CIGRÉ-43 2018 CIGRÉ Canada Conference http : //www.cigre.org Westin CalgaryAlberta, Canada, October 15-18, 2018 Assessing Capacity Value of Wind in Alberta S. AWARA, A. JAHANBANI ARDAKANI, H. ZAREIPOUR, A. KNIGHT University of Calgary Canada SUMMARY As wind-powered generators are increasing, system planners are becoming more interested in calculating the contribution of these generators to the resource adequacy of the power system, known as the capacity value of wind. Developing reliable methods to calculate the capacity value of wind will avoid the unnecessary costs of over-planning to maintain a reliable power system. This paper compares the results from the effective load carrying capability (ELCC) probabilistic-based method and capacity factor (CF) approximation-based method to calculate the capacity value of wind. It also compares the capacity value of wind when using different reliability indices such as the loss of load hours and loss of load expectation. The capacity value of wind based on winter and summer seasons is also assessed. The analysis is performed for the periods from November 2012 to October 2017, using Alberta s data. The input to the generation model used in the ELCC method is the June 2018 generation unit data and the load model used is based on the historical demand and total wind generation. The CF approximation-based method uses the historical total wind generation. No general hypothesis can be drawn that the ELCC method that uses loss of load hours will give a higher or lower capacity value compared to the ELCC method that uses the loss of load expectation. The analysis shows that the capacity value of wind during the winter months is higher than the summer months in Alberta. This is shown with both the ELCC and CF methods investigated in this analysis. The results from the CF method give a higher capacity value than the ELCC method when the same number of hours are used in both methods. The CF method gives values close to the ELCC method when the 250 tightest supply cushion hours per year are used. KEYWORDS Capacity Value, Capacity Credit, Wind, Power System Reliability, Capacity Factor, Effective Load Carrying Capability, Energy-Limited Resources, Capacity Market saawara@ucalgary.ca
1. INTRODUCTION As countries adopt decarbonisation agendas, the level of renewable generation in power systems is increasing. In Alberta, the Climate Leadership Plan aims to generate 30% of the Alberta s electricity from renewable resources and to phase out coal power plants by 2030 [1]. Due to the intermittent nature of renewable resources, calculating the capacity contribution of renewable resources to ensure a reliable operation of power systems is currently a topic of interest for system planners. There are two types of assessments for power system reliability, i.e. adequacy and security [2]. This paper focuses on estimating the capacity value of wind in Alberta, which is an adequacy question. Capacity value, or capacity credit, is the contribution of a generating unit to the generation adequacy of the power system. Some of the factors that affect the capacity value of a generating unit are location, forced outage rate, and technology of the unit. The literature shows that the capacity value of a renewable facility can range between 5% and 95% of the nameplate capacity due to several factors, such as geography, penetration levels of the technology, and the correlation of generation and demand [3]. In the literature, there are multiple methods to calculate the capacity value of wind. There are two major classes for capacity value calculation, i.e. probabilistic-based and approximation-based methods [4]. The IEEE Wind Power Coordinating Committee Task Force paper recommended the use of the probabilistic-based approach for calculating the loss of load probability (LOLP) when calculating the capacity value of wind and specifically using the effective load-carrying capability (ELCC) method [5]. However, system planners in certain jurisdictions use the capacity factor (CF) approximation-based method to estimate the capacity value of wind, such as PJM in the United States [6]. The accuracy of CF approximation-based method is greatly influenced by the number of hours used and the method used to select these hours [7]. Over the past decades, there has been extensive work done to develop accurate methodologies to calculate the capacity value of wind in different regions. Milligan et al. describe the recent research on the capacity value of wind, including methodologies, data requirements, and current challenges [8]. Holttinen et al. provide a review of the capacity value of wind determination in different systems around the world [9]. This paper assesses the effect of the calculation method, season of the year, and the reliability metric used for the capacity value of wind using Alberta s data. Section 2 provides a description of the methodologies used in this paper to calculate the capacity value of wind. The methodologies used in this paper: probabilistic-based ELCC and approximation-based CF. Section 3 provides a brief background on Alberta s power system, the data used in this analysis and the assumptions that are made. Section 4 provides a discussion of the results followed by the conclusion and future work. The main findings of this paper are that the capacity value of wind during the winter is higher than summer in Alberta. Also, when the 250 tightest supply cushion hours in a year are used for the CF approximation-based method, the capacity values of wind from the ELCC and CF approximation-based method are within the same range. Possible future work includes studying the correlation between the peak load hours and the wind generation in Alberta. Also, potential future work would include studying mechanisms to reliably calculate the capacity credit for fair remunerations in the capacity market. 2. METHODOLOGY The capacity value of wind can be expressed using different metrics. Soder and Amelin demonstrate the different probabilistic-based methodologies used for calculating the capacity value of wind: effective load carrying capability (ELCC), equivalent firm capacity and equivalent conventional capacity [10]. System operators usually use approximation-based methods since it does not require extensive data as the probabilistic-based methods [10]. Examples of approximation-based methods are the capacity factor approximation-based method [6], Garver s approximation method [11], and the Z-method [11]. In this paper, the results from the ELCC probabilistic-based method and the results from the CF approximation-based method are compared for each time-period. 2.1 Effective Load Carrying Capability Probabilistic-Based Method The probabilistic-based method used in this paper is the ELCC developed by Garver [12]. In the first step, a capacity outage probability table (COPT) is computed which is a list of the outage capacities and their associated probabilities. The COPT is referred to as the generation model. The generation model is convolved with the load model to give the risk model as shown in Fig. 1. The risk model calculates the probability that the load exceeds the available generation. The risk model yields the loss of load probability (LOLP) for each hour for the power 2
system without any wind power integration. The risk model results depend on the load model used. If the load model uses the daily peak load to represent the load in a single day, then the loss of load index is expressed in days/year. However, if the load model uses the individual hourly load values in a day, then the loss of load index is expressed in hours/year; this index is sometimes expressed as Loss of Load Hours (LOLH). The loss of load expectation (LOLE) index is calculated by taking the summation of the LOLPs. When the hourly load time series is used, the loss of load expectation is referred to as LOLH and, when the daily peak load hours are used, the loss of load expectation is referred to as LOLE. In the next step, the wind generation is treated as a negative load when combined with the load time series resulting in a net load time series. The LOLE index is calculated using the net load obtained from the inclusion of the wind generation. The calculated LOLE should be lower than the LOLE calculated before adding wind to the system. This is shown in the green curve in Fig. 2. This implies that additional load can be added to the system to reach the original LOLE of the system. The additional load is added through an iterative process and the LOLE is recalculated in every step until the original LOLE is reached. This additional load is the ELCC. In the example presented in Fig. 2, the ELCC is 400 MW. Fig. 1. Probabilistic-based Capacity Value Methodology [13] Fig. 2. A graphical representation of ELCC [14] 2.2 Capacity Factor Approximation-Based Method For the approximation-based method, the capacity factor of wind generation is used to calculate the capacity value of wind. In this analysis, the capacity factor was calculated considering all the hours as the ELCC method that uses the LOLH index, the daily peak load hours as the ELCC method that uses the LOLE index and the 250 tightest supply cushion hours of the year. The capacity value of wind was calculated using Eq. (1): Capacity Value =./012 3456 78589104/5 /:89 1 ;890145 04<8 =894/6 >5?012286 @456 ;1=1;40A.4<8 C894/6 (E9? /9 61A?) (1) 3. SYSTEM DESCRIPTION 3.1 Alberta Power System Most of Alberta s electricity generation, as of March 2018, comes from conventional generation. Coal-fired power plants constitute 37.79% of Alberta s generating power while wind constitutes 8.69% of Alberta s electricity generating resource [15]. Alberta s power system has 20 wind farms with a total capacity of 1,445 MW [15], which are mostly located in central and southern Alberta. The generation in Alberta is 16,626 MW and the peak demand is 11,697 MW [15]. Alberta has three interties to British Colombia, Saskatchewan, and Montana. Information on Alberta s generation system can be found in the Current Supply Demand Report [16]. 3.2 Data Requirements The data used in the analysis is based on Alberta s power system. The period that is considered in this analysis is from November 2012 to October 2017. This paper analyses the capacity value of wind for seasonal and annual periods in Alberta. Since Alberta s capacity market design considers the obligation year from November 1 st to October 31 st, the annual capacity value of wind was calculated for the periods of November 1 st to October 31 st of the following year [17]. The seasonal capacity value of wind was calculated based on the winter season and the 3
summer season months. The winter season, as defined here, starts on November 1 st and ends on April 30 th of the following year and the summer season, as defined here, starts on May 1 st and ends on October 31 st of the same year [17]. The ELCC method requires extensive data, as opposed to the CF approximation method. For the ELCC method, the number of generation units, their capacities, and their forced outage rates are required. The Current Supply Demand Report is used to obtain the capacities of each plant [16]. The number of units in each plant is obtained from the 2017 Planning Base Case Model as provided by the Alberta Electric System Operator (AESO). The generation unit data as of June 2018 is used as the input for the generation model for all the periods under study for purposes of comparison. The forced outage rates of coal- and hydro- generating units are assigned based on NERC s 2016 Generating Unit Statistical Brochure [18]. For simple cycle, combined cycle, co-generation, biomass, and other technologies, the forced outage rates are obtained from the capacity market working group presentations [19]. Hourly and daily peak load data with their corresponding wind generation data are available for the period under study from the Nrgstream website. For the ELCC methodology, chronological wind generation data is synchronized with the load data mainly since wind-powered generation plants and electric load are weather drivers; therefore, it is critical to maintain chronology between them. Hasche et al. show that the capacity value of a power plant differs if different initial LOLEs are used [4]. Therefore, the load data is scaled through an iterative process before calculating the capacity value to start at the same reference LOLE. For the annual analysis that used the LOLH index, the reference LOLE used is 2.4 hours/ year, in compliance with the Southwest Power Pool (SPP) practice [20] [21]. For the annual analysis that used the LOLE index, the reference North American LOLE reference is used, which is 0.1 days/ year [21]. 3.3 Assumptions The main assumptions used in calculation of the LOLE are that there are no transmission constraints and that there are no generation deficiencies at any specific load point [2]. Also, since the forced outage rate of each generating unit is not available, a class average for each technology is used for the forced outage rate as described in Section 3.2. Additionally, the maintenance schedule of generation units is not taken into consideration when calculating the LOLE. The wind generation used in this analysis is based on the total wind generation in Alberta. The effect of location on the capacity value of wind is not taken into consideration. 4. RESULTS AND DISCUSSION 4.1 Comparison of ELCC Results using Different Reliability Indices The ELCC method that uses the LOLH index uses the net hourly load time series, which is the actual hourly demand time series minus the corresponding wind generation at each hour (the wind generation is treated as a negative load). The net load time series from the actual demand time series and the wind time series is used as the load model for this method. The ELCC method that uses the LOLE index uses the net load time series, which is the daily peak load time series minus the corresponding wind generation from the daily peak load hour. No general hypothesis can be drawn that the ELCC method that uses the LOLH method will give a higher or lower capacity value compared to the ELCC method that uses the LOLE method as shown in the blue and orange columns in Fig. 3 and Fig. 4. This is because the LOLE method uses the wind generation that corresponds to the daily peak load hour and the wind level could be at its lowest or its highest during the day or somewhere in between. It is important to study the correlation between the peak load and the wind generation to be able to provide a reasonable explanation for this observation. This topic will be further investigated as an extension to this work. 4.2 Seasonal Capacity Value Evaluation Based on the results provided in Fig. 3, the capacity value of wind during the winter months is always higher than the summer months in Alberta using both the ELCC and CF approximation-based methods. The CF approximation-based method used in this analysis is based on the same hours that are used for the ELCC method. For the seasonal analysis, the 250 tightest supply cushion hours is not used since the seasons under study are 6-month periods. Over the past few years, Alberta has been observing higher wind generation during the winter rather than summer [17]. Also, Alberta is winter-peaking so the system s loss of load probability is higher during the winter season than the summer season. Therefore, the wind generation would have a higher 4
contribution in a system with a high loss of load probability compared to a system with a low loss of load probability. Fig. 3. Seasonal Capacity Value (CV) of Aggregated Wind 4.3 Comparison of Results from the ELCC and CF Approximation-Based Method The capacity factor method gives a higher capacity value when compared to the ELCC method when the same hours are used for both methods. The results are shown in the columns CF using LOLH Hours and CF using LOLE Hours in Fig. 4. As can be observed from Fig. 4, like the ELCC that uses LOLH and the ELCC that uses LOLE, there is no general hypothesis on which set of hours in the capacity factor methodology yields a higher capacity value. Due to using a larger number of hours as opposed to those used by system operators, the capacity factor methodology shows the general historic performance of wind in Alberta rather than showing only the capacity factor during peak load hours. However, the ELCC considers the availability of the conventional generators and how wind generation impacts the load time series at each hour. System planners usually use the peak hours for the peak load months when calculating the capacity value using the CF approximation-based method. For example, PJM uses the capacity factor methodology by taking the wind generation for the prior three summers for hours ending 3:00 pm 6:00 pm [22]. However, when the 250 tightest supply cushion hours are used as shown in the column labelled CF using the 250 tightest supply cushion hours in Fig. 4, the capacity values of wind from ELCC methods (ELCC (LOLE index) and ELCC (LOLH index) in Fig. 4) and the CF based on the tightest supply cushion hours are within the same range. The reason for using the 250 tightest supply cushion hours per year is based on AESO s Comprehensive Market Design (CMD) as published on June 2018 [23]. Fig. 4. Annual Capacity Value (CV) of Aggregated Wind 5
5. CONCLUSION AND FUTURE WORK The capacity value of wind in Alberta using the ELCC and CF approximation-based methods is assessed in this paper. The paper assessed the seasonal and annual capacity value of wind. The conclusion is that the capacity value of wind during the winter months is higher than the summer months regardless of whether the ELCC or CF approximation-based method is used. The CF approximation-based method is assessed using the same hours as the ELCC method that used the LOLH index, the same hours as the ELCC method that used the LOLE index and the 250 tightest supply cushion hours per year. The CF approximation-based method that used the 250 tightest supply cushion hours per year and the ELCC method gave capacity values within the same range. The observations discussed in the results section will be further investigated in future work. This future work will involve assessing the correlation between the peak load and wind generation and the contribution of transmission and its impact on the capacity value of wind. Assessing the number of years of data that are needed to provide a robust estimate of wind capacity value in Alberta is also part of the future work. The impact of location, data resolution, and integration of storage and demand response on the capacity value of wind will be assessed on the capacity value of wind. Potential future work also involves studying mechanisms to reliably calculate the capacity credit for fair remunerations in the capacity market. 6. ACKNOWLEDGEMENTS We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), CRDPJ 477323 14 (Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG), CRDPJ 477323 14). This project was funded in part by Alberta Innovates, which supports and accelerates research, innovation and entrepreneurship. This project is also supported by Rocky Mountain Power. 7. REFERENCES [1] Government of Alberta, "Climate Leadership Plan: Implementation Plan 2018-19," (June 2018. [Online]. Available: https://open.alberta.ca/dataset/da6433da-69b7-4d15-9123- 01f76004f574/resource/b42b1f43-7b9d-483d-aa2a-6f9b4290d81e/download/clp_implementation_planjun07.pdf. [Accessed 10 July 2018]). [2] R. Billinton and R. Allan, Reliability Evaluation of Power Systems, New York: Plenum Press, 1996. [3] D. Gami, R. Sioshansi and P. Denholm, "Data Challenges in Estimating the Capacity Value of Solar Photovoltaics," IEEE Journal of Photovoltaics, vol. 7, no. 4, pp. 1065-1073, 2017. [4] B. Hasche, A. Keane and M. O'Malley, "Capacity Value of Wind Power, Calculation, and Data Requirements: the Irish Power System Case," (IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 26, no. 1, 2011, pp. 420-430). [5] A. Keane, M. Milligan, C. J. Dent, B. Hasche, C. D'Annunzio, K. Dragoon, H. Holttinen, N. Samaan, L. Soder and M. O'Malley, "Capacity Value of Wind Power," (IEEE Transactions on Power Systems, vol. 26, no. 2, 2011, pp. 564-572). [6] M. Milligan and K. Porter, "Determining the capacity value of wind: an updated survey of methods and implementation," (National Renewable Energy Laboratory, 2008. [Online]. Available: https://www.nrel.gov/docs/fy08osti/43433.pdf. [Accessed 23 July 2018]). [7] M. Milligan and B. Parsons, "A Comparison and Case Study of Capacity Credit Algorithms for Intermittent Generators," (Solar '97, Washington, 1997). [8] M. Milligan, B. Frew, E. Ibanez, J. Kiviluoma, H. Holttinen and L. Soder, "Capacity value assessments of wind power," (WIREs Energy and Environment, vol. 6, no. 1, 2017). [9] H. Holttinen, J. Kiviluoma, M. Milligan, B. Frew and L. Soder, "Assessing capacity value of wind power," (Wind Integration Workshop, Vienna, 2017). [10] L. Soder and M. Amelin, "A review of different methodologies used for calculation of wind power capacity credit," (IEEE Power and Energy Society- General Meeting, Pittsburgh, 2008). [11] S. H. Madaeni, R. Sioshansi and P. Denholm, "Comparison of Capacity Value Methods for Photovoltaics in the Western United States," (National Renewable Energy Laboratory, Golden, CO, 2012). [12] L. L. Garver, "Effective Load Carrying Capability of Generating Units," (IEEE Transactions on Power Apparatus and Systems, Vols. PAS-85, no. 8, 1966, pp. 910-919). 6
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