatic Generation Control (AGC) equipment) and other important conditions for the integration of the VRE. Beyond the technical requirements one of the important requirement determined as a condition of VRE integration to the grid is the existence of VRE power production forecasting tools. The expected/required VRE production uncertainty level is in the range of 8-10%. Wind and solar energy are among the most difficult weather variables to forecast. Topography, surface roughness, ground cover, temperature inversions, foliage, gravity waves, low–level jets, clouds, and aerosols9, all affect wind and solar energy prediction skill. As wind and solar energy portfolios expand, this forecast problem is taking on new urgency because wind and solar energy forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small–scale weather events are crucial for energy management, as is the need to further develop and implement advanced technologies10.
5 Variability – due to temporal availability of resources (wind and solar power output vary over time). 6 Uncertainty – due to unexpected changes in resource availability (factors: wind speed, cloud cover, etc.). 7 In this particular case refers to Wind Projects only 8 Source: TYNDP of Georgia 2017-2027 GSE Web Page Accessed 4/10/2018 http://www.gse.com.ge/sw/static/file/TYNDP_GE-2017- 2027_ENG.pdf 9 Aerosol Smoke, dust, ash and SO2 particles from events such as volcanic eruptions Dust Particles, usually sand, carried in the atmosphere. Also includes: Haboob, Sandstorm. Source: EUMETSAT Webpage https://www.eumetsat.int/website/home/Images/Imageglossary/index.html 10 NCARS Contribution to Wind and Solar Energy prediction NCAR Webpage Accessed 03/05/2018 https://ral.ucar.edu/projects/ncars-contribution-to-wind-and-solar-energy-prediction
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METHODOLOGY In general, the methodology that was applied for the development of the report considers: • Interviews with stakeholders; • Documentation review/desk study helped to identify the VRE Forecasting tools; • Collection of all available documentation pertaining to the available VRE forecasting tools; • Conduct the review of collected materials; • Develop a draft outline on available VRE forecasting tools; • Develop a draft report on available VRE forecasting tools. Interviews have been held with the following organizations and individuals: GSE, Georgian National Energy and Water Supply Regulatory Commission (GNERC), National Environmental Agency (NEA), Denmark Technical University (DTU), Qartli Wind Farm (QWF). The VRE integration team reviewed all relevant sources of information, such as the project document, project reports, national strategic and legal documents, and any other materials that the team considers useful for this report. Supervision of the work of the assessment team have been provided during the entire assessment period. The deliverables from the assessment team are listed below: 1. Draft Outline on Available VRE Forecasting Tools; 2. Draft Final Report on Available VRE Forecasting Tools.
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REGULATORY INSTRUMENTS FOR SUPPORTING VRE INTEGRATION IN GEORGIA Energy production from variable renewable energy are often dispersed and located in remote areas far away from large consumption centers. This therefore creates a need for significant new investment in electricity networks infrastructure. According to the European Network of Transmission System Operators -Electricity (ENTSO-E), the VRE producers should be bound by the same duties and responsibilities as all other electricity generators. Providing incentives for VRE producers to correctly forecast their feed-in and hedge their volatility improves system security and economic efficiency. The integration of VRE not only require technological solutions, but also a higher level of regulatory and policy coordination, as well as innovation in market design to increase flexibility in the system. Four categories represent the key domains of VRE regulation. 1. Facilitating New VRE Generation While the impetus for new VRE generation is typically driven by policy or economic factors, energy regulators play a crucial role in, inter alia, setting tariffs, facilitating auctions, adopting grid codes, and influencing the interconnection of new VRE generation. These regulatory functions can strongly influence the pace of new VRE deployment. 2. Ensuring Adequate Grid Infrastructure Grid infrastructure enables VRE deployment, and regulators play a crucial role in shaping the grid investment landscape, especially with regard to planning, siting, cost allocation, and cost recovery. 3. Ensuring Short-term Security of Supply (Flexibility) Significant penetration of VRE brings increased variability and uncertainty to power system operations. Regulators play a crucial role in employing strategies that ensure system flexibility in a cost-efficient manner, such as encouraging the integration of forecasting into system operations and encouraging investment in flexible demand- and supply-side resources. 4. Ensuring Long-term Security of Supply (Resource Adequacy) The impact of VRE on resource adequacy is important in all settings, though the regulatory role varies considerably depending upon the level of excess generation capacity in the existing power system. In systems with excess capacity, VRE generation can disrupt the volume of conventional generation and suppress average market prices for energy, placing financial stress on legacy conventional generators, which leads to concerns over sufficient conventional dispatchable capacity. In systems with capacity scarcity, the contribution of VRE generation instead tends to mitigate overall capacity shortages and the contribution depends significantly upon the resource profile and the ability of the system to accommodate all of the resulting generation (in other words, to minimize curtailment of the resource). Also important are the interactions between these domains—for example, the interdependency between VRE generation and grid infrastructure planning. Actions to address issues in these four domains are not static—they evolve as VRE deployment grows as a percentage of annual generation. Table 1 illustrates some potential regulatory actions that may be appropriate in each of these categories at early, intermediate, and advanced stages of VRE deployment. Table 1: Potential Regulatory Actions at different stages of VRE development
Short term Security of Long term Security of VRE Generation Grid Infrastructure supply supply (Flexibility) (Resource Adequacy) Early Stage Establish appropriate Initiate data collection Establish efficient siting Initiate data collection VRE VRE support efforts that will processes Simplify efforts that will facilitate approximately mechanisms Establish facilitate formal grid interconnection protocols formal grid integration < 5% queue management integration Establish VRE grid codes Initiate formal grid Initiate formal grid Intermediate Refine VRE support and designated transmission integration Improve integration, with Stage mechanisms if zones Coordinate forecasting Broaden capacity credit or VRE necessary generation and grid planning balancing-area footprints resource adequacy approximately Refine siting and Establish distribution Improve system components as 5 - 20% queue management network standards for VRE operation methods needed Encourage alignment Expand grid interconnection Employ advanced Improve adequacy Advanced Stage between demand and and market coupling Employ system operation mechanism in
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VRE VRE production locational pricing Incentivize Incentivize Demand accordance with approximately Incentivize VRE active network management Response (DR) predominant paradigm > 20% dispatch ability Incentivize flexible (e.g., capabilities generation and/or market; strategic storage reserve requirement; full scarcity pricing) Table 1 reinforces how regulatory priorities evolve - and issues become more interdependent - as shares of VRE increase. For example: In early stages (normally less than 5% of annual generation) regulatory concerns typically center on the establishment of mechanisms for procuring new Renewable Energy generation and defining interconnection standards. Complex system integration issues are of a lower priority at these stages. In intermediate stages (typically between 5-20% VRE penetration) regulatory concerns increasingly center on the interactions between VRE and existing systems, such as how to achieve cost-efficient planning for grid expansion, how to identify VRE integration needs and evaluate costs, and how to allocate various charges to specific actors. In advanced stages (as VRE generation surpasses 20% of annual generation) regulatory concerns increasingly focus on the evolution of the entire power system, such as significant changes to institutional arrangements, grid infrastructure, conventional generation assets, demand elasticity, and interactions with neighboring systems, which can complicate regulatory initiatives. SOLAR INTEGRATION INTO THE POWER MARKET Recent global investments in the clean energy sector have exceeded those in conventional or fossil fuel based power generation technologies. This has been driven by an availability of a variety of solar technologies catering to different needs (power generation, lighting, heating etc.) and recent efficiency advancements achieved in those technologies. As an intermittent resource, solar only generates during daylight hours when prices in the electricity markets are highest. Solar has high capital costs but near zero operating costs – solar will always produce when it is able. This drives down power prices when solar operates, displacing other, more expensive forms of generation. Distributed solar electricity, on the other hand, rarely participates in wholesale markets, but has an indirect effect by reducing net demand levels during midday hours that used to represent peak price hours. Boosted by a strong solar Photovoltaic (PV) market, renewable energy sources accounted for almost two-thirds of net new power capacity around the world in 2016, with almost 165 GW coming online. This was another record year, largely as a result of booming solar PV deployment in China and around the world, driven by sharp cost reductions and policy support. In 2016 new solar PV capacity around the world grew by 50%, reaching over 74 GW, with China accounting for almost half of this expansion. For the first time, solar PV additions rose faster than any other generating resource, surpassing the net growth in coal. This deployment was accompanied by the announcement of record-low auction prices as low as 3 cents per kilowatt hour. Low announced prices for solar PV farms were recorded in a variety of countries, including India, the United Arab Emirates, Mexico and Chile. These announced contract prices for solar PV power purchase agreements are increasingly comparable or lower than generation cost of newly built gas and coal power plants. The growing proliferation in solar deployment, especially at the distribution level, has made the case for power system operators to develop more accurate solar forecasting models. They are characterized by the forecast horizon, the time resolution, and the update frequency, all depending on the purpose. For power system or power market related purposes, forecast horizons are typically below 48 hours and the time resolution is 15 minutes to one hour, in line with the program time unit of the power system or the market. Common products are day-ahead forecasts, intra-day forecasts and combined forecasts. Day-ahead forecasts are typically delivered in the morning for the next day from 0 to 24 and updated once or twice. Intraday forecasts are delivered and updated several times per day for the rest of the day. For long-term planning of unit commitment and maintenance decisions, forecasts with longer time horizons are used, typically one week or more.
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WIND POWER FORECASTING TOOLS This chapter reviews the available WPF tools and models. The chapter includes an overview of the methodologies underlying the physical and statistical modeling approaches used in state-of-the-art wind power forecasting systems. The chapter pays specific attention to how the uncertainty in the forecasted wind power can be estimated and presented. A review of existing commercial and operational WPF tools is also provided, along with an overview of existing information on benchmarking studies of WPF models. The chapter also discusses how WPF can be efficiently integrated into power system operations, focusing on the unit commitment problem with wind power uncertainty. TIME HORIZONS AND TYPE OF WIND POWER FORECASTING The forecasting time horizon of WPF might range from several hours to the week ahead and more. The areas of application depending on time horizon of the forecast ranging from transmission outage and maintenance planning, peak load analysis to reliability unit commitment, day ahead or hour- ahead market bidding, and real-time commitment and dispatch. Time-scale Classification for Forecasts Very (Ultra) short term. The time horizon range is a few hours. The application of this time horizon WPF for the listed in Table 2 entities depends on the market rules; for example, these forecasts can be useful for trading in intraday markets. For the System Operator, the usefulness of these forecasts is related to the ancillary services management of the power system, as well as for unit commitment and electricity dispatch. Short term. The time horizon ranges from the very-short–term limit up to 72 hours. This time horizon is mainly interesting for electricity trading in the day-ahead market. For example, if the energy sale bids for the next day should be submitted before 10:00 AM, the 38-hour time horizon covers the entire following day as well. Depending on the country, the period for submitting bids and offers might be different so the number of hours in the time horizon can also vary. These forecasts might be used for maintenance planning when the time horizon is 72 hours. Medium term. The time horizon ranges from the short-term limit of 7 days and more depending on the capability of the method applied for the forecast and acceptable level of uncertainty. With the increase of time horizon of forecast the uncertainty level is increasing. Such forecasts applicable for decision on unit commitment of conventional generation with low ramp up and rump down speed as well as in the maintenance planning of the generation plants and maintenance scheduling of transmission system. To perform forecast with the time horizon which can be attributable to the medium-term type forecast, NWP such as the European Centre for Medium Range Weather Forecasting (ECMWF) or the Global Forecast System (GFS) should be employed. Wind power forecasting is used for different purposes, as summarized in Table 2 provided below. Table 2: Forecast time horizon and potential users
Time Horizons a GENCOs ISO/RTO/TSO S models Intraday market (1h) Ancillary services management (5-10 min.) (H up to 6 h) Real-time market (1h) Unit commitment (up to 3 h) (S – 10 min.) Economic dispatch (up to 3 h) (R 10 to 60 min.) Ancillary services management (10 min.) Congestion management (up to 3 h) Time Horizons a GENCOs ISO/RTO/TSO R NWP/S models Intraday market (3 hr to 24 hr) Unit commitment 3h to 12 h) (H up to 72 h) Wind farm and storage devices coordination (3 h Economic dispatch (1 h to 12 h) (S – 30 min.) to 72h) Maintenance planning of wind farms (R 30 to 60 min.) Congestion management (1 h to 12 h) (3 h to 12 h) Time Horizons a GENCOs ISO/RTO/TSO Maintenance planning of network lines R NWP models Day-ahead market (>12 h) (12 h to 72 h) (H up to 72 h) Congestion management (12 h to 72 h) (S – 60 min.) Maintenance planning of wind farms Day-ahead reserve setting (12 h to 72 h) (R 12 h) (12 to 72 h) Unit commitment and economic dispatch
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(12 h to 72 h) Time Horizons a GENCOs ISO/RTO/TSO G NWP models Maintenance planning of wind farms (up to 7 days) (72 h to 168 h) Maintenance planning of network lines (H up to 7 days) Maintenance planning of conventional generation (72 h to 168 h) (S – 60 min.) (72 h to 168 h) (R 24 h) a H – Horizon (h); S – Time Step (min.); R – Refreshment (h). G NWP – Global NWP, R NWP Regional NWP.
GENCO- Generation Company ISO – Independent System Operator RTO – Regional Transmission Operator TSO Transmission System Operator
WIND POWER FORECAST METHODS As Jones states11, “94% of grid operators say that integrating a significant amount of wind [power] will largely depend on the accuracy of the WPF.” Wind farms are often located in remote areas which are serviced by “stringy” and constrained transmission networks. In such cases, the need for accurate forecasting of wind farm outputs is required to ensure appropriate loading and secure operation of the constrained transmission networks servicing them. The precise WPF is necessary to enable successful integration of wind generation into power systems and electricity markets. Data time series representing wind parameters and wind power are chaotic or stochastic in nature and this presents challenges for accurate prediction, particularly in the short-term time frame. From the one hand the data requirements of WPF tools are conditioned by the type of model, and from the other hand, models are classified by the inputs used. The WPF models could be classified as those using12: • Only Supervisory Control and Data Acquisition (SCADA)13 data (S): applicable only for very- short–term applications, with a time horizon less than 6 h; • NWP regional models refreshed with SCADA data (R NWP/S): applicable for short-term forecasting problems with typical horizons of between 3 and 24 h; • NWP regional models without refreshment of SCADA data (R NWP): applicable for short-term forecasting problems with typical horizons of between 12 and 72 h; • NWP global models (G NWP): applicable for medium-term forecasting problems with typical horizons of between 72 and 168 h. These global models are less accurate; however, they are the only ones capable of producing forecasts for these horizons. With the consideration of the above, it’s obvious that input data to a WPF tool require information from distinct sources. Also, it seems that the short-term WPF applications require NWP as inputs to forecast for horizons ranging from 6 to 72 hr. In case of High-Resolution NWP, physical wind power algorithms compute local wind power from large-scale wind forecasts (typically between 7 to 40 km horizontal resolution) as follows14: - Spatial refinement (e.g. horizontal interpolation); - Calculation of the wind speed at hub height (e.g. extrapolation of 10 m surface wind considering thermal stability or use of high level NWP model fields); - Consideration of orography effects and surface roughness; - Losses due to turbine wakes in the wind park and accounting the availability of turbines with respect to damages, maintenance or cut-off at high wind speeds. In statistical algorithms at least three of the above-mentioned aspects of wind power prediction do not necessarily require physical modeling, i.e. orography effect, surface roughness and turbine wakes.
11 L. E. Jones, Strategies and Decision Support Systems for Integrating Variable Energy Resources in Control Centers for Reliable Grid Operations. Washington, DC, USA: Alstom Grid Inc., 2011. 12 Argon National Laboratory Wind Power Forecasting: State-of-the-Art 2009 13 SCADA in the context of WPF considers Generator and Meteorological parameters. For the example please refer to the Annex 1 Generator and Meteorological parameters 14 Source : Combination of Deterministic and Probabilistic Meteorological Models to enhance Wind Farm Power Forecasts Accessed 03/05/2018 https://www.uni- oldenburg.de/fileadmin/user_upload/physik/ag/ehf/enmet/publications/wind/journal/2007/combination_of_deterministic_probabilistic_models_to_ enhance_wind_farm_power_forecasts.pdf
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES 17
The quality of the forecast, typically on day ahead basis, is a metric according to which wind farm owners can be charged penalties due to the imbalance between what is dispatched into the grid and what was declared it would be dispatched. Usually, the farm owner provides a 24-hour forecast in the morning for the day after. Forecasts rely on high quality data made available in a timely manner to the forecast providers for use within their models. The set of information required for forecasting could differ for different types of users. This difference in requirements is related to the time frame of forecast, as well as to the trade-off between the value of forecast and the cost of data. The sources of data could be the following: Table 3: Data for Farm Level
Data Historical On-line Forecast Source Useful Useful SCADA Wind Speed &Wind Essential Essential NWP services Direction Important Important Meteorological station Temperature Useful Useful Meteorological station Pressure Useful Useful NWP services Humidity Essential Important WF SCADA Power Production Important Important Utility SCADA Useful Reference forecast Essential Essential WF SCADA Turbine Availability Essential Essential WF Operator SCADA – Supervisory Control and Data Acquisition; NWP – Numerical Weather Prediction; WF – Wind Forecast.
Table 4: Static Data Requirement 15
Level of Detail Wind Turbine Wind Farm Regional Terrain Modeling Data Essential Essential Useful Terrain Roughness Useful Useful Information about Obstacles Useful Useful Wind Farm Capacities Essential Important Wind Farm Layout Essential Essential Power Curve Information Useful Useful Useful Wind Turbine Location Essential Useful Wind Turbine Characteristics Essential Essential Location and Characteristics of Meteorological Station Important Important Wind Farm Locations Essential Essential Useful The provision of meteorological data to System Operator is a must for VRE operators in case of California Independent System Operator (CAISO). According to CAISO, the requirement for VRE operators to have a Meteorological Station and provision of the data is a must. According this requirement: “Each Generator with a wind Eligible Intermittent Resources (EIR) must install and maintain equipment required by the CAISO to support accurate power generation forecasting and the communication of such forecast, meteorological, and other required data. A Generator with a wind EIR shall install a minimum of one meteorological tower and two meteorological stations measuring barometric pressure, temperature, wind speed and direction, except that the second meteorological station is only required for plants with a rated capacity of 5 MW or greater. The meteorological tower should be located on the windward side of the wind farm. One meteorological station is required to be installed at the average hub height of the wind turbines. The second meteorological station may be co-located on the primary meteorological tower and installed approximately 30 meters below the average hub height.”16 Some forecast vendors or system operators performing the WPF prefer to have data from meteorological (met) towers, as opposed to plant-mounted sensors. Data from plant-mounted
15 For the practical Example on requirement of Static Data for WPF please refer to Appendix “Static Data Requirement for Energy Conversion Model – Australian Wind Energy Forecasting System of AEMO” 16 Source : CAISO Business Practice Manual for Telemetry Accessed 4/28/2019 https://bpmcm.caiso.com/BPM%20Document%20Library/Direct%20Telemetry/BPM_for_Direct_Telemetry_Redline%20v3.pdf
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES 18
sensors can be affected by the movement of the wind turbine blades. Some vendors and System Operator require data from both met towers and plant-mounted sensors. With the consideration of data requirements listed above, the main feature that distinguishes WPF applications is how they deal with the consideration of wind flow around and inside the wind farm, i.e., how in the forecast model physical phenomena could be considered. Figure 2 below graphically shows the interconnection of data requirement and methods used for WPF
Figure 2: Wind Forecast Data Requirements and Methods17
A WPF can be considered as a “black-box”. This “black-box” takes various data as inputs and generates wind power production forecasts as outputs. Depending on the complexity of WPF, the number of inputs can be either small or large. For example, the persistence model only needs one input: current power generation. Other systems operate upon a wide range of input data such as online meteorological data (wind speed, wind direction, temperature, pressure, etc.) measured by on- site and off-site met towers, online power production data provided by wind farm owners, historical power production data of a wind farm, and turbine availability data for a wind farm. According to this important difference, the forecasting methods are generally divided into two main groups. The first group is called the physical approach, and second one, statistical. There is another group of models which benefits from both physical and statistical models and such models has been called hybrid models. Therefore, it is common to classify WPF into the following three categories: - Physical model; - Statistical Model; - Hybrid model. Potential Users of WPF output The basic classification of wind prediction end-use specifics and requirements is related to the details of a power market. However, a generic representation of entities interested in wind power forecasting can be stated. Some of the possible WPF end-users are: - Generation companies (wind power plant operators); - Wind farm owners; - System operators; - Market operators; - Energy regulators; Table 5 provides examples of how forecasts are used in system operations in North America. 18
17 National Renewable Energy Laboratory (NREL) Forecasting Wind Barbara O’Neill, Grid Integration Manager Presented to the Southeastern Wind Coalition Presented to the Southeastern Wind Coalition UAG Forecasting and Integration Meeting Raleigh, North Carolina March 30, 2016 Source NREL Webpage: https://www.nrel.gov/docs/fy16osti/66383.pdf 18 NREL Forecasting Wind Barbara O’Neill, Grid Integration Manager Presented to the Southeastern Wind Coalition Presented to the Southeastern Wind Coalition UAG Forecasting and Integration Meeting Raleigh, North Carolina March 30, 2016 Source NREL Webpage: https://www.nrel.gov/docs/fy16osti/66383.pdf
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES 19
Table 5 Utilization of wind forecasts in system operation
Balancing Authority Type of variable Forward Unit Intra-day Transmission Reserves Manageme Generation/ RE forecasted Commitment Unit Congestion nt of Hydro Transmission (Day-ahead, Commitment Management or Gas Outage week-ahead, Storage Planning etc.) Alberta Electric System Wind X X Operator Arizona Public Service Wind X X X
Bonneville Power Wind X X X Administration (BPA) California Independent Wind and solar X System Operator (CAISO) Glacier Wind Wind X X
Idaho Power Wind X X X X
Northwestern Energy Wind X X X
Sacramento Municipal Utility Solar X District* Southern California Edison* Wind* and solar X X X**
Turlock*** Wind
Xcel Energy Wind and solar X X X X X
RE – Renewable Energy *Also, participants in the CAISO’s Participating Intermittent Resource Program **For hydro only, not natural gas **Uses forecast for trading, optimization, marketing, and compliance with BPA scheduling directives Source: Porter and Rogers, 2012. Survey of Variable Generation Forecasting in the West1910
PHYSICAL WIND POWER PREDICTION MODELS Physical models focus on the description of the wind flow around and inside the wind farm, in addition to using the turbine manufacturer’s power curve to propose an estimation of the wind power output. Since many wind farms can extend over several kilometers wide, wind speed over the wind farm area is not uniform. It is conceivable to have a strong gust at one end of the farm, with no or very little wind at the other end. The physical models are capable to deal with this phenomenon. Figure 3 illustrates different approaches used for wind power forecasting20.
19 Source: Survey of Variable Generation Forecasting in the West August 2011 — June 2012 K. Porter and J. Rogers Exeter Associates, Inc. Columbia, Maryland Webpage: https://www.nrel.gov/docs/fy12osti/54457.pdf 20 California Renewable Energy Forecasting, Resource Data and Mapping Wind Forecasting
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES 20
Figure 3: Wind Power Forecasting Methods
Physical systems are based on modelling the physical atmosphere and environmental conditions of the wind power plant and often use weather services on a course grid adapted to the location and topology of the site. Complex mathematical models used by NWP approaches are provided by weather services to produce forecasts based on temperature, pressure, surface conditions and roughness and are run a few times over a single day for timescales up to 7 days21. Physical Model Advantages22: - The modern physical models are, in fact, hybrid models; - Model parameterizations are empirically derived (e.g., cloud and radiative processes); - Atmospheric physics feedback incorporated; - Physically consistent forecast solution with value out to 7 days. Physical Model Disadvantages: - Most global scale models update only 2 or 4 times per day; - Not optimized for sub hourly processes (e.g., 15-minute scales); - Model improvement scorecard NOT validated against hub height (80m or 100m) observations as there are so few publicly available; - Biases at wind plant locations exist; - More expensive to run internally (expertise required). The next model group is called the statistical approach, and it consists of emulating the relation between meteorological predictions, historical measurements, and generation output through statistical models whose parameters have to be estimated from data without taking any physical phenomena into account.
STATISTICAL MODELS In the statistical approach, a WPF uses statistical models to find relationships between a wealth of explanatory variables (including results from NWP models and online measured power data. Usually, the statistical models are developed by employing one or more of several different statistical algorithms.
21 Short term forecasting of wind power plant generation for the provision of ancillary services By Harley MacKenzie | Published Tue, 6 March 2018 at http://www.wattclarity.com.au/2018/03/short-term-forecasting-of-wind-power-plant-generation-for-the-provision-of-ancillary-services/ 22 VAISALA- Practices in Wind & Solar Power Forecasting A Forecast Provider’s Perspective G M Vishwanath, Head of Renewable Energy Operations 22 January 2018
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES 21
The very short-term forecasting approach consisting of statistical models use as a basis the time series approach, such as the Kalman Filters, Auto-Regressive Moving Average (ARMA), Auto- Regressive with Exogenous Input (ARX), and Box-Jenkins forecasting methods. One of the most typical statistical methods to forecast wind power are Artificial Neural Networks (ANN)23. The essence of the artificial intelligence approach is to establish the relationship between input and output by artificial intelligence methods, rather than using the analytical method. Such models utilize past values of input variables (e.g., wind speed, wind generation). At the same time, they can also use other explanatory variables (e.g., wind direction, temperature), which can improve the forecast error. For time horizons greater than 6 h, NWPs should be used as inputs. Fig 4: Block diagram of ANN Wind Power Forecaster24
WRF – Weather Research and Forecasting; NWP – Numerical Weather Prediction; SCADA – Supervisory Control and Data Acquisition; ANN – Artificial Neural Networks. The ANN wind forecasting model depicted in Figure 4 used as a basic data sources for wind power prediction the historical measurement records of wind turbine SCADA and numerical weather prediction (NWP), It was trained utilizing 12 months data with 10 minutes interval provided from Goldwind wind turbine SCADA system which is recording wind speed and wind power data. The forecasted wind speed data provided from NWP model (Weather Research and Forecasting (WRF)) projected around the vicinity of the wind farm has been preprocessed and applied to the developed ANN forecasting model in order to estimate wind power for the next 24 hours of the next day on 10 minutes interval basis25 Figure 5 illustrates the architecture of ANN for WPF.
23 Wind Power Forecasting techniques in complex terrain: ANN vs. ANN-CFD hybrid approach Francesco Castellani, Davide Astolfi, Matteo Mana, Massimiliano Burlando, Cathérine Meißner and Emanuele Piccioni Published under license by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 753, G. Modeling and simulation technology Webpage: http://iopscience.iop.org/article/10.1088/1742- 6596/753/8/082002/pdf 24 Source: International Journal of Science and Engineering Applications Volume 5 Issue 3, 2016, ISSN-2319-7560 (Online) www.ijsea.com 144 Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids Webpage: https://pdfs.semanticscholar.org/e178/10475401fc928e270e35dbdb8b63044e03d7.pdf 25 Source: International Journal of Science and Engineering Applications Volume 5 Issue 3, 2016, ISSN-2319-7560 (Online) www.ijsea.com 144 Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids Webpage: https://pdfs.semanticscholar.org/e178/10475401fc928e270e35dbdb8b63044e03d7.pdf
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES 22
Figure 5: Example of Artificial Neural Network Architecture for Wind Energy
Input Layer Hidden Layer Output Layer
Wind Speed
Humidity
Wind Energy
Generation Time in Hours
There are three types of layers, input layer, hidden layer, and output layer in an ANN. The input layer receives three inputs (average wind speed, average relative humidity, and average generation hours per month) processed by three processing units. The output parameter is the wind energy generation by the wind farms. The model could be implemented using MATLAB (Programming language) to forecast wind power. Statistical Model Advantages26: - Very quick to run (order of seconds). Can capture short term variability (forecasts less than 2-3 hours on 15-min or shorter scale); - Many different algorithms are now available Open Source (e.g., Octave, R CARET, Python scikitlearn); - Doesn’t take an advanced-degreed atmospheric scientist to develop and apply; - Machine Learning, Big Data, AI is advancing much faster than physical modeling of renewable power forecasting. Statistical Model Disadvantages: - Most models improve with longer history (1-year+) to learn; - Forecast performance generally inferior to physical models beyond 6 hours; - Forecast skill degrades precipitously in the absence of real time observations; - Forecast skill degrades without the use of physical model forecasts as input predictors.
HYBRID (PHYSICAL + STATISTICAL) MODELS The hybrid methods combine different approaches such as mixing physical and statistical approaches to combine the two approaches to join the advantages of both statistical and physical approaches or combine short-term and medium-term models and thus improve the forecasts and such system are called hybrid models. Today most of the physical models represents hybrid models. The main advantage of hybrid models is to benefit from the advantages of each model and obtain an optimal forecasting performance. Many types of hybrid models are utilized to predict wind power. The types of combinations can be27: - Combination of physical and statistical approaches; - Combination of models for short term and medium term; - Combination of alternative statistical models; - Combination of alternative models of artificial intelligence. Since the information contained in the individual forecasting method is limited, hybrid method can maximize the available information, integrate individual model information and make the best use of the advantages of multiple forecasting methods thus improving the prediction accuracy.
26 VAISALA- Practices in Wind & Solar Power Forecasting A Forecast Provider’s Perspective G M Vishwanath, Head of Renewable Energy Operations 22 January 2018 27 Energies Different Models for Forecasting Wind Power Generation: Case Study Web Page http://www.mdpi.com/1996-1073/10/12/1976
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NATIONAL ENVIRONMENTAL AGENCY (NEA) FORECASTING OF WIND SPEED For wind speed forecasting, NEA employs the Weather Research and Forecasting (WRF) Model developed by the US National Center for Atmospheric Research. It is a mesoscale NWP system designed for both atmospheric research and operational forecasting applications. It features two dynamical cores, a data assimilation system, and a software architecture supporting parallel computation and system extensibility. The model serves a wide range of meteorological applications across scales from tens of meters to thousands of kilometers. The WRF model input comprise data from Global Forecast System (GFS) and ECMWF model together with the data from the local meteorological stations. The GSF from US National Centers for Environmental Prediction (NCEP) is the most widely used data source as it is free of cost, and a good model. The models are run at 27km resolution globally, which is not enough resolution to predict local geographically and thermal effects such as sea breezes. In March 2016 ECMWF28 increased the resolution of their model to a 9km resolution, which is currently the highest resolution global model available. ECMWF data has a high acquisition cost, and therefore the data is not widely used by many weather websites and has been traditio nally used only by top yacht racing teams and meteorologists. There is the Associated Member fee up to 25,000 €. The figure below highlights the meteorological forecast published on NEA’s webpage29 for Gori. 3 km far from this city the QWF is located. Figure 6: General daily-published weather forecast
The number circled in red is the forecast for wind speed with the time step of 12 hours with a difference 5 m/s between the minimum and maximum values of wind speed forecast. For the purposes of example provided below, this forecast will be referred to as the “general meteorological forecast”. In fact, NEA is capable to provide much more precise forecasts depending on the time stamp and spatial scale. The provision of such kind of forecast is included in the list of services30 NEA is capable to provide. Historically, wind-speed forecast errors of 1–2 m/s were acceptable to most users of NWP forecasts. However, since wind power is a function of wind speed cubed, these relatively small errors in wind speed produce significant wind power forecast errors between turbine cut-in and rated wind speeds. Just as an example, the power curve shown below is for a Vestas wind turbine, V117-3.4531, which is the turbine type operated by the QWF.
28 ECMWF Web page: https://www.ecmwf.int/en/newsletter/147/meteorology/new-model-cycle-brings-higher-resolution 29 Web Page of National Environmental Agency Accessed 4/18/2018 (http://meteo.gov.ge/index.php?d=10&q=%E1%83%92%E1%83%9D%E1%83%A0%E1%83%98 ) 30 NEA Webpage Annexes 1-9 (http://nea.gov.ge/ge/service/hidrometeorologia/2/hidro-meteorologia/l 31 Web Page Accessed 4/19/2018 https://en.wind-turbine-models.com/turbines/1248-vestas-v117-3.45
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES 24
Graph 5: Vestas V117-3.45 Turbine Power Curve
At 7m/s of wind speed at the turbine, the generation output is 1 MW whilst at 12 m/s the turbine is capable to operate at its rated capacity, which in case of Vestas V117-3.45 turbine might be up to 3.5 MW. if we consider the number of units in the QWF and the difference 5 m/s in wind speed forecast, only in one hour this parameter would result difference in generation 6*(3.45MW-1MW) =14.7MWh/h. Such kind of deviation from the planned figures in an environment where the day ahead and intraday electricity market is functioning, would result either in significant penalties or loss in revenue due to the curtailment from Transmission System Operator (TSO) or imbalance service penalties. Moreover, another example with actual numbers and one year of observation performed on Botievo Wind Farm (BWF)32 which is provided below, emphasizes how precise should be wind forecast not to result in the significant uncertainty in wind power production forecast. Figure 7: Problems of Short Term Energy Forecast in Ukraine33
Uncertainty of Stage Meteorological Prognosis
DWD Germany Hourly uncertainty – MAE=26% Met Office UK
Forecasts on Power Production Botievo WF
Germany
Hourly uncertainty – MAE=77% Denmark
Transmission System Operator
DWD – Germany's National Meteorological Service; MAE – Mean Absolute Error; WF – Weather Forecast
32 Web Page Accessed 4/19/2018 http://botievskaya.dtek.com/en/ 33 Problems in Short-Term Power Forecast in Ukraine- Case of Botievo Wind Farm / Presentation Vyacheslav Molibog Accessed 04/18/2018
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Depending on the spatial scale of wind power forecast such as turbine level, wind farm or regional level, forecasts of meteorological parameters listed in Table 3 to be applicable for power forecasting should be delivered with the same spatial scale and the consideration of required time scale for forecasting. Through the consultation with NEA it was preliminary identified possible fields of wind speed forecast accuracy improvement which are listed below: - Fully utilize ECMWF outputs by becoming the associate member (€25,000 annual fee); - Forecast verification procedure improvement; - WRF 3D (Dimensional) model upgrade to Real-time Four-Dimensional Data Assimilation and forecasting system (WRF RTFDDA)34; - Systematization of data acquisition and its validation from local meteorological stations. Also, as one of the possible fields for improving the accuracy of wind speed forecasts was obtaining data for Nowcasting from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) which an intergovernmental organization was founded in 1986 with the purpose to supply weather and climate-related satellite data, images and products to the National Meteorological Services of Member and Cooperating States in Europe, and other users worldwide. The last is the delivery of data, products and services to a user reception station, transmitted directly from Meteosat and Metop satellites. With the consideration of above, USAID Energy Program proposes forecasting wind parameters by NEA in Test Mode at the QWF site. Test Mode considers the forecast of wind parameters at the hub height which in case of QWF is 90-100 m Above Ground Level (AGL). For verification of wind measurement accuracy using NEA equipment, the Test Mode working group will use readings from anemometers installed on QFW wind turbines and meteorological tower located near the QWF. Figure 8: Schematics of Test Mode
MAD – Mean Absolute Deviation; MSE – Mean Square Error; RMSE – Roof Mean Squared Error; MAPE – Mean Absolute Percentage Error; NEA – National Environmental Agency; GSE – Georgian State Electrosystem; QWF – Qartli Wind Farm. QWF will share the historical data on measurements of meteorological parameters performed at devices installed on nacelles and meteorological tower located near wind farm. Data would be share with the NEA on 2 days delay. NEA would perform forecast of meteorological parameters provided below with the time horizon and Time stamp indicated in the table: Table 6: List of Meteorological Parameter for Test Mode
Wind Speed 90-100 m AGL QWF Parameter Wind Direction 90-100 m AGL QWF Parameter Air Density 90-100 m AGL QWF Parameter Time Horizon 12-72 hours Time stamp Hourly averaged on 60 units
34 RTFDDA (Real-time Four-Dimensional Data Assimilation and forecasting system) is a mesoscale numerical weather modeling system. RTFDDA is built upon the WRF (Weather Research and Forecasting) model and is designed to effectively and efficiently assimilate diverse available weather observations into WRF. An important feature of RTFDDA is that it allows for smooth and uninterrupted assimilation of diverse weather observations and produces physically consistent and dynamically balanced 4D weather analyses.
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The values of meteorological parameters are proposed to be rounded to tenth. Before launching the test mode, the creation of Working Group (WG) would be important in terms of to facilitate the exchange of information through the cloud technology, analysis of test mode performance, selection of metrics for uncertainty, and to determine the duration of Test Mode. For the metrics which would be employed for weather forecast model evaluation, refer to the paragraph Evaluation and Comparison of forecasting models. Forecasting research is very active in EU and USA due to the commercial realities of operational utility-scale plants and the expectation of more renewable generation integration to the grid as a result of government policies. The same concerns are now emerging in Georgia. It can be expected that VRE forecasting efforts are now a topic of growing interest since it can be perceived as a least cost alternative and/or preliminary determined condition for the integration of VRE to the grid. Currently forecasting of meteorological parameters specific to WPF is underdeveloped in Georgia. NEA is the only local provider of the forecasting of meteorological parameters. This together with the prospective of VRE development in Georgia, with the consideration of importance of meteorological parameters as an input for WPF opens a new opportunity (at least theoretical which should be tested) to NEA to make its service on forecasting meteorological parameters valuable for performing WPF. Nevertheless, performing forecast of specific to WPF input meteorological parameters in Test Mode at some extent would provide notion on strength and weaknesses of the system currently employed for the forecasting. This would allow identification of issues challenging the commercial viability on provision of services required for WPF and cost effectiveness required improvements and upgrades of models employed. More specifically it would result in: - Identification of the accuracy of wind forecasts produced by NEA through forecasting system which comprise WRF and ECMWF models with limited access and without the upgrade; - Identification of the performance of the models and techniques of the consequences the reduced temporal resolution of models or upgrade of models might have on short-term forecasts; - Identification of the accuracy of wind forecasts on different time horizons; - Identification of the cost effectiveness of improving the accuracy of wind forecasts in order to reduce “causer pays” costs. Nevertheless, even if the test mode is performed and its results successfully accessed, there is much further work to be done to integrate VRE generators into the grid and more specifically to develop VRE forecasting system.
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SOLAR POWER FORECASTING METHODS The issues for integration into the grid for solar and wind energy are quite a similar and much of the effort to integrate wind power has paved the way for integration of solar plants, particularly with respect to forecasting and scheduling. Moreover, a cost-effective utilization of solar energy over a grid strongly depends on the accuracy and reliability of the power forecasts available to the TSOs. Furthermore, several countries have in place legislation requiring solar power producers to pay penalties35 proportional to the errors of day-ahead energy forecasts, which makes the accuracy of such predictions a determining factor for producers to reduce their economic losses. The output power depends on the incoming radiation and on the solar panel characteristics thus the energy produced by PV installations has a variable nature depending on several factors. Those general factors are astronomical and meteorological factors. The former are the solar elevation and the solar azimuth, which are easily predictable whilst the meteorological factors influencing the solar power production are cloud cover and temperature. The short wave solar radiation is reflected by the water particles and cannot reach the earth surface. The total cloud cover is a meteorological variable often used to indicate the presence of liquid water in the troposphere and has a limited predictability, which is also reflected on the Global Horizontal Irradiance (GHI)36 and, therefore on solar photovoltaic power prediction. Above the certain threshold with the increase of temperature, the efficiency of panels goes down and so goes the PV power production. Hence, the total Cloud Cover (CC), the GHI and air temperature at 2 m above the ground are the meteorological variables directly related to PV production and their limited predictability is also reflected on PV output predictions.37 Table 7: Quantities relevant to solar forecasting
Forecast Quantity Application Primary Determinants Importance to market Forecast Skill Global Irradiance PV Clouds, Solar geometry high medium Cell temperature PV Global Irradiance, air temperature wind low high SPF methods are generally characterized as physical or statistical, however in practice the lines between these might be blurred in case of use combination of physical and statistical models (hybrid models). Figure 9: Solar Forecasting Structure38
35 ELSEVIER An analog ensemble for short-term probabilistic solar power forecast Webpage: http://geoinf.psu.edu/publications/2015_AppliedEnergy_AnEn-solar_Alessandrini.pdf 36 Global Horizontal Irradiance (GHI): The sum of DNI and DHI. Direct normal irradiance (DNI): The amount of solar radiation received per unit area by a surface that is always held perpendicular (or normal) to the rays that come in a straight line from the direction of the sun at its current position in the sky. Diffuse Horizontal Radiation (DHI): The amount of radiation received per unit area by a surface (not subject to any shade or shadow) that does not arrive on a direct path from the sun but has been scattered by molecules and particles in the atmosphere and comes equally from all directions. 37 An analog ensemble for short-term probabilistic solar power forecast S. Alessandrinი, L. Delle Monache, S. Sperati G. Cervone Accessed 4/29/2018 http://geoinf.psu.edu/publications/2015_AppliedEnergy_AnEn-solar_Alessandrini.pdf 38 Solar Electric Power Association Predicting Solar Power Production: Irradiance Forecasting Models, Applications and Future Prospects
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The choice of solar-forecasting method depends strongly on the timescales involved, which can vary from horizons of a few seconds or minutes (intra-hour), a few hours (intraday), up to 168 hours (day- ahead) or a few days ahead (intraweek). Figure 10: Solar Forecast Types and Horizons39
As it depicted on Figure 10 the forecasting of PV power method and technique depends on the forecast horizon required: NWP40 requires and perform best for horizons of 1 or 2 days ahead, whereas statistical models based on historical and real time data from local ground measurements, possibly combined with sky imager or satellite data of cloud motion requires for short‐horizons of less than 6 hours. Accuracies typically decrease with increasing forecast horizon, with a steeper decrease for methods such as persistence forecasting41 based solely on past data. Table 8 Characteristics of solar forecasting techniques
Technique Sampling rate Spatial resolution Spatial extent Suitable Forecast horizon Persistence (Statistical) High One point One Point Minutes Total Sky Imagery 30 sec 10s to 100 meters 3-8km radius 10s of minutes GOES satellite imagery 15 min 1 km US 5 hours NAM weather model 1 hour 12 km US 10 days
GOES – Geostationary Operational Environmental Satellite; NAM – North American Model North American Model (NAM) is officially called the - North American Model. All of its data can be accessed at the NCEP website. It initializes once every 6 hours, like the GFS, and simulates the atmosphere out to 84 hours for North America, only. CAISO uses the following forecasts: - The day-ahead forecast is submitted at 05:30 prior to the operating day, which begins at midnight on the day of submission and covers (on an hourly basis) each of the 24 h of that operating day; - Day-ahead forecast is provided 18.5 to 42.5 h prior to the forecasted operating day; - The hour-ahead forecast is submitted 105 min prior to each operating hour. It also provides an advisory forecast for the 7 h after the operating hour. According the literature42 the intraday forecasts are currently of smaller economic value than are Day- ahead forecasts; however, with increasing solar penetration and the expected accuracy improvement of intraday compared to Day-ahead forecasts, substantial market opportunities will likely materialize.
39 Solar Energy Forecasting Advances and Impacts on Grid Integration Jan Kleissl University of California, San Diego Subject Editor, Solar Resources and Energy Meteorology, Solar Energy Journal Accessed 4/28/2018 https://www.energy.gov/sites/prod/files/2016/08/f33/1.Jan_Kleissl-PVSCPlenary.pdf 40 Numerical Weather Prediction (NWP): The use of mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. 41 Persistence Forecast: Forecasts based on extrapolating current conditions into the future. 42 IEA International Energy Agency Photovoltaic and Solar Forecasting: State of the Art
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For this reason, less than 48 hours solar forecasts are useful for energy resource planning and scheduling whereas intraday forecasts are useful for load following and pre-dispatch, reducing the amount of frequency control (“regulation”) in “real” time.
PHYSICAL METHOD The physical method is based on the NWP, Cloud Observations by Satellite or Total Sky Imager (TSI) or atmosphere by using physical data such as temperature, pressure, humidity and cloud cover. Currently, physically-based forecasting is primarily conducted with NWP and cloud observations by satellite or TSI. NWP provides information up to several days ahead, however there are significant biases and random errors in the irradiance estimates.
NUMERICAL WEATHER PREDICTION NWP is a computer simulation of the atmosphere43 and this simulation relies on mathematical modeling of atmospheric physics. It is the study of how the observations of the weather are used and then processed to predict the meteorological parameters in the future. Knowing the current state of the weather is just as important as the numerical computer models processing the data44. In the case of weather this involves a complex system of measurements of the atmosphere from terrestrial and space-based systems. All the processes of calculation and computation are done with the help of super computers45. Below provided Table 9 together with the Figure 11 provide some examples of type and location of measurements assimilated in ECMWF Numerical Weather Prediction Model.
Table 9: Terrestrial and Space based measurement Figure 11: ECMWF Data Coverage46
Terrestrial based Space based Surface synoptic and Cloud motion winds ships Data buoys, drifting and Surface winds - moored Scatter meter Aircraft Microwave Radio Zond Infra-red Balloon winds and GPS, etc. profilers
NWP processes as follows: In the first step the initial states of atmosphere are collected with the help of different sources such as space and terrestrial based observations. The observations are typically inhomogeneous in space, in time and importantly, in quality; a major task of assimilation algorithms is to address quality control, observation density issues and the error characteristics of both the model issues and the error characteristics of both the model and the observations. “Data assimilation” is the process whereby observations of the state of the atmosphere used to update the state of the corresponding models. The output of the assimilation step is an “initialization”, which is an estimate (model) of the state of the physical system at an instant in time. Then “Forecasting” is the evolution of the model state from its initialization into the future, where (by definition) no observations are available47. The fidelity of the forecast is constrained by the fidelity of the initialization, the quality of the physical parameterization of key meteorological processes, and the overall resolution of the
43 Solar Power Forecasting: A Review D. K. Chaturvedi Accessed 4/25/2018 https://pdfs.semanticscholar.org/a679/0993e18db1b5d488b161194fa07a3d5c139f.pdf 44 Numerical Weather Prediction NOAA Accessed 4/29/2018 https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/numerical- weather-prediction
46 ECMWF Webpage Accessed 4/29/2018 https://www.ecmwf.int/en/forecasts/charts/monitoring/dcover?facets=undefined&time=2018042900,0,2018042900&obs=synop-ship&Flag=all 47 Application of Numerical Weather Prediction to Rapid Environmental Assessment Stuart Anstee Maritime Operations Division Systems Sciences Laboratory DSTO–GD–0403
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assimilation/prediction system and the observations. The significant source of the NWP error is data- assimilation, which is a complex process. This occurs because sources measure different quantities of current states over different volumes of a space and that creates an error in the measurement. A process called assimilation is done so as to process the current weather states and produce outputs of temperature, wind, irradiance and other hundreds of meteorological elements.48 In the second step, the main important equations of atmosphere, such as dynamics equations, Newton‘s second law for fluids flow, thermodynamics equations, and radiative transfer equations are integrated and solved49. Last step is statistical post-processing step where the output of the NWP is manipulated using Model Output Statistics (MOS) application or Kalman Filtering in order to compare the outputs with observations and find the statistical relation, and hence correct the error 50. MOS is a post-processing technique which is used for interpreting numerical model output and producing site-specific forecasts. A statistical approach is used by MOS for relating observed weather elements with appropriate variables (predictors). These predictors can be NWP model forecast, prior observations, or geo- climatic data. NWP models can be classified either to global models or to regional models. In global models, global or worldwide simulation of the behavior of the atmosphere is carried out, where as in regional (mesoscale) models, simulation is done on a continent or a country scale. Hence, global models have the coarsest resolution, regional models have intermediate resolution and mesoscale models have the finest resolution. For different resolutions, different models of the physical processes taking place in the atmosphere, such as cloud generation, are required. In their current development of NWPs might be quite a challenging the prediction of the exact position and extent of cloud fields and resolving the micro-scale physics that are associated with cloud formation due to their relatively coarse spatial resolution (typically on the order of 1 – 20 km) and lack of local observations. The benefits given by NWP are that it works for long time horizons (15 to 240 hours). With the help of regional and global modelling of atmospheric physics, it is possible to obtain information about the propagation of large scale weather thus as compared to satellite-based methods NWPs shows more accurate results of forecast for time horizons exceeding 4 hours51. One way to improve solar and PV forecasting is to combine forecasts from different NWP models or from different members in an ensemble forecast. This approach is employed in the case of wind forecasting, where significant improvements in forecast accuracy have been achieved by combining models.
THE SATELLITE AND CLOUD IMAGERY For physically-based forecasting, cloud cover and cloud optical depth are the most important parameters affecting solar irradiance. Through processing of satellite or ground imagery, clouds can be detected, characterized, and advected to predict GHI accurately up to 6 h in advance ahead. The satellite and cloud imagery-based model is a physical forecasting model that analyzes clouds. The satellite imagery deals with the cloudiness with high spatial resolution. The high spatial resolution satellite has the potential to derive the required information on cloud motion.
48 NOAA Numerical Weather Prediction Accessed 4/21/20018 https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/numerical- weather-prediction 49 Solar Power Forecasting: A Review D. K. Chaturvedi Accessed 4/25/2018 https://pdfs.semanticscholar.org/a679/0993e18db1b5d488b161194fa07a3d5c139f.pdf 50 Post-processing of solar irradiance forecasts from WRF model at Reunion Island Hadja Maïmouna Diagne, Mathieu David, John Boland, Nicolas Schmutz, Philippe Lauret Accessed 4/24/2018 https://hal.archives-ouvertes.fr/hal-01089749/document 51 Forecast of ensemble power production by grid-connected PV systems E Lorenz, D Heinemann, H Wickramarathne, H G Beyer and S. Bofinger Irradiance forecasting for the power prediction of grid connected photovoltaic systems E Lorenz, J Hurka, D
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Picture 1: NOAA GOES East Image Viewer Band & and Geocolor Images52
Picture 2: EUMETSAT Near Real Time Image
Geostationary satellite images, such as those obtained from the satellite, have been used for the determination and forecasting of local solar radiation conditions. The basis of this method relies upon the determination of the cloud structures during the previous recorded time steps. Extrapolation of their motion leads to a forecast of cloud positions and, as a consequence, to the local radiation situation. This method has the advantage of producing a spatial analysis of an area within certain resolution capabilities53. The cloud motion helps in locating the position of cloud and hence solar irradiance can be forecasted. The parameters which have the most influence on solar irradiance at the surface are cloud covers and cloud optical depth54. The processing of satellite and cloud imageries are done in order to characterize clouds and detect their variability and then forecast the GHI up to 6 hours ahead. This model works by determining the cloud structures during earlier recorded time steps. The structure of the clouds and their positions helps in predicting solar irradiance.55 In most cases, the limitations56 of this approach are in infrequent updates of the original images, poor understanding of cloud altitudes which pose problems for sunrise and sunset predictions, and a set of challenges posed by estimating Clearness Index57 through calculation of dynamic pixel range. The lower spatial and temporal resolution causes satellite forecasts to be less accurate than sky imagery on intra-hour time scales. Satellite imagery is the best forecasting technique in the 1 to 6 hour forecast range. Classical satellite methods only use the visible channels (i.e. they only work in day time), which makes morning forecasts less accurate due to a lack of time history. To obtain accurate
52 https://www.star.nesdis.noaa.gov/GOES/index.php 53 Solar Irradiation Forecasting: State-of-the-art and Proposition for Future Developments for Small-scale Insular Grids Web Page:https://ases.conference-services.net/resources/252/2859/pdf/SOLAR2012_0617_full%20paper.pdf 54 The vertical optical thickness between the top and bottom of a cloud. http://glossary.ametsoc.org/wiki/Cloud_optical_depth 55 Review of solar irradiance forecasting methods and a proposition for small scale insular grids H M Diagne, M David, P Lauret, J Boland and N. Schmutz, 56 Solar Power Forecasting Performance – Towards Industry Standards V. Kostylev and A. Pavlovski Acessed 4/29/2018 https://greenpowerlabs.com/gpl/wp-content/uploads/2013/12/wp-sol-pow-forecast-kostylev-pavlovski.pdf 57 The clearness index is a measure of the clearness of the atmosphere. It is the fraction of the solar radiation that is transmitted through the atmosphere to strike the surface of the Earth. It is a dimensionless number between 0 and 1, defined as the surface radiation divided by the extraterrestrial radiation. The clearness index has a high value under clear, sunny conditions, and a low value under cloudy conditions. Accessed 4.29/2018 https://www.homerenergy.com/products/pro/docs/3.11/clearness_index.html
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morning forecasts, it is important to integrate infra-red channels (which work day and night) into the satellite cloud motion forecasts (Perez, et al. 2010). For a given time/location, a cloud index is derived from image’s pixel brightness in relation to the local pixel’s dynamic range -- i.e., the possible range of pixel brightness at the considered location, with the darkest pixels corresponding to clear conditions and the brightest to cloudy conditions. Pixel dynamic range varies as a function of location and time because of ground reflectivity (albedo), ground bidirectional – specular – reflectivity, the presence of snow cover, and the satellite sensor’s calibration58. High bare ground albedo is the most common problem weakening the approach in arid environments and as a result of seasonal snow cover.
TOTAL SKY IMAGERY (TSI) BASED FORECASTING Success of TSI-based forecasting depends on accuracy of the cloud detection algorithm, and correctness of forecasted 2-dimensional cloud mask. Intra-hour forecasts with a high spatial and temporal resolution may be obtained from ground-based sky imagers. For a temporal range of 30 minutes up to 6 hours satellite images-based cloud motion vector forecasts show good performance.59 Typically, the methodology for ground based images is similar to satellite based techniques. Projections of observed solar radiation conditions based on immediate measured history while the position and impact of clouds is deduced from their motion. Cloud motion vectors are generated by cross-correlating consecutive sky images and used to predict cloud locations short time ahead, dependent on velocity of cloud movement. This approach allows for high spatial and temporal resolution in GHI forecasts at timescales shorter than about 5-min.60 In the case of TSIs, the Charged Coupled Device (CCD) 61 image is digitally processed in order to detect locations of the sky covered by clouds. The cloud image is then propagated forward in time resulting in a forecast. TSI images are useful for prediction of GHI on time horizons up to 15 minutes. Picture 3: TSI Images
Raw Sky Image Processed Sky Image Cloud Motion Vectors Picture 4: TSI-880 Automated Total Sky Imager62
58 Producing Satellite-Derived Irradiances in Complex Arid Terrain Richard Perez & Marek Kmiecik Kathleen Moore https://openei.org/datasets/files/674/pub/perez2003_17.pdf 59 Solar Power Forecasting: A Review International Journal of Computer Applications (0975 – 8887) Volume 145 – No.6, July 2016 60 Solar Power Forecasting Performance Towards Industry Standards V. Kostylev and A. Pavlovski 61 The CCD is a type of sensor that is used to capture an image by taking the light and translating it into digital data. Accessed 4/29/2018 http://photographycourse.net/what-is-the-ccd/ 62 Model TSI-880 Automated Total Sky Imager http://www.yesinc.com/products/data/tsi880/index.html
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Images from the sky are captured via a solid-state CCD imager looking downward onto a heated, rotating hemispherical mirror. A shadowband on the mirror blocks the intense direct-normal light from the sun, thereby protecting the imager optics. An embedded image-processing algorithm captures and displays the images. Results are presented in real time via a web server, both statically and via panoramic views and animations. The forecast by TSI is challenged by the lack of information on 3-dimentional structure and multi-level dynamics of the observed clouds. The forecast of GHI is most successful in tracking single layer broken clouds moving across the sky without rapid deformation. Cloud velocity at different altitudes are the major sources of error in this approach. Unrealistic assumption of steady state cloud cover (i.e. that cloud pattern moves as a single, unchangeable layer) is probably the main weakness of the approach. Clouds moving at different altitudes and their shadows on the ground as well as on top of other clouds produce an additional challenge in correct interpolation of the next cloud image and determination of clearness index63. For low and fast clouds, the forecast horizon may only be 3 minutes while for high and slow clouds may be over 30 minutes, but generally horizons between 5 to 20 minutes are typical. Even if cloud size and velocity could be determined accurately, the forecast accuracy depends on the rate at which the cloud field is departing from the evolution defined by the cloud motion vectors (i.e. development, dissipation, etc.).64 Provided below is the schematics of GHI forecast through TSI and description of schematics: Figure 12 Schematics of GHI prediction with TSI 1. Image acquisition; 2. Image pre-processing. The horizon (i.e. topological features and nearby objects) is removed from the image, and the distortion due to the fish-eye lens is corrected with a geometrical transformation. Besides, the sun position is determined as a function of the sun zenith and azimuth values; 3. Cloud detection. Each pixel of the image is labeled as a cloud or clear sky. The result of the segmentation is a binary image called cloud map; 4. Cloud motion identification. It consists in estimating the cloud movement. Cloud motion algorithm returns one motion vector (a single one for the whole image), called global motion vector; 5. Cloud map forecasting: the cloud map is translated by applying the global motion vector. This leads to the forecasted cloud map; 6. Local cloud cover computation. Given the forecasted cloud map, it consists in computing the percentage of cloudy pixels in a specific area around the sun; 7. GHI prediction intervals computation. Time series-based probabilistic prediction tool.
63 Solar Power Forecasting Performance – Towards Industry Standards V. Kostylev and A. Pavlovski Webpage : https://greenpowerlabs.com/gpl/wp-content/uploads/2013/12/wp-sol-pow-forecast-kostylev-pavlovski.pdf 64 Report IEA PVPS T14‐01:2013 http://www.meteonorm.com/images/uploads/downloads/Photovoltaic_and_Solar_Forecasting_State_of_the_Art_REPORT_PVPS__T14_01_2013 .pdf
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STATISTICAL METHODS For intra‐hour, and up to 2 or 3 hours ahead forecasts, statistical methods without exogenous input (i.e. only the power plant output is used) can be used to achieve certain accuracy. This is relatively easy especially when advanced forecasting engines are used. However, inclusion of relevant exogenous data from sky imagery, satellite, and NWP (in order of increasing forecast horizon) can significantly increase accuracy and forecasting skill. These requires more comprehensive stochastic learning techniques such as various ANNs (number of neurons, layers, initial weights, size of the training set, etc.) to outperform conventional regression (ARMA, Autoregressive integrated moving average ARIMA, etc.) producing higher‐fidelity forecasts with exogenous variables at various horizons. Linking statistical modeling with real-time data from monitoring site would lead to better accuracy of predictions. Statistical methods can be applied to correct for known deficiencies of different forecasting methods through corrections for known model biases or automated learning techniques. Examples are MOS, autoregression techniques, and ANN. For example, MOS uses statistical correlations between observed weather elements and climatological data, satellite retrievals, or modeled parameters to obtain localized statistical correction functions. This allows correcting systematic deviations of a numerical model, satellite retrievals, or ground sensors. A disadvantage of statistical methods is the large amount (typically at least one year) and accuracy of measurement data needed to develop statistical correlations separately for each location. This means that MOS-based forecasts are not immediately available for larger areas or for locations without prior measurements, such as most non-urban solar power plants which development is proposed in Georgia.
HYBRID METHODS Hybrid models are the combination of two or more forecasting techniques so as to improve the accuracy of the forecast. Therefore, they are also known as combined models. The idea behind using the hybrid models is to overcome the deficiencies of the individual models and to utilize the advantages of individual models, merge them together and provide a new hybrid model to reduce forecast errors. For instance, the NWP model can be combined with the ANN by feeding the outputs from the NWP as input to the ANN models. Many studies have showed that integrated forecast methods outperform individual forecast.65
POTENTIAL USERS OF SOLAR POWER FORECASTING OUTPUT These different uses of SPF require different types of forecasts. For example, a forecast may apply to a single PV farm (point forecast) or refer to the aggregation of large numbers of distributed PV systems spread over an extended geographic area (area forecast). A forecast that focuses on the rate of change in solar power output may be needed for decision support tools designed to predict significant ramp events on regional grids. Table 10: Factors determining type of forecast66
Day-ahead, hour-ahead, & intra-hour Forecasts based on time horizon. Central utility-scale or Distributed Generation scale Large-scale or small scale solar installations. A range of possible solar system output based on probability or a Probabilistic67 or Deterministic68 specific output value. Forecasts for one solar farm or forecast for an aggregate of Point69 or Area forecasts geographically dispersed solar energy systems.
65 International Journal of Computer Applications (0975 – 8887) Volume 145 – No.6, July 2016 28 Solar Power Forecasting: A Review Webpage: https://pdfs.semanticscholar.org/a679/0993e18db1b5d488b161194fa07a3d5c139f.pdf 66 Solar Electric Power Association Predicting Solar Power Production: Irradiance Forecasting Models, Applications and Future Prospects 67 A probabilistic system is one in which the occurrence of events cannot be perfectly predicted. Though the behavior of such a system can be described in terms of probability, a certain degree of error is always attached to the prediction of the behavior of the system. 68 A deterministic system is one in which the occurrence of all events is known with certainty. If the description of the system state at a point of time of its operation is given, the next state can be perfectly predicted. Accessed 4/28/2018 http://ecomputernotes.com/mis/information-and- system-concepts/differentiate-between-deterministic-and-probabilistic-systems 69 Point Solar Forecast: A solar forecast for a single solar energy plant.
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Forecast of power output or expected change in output over the Output or Rate of Load Change forecast time step. Most common industry-requested operational forecasts and their corresponding granularity are the following70: - Intra–Hour: 15 minutes to 2 hours ahead with 30 seconds to 5-minute granularity (relates to ramping events, variability related to operations); - Hour Ahead: One to 6 hours ahead with hourly granularity (related to load following forecasting); - Day-Ahead: One to 3 days ahead with hourly granularity (relates to unit commitment, transmission scheduling, and day ahead markets); - Medium-term: Week to 2 months ahead, with daily granularity (hedging, planning, asset optimization); - Long-term: typically, one or more years, with diurnal monthly and annual granularity (long- term time series analysis, resource assessment, site selection, and bankable documentation). Solar irradiance forecasting provides a critical input to predicting a solar power plants’ output at various points in the future. SPF provides grid operators, utilities, and market participants data for use in decision support tools, including scheduling reserve capacity or developing bidding strategies for hour-ahead and day-ahead wholesale power markets. Table 11: Solar Forecast Possible End-Users and Potential Applications71
Organizations Potential Application Day-ahead reliability planning Hour-ahead reliability management Security constrained unit commitment ISOs/RTOs & Balancing Authorities Real time dispatch Load forecasting Ramp event prediction Transmission security planning and outage coordination Distribution system planning Distribution Utilities (either serving load or Distribution management systems not serving load in unregulated or Outage management systems regulated markets) Smart grid infrastructure management Load forecasting Scheduling Coordinator (participant in Day-ahead scheduling in competitive markets competitive wholesale market) Hour-ahead scheduling in competitive markets Day-ahead scheduling Scheduling Coordinator (regulated market) Hour-ahead scheduling Load Serving Entity (participant in Day-ahead load bids competitive wholesale market) Hour-ahead load bids Day-ahead load forecasts Load Serving Entity (regulated markets) Hour-ahead load forecasts Energy Traders Day-ahead, hour-ahead, and intra-hour bidding strategies Day-ahead, hour-ahead, and intra-hour simulations for Research Labs variable generation integration studies Day-ahead, hour-ahead, and intra-hour simulations for Project Developers project pro forma ISOs & Balancing Authorities Day-ahead reliability planning
ISO – Independent System Operator; RTO – Regional Transmission Operator.
DATA REQUIREMENT FOR SOLAR FORECASTING The CAISO, which maintains a centralized VRE forecasting system for the grid operation and forecasting purposes, as a must requests both static and meteorological data from the VRE generators. Provided below are examples of static and meteorological data that should be provided to CAISO: Table 12: CAISO Data Requirement for Forecasting and Grid Operation
Static Data Meteorological Data Generation Capacity (MWs): Direct Irradiance (DIRD) Measured by pyranometer
70 Solar Power Forecasting Performance – Towards Industry Standards V. Kostylev and A. Pavlovski 71 Solar Power Electric Association Predicting Solar Power Production: Irradiance Forecasting Models, Applications and Future Prospects
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Global Horizontal Irradiance (GHIRD) Pyranometer or Plant Type (PV, CPV or Thermal): equivalent equipment Topographical map Wind Speed (Meter / Second) Plant Location points as necessary to describe the site Wind Direction (Degrees) (Use World Geodetic System (WGS) 84 only) Meteorological Stations Location WGS 84 datum only Air Temperature (Degrees Celsius) Panel Manufacturer/ Panel Model Barometric Pressure (Hecto Pascals) Number of Panels / Number of inverters Back Panel Temperature (Degree C) Panel Power Rating Plane of Array Irradiance Watts\Meter Sq. Inverter ratings Global Horizontal Irradiance Watts\Meter Sq. Tracking (Yes or No) Direct Irradiance Watts\Meter Sq. Single or Dual Axis Tracking Tracker Manufacturer /Tracker Model CPV – Concentration Photovoltaics; PV – Photovoltaic; WGS – W; MW – Megawatt; Sq. – Square meter; Moreover, to ensure that this data is delivered, the installation of meteorological station is a must before the integration to the grid: “Each EIR whose capacity is one MW or greater shall install a minimum of one meteorological station. Each EIR facility whose capacity is five MW or greater shall provide a minimum of two meteorological stations. Solar generating facilities that require Direct Normal Irradiance (DNI) and GHI measurements may provide alternate radiometry meteorological station data. For example, meteorological station one may report DNI where meteorological station 2 may report GHI. All other meteorological data reporting requirements shall remain the same. Solar generating facilities’ meteorological stations shall cover 90% of the facility’s footprint for each Resource Identity Document (ID). Each meteorological station shall have a coverage radius of 7 - 10 miles.”72
72 CAISO Business Practice Manual for Direct Telemetry
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SURVEY OF THE SERVICE PROVIDERS (CONFIDENTIAL) Accurate VRE production forecasts are an indispensable tool for system operators to anticipate possible risks to system security and to take counter-measures in a timely manner. Experience of other countries practicing forecasting for a long period shows that centralized forecast for the entire power system is a strict requirement and should not be substituted by forecasts based on the aggregation of individual VRE generators. These forecasts benefit from real-time monitoring of VRE generation and need to consider information on VRE plant status (such as forced outages, maintenance).73 A centralized system has some advantages: it is more cost effective and can be more consistent and efficient in the use of source information. The decentralized VRE power forecasting system is justifiable when several actors are involved and when the confidentiality of information and the distribution of forecasting costs are needed. It’s commonly accepted that the centralized system, when using the same data detail, could achieve better accuracy than the decentralized system of VRE power forecasting. This arrangement implies the existence of proper tools, professional forecasting structure that is expensive and difficult to maintain and update if it relies on in-house capacity instead of outsourcing and rely on service provision from vendors. However, for both cases – in-house capacity or supply of forecasting services the forecasting tool platform should be compatible with all platforms on which the client/hosting agency platform is capable of running, as well as with the user database technologies and interfaces with the related SCADAs. In 2012, US National Renewable Energy Laboratory (NREL) surveyed Balancing Authorities in the US in regard to the cost data for their VRE forecasting system. About half of the Balancing Authorities provided cost data for their VRE forecasting systems, but in very different formats, so making comparison difficult. Below is an inventory of cost data from the Balancing Authorities that provided information74: - The CAISO charged wind and solar generators $0.10 per MWh and reported that the fee covers roughly all of the forecasting costs in 2012; - Glacier Wind uses multiple wind forecasting companies and pays monthly fees on a project basis. Glacier Wind also receives forecasts for free from some forecasting companies on a trial basis; - Idaho Power developed its wind forecasting model internally. The company estimated it spent about $500,000 in initial development costs, with half of it coming from internal company funds and the other half from Idaho Power’s Smart Grid grant from the U.S. Department of Energy; - Southern California Edison (SCE) used multiple variable generation forecasting companies and said the costs are equivalent to two full-time staff people per year; - Turlock Irrigation District spent between $10,000 and $15,000 annually for wind forecasting or approximately 3 cents/MWh for wind generation. With the consideration of above survey results together with the aim of USAID Energy Program to support integration of VRE generation to the grid, USAID Energy Program performed its own survey of VRE forecasting service providers. The main purpose of the survey was to identify the services on VRE forecasting applicable to Georgia and rise the interest of vendors on service or software provision. A list of general questions below were asked to potential suppliers of VRE forecasting: - The availability of forecasting tool or services on solar and wind power prediction for the region; - Type of model - its capability, and to some extent, technical specification; - Proposed time intervals for forecasts (intra hour, hourly, day ahead, etc.); - Expected level of uncertainty on prediction for certain time interval; - Update intervals;
73 System Integration of Renewables: Implications for Electricity Security Report to the G7 74 National Renewable Energy Laboratory - Survey of Variable Generation Forecasting in the West August 2011 — June 2012 K. Porter and J. Rogers Exeter Associates, Inc. Columbia, Maryland
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- Real time and historical data (meteorological and power generation) requirement for expected level of uncertainty; - Numerical Weather Prediction (If NWP is required to be obtained from local meteorological agency please specify); - Raw estimation of cost required for project implementation75; - Monthly forecasting services provision fee. USAID Energy Program contacted surveyed 20 suppliers of VRE Forecasting Services (For the full list of contacted vendors, please refer to Appendix – “Surveyed Forecasting Service and Tools Providers”, Surveyed forecasting service and tool providers). The table below provides the list of vendors that have remained in contact with the Program due to their responses and expression of interest either in provision of VRE forecasting services or in-house capacity development for forecasting. Additionally, Skype conversations were held at the request of potential service suppliers, with the main purpose to clarify the existing situation regarding the integration/integrated capacity of VRE.
Confidential Part Removed
75 With the purposes to keep confidentiality on provided raw estimation of project costs the information is delivered to USAID separately and not included in this report.
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SERVICES AND TOOLS OF RESPONDED VRE FORECAST PROVIDERS USAID Energy Program contacted the DTU because the Wind Power Prediction Tool (WPPT)8 was developed by the Institute for Informatics and Mathematical Modelling (IMM) of the DTU. WPPT is a forecasting system that is capable of forecasting for a single wind farm, for a group of wind farms, or for a wide region (e.g., the western part of Denmark). DTU is the developer of WPPT. The summary of results provided in above table are missing the responses of Denmark Technical University. The main reason for this is that currently WindFor™ (formerly known as WPPT) i.e., the software solution for wind power forecasting now is under the disposal of ENFOR. Nevertheless, DTU expressed readiness to provide workshops and other necessary actions to build up in-house capacity of stakeholders and agency which in the future will be engaged in forecasting of VRE power.
NATIONAL CENTER FOR ATMOSPHERIC RESEARCH The National Center for Atmospheric Research (NCAR) is a world–renowned atmospheric scientific research, development and technology transfer center which works to advance weather capabilities for mission agencies and the public and private sectors. NCAR is operated by the University Corporation for Atmospheric Research (UCAR), a non–profit organization established in 1960 to oversee a wide range of programs and facilities that support its 100+ university affiliates, as well as the national and international scientific community. As a national center, NCAR is able to utilize advancements developed not only at NCAR, but at research centers, institutes, universities, and national laboratories worldwide.
NCAR – SUN4CASTTM SOLAR POWER FORECASTING SYSTEM76 Sun4CastTM is NCAR’s comprehensive approach to forecasting the power produced from the sun’s irradiance and includes a variety of components. NCAR has worked closely with utilities and ISOs to produce forecasts that allow them to effectively balance the variable generation resources with conventional energy sources. In order to meet both the short-range (nowcast) and longer-range (day ahead and beyond) needs, NCAR forecasts the expected irradiance and the resulting power output from 15 min through 168 h. For NCAR forecasts the expected irradiance and the resulting power output are from 15 min through 168 h. This system was recently built through a Public-Private-Academic Partnership funded by the U.S. Department of Energy (DOE) to advance solar power forecasting. The system was designed with both the needs of the intraday unit commitment and dispatch, as well as longer-range unit scheduling and planning in mind. The architecture is summarized in Figure 13.
76 Source: Variable Generation Power Forecasting as a Big Data Problem Sue Ellen Haupt and Branko Kosović
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Figure 13: Sun4Cast Architecture
GFS - Global Forecast System; GEM – Global Environmental Mesoscale; HRRR – High Resolution Rapid Refresh; RAP – Rapid Refresh; CIRA - Cooperative Institute for Research in the Atmosphere; DNI - Direct Normal Irradiance; DIF – Diffuse Irradiance; POA – Plane of Array.
Day-Ahead Forecast For the forecast periods beyond the nowcasting period (beyond about 4 hours), Sun4Cast refers to Day-Ahead system and leverages NWP models run by the NCEP of the National Oceanographic and Atmospheric Administration (NOAA) and other national centers, as well as deploying NCAR’s WRF- Solar. Each of these models has its own grid and timeframe: (a) WRF-Solar™ WRF-Solar™ is a branch of the Weather Research and Forecasting model designed specifically to improve solar irradiance forecasts. This version includes an improved radiative transfer scheme, improved cloud physics parameterization, new shallow convection scheme, improved aerosols with the radiation, and output tailored to the specific application. Initial and boundary conditions for the WRF-Solar forecasting system derive from the Rapid Refresh (RAP) model analysis. The irradiances (GHI, DNI, and diffuse irradiance - DIF) are output every model time step (20 seconds) and one-minute averages are computed77. It is run with one primary domain of 3-km horizontal grid spacing over the US and two domains of 1- km grid spacing over regions with solar farms – the San Luis Valley in Colorado and Sacramento, California. One run per day is configured to meet operational needs of the private partners targeting the day-ahead forecast. Because the computational cost of activating the 1-km domains is high, they are only activated for the daytime of the second day of the simulation to enable the simulation to complete in time for the forecast.
77 Variable Generation Power Forecasting as a Big Data Problem Sue Ellen Haupt and Branko Kosović
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(b) HRRR NCEP’s High Resolution Rapid Refresh (HRRR) model forecasts the weather over a limited area domain, in this case the CONUS, using a relatively fine 3-km grid cell size. It produces forecasts hourly for the next 15 hours. (c) RAP The RAP model, Version 2, includes a wider domain than the HRRR. It is run hourly with a coarser grid to produce forecasts for the following 18 hours. (d) GFS NCEP’s GFS is representative of the available global models. GFS is run at 2.5°, 1.0°, and 0.5° globally. The model forecasts are produced every 6 hours out to 384 hours. Recently an additional 0.25° simulation was added and produces forecasts for the next 168 hours. The Sun4Cast forecasting system employs the 0.5° forecast. Other global models, such as those from Canada, Europe, and other national centers can also be blended. Table 15: Summary of the data output by some of the NWP models used in the Sun4Cast system
Model Forecast frequency Hours ahead Grid cell size Daily output [GB] HRRR hourly 15 3 km 130 RAP hourly 18 9 km 5.7 NAM 6 hours 84 12 km 5.5 GFS 6 hours 384 0.5° 68 GEM 12 hours 240 1° 4.3 WRF- Solar Irradiance only: 20 secs 30 3 km (CONUS) 1 km Domains 4.2
HRRR – High Resolution Rapid Refresh; RAP – Rapid Refresh; NAM - North American Model; GFS - Global Forecast System; GEM - Global Environmental Mesoscale; WRF - Weather Research and Forecast.
Nowcast System Five models comprising the Nowcast system producing a most accurate forecast. These nowcasting methods leverage a variety of disparate observational data, statistical and computational intelligence methods, and physical understanding of the atmosphere to produce a “best practices” blended forecast. Each is briefly described below. (a) TSICast TSICast, uses three total sky imager cameras to observe current cloud cover. Because they deploy multiple cameras, they can deduce the height, base, location of the clouds, as well as the speed and direction of each cloud layer by observing the changes in time. Thus, they can predict where the clouds will be in the next 15-30 min. TSICast processes in about 2-3 min to provide this short-range prediction. (b) StatCast StatCast was developed to leverage irradiance measurements from pyranometers located at solar plants. There are several versions of StatCast each of which uses a computational intelligence method to predict the cloud cover and the resulting clearness index for the next 3 hours. It ingests surface irradiance measurements, nearby weather data, and, when available, satellite data to estimate the clearness index. StatCast requires at least a year’s worth of data to train the forecast model. Once trained, it runs in a matter of seconds. (c) CIRACast CIRACast designed to detect geostationary satellite-observed clouds, process the data to remove parallax and shadowing, and advect those clouds with derived motion vector and model winds Thus model is able to predict cloud coverage over the coming hours typically around 15-30 min. (d) MADCast The Multi-sensor Advection Diffusion foreCast (MADCast) system uses the Multivariate Minimum Residual (MMR) scheme of Auligné to assimilate satellite infrared radiance observations into the dynamic core of the WRF model. The dynamics of WRF then advects the observed clouds accordingly. It predicts out to 6 hours, with a latency of only about 10 min due to not employing the computationally expensive physics packages of WRF.
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(e) WRF-Solar-Now WRF-Solar-Now is an implementation of the specially configured version of the WRF model, WRF- Solar that optimizes computation of solar irradiance. It is run in a nowcasting mode at 9-km horizontal grid spacing over the contiguous United States (CONUS) hourly. It predicts out to 6 hours with approximately 1 h of latency to complete the run. The Nowcast system has different data needs, most of which are more modest than for NWP. The amount of data produced by the Nowcast system is displayed in Table 16. Table 16: details of several nowcast model daily output
Model Forecast Frequency (Minutes) Hours ahead Daily Output (MB) MADCast 15 6 2,100 CIRACast 15 6 1.4 StatCast 15 3 13 WRF-Solar-NOW 15 6 24,000 MADCast 15 6 2,100
Completing Forecasting Process The integrator for the various NWP models is the computational intelligence algorithm, the Dynamic Integrated Forecast System (DICast®) as depicted in Figure 14. DICast® produces automated forecasts using a method that was designed to emulate the human forecast process. Figure 14: Diagram of the DICast® blending process.
DMOS – Dynamic Model Output Statistics The Dynamic Integrated foreCast (DICast®) system78 is tasked with ingesting meteorological data (observations, models, statistical data, climate data, etc.) and producing meteorological forecasts at user defined forecast sites and forecast lead times. DICast employs a two-step process: it first statistically corrects the bias of each input model using Dynamic Model Output Statistics (DMOS), and second, it optimizes the model blending weights for each lead time, producing a consensus forecast. DICast typically works with up to 90 days of data; this is an advantage because many other methods require a year or more of data. DICast generates forecasts by optimizing the combination of NWP model data. DICast typically reduces root mean square error by about 10-15% and essentially eliminates bias as compared to the best input model. DMOS is the first forecast optimization step in DICast®. It is a statistical post-processing step that attempts to optimize the raw forecast from each NWP model. This process is like what NCEP does in
78 https://ral.ucar.edu/solutions/products/dynamic-integrated-forecast-dicast%C2%AE-system
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its MOS79 product generation. A key difference is that DMOS has been designed to work on relatively short forecast/observational histories.80 The second optimization step in DICast® is the integration of the DMOS forecasts into a single consensus forecast. The DICast® forecast integrator does this by objectively determining the optimal combination of DMOS model forecasts. The forecast integrator performs a bias-corrected weighted average of the input DMOS forecasts. Each day, the weights are nudged based on the models’ performance compared to the observations. Overall, the configuration of DICast used in Sun4Cast employs irradiance values obtained from seven NWP models as seen in Figure 14, as well as observations from the sites of the solar plants. These models include those run operationally by NCEP in the USA: - Global Forecast System (GFS); - North American Model (NAM); - Rapid Refresh model (RAP); - High Resolution Rapid Refresh (HRRR-NCEP); - HRRR-ESRL research model developed at the Earth System Research Laboratory of NOAA; - Environment Canada runs the Global Environmental Mesoscale (GEM) model; - WRF-Solar was run quasi-operationally by NCAR. The data from each of these models is ingested and blended in real time to produce forecasts hourly. The Nowcast systems discussed above are integrated separately using a unique Nowcast expert system integrator that utilizes the recent performance scores of each component model, whether it be from a computational intelligence method (StatCast), based on cloud observations (TSICast and CIRACast), or includes NWP components (MADCast and WRF-Solar-Now). Although the Nowcast system is currently optimized via an expert system, dynamic methods are planned for future applications. The DICast® and Nowcast irradiance forecasts are integrated and blended during the transition period (2 h - 6 h to produce irradiance forecasts for each 15-min interval out to 3 hours then hourly out to 168 hours. GHI is the most useful forecast variable for photovoltaic panel operations while DNI is the only component useful for concentrated solar plants. The meteorological irradiance values are not the final output variables. Predicting irradiance is a step toward predicting power, the variable that the utility and balancing authority need in order to plan day ahead unit commitment and balance the grid in real time. The power conversion depends on the particular type of hardware installed at the solar farm as well as local conditions. Thus, the power conversion module philosophy is that rather than specifying the physics, one obtains a sufficiently long time series of matched irradiance and power output data, and then trains an artificial intelligence model to predict power from irradiance. This approach is straightforward, and it is typically superior to more direct prediction methods. Specifically, NCAR has applied regression tree analysis to train conversion algorithms to best match historical observed irradiance/power relationships. A model regression tree (Cubist)81 is used in Sun4Cast to train the relationship between the measured irradiance value and the coincident power produced. The empirically derived relationship is then applied in real-time to the irradiance forecast to produce a power forecast. A separate power conversion algorithm must be trained for each generation site. NCAR applies the Analog Ensemble (AnEn) approach to produce an appropriate Probability Density Function (PDF) of the forecast uncertainty. The AnEn assumes that if a forecast made in the past under meteorological conditions analogous to today’s forecast, then it is likely to produce the same error characteristics as is probable in today’s forecast. Thus, analogs in those past forecasts are identified so that: 1) observations corresponding to analog forecasts are selected as members of
79 Model Output Statistics (MOS) is an objective weather forecasting technique which consists of determining a statistical relationship between a predictand and variables forecast by a numerical model at some projection time(s). Accessed 4/28/2018 https://journals.ametsoc.org/doi/10.1175/1520-0450%281972%29011%3C1203%3ATUOMOS%3E2.0.CO%3B2 80 A turbine hub height wind speed consensus forecasting system William Myers* and Seth Linden National Center for Atmospheric Research, Research Applications Laboratory https://ams.confex.com/ams/91Annual/webprogram/Paper187355.html 81 The Sun4Cast® solar power forecasting system: the result of the public-private-academic partnership to advance solar power forecasting https://opensky.ucar.edu/islandora/object/technotes:539
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AnEn and used to correct the forecast, and 2) a PDF of multiple analogs is used to estimate the uncertainty of the forecast. More details about the Sun4Cast Project can be found on UCAR web pages: https://opensky.ucar.edu/islandora/object/technotes:539 https://wiki.ucar.edu/pages/viewpage.action?pageId=321619872
NCAR –Wind Power Forecasting System82 NCAR has configured an enhanced Wind Power Forecast System that integrates high-resolution WRF RTFDDA modeling and the analog ensemble approach with artificial intelligence methods. Advances to the forecasting system include prediction technologies for short-term prediction of wind power ramps, including an observation-based expert system and the Variational Doppler Radar Assimilation (VDRAS) model. The statistical learning system, DICast®, has been enhanced, as have the power conversion algorithms through deploying a quantile data quality control scheme. Additional modules provide estimates of extreme events, including icing, high winds, and extreme temperatures. According the flow chart provided below in Figure 15 the system is configured to include tailored high resolution mesoscale model data with assimilation (RTFDDA) of wind farm-specific data, including the nacelle winds; a 30-member mesoscale ensemble system (Ensemble - Real-Time Four-Dimension Data Assimilation (E-RTFDDA)); model data from the national centers; ANalog-space Kalman Filter (ANKF) and a Quantile Regression (QR) calibration scheme; a statistical forecasting system (DICast) to perform MOS and optimize weights to best match the nacelle wind speeds; empirical power conversion; and a nowcasting system based on VDRAS. Figure15: WPF System Developed for Xcel Energy
RUC – Rapid Update Cycle; GUI - Graphical User Interface.
(f) RTFDDA–WRF Figure 16: RTFDDA–WRF Domains 83RTFDDA–WRF Real-time Four-Dimensional Data Assimilation version of the Weather and Forecasting Mesoscale Model. This mesoscale numerical weather prediction model has been designed for high- resolution applications, featuring rapid forecast updates and continuous real-time assimilation of observational data. This project utilizes a customized version of the model, optimized for wind energy applications. Inner, high-resolution, D3 domain covers targeted wind farm sites in Colorado, New Mexico, Texas & Minnesota. 41 levels in the vertical for all domains.
82 Scientific Advances in Wind Power Forecasting Brank Kosovic, Sue Ellen Haupt, Drake Bartlett, Daniel Adriaansen, Stefano Alessandrini, Gerry Wiener, Luca Delle Monache, Yubao Liu, Seth Linden, Tara Jensen, William Cheng, Marcia Politovich, Paul Prestopnik 83 NCAR’s Xcel Energy Project Overview David Johnson Research Applications Laboratory, NCAR Wind Energy Prediction, Research & Development Workshop National Center for Atmospheric Research May 11, 2010
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- D1 resolution = 30 km - D2 resolution = 10 km - D3 resolution = 3.3 km D3 domain is only used for the first 24 hours of the forecast cycle. D1 & D2 domains operate for the entire 72 hour forecast cycle, with hourly output (forecasts). NCAR WPF Computer Model with Three Separate Computational Domains84. The RTFDDA system assimilates diverse observations including World Meteorological Organization (WMO) Global Telecommunication System (GTS) standard upper-air and surface stations, NOAA wind profilers, cooperative agency wind profiler, aircraft weather reports, satellite derived atmospheric winds, doppler radar wind profiles, a large number of surface mesonet and other weather data sources, as well as wind farm meteorological tower and turbine nacelle anemometer wind speed measurements. This system is run eight times a day, and in each cycle, produces 24-h forecasts on the one mesh (3.3 km) domain, output every 15 min, as well as producing a 72-h forecast for the two coarser domains, output hourly. (g) E-RTFDDA In addition to the deterministic RTFDDA component, NCAR implemented the mesoscale Ensemble Real-Time Four-Dimensional Data Assimilation (E-RTFDDA) system based on the probabilistic forecasting technology. The system includes multiscale continuous-cycling probabilistic data assimilation and forecasting. The Xcel Energy E-RTFDDA system has 30 members, with 15 members based on the Pennsylvania State University (PSU)-NCAR Mesoscale Model 5 (MM5) and the other 15 members based on WRF. The ensemble model runs on two nested-grid domains with 30- and 10-km grids. The 30-km grid is the same as the domain (D1 in Figure 16) of the deterministic WRF RTFDDA model, but the 10-km grid is only run on the D3 domain. Furthermore, the ensemble system runs four forecast cycles a day, producing 6-h analyses and 48-h forecasts in each cycle. (h) DICast The centerpiece of the forecast system is DICast, which tunes, integrates, and optimizes the contributions of the individual component forecasts. DICast is a robust consensus forecast system. Its role is to integrate a variety of data and produce a single, optimized forecast for each user defend site. DICast generates an optimized consensus hub height wind speed forecast for each wind turbine. Downstream processes are responsible for turning these wind speed forecasts into turbine-specific power forecasts and aggregating these into farm and connection node power forecasts. DICast generates new wind speed forecasts every hour by taking advantage of the latest available forecast (e.g., model, MOS) and observational data. A forward error correction scheme is then applied every 15 min for the first 3 h of the forecast period. (i) DICast Input Data 1. The Xcel Energy version of DICast currently uses seven input models. The publicly available models include NCEPgs GFS, NAM, and RUC as well as the Canadian GEM model. In addition, it ingests the high-resolution (3.3-km grid) deterministic WRF RTFDDA simulation and the means from each of the two 15-member WRF and MM5 model ensembles (10-km grid). For each input model, only the most recent model run is used. 2. Observational Input Data: Observational data are critical to DICast since as an automated learning system, it depends on an historical observational dataset. DICcast predicting wind speed at hub height whilst regarding the measurement its focused-on nacelle wind speed measurements. The raw nacelle observations are available at high frequency, exceeding one per minute. System utilize 15-min averages of the measured wind speed, which are more statistically representative of the wind and power produced during that period.
84 NCAR’s Xcel Energy Project Overview David Johnson Research Applications Laboratory, NCAR Wind Energy Prediction, Research & Development Workshop National Center for Atmospheric Research May 11, 2010
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(j) Dynamic Model Output Statistics (DMOS) DMOS is the first forecast optimization step in the DICast forecast process. The optimization performed of the forecast from each model independently based on available observation verification data. DMOS finds relationships between each model’s data and the observations valid at a particular time of day. (k) Forecast Integration: Once the DMOS process has generated optimized forecasts from individual forecast models, DICast combines these forecasts to produce a consensus forecast. At each forecast location and lead time, comparison of the observations and the individual models, DMOS forecasts are used to determine which models have performed better than others in the recent past and gives more weight to the better performers. (l) Power Conversion At this stage DICast forecasted wind speed at hub height converted to the power utilizing the utilize historical wind and power empirical relationship. In the actual implementation, the current nacelle wind speed, the current turbine power, and the next forecast nacelle wind speed are substituted into the data mining model in order to forecast the next turbine power value. Consecutive turbine power forecasts are then generated by utilizing consecutive wind forecasts and employing recursion. In case of missing observed nacelle winds and turbine power data, forecast wind speeds and ideal power curve estimated power are used in the above recursion. Finally, at farms where no wind/power observations are readily available, forecast wind speed is simply fed into the appropriate idealized power curve in order to determine generated power. Once the turbine power is forecast for each turbine, the powers are summed for each connection node and operating region in order to estimate the overall connection node and regional power. (m) Bias Correction and Ensemble Calibration Two statistical post-processing approaches are employed to reduce errors in physical model forecasts in the wind power modeling system. The first technique is the ANKF that corrects the bias errors of the WRF RTFDDA forecasts; and the second is a QR calibration scheme. Both ANKF and QR require a significant recent history of the model forecast data and wind plant measurements. (n) Incorporating Forecast Availability Data Wind turbines may be taken offline for routine or special maintenance and as a result the expected power production at affected farms may be substantially reduced. To account for this, the forecast system incorporates a percentage turbine availability forecast for each farm. The available power forecasts are produced by the contributing farms in accordance with their maintenance schedules. This information is automatically incorporated in the production of an availability forecast that can be viewed separately from the full potential power forecast. (o) NOWCASTING NCAR’s Variational Doppler Radar Assimilation System (VDRAS) was designed to produce high- resolution and high-frequency atmospheric analyses using high-resolution observations from Doppler radars, Light Detection and Ranging (LiDARs), and surface networks. The system provides wind, thermos-dynamical, and microphysical analyses with a typical spatial resolution of 1–4 km and temporal update frequency of 15–20 min. The major processes of VDRAS include data ingest, data preprocessing, data assimilation, and output generation. The central process of VDRAS is the four-dimensional variational analysis (4DVAR) radar data assimilation, which includes a cloud-scale numerical model, the adjoint of the numerical model, a cost function, a minimization algorithm, background analysis, and the specification of background and observations error statistics. VDRAS is used for short-range forecasting to distinguish between large-scale features such as cold fronts, thunderstorm gust fronts, low-level jets, and other weather phenomena that have strong wind gradients. VDRAS is based on a numerical cloud scale model that produces high-resolution boundary-layer wind fields. More detailed information can be found at NCAR webpage:
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES 47
https://ral.ucar.edu/solutions/products/wind-energy-prediction-system-0 https://ral.ucar.edu/solutions/products/real-time-four-dimensional-data-assimilation-rt-fdda https://ral.ucar.edu/solutions/products/analog-kalman-filter-ankf https://ral.ucar.edu/solutions/products/analog-ensemble-anen
UL AWST UL AWST 85 is the renewable energy forecasting provider for ISOs and balancing authorities in North America. As the renewable forecast service provider for 60% of the ISOs in North America, UL AWST has created power forecasts for over 65% of the installed solar capacity and 47% of the installed wind capacity in the United Stated of America and Canada. Table 17: Forecasting for Energy Integration
- Individual Plant & Fleet Power Production Forecasting Minutes-, - Hours-, Days-Ahead & Seasonal Power & Resource Forecasting - Load Forecasting, Dynamic Line Rating - Forecasting for Energy Traders - Services to Support Real-Time Dispatch Operations - Distributed Energy Resources (DER) Forecasting - Wind & Solar Power Forecast Ability Studies - Services to Support Emergency Management & Response UL AWST forecasts are customized for each individual wind and solar resource and are not simply control area aggregate predictions. They advance their forecasting techniques and implement cutting- edge strategies in their operational forecasting services through their client-driven solutions, 24/7 on- call support, and the determination to remain at the forefront of scientific research. Table 18: Managing Renewable Energy Variability
- Capacity Expansion Modeling & Infrastructure Planning - Transmission & Distribution Planning & Investment - Grid Reliability Sensitivity Studies - High Penetration Scenarios & Modeling for DER - Optimization of Future Wind & Solar Plant Build-out on Grids - Estimating Potential Wind & Solar Plant Capacity - High Frequency, Sub-Hourly Power & Resource Profiles (Seconds to Hour) - Load Coincidence Studies - Demand Response Planning - Smart & Micro-Grid Storage Optimization Studies With the combination of advanced models with production data, UL AWST provides site-specific energy-generation profiles. The synthesized data characterizes critical seasonal and diurnal generation patterns, power ramp behavior, and resource variability with generation on time scales of seconds to years. The advanced techniques allow UL AWST to create power and resource profiles for historical or future time periods to support a wide range of client needs. Table 19: Atmospheric Modeling & Applied Research
- Atmospheric Modeling & Applied Research - Modeling Plant Availability - Occurrence & Energy Impact of Icing Events - Synthetic Power & Resource Profiles - Historical Plant or Geographic Region Forecasts - Storage Planning, Capacity & Discharge - Impact of Climate Change on Renewables - Advanced Forecasting Research - Uses of Stochastic Data in Grid Operations If a client needs to estimate the potential generation from a planned solar or wind plant, UL AWST can accurately simulate the plant’s generation and variability on multiple time scales, spanning monthly generation to second-by-second generation. This expertise also extends to tasks like planning optimal power scheduling approaches, creating power forecasts to calculate a renewable plant’s forecastability, determining expected coincident generation from other centralized or distributed resources, and even predicting the impact of new efficiency technologies (e.g. storage, smart appliances, electric vehicles, etc.) on power system load profiles.
85 UL now delivers an even more extensive portfolio of renewable energy services, through the acquisition of AWS Truepower (2016)
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Moreover UL AWST developed tools allows to estimate resource at the project proposed location and predict the real-time impact of renewable resources on smart grid applications and the coincident changing weather conditions on load profiles or grid operations. The experience combined with massive quantities of atmospheric data and a high-performance computing environment, allows UL AWST to conduct long-term climate change assessments and impact mitigation studies for their clients. Table 20: Wind Data & Reports Pricing86
Product Price GIS Data & Wind Speed Maps $3,000 + $0.045/km2* WRF Time Series $700 and up Typical Year $950 Global Reanalysis $250 Wind Resource Grid (WRG) $2,300 Basic Wind Site Assessment Report $500 Advance Wind Site Assessment Report $1,200 *Prices subject to change GIS – Geographic Information Systems; WRG – Wind Resource Grid. UL AWST suite of software and data products supports the entire wind farm development process, from initial site prospecting to final design and energy estimation. The tools are available for clients to work on their own, as well as a platform to collaborate powerfully and efficiently with UL AWST experts. Table 21: UL AWST software key futures
Windnavigator Windographer Openwind Create the optimal wind turbine layout before Best resolution in industry: 200 m Quickly import virtually any data file construction begins, saving time & money site prospecting online or in client and see the results in multiple Apply constraints to produce buildable design preferred spatial analysis platform graphs advanced multi-threading to handle large Rapid screening of sites with Combine multiple data files into one complex sites resource & energy comparisons data set for easy analysis Advanced multi-threading to handle large complex sites Enter any hub height between 10m Spot and fill gaps in data quickly 5 wake modules to choose from and 140m Track all modifications to the data Perform many common GIS functions within Generate PDF maps and reports set for your records the software High quality data leads to optimal Apply (MCP) to your data with a Leading-edge Deep Array Wake Model site selection suitable reference data set Time-series energy modeling for more The world’s most validated Graphically compare accurate simulation of wakes, curtailment, and resource modeling system any number of data other losses, and to account for time-of-day Long-term time series data for sets even if they have pricing Measure-Correlate-Predict (MCP) different time steps Road and cabling design and costing and energy modeling UL AWST offers free use of software for student research and academic programs teaching wind energy and resource assessment. Windnavigator Rapid, effective site selection with the best resolution and most accurate maps in the industry. Interactive global, 200-meter-resolution wind maps, speed and direction frequency data, downloadable reports, and a wide range of resource data (WRGs) including reanalysis data and custom WRF-based time series. Windographer The market-leading software for importing, flagging, and analyzing wind resource data collected at wind project sites. It allows rapid quality control and statistical analyses including MCP and for preparing the data for use in wind flow and plant design software. Openwind The industry’s most advanced software for creating and optimizing turbine layouts, designing and costing balance-of-plant (roads and electrical), performing energy estimates, and conducting ancillary analyses to produce a plan banks will approve.
86 UL AWST https://www.awstruepower.com/software/pricing/
USAID ENERGY PROGRAM AVAILABLE VARIABLE RENEWABLE ENERGY FORECASTING TOOLS AND METHODOLOGIES 49
Software Training Hours The Wind Developer Suite includes 10 hours of custom training time per year. The Client decides what software to be trained on and how many hours per session.
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Table 22: Software Pricing87
Additional User Permanent Additional User Software Subscription (Subscription) License (Permanent) Wind Developer Suite $8,000 $2,000 Windnavigator Global Subscription $6,000 $1,500 Windnavigator Global Subscription Renewal $6,000 $1,500 Windographer Standard $420 $420 $1,050 $1,050 Windographer Professional $780 $780 $1,950 $1,950 Windographer Enterprise $1,260 $1,260 $3,150 $3,150 Openwind $6,000 $1,500 $11,000 $3,000 Openwind Renewal (Maintenance cost) $6,000 $2,750 $750 *Additional options are available for Windnavigator. ** Volume discounts for 3 or more are available. Contact a Product Specialist for more information. More details about the tools and services offered by the UL AWST can be found on Webpage: https://www.awstruepower.com/software/
VAISALA WIND FORECASTING SYSTEM VAISALA acquired 3TIER in December 2013. In 2015 Schneider88 selected Vaisala as their Global Wind Forecasting Partner. 3TIER were providing project feasibility, asset management and forecasting services to companies operating in the renewable energy market globally. While the majority of 3TIER's business were from the wind energy market, company also served customers in the solar and hydro energy markets. Currently 3TIER complements Vaisala's strong environmental sensing business.89 Digital Services division has experience in: Wind and Solar Power Forecasting, Hydro Streamflow forecasting, Solar and Wind Resource Assessment. VAISALA regional wind forecast system is a combination of publicly available global and regional weather forecasts, high resolution surface data, and highly customized mesoscale NWP models. The result is an aggregate wind speed forecast converted into real world values of hourly power generation in megawatts using asset-specific manufacturer power curve data. Where aggregate power data is publicly available, Vaisala will incorporate observations to deliver statistically corrected power forecasts. In case of regional wind power forecasting, Vaisala only requires from client to select region of interest and provide sub-region geographic boundary specifications or wind project lists. Graph 5: VAISALA Wind Forecast System GUI
In case of site specific forecasting VAISALA employs high-resolution forecast system which is a combination of advanced statistical algorithms, highly customized mesoscale NWP models, self- learning artificial intelligence models, and publicly available weather forecasts, including data from the US National Weather Service as well as other global weather forecast centers. Site specific
87 UL AWST https://www.awstruepower.com/software/pricing/ 88 Vaisala Webpage Accessed 4/26/2018 https://my.vaisala.net/en/press/news/2015/Pages/Page_1967562.aspx 89 Web page accessed 4/26/2016
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model also incorporates the climatology and terrain for client project location using diurnal variability averages on a monthly time-scale and can incorporate real-time data clients project and met tower as well as thousands of surface weather stations Figure 17: VAISALA forecasting System Schematics90 The forecasting system comprise the tools listed below: Wind Power Forecast Tools - Hour-ahead forecast for power; - Day-ahead forecast for power; - Week-ahead forecast for power; - Downloadable forecast data files; - System status tool. Weather Forecast Tools - Meteograms for air temperature, precipitation, and hub height wind speed; - Downloadable forecast data files. Verification Tools - Hourly and Daily Time Series Verification; - Hourly Horizon Time Verification; - Wind Error Histogram; - Wind Cumulative Advantage; - Wind Power Curve Verification; - Wind Recent Performance. Optional Features - An Outage Scheduler allows Vaisala to incorporate planned curtailments into the forecast; - MOS-correction can be applied to improve forecast accuracy and reduce bias when at least one year of historical met tower or power production data is available; - A Seasonal Wind Power Forecast can be added to your wind forecast, which projects 12 months into the future and is based on 40 years of site-specific climatology and climate index forecasts; - An Multi-Forecast Tool allows you to view an aggregate forecast of all your wind projects and perform scenario analysis by clicking specific projects and sub-regions 'on or off;' - An Application Programming Interface (API) is also available for faster integration of downloadable forecast data into your internal analysis tools and programming software. To provide wind forecasting, Vaisala must be supplied with meta data such as: time zone, project location, hub height, turbine model, naming conventions, and other identifiers for met towers and turbines. For data integration, the skill of the hour-ahead forecasts greatly depends on the timely receipt of high-quality, real-time observations from the client. While hour-ahead forecasts can be provided without observations, the quality of this forecast is greatly improved with real-time data including: project power output, turbine availability, and near hub height wind conditions from met towers. To facilitate the transfer of real-time data, Vaisala will work with client to set up an automated and robust way of transferring this information.
VAISALA SOLAR POWER FORECASTING SYSTEM VAISALA maintains and provides regional solar forecast system which is a combination of publicly available global and regional weather forecasts, high resolution surface data, and highly customized mesoscale NWP models.
90 VAISALA- Practices in Wind & Solar Power Forecasting A Forecast Provider’s Perspective G M Vishwanath, Head of Renewable Energy Operations 22 January 2018
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Under his portfolio, VAISALA has Solar Energy Performance Reconciliation service, Solar Resource Assessment service and Regional Solar Energy Forecast service. The product of Regional Solar Energy Forecast Service is an aggregate solar forecast converted into real world values of hourly power generation in megawatts. Where aggregate power data is publicly available, Vaisala will incorporate observations to deliver statistically corrected power forecasts. Solar forecasting system leveraging powerful atmospheric models, comprise machine learning, and 20 years of high-quality historical information from global dataset. Forecasting system is capable to tailor each forecast to its unique local environment. Key Features: - Solar Forecast Tools –MOS -corrected day-ahead GHI or power forecast with downloadable forecast data files; - Rewind Tool allows client to compare the current forecast with previous predictions; - API available for faster integration of downloadable forecast data into your internal analysis tools and programming software; - Customizable dashboard interface; - Guaranteed 24/7 availability; - Frequency - The forecast is updated every 6 hours, provides hourly predictions, and projects 60 hours into the future; - Security - Easily set your own permissions system for access to forecast information with unique usernames and passwords. In addition, Vaisala provides a secure password protected web host server for all data transfers. In the forecast model initialization process, forecast system statistically integrate historical observations provided by the client or hourly, high-resolution (3km), satellite derived solar data, which are well calibrated to ground measurements. This statistical process is called model output statistics (or MOS) and significantly reduces forecast error and bias. In addition, Vaisala validates the solar forecast using their global solar dataset, which is based on over a decade of actual, high- resolution visible satellite imagery observations. To provide solar forecasting, Vaisala must be supplied with all the meta data such as: time zone, project location, and panel information. For power forecasting the client must provide historical irradiance, power production data, or a power conversion formula. Historical data is not required for GHI forecasting.
ENFOR - WINDFOR™ Since 2006 ENFOR provides forecasting and optimization solutions for the energy sector. Utilities, energy traders, transmission and distribution system operators use solutions for forecasting of renewable energy production, electricity and heat demand as well as optimization of district heating systems. ENFOR was established in 2006 as a spin-off from the Technical University of Denmark. The company has a solid operational track record and has successfully served customers all over the world for many years. WindFor™ is capable to deliver predictions of wind power production for the operational horizon (ranging from a few minutes ahead in time, up to a couple of weeks). WindFor™ is very flexible and has a long track record of producing accurate forecasts in almost any condition. Forecasting system is available as a software package installed locally on the client’s servers or as a service hosted on servers operated and maintained by ENFOR. Weather forecasts (incl. ensemble forecasts) are typically supplied as an integrated part of the solution. The system can use one or more weather forecast providers as input and automatically detect the optimal prioritization of the different weather forecasts for each wind farm and for different forecast horizons. Weather data is also made available to the client such that the client can compare power forecasts and weather inputs. Highly flexible and configurable to almost any condition WindFor™ has been deployed all over the world for both off-shore and on-shore wind farms in every type of climate and terrain: from mountainous, icy and complex terrain to hot, dry and flat terrain.
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Figure 18: Flexibility on Configuration
Example configuration 1: Limited data available for Example configuration 2: Mixture of online and Example configuration 3: Mixture of offline and a portfolio of frequently changing assets offline data available and limited geographic online farms with geographical information to information create sub-groups
Background Background Background 1.FREQUENT CHANGES IN FARM POPULATION (FARMS 4.ONLINE DATA IS ONLY AVAILABLE FOR A SOME OF THE 7. OFFLINE PRODUCTION DATA IS AVAILABLE FOR REMOVED OR INTRODUCED ON AN ONGOING BASIS) FARMS IN THE AREA FARMS IN THE PORTFOLIO 2.LIMITED INFORMATION ABOUT THE FARMS AVAILABLE 5.OFF-LINE DATA FOR THE REMAINING FARMS IS 8. ONLINE DATA AVAILABLE FOR SOME FARMS IN THE (NO ONLINE DATA) AVAILABLE WITH A DELAY PORTFOLIO 3.OFF-LINE PRODUCTION DATA AVAILABLE WITH A TIME 6.GEOGRAPHICAL INFORMATION NEEDED FOR GROUPING 9. GEOGRAPHICAL INFORMATION IS AVAILABLE TO DELAY THE FARMS (INTO SUB-GROUPS) IS NOT AVAILABLE GROUP FARMS INTO SUB-GROUPS
Key features provided from special modules: - Module for forecasting of uncertainty bands (quantiles) which can be used for trading/bidding strategies and risk assessment; - Module for forecast scenario generation; - Cut-out module. Estimates the probability/risk of cut-out at high wind speeds; - Ramping module. Estimates the probability of a ramp occurrence of a certain size for a given time interval; - Module for ice detection and forecasting of ice decay; - Combination module. Combines multiple internal forecasts (based on different weather forecasts) and/or external forecasts. Calculates optimal weighing of individual forecasts, and produce high accuracy combined forecast; - Downscaling module available for adapting weather forecasts and power predictions to local conditions in complex and mountainous regions; - Upscaling module for using online measurement from some wind farms improve forecast for other wind farms without on-line measurements; - Ensemble weather forecast module. Use ensemble forecast as input for improving forecast accuracy on both short term and long-term horizons; - Curtailment module for estimating “lost production” during curtailment; - High resolution forecasting module. Forecasting of time resolution of 5 minutes or less; - Large number of wind farms and stand-alone wind turbines grouped into three areas; - Frequent changes in the number of wind turbines and wind farms (turbines/farms are removed or introduced in the portfolio); - Power curve model for each area combined with weather forecast for the three areas; - Off-line production data available and updated daily; - Forecasts are provided for each area as well as a total. More details can be found on ENFORs Webpage: https://enfor.dk/services/windfor/
ENFOR-SOLARFOR™ SolarFor™ is a self-learning and self-calibrating software system based on a combination of physical models and advanced machine learning. This combines the best of artificial intelligence with solar power domain knowledge in order to produce the most accurate solar power forecasts available. SolarFor™ is available as a software package installed locally on the client’s servers, or as a service hosted on servers operated and maintained by ENFOR™. The self-learning and self- calibrating algorithms will continuously learn about the solar farm characteristics and will adapt to
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changing conditions, seasonal variations, and as the photovoltaic module ages, such that forecasts stay accurate over time without the need for manual configuration. Weather forecasts (incl. ensemble forecasts) are typically supplied as an integrated part of the solution. The system can use one or more weather forecast providers as input and automatically detects the optimal prioritization of the different weather forecasts for each solar farm and for different forecast horizons. Weather data are also made available to the client such that the client can compare power forecasts and weather inputs. SolarFor™ is initialized using historical weather and production data to train the models or relevant data describing the power curve from the design of the solar farm. After initialization, forecasts are produced every time the system receives new data, which can be either updated weather forecasts or new production data. SolarFor™ can run in either online mode and continuously receive real-time production data or in off-line mode where historical data are retrieved monthly, or any other time interval. By integrating SolarFor™ directly with the SCADA system and thereby providing real-time production data, very accurate short-term forecasts can be achieved. Figure 19: Flexibility on configuration
Example configuration 2: Mixture of online and Example configuration 3: Mixture of offline and Example configuration 1: Limited data available for offline data available and limited geographic online farms with geographical information to a portfolio of frequently changing assets information create sub-groups
Background Background Background - Frequent changes in farm population (farms removed - online data is only available for a some of the farms - offline production data is available for farms in the or introduced on an ongoing basis) in the area portfolio - Limited information about the farms available (no - off-line data for the remaining farms is available with - online data available for some farms in the portfolio online data) a delay - geographical information is available to group farms - Off-line production data available with a time delay - geographical information needed for grouping the into sub-groups farms (into sub-groups) is not available
Key features provided from special modules - Module for forecasting of uncertainty bands (quantiles) which can be used for trading/bidding strategies and risk assessment; - Module for forecast scenario generation; - Support for multiple weather forecast providers/ NWP; - Combination module. Combines multiple internal forecasts (based on different weather forecasts) and/or external forecasts. Calculates optimal weighing of individual forecasts, and produce high accuracy combined forecast; - Upscaling module for using online measurement from some solar farms improve forecast for other solar farms without on-line measurements; - Ensemble weather forecast module. Use ensemble forecast as input for improving forecast accuracy on both short term and long-term horizons; - High resolution forecasting module. Forecasting of time resolution of 5 minutes or less; - Automatic shadow detection; - NWP error correction models based on satellite data and online measurements. More details can be found on ENFORs Webpage: https://enfor.dk/services/solarfor/
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DNVGL FORECASTER Previously known as GH Forecaster, DNV GL's short-term energy forecasting service is capable to handle accurate, up-to-date predictions of energy generation through in-depth, site-specific weather forecasting and unparalleled understanding of renewable energy systems. “Forecaster” uses base weather data from the world's top weather models, downscaling and localizing them to produce the most accurate forecasts for client’s particular power plant. Through detailed analysis and modeling, DNVGL (Global Quality Assurance and Risk Management Company) is capable to build a model for each and every plant generation client wants to forecast, accounting for machine specifics and layouts. DNV GL short-term forecasting is a flexible service comprised of Forecaster Now, Forecaster Live, Forecaster Plus and Forecaster Solutions. It’s possible to supply predictions of conditions hour-by- hour up to 15 days ahead, updated as frequently as every 5 minutes in a format of client’s choice: - Raw data in formats such as Extensible Markup Language (XML) and American Standard Code for Information Interchange (ASCII) or customized for client systems; - Interactive graphs covering live forecasts and historical performance; - Monthly reports summarizing accuracy, performance and the metrics you need; - Data to client can be delivered via email, Secure Shell (SSH) protocol File Transfer Protocol (sFTP)91, web services or accessed via our dedicated short-term forecasting website, which is used every day in our customers' control rooms around the world. Forecaster offers straight forward, regional and plant level, wind and solar power forecasts:
Figure 20: Forecasting Options Figure 21: Forecasting Outputs
Forecaster Live is subscription-based service, providing 24/7 streaming forecasts built on our proprietary modelling engine, and provides hourly-to-15-minute resolution out to 14 days. Accessed via DNV GL website or sFTP feed, Forecaster Live provides on-going access to predictions of weather conditions and renewable power, giving to client the critical guidance. “Forecaster Plus” takes “Forecaster Live” to the next level, merging state-of-the-art forecasting capabilities with high-availability, failover systems, as well as advanced machine learning capabilities. Valuable predictions are enhanced with real-time feedback, providing the highest levels of accuracy, trusted by ISOs and utilities in multiple countries.
91 SFTP (SSH File Transfer Protocol) is a secure file transfer protocol. It runs over the SSH protocol. It supports the full security and authentication functionality of SSH. Web Page: https://www.ssh.com/ssh/sftp/
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“Forecaster Plus” provides customizable forecasts along a flexible time horizon, with flexible update frequencies. In addition, Forecaster Plus service includes ramp warnings, uncertainty bands, and key situational awareness information; all of which can be presented on the Forecaster web portal or on customizable dashboards to meet Client needs. “Forecaster” Solutions brings together the accurate forecasting and weather modeling expertise, as well as analysis capabilities. Using Forecaster platform capable to provide solutions client specific solutions of challenges in forecasting. These one-of-a-kind customized solutions can be designed to a specific project or an on-going program need. In addition, their scientists, engineers, and developers can build, deploy and support unique software and applications for client own in-house use. More detailed information can be found on DNV GL Webpage: https://www.dnvgl.com/services/forecaster-introduction- 3848?utm_campaign=solar&utm_source=google&utm_medium=cpc&utm_content=220969667691&utm_term=solar+pv+forecasting&gclid=Cj0K CQjw_ODWBRCTARIsAE2_EvUdYKr-U6OdAcF4VltaLqsKIQbnTk92wXXN5eaXNQ2ry7LZBmlqPxwaAtvxEALw_wcB
METEOLOGICA Table 23: List of forecast Services Solar Forecast Weather Forecasts Wind Forecast Variables: wind, precipitation (quantity 168h forecasts of solar power output and type: rain/snow), lightning, 360h forecasts of power output temperature Prediction's uncertainty expressed by 240h forecasts Hourly updates 10 and 90 percentiles Clear and intuitive web interface with: Hourly resolution Customizable delivery format graphical display of forecast and recent Probabilistic information User-friendly web interface with: observations up-to-date record of the prediction's Graphical display of forecast and recent performance (observation vs forecast Clear and intuitive web interface with: observations graphics) Up-to-date record of the forecast's Graphical display of forecasts and 168h forecasts of solar power output performance (observation vs forecast recent observations graphics) Up-to-date record of prediction's Prediction's uncertainty expressed by performance (observation vs forecast Interactive availability display 10 and 90 percentiles graphics) User defined alerts on probability Clear and intuitive web interface 360h forecasts of power output thresholds or severe events.
More detailed information about the services provided by the Meteorologica can be found on webpage: 1 http://www.meteologica.com/meteologica/content/renewable-energies
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EVALUATION AND COMPARISON OF FORECASTING MODELS Mean absolute deviation (MAD) and root mean square error (RMSE) are the most commonly used statistics to VRE power, solar irradiation and wind speed forecast performance. In evaluations of wind speed and solar irradiation radiation models, these are used together, interchangeably or added to each other for evaluation of total score and expressed either in absolute values (e.g. W/m2 or m/s) or as fraction of some metrics of observed data. The Mean Absolute Deviation (MAD)
The error measure to identify the contribution of both positive and negative errors to a forecasting method’s lack of accuracy is the Mean Square Error (MSE), which consists of the average of the squared errors over the test set.
RMSE statistic is the most commonly reported in forecast accuracy claims. MAE has a specific meaning and it is related to RMSE because it puts less emphasis on the extreme discrepancies between forecasted and observed values. MBE also has different meaning and value to forecast performance evaluation. It relates more to general over, or under-prediction over the analysis time span, rather than to predictive power of forecast. The Root Mean Squared Error (RMSE)
A common metric used to evaluate load forecast performance is the Mean Absolute Percentage Error (MAPE). This metric can be interpreted as the average percentage error in absolute terms that can be expected from a load forecast model. In general, load forecast MAPEs become bigger the longer the forecast horizon. Formally, the MAPE is computed as:92
Therefore, if quality of the evaluation set is under doubt93, the MAE should be preferred as a main evaluation criterion since it presents greater robustness when confronted with large prediction errors. By taking this approach, one can avoid concluding that a certain prediction method would have poor accuracy when the observed high RMSE values would be the result of the poor quality of the measured data. The MAE and RMSE, divided by the installed capacity or the average production of the wind farm, are called NMAE (Normalized Mean Absolute Error) and NRMSE (Normalized Root Mean Square Error).
92 Energy Research and Development Division Interim Project Report Improving Short-Term Load Forecasts by Incorporating Solar PV Generation http://www.energy.ca.gov/2017publications/CEC-500-2017-031/CEC-500-2017-031.pdf 93 Argonne National Laboratory Wind Power Forecasting: State-of-the-Art 2009 Decision and Information Sciences Division
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Table 24: Example on Components of MAE and RMSE Calculation Period Actual Forecast Error Absolute Value of Error Square of Error A F A -F | A -F | ( A -F )^2 t t t t t t t t t 1 27.580 27.580 0.000 0.000 0.000 2 25.950 26.765 -0.815 0.815 0.664 3 26.080 26.015 0.065 0.065 0.004 4 26.360 26.220 0.140 0.140 0.020 5 27.990 27.175 0.815 0.815 0.664 6 29.610 28.800 0.810 0.810 0.656 7 28.850 29.230 -0.380 0.380 0.144 8 29.430 29.140 0.290 0.290 0.084 9 29.670 29.550 0.120 0.120 0.014 10 30.190 29.930 0.260 0.260 0.068 11 31.790 30.990 0.800 0.800 0.640
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BENCHMARKING FORECASTING TOOLS AND SELECTION OF VENDORS As it can be seen from the paragraph related to the surveying of VRE forecasting service providers there are plenty of companies operating today in the field of provision of VRE forecast services. And with the increase of penetration level of VRE to the grid the number of companies engaged in provision of VRE forecasting services due to the increase in demand would be getting more and more. The significant issue that these firms face today is related to finding new business opportunities. With the consideration of methods and models described above it’s a fact that the provision of such a services or in-house development of a forecasting system can’t be perceived as a “plug and play” activity. Moreover, the complexity surrounding the purchase of these services, tools or development of forecasting systems making it a complex subject that is hard to fully grasp. As examples provided in this paragraph indicates “Benchmarking”, “Pilot Project”, “Trial” those are approaches applied before starting the development of VRE forecasting of system or selecting the vendor of forecasting tool or services. Those approaches have one common preliminary determined condition: The existence of the plan, before performing benchmarking, trial or pilot is a must. Below is a good example94 of the required capacity and effort to perform planning for trial/benchmark project. Figure 22: Plan Development Stages
Trial planning & Setup Evaluation Data Representativeness of Sample • Accurate locations of forecast sites • Exactly how the raw evaluation • SIZE: should be large enough to • Content and format of data data will be quality-controlled so produce statistically meaningful • Mechanism and frequency of that the providers can perform the results providing data identical Quality Control (QC) • Adjacent forecast cases are often • Precise definition of forecast target • Provide the exact QC’d dataset highly correlated variables that will be used to evaluate the • Differences in forecast forecasts • Mechanism and frequency of performance may be variable and forecast delivery noisy • Expected outcomes (selection • 3 months may be adequate under criteria etc.) ideal circumstances • important modes of variability for the forecast parameter • Trial timing (winter, summer etc.) & duration should be chosen carefully
Trial planning & Setup Evaluation Data Representativeness of Sample • Accurate locations of forecast sites • Exactly how the raw evaluation • SIZE: should be large enough to • Content and format of data data will be quality-controlled so produce statistically meaningful • Mechanism and frequency of that the providers can perform the results providing data identical QC, OR • Adjacent forecast cases are often • Precise definition of forecast target • Provide the exact QC’d dataset highly correlated variables that will be used to evaluate the • Differences in forecast forecasts • Mechanism and frequency of performance may be variable and forecast delivery noisy • Expected outcomes (selection • 3 months may be adequate under criteria etc.) ideal circumstances • important modes of variability for the forecast parameter • Trial timing (winter, summer etc.) & duration should be chosen carefully
Below are provided several examples95 of wind forecasts pilot projects which were performed with the main aim to determine the main challenging issues for performance of the different WPF models at different locations. Moreover, the main objectives as it’s indicated in the cases listed below were the
94 wind and solar forecasting trials experience: do’s and don’ts Part 2: introduction to the IEA wind task 36 guideline for evaluation of forecasting approaches and selection John W Zack, Ph.D. Vice President – Grid Solutions [email protected] 95 ARGON National Laboratory Wind Power Forecasting: State-of-the-Art 2009
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comparison of different global NWP (ECMWF, GFS), identification of most effective WPF approaches together with evaluation of forecasting models with the consideration of local distinctive weather patterns and terrain specification. Case 1: ANEMOS Project (launched in 2002). During the exercise, there was an evaluation of 11 forecasting models (e.g., AWPPS, LocalPred, Prediktor, Previento, Sipreólico, WPPT) from nine different institutes in six test-case wind farms with different types of climatology and terrain. Case 2: Asociación Empresarial Eólica (AEE) - Spanish Wind Energy Association). Seven wind farms located in Spain were chosen for this exercise as they represent different types of terrain. Eight companies (Meteológica, Meteotemp, CENER, Casandra, Garrad Hassan, Meteosim/AWS Truewind, Aleasoft, Aeolis) provided forecasts for these wind farms for a period of 13 months. Throughout this forecasting exercise, it was possible to compare different global NWP (ECMWF, GFS) models. Case 3: Alberta Electric System Operator (AESO) has promoted a forecasting pilot project in 12 wind farms and five regions during a period of one year. The purpose of this project was to figure out which were the most effective WPF approaches for Alberta’s distinctive weather patterns and complex terrain. The project tested three different WPF providers with different models: AWS Truewind (eWind), EMSYS (Previento), and WEPROG (MSEPS). In case of Alberta Energy System Operator, the pilot project performed in 2007-2008 was predecessor96 of announcing the Request for Proposals (RFP) on provision of WPF in 2009. Using the knowledge and experience gained from the pilot project, the AESO worked with stakeholders to develop a recommendation for a wind power forecasting service which provides the AESO with wind power forecasts updated at least once per hour and for the next 48 hours for the province of Alberta. The AESO's intention to have the wind power forecast service were successfully implemented and now it is utilizing “Centralized Forecast Service”. Near real-time and day-ahead forecasting for wind aggregated generating facilities in Alberta, performed using a centralized approach of collecting the real time data and predicting output for all plants. The AESO has contracted with a forecast vendor to produce all the forecasts. Currently, Wind Power Generation makes up nine per cent of Alberta's generation capacity and AESO97 is working with the government, wind generation developers, and stakeholders to bring even more wind-generated power to the grid.
96 CISION Article on Request for Proposals AESO Acessed 4/30/2018 https://www.newswire.ca/news-releases/aeso-issues-rfp-for-wind-power- forecasting-service-in-alberta-537891202.html 97 AESO Wind Power Forecasting Acessed 4/30/2018 https://www.aeso.ca/grid/forecasting/wind-power-forecasting/
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RECOMMENDATIONS The aim of this report is to provide policy guidance and recommendations to policy makers in Georgia when considering VRE integration and electricity market arrangements. Wind and solar forecasting is a dynamic research and development area, with new models and findings emerging rapidly. Likewise, the advent of smart grids with predictive control of buildings and electricity loads will place its own requirements on wind and solar forecasting and help spur new developments. The aim of this chapter is to provide recommendations for how to improve the VRE forecasting and how to use it in power system operation. Table 25 summarizes the content of paragraphs describing the WPF and SPF methods: Table 25: Summarizing the Wind and Solar Forecasting methods SPF WPF Statistical Statistical - For intra‐hour, and up to 2 or 3 hours ahead forecasts, - Only power generation and meteorological data for statistical methods without exogenous input (i.e. only the persistence models: applicable only for very-short–term power plant output or irradiance measurement) can be applications, with a time horizon less than 6 hr. used to achieve the certain accuracy. Physical Models Physical Models - NWP regional models refreshed with power generation and meteorological data (Physical and Statistical): applicable for - For a temporal range of 30 minutes up to 6 hours short-term forecasting problems with typical horizons of satellite images-based cloud motion vector forecasts between 3 and 24 h show good performance.98 - NWP regional models without refreshment of power - TSI cloud imagery forecast - for low and fast clouds, the generation and meteorological data - Hybrid Models: forecast horizon may only be 3 minutes while for high applicable for short-term forecasting problems with typical and slow clouds it may be over 30 minutes, but generally horizons of between 12 and 72 h. horizons between 5 to 20 minutes are typical. - NWP global models (G NWP) global models: applicable for - NWPs such as WRF or ECMWF can outperform in the medium-term forecasting problems with typical horizons of range of 5 up to 72 hours between 72 and 168 h. These global models are less accurate; however, they are the only ones capable of producing forecasts for these horizons A forecasting system as an input requires historical and real time power data and if it’s supplemented by the static and meteorological data, it’s used as an input to reduce the uncertainty. In short, power generation and meteorological data requirement exist. There is no utility scale solar project in Georgia yet and currently only one project, QWF is operating in Georgia. As described in the Background paragraph there is uncertainty in regard the timing on the start of the implementation of other wind and solar power projects which installed capacity is up to 1500 MW. Respectively launching the pilot project on forecasting with the similar structure and purpose AESO launched before the announcement of RFP on procurement of forecasting seems unreasonable. Referring to the structure of forecasting system developed by the NCAR alternative might be to start with the forecasting meteorological parameter plus power for commenced projects like QWF. The rationale behind this alternative relies on fact that for the forecasting time horizon beyond 5-6 hours WPF and SPF mostly rely on NWPs forecasting of wind parameters and solar irradiance and then with the consideration of static data containing turbine or solar PV parameters and its conversion to the power. Each wind developer which has signed MoU obliged for resource assessment. In case of wind its performed through the measurements with the anemometers installed on meteorological mast which height is up to 100m. For instance, Infinite Energy for assessment of wind resource with the consideration of farm layout and local terrain conditions for the precise assessment of wind resource currently operates 4 meteorological mast. Georgian Energy Development Fund (GEDF) in cooperation with Calik Energy operating two meteorological masts near Gori. The existence of measurement equipment and devices under the disposal of developers provide opportunity to supply the selected WPF with real time and historical data. Which means that both
98 Solar Power Forecasting: A Review International Journal of Computer Applications (0975 – 8887) Volume 145 – No.6, July 2016
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statistical and physical methods can be applied for the forecast of wind speed and wind direction and forecasting time horizon would be starting from 15 min up to 72 hours. In case of solar, the resource assessment also exists in requirements of signed MoUs. But it does not specify the method for resource assessment. Here are two options for resource assessment that could be derived from satellite data or ground-based measurements which are more desirable to provide an increased level of confidence99. Table 26: List of hardware required for the proper measurement of the GHI Data Logger Steel cabinet with solar power supply and communication system Sun Tracker Pyranometer for GHI GHI and DNI Pyrheliometer installed on tracker for DNI Anemometer for wind speed measurement Temperature sensor Precipitation sensor 100 The existence the of pyranometers under the disposal of PV project developer, could be the opportunity to supply the selected SPF with real time and historical data. Which means that both statistical and physical methods can be applied for the forecast of irradiance and forecasting time horizon would be starting from 15 min up to 72 hours. With the consideration of the above, it is recommended to start with the forecasting of applicable meteorological parameters for the points where currently developers are performing the measurement of wind speed and direction or solar irradiance could be perceived as a good start in terms of to gain the relevant experience. To check how correct the forecast of wind and solar measurement, it could be compared to the actual measurements. The correctness of the actual measurements could be checked with the devices which employ sound and light technologies remote measurements of wind parameters, and in case of solar, it could be performed with the non-stationary pyranometers. Moreover, the utilization of power conversion tools and the respective estimation of power production annual yield from proposed VRE project might in the future utilized for the VRE grid impact and planning study. Also, it might be very useful in the process of incentive mechanisms development as well. Hence, with the consideration of above, it is recommended: 1. Start with launching Test Mode on Wind parameter forecasting at QWF site; 2. Access the current capability and prospective of NEA on forecasting meteorological parameter and if necessary expand test mode to other sites where the measurement available; 3. At the initial stage focus on Power, Wind Speed & Direction and Solar Irradiance GHI forecast for 9 zones mentioned in the Transmission TYNDP, specifically at VRE projects scale where the data on measurement and/or power generation are available; 4. If it’s not included in the tool or services of vendors selected for Item 2, look for available tool for wind and solar to power production conversion; 5. Together with items 1,2,3,4 from the list, look for the availability for capacity building (on job training and study tours); 6. Together with items 1,2,3,4 items from the list, look for the availability to check the correctness of the wind and solar forecasts provided for each zone or VRE production point. In case of wind power, the 6th line items considers the availability of the non-stationary equipment capable to measure wind speed at different height up to 120 m without the installation of meteorological tower and fixing anemometers on the tower. This could be performed with the devices called Sonic Detection and Ranging (SoDAR) and LiDAR. The systems measure the wind conditions from the ground up to a height of 200 meters. Thus, the wind speed and wind direction up to rotor blade tip-height or for the whole rotor blade area can be used to calculate the wind energy yield forecast. There are two types of non-stationary remote sensing systems on the market: SoDAR (Sonic Detection and Ranging) and LiDAR (Light Detection
99 Utility-Scale Solar Photovoltaic Power Plants In partnership with A Project Developer’s Guide https://www.ifc.org/wps/wcm/connect/f05d3e00498e0841bb6fbbe54d141794/IFC+Solar+Report_Web+_08+05.pdf?MOD=AJPERES 100 Ammonite Professional Solar Measurement
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and Ranging). SoDAR instruments measure the wind conditions by means of sound; LiDAR instruments use light to measure the wind characteristics.
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APPENDIX
GENERATOR AND METEOROLOGICAL PARAMETERS
Wind generator parameters Description Wind generator generation (MW) The power output from the wind generator as measured at the connection point. MW Setpoint applied in the wind generator's control system to limit (down regulate) Wind generator control system its output to at or below the level required by Australian Energy Market Operator setpoint (MW Setpoint) (AEMO) or the Network Service Provider. No. of Turbines On The number of turbines (within the entire wind generator) actively generating. The number of turbines (within the entire wind generator) that are available to generate, including turbines actively generating, turbines that are cut-off due to high No. of Turbines Available ambient wind speed / temperature conditions as well as turbines that are paused due to down regulation. It excludes turbines that are under maintenance/repair and turbines that are being manufactured/installed. Meteorological parameters Description Wind speed Wind speed as measured from the meteorological mast/turbine nacelle. Wind Direction Wind direction as measured from the meteorological mast/turbine nacelle. Ambient Temperature Ambient Temperature as measured from the meteorological mast/turbine nacelle.
STATIC DATA REQUIREMENT FOR ENERGY CONVERSION MODEL – AUSTRALIAN WIND ENERGY FORECASTING SYSTEM OF AEMO
Static Data Wind generator Parameters Description Proposed Dispatchable Unit Identifier DUID of the wind generator to be used in AEMO’s Market Systems. The DUID in the (DUID) Energy conversion model should be the same as the DUID in the Registration Application. Cluster ID IDs for each cluster of wind turbines, to be used in AEMO’s Market Systems. Expected Date of Expected Date when the wind generator is to commence generation or commissioning energisation/connection to grid tests. Expected Date when the wind generator would have completed commissioning tests and Expected Date of commercial operation begin commercial operation. Registered Capacity Installed capacity of the wind generator. Wind generator geographical co- Geographical co-ordinates of a representative location of the wind generator ordinates Representative value for the wind generator altitude (average of the ground altitude of Wind generator altitude turbine locations) Wind generator geometry Wind generator map with wind turbine locations marked on it. Met Mast measuring height The height of the meteorological mast that measures the real-time meteorological data. Met Mast geographical coordinates The geographical co-ordinates of the meteorological mast. Orography Map of wind generator area in numerical format (dimensions of land surface) Roughness Map of terrain roughness in numerical format. Individual Cluster Details Description No. of wind turbines in the To determine the capacity of each cluster, the number of turbines in each Type of Turbines Manufacturer type and model of turbines in the cluster. Hub Height of the turbines Height from the turbine platform to the rotor of an installed wind turbine Rotor Diameter Diameter of the rotor blades Manufacturer supplied power curve that shows correlation between power output from the Turbine Power vs. Speed curve turbine vs. wind speed. Nominal power of turbines Nominal capacity of the wind turbine Wind speed above which the turbines would cut-out to avoid damage to blades, and Cut-out and restart after Cutout wind restart after Cut-out wind speed refers to speed below which the turbine can generate speeds again, after the cut-out. Temperatures above which the turbines would stop operating to avoid damage, and Cut-out and restart after cut-out restart after cut-out temperatures refer to the temperature below which the turbine gets temperatures back in operation after a cut-out due to temperature. High Details about any Network Service Provider imposed control schemes which could Control Schemes in operation limit the output from the wind generator to avoid overloads etc. Historical Measurements Description Historical measurements since This is only required if an existing wind generator had to be modelled into Australian Wind operation of wind generator Energy Forecasting System (AWEFS). Historical wind measurements from the All new wind generators would need to provide historical wind speed and wind direction site measurements for a period of atleast one year, with at least hourly resolution.
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SURVEYED FORECASTING SERVICE AND TOOL PROVIDERS
Confidential Part Removed
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USAID Energy Program Deloitte Consulting Overseas Projects LLP Address: 29 I. Chavchavadze Ave.,0179, Tbilisi, Georgia Phone: +(995) 595 062505 E-mail: [email protected]