CIGRÉ CIGRÉ Canada Conference http : // Westin CalgaryAlberta, Canada, October 15-18, 2018
|
|
- Phebe Wilcox
- 5 years ago
- Views:
Transcription
1 CIGRÉ CIGRÉ Canada Conference http : // 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
2 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
3 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 =./ /5 /:89 1 ; <8 =894/6 ;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 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
4 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
5 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
6 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 (Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG), CRDPJ ). 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 ," (June [Online]. Available: 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, [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 , [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 ). [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 ). [6] M. Milligan and K. Porter, "Determining the capacity value of wind: an updated survey of methods and implementation," (National Renewable Energy Laboratory, [Online]. Available: [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 ). 6
7 [13] S. Awara, H. Zareipour and A. Knight, "Solar Power Capacity Value Evaluation- A Review," (CCECE 2018, Quebec City, 2018). [14] M. Milligan, "Determining the Capacity Value of Wind: An Updated Survey of Methods and Implementation," (WindPower 2008, Houston, 2008). [15] "Electricity in Alberta," (Alberta Electric System Operator, [Online]. Available: [Accessed 30 July 2018]). [16] "Current Supply Demand Report," (Alberta Electric System Operator, [Online]. Available: [Accessed 30 July 2018]). [17] Alberta Electric System Operator, "Annual market statistics reports," (26 March [Online]. Available: [Accessed 30 July 2018]). [18] "Reports- Generating Unit Statistical Brochure," (North American Electric Reliability Corporation, 17 August [Online]. Available: [Accessed 30 July 2018]). [19] Alberta Electric System Operator, "Reliability Modeling: Demand, Outage, Intermittent Generation & Import," (15 November [Online]. Available: [Accessed 30 July 2018]). [20] Capital Power, "Resource Adequacy, A Comparison of Reliability Metrics," (2017. [Online]. Available: [Accessed 30 July 2018]). [21] North American Electric reliability Corporation, "Probabilistic Adequacy and Measures," (April [Online]. Available: [Accessed 30 July 2018]). [22] "Rules and Procedures for Determination of Generating Capability," (5 March [Online]. Available: [Accessed 30 July 2018]). [23] Alberta Electric System Operator, "Calculation of Unforced Capacity Ratings (UCAP)," (June [Online]. Available: Rationale-FINAL.pdf. [Accessed 10 August 2018]). 7
Solar Power Capacity Value Evaluation- A Review
Solar Power Capacity Value Evaluation- A Review Sarah Awara saawara@ucalgary.ca Hamidreza Zareipour hzareipo@ucalgary.ca Andy Knight aknigh@ucalgary.ca Abstract With the increase in renewable generation
More informationImpact of capacity value of renewable energy resources on RAPS system energy management
University of Wollongong Research Online Faculty of Engineering and Information Sciences - Papers: Part A Faculty of Engineering and Information Sciences 213 Impact of capacity value of renewable energy
More informationAssessing capacity value of wind power
Assessing capacity value of wind power Hannele Holttinen, Juha Kiviluoma VTT Espoo, Finland Hannele.holttinen@vtt.fi Michael Milligan, Bethany Frew NREL Golden, US (CO) Lennart Söder KTH Stockholm, Sweden
More informationReliability Modelling: Review Process & Methodology
Reliability Modelling: Review Process & Methodology Adequacy and Demand Curve Workgroup Sept 20 th, 2017 Public Reliability Modelling Background Per SAM 2.0: AESO is the responsible party for modelling
More informationResource Adequacy Modeling update. Technical Workgroup #4 June 14, 2018 AESO External
Resource Adequacy Modeling update Technical Workgroup #4 June 14, 2018 Demand Curve Workgroup Objective: AESO Resource Adequacy Model Through the WG process, AESO seeks workgroup members review and feedback
More informationIEEE Transactions on Power Systems, 26 (2):
Provided by the author(s) and University College Dublin Library in accordance with publisher policies. Please cite the published version when available. Title Capacity value of wind power Author(s) Publication
More information2016 Probabilistic Assessment. December 5, 2016 Southwest Power Pool
2016 Probabilistic Assessment December 5, 2016 Southwest Power Pool Table of Contents 1. Summary...2 a. SPP Planning Coordinator Footprint...2 b. Seasonal Capacity Totals... Error! Bookmark not defined.
More informationWind Power Capacity Value Metrics and Variability: A Study in New England
University of Massachusetts - Amherst ScholarWorks@UMass Amherst Doctoral Dissertations May 2014 - current Dissertations and Theses 2015 Wind Power Capacity Value Metrics and Variability: A Study in New
More informationEstimation of the Capacity Value for Wind Power Generation in India Wind Discussion Forum August 22, 2014 New Delhi
Estimation of the Capacity Value for Wind Power Generation in India Wind Discussion Forum August 22, 2014 New Delhi 1 Presentation Outline Introduction to the Power System Reliability and Capacity Value
More informationDETERMINING POWER SYSTEM CAPACITY VALUE AND EMISSIONS OF STEAM-CONSTRAINED COGENERATION. Daniel Ryan
DETERMINING POWER SYSTEM CAPACITY VALUE AND EMISSIONS OF STEAM-CONSTRAINED COGENERATION By Daniel Ryan A project submitted in partial fulfillment of the requirements for the degree of Master of Science
More informationIntegration of Variable Generation: Capacity Value and Evaluation of Flexibility
1 Integration of Variable Generation: Capacity Value and Evaluation of Flexibility Eamonn Lannoye, Student Member, IEEE, Michael Milligan, Member, IEEE, John Adams, Aidan Tuohy, Member, IEEE, Hugo Chandler,
More informationAPRIL 23, Capacity Value of Wind Assumptions and Planning Reserve Margin
APRIL 23, 2014 Capacity Value of Wind Assumptions and Planning Reserve Margin Executive Summary Effective Load Carrying Capacity (ELCC), or capacity value, of variable generation and required planning
More informationA New Reliability Criterion for Calculating Wind System Capacity
1 A New Reliability Criterion for Calculating Wind System Capacity Alex Pavlak, Member, IEEE and Cynthia Bothwell, Member, IEEE Abstract After correcting for wind system capacity (WSC), the load imposed
More informationResource adequacy in grids with deepening penetrations of integrated renewable resources
Resource adequacy in grids with deepening penetrations of integrated renewable resources Mariola Ndrio and George Gross Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign,
More informationCalculation of Demand Curve Parameters
Calculation of Demand Curve Parameters Rationale 4.1 Resource adequacy standard 4.1.1 The resource adequacy standard announced by the Government of Alberta prescribes a minimum level of reliability as
More informationCapacity Value of Concentrating Solar Power Plants
Capacity Value of Concentrating Solar Power Plants Seyed Hossein Madaeni and Ramteen Sioshansi Ohio State University Paul Denholm National Renewable Energy Laboratory NREL is a national laboratory of the
More informationOptimizing the Generation Capacity Expansion. Cost in the German Electricity Market
Optimizing the Generation Capacity Expansion Cost in the German Electricity Market Hamid Aghaie Research Scientist, AIT Austrian Institute of Technology Novemebr 2017 1 Motivation Energy-only Market Generators
More informationA Revised Resource Adequacy Standard for the Pacific Northwest. NERC LOLE Work Group November 7-8, 2011 Austin, TX
A Revised Resource Adequacy Standard for the Pacific Northwest NERC LOLE Work Group November 7-8, 2011 Austin, TX OUTLINE Makeup of the PNW s Power Supply NERC Definition for Adequacy PNW s Approach Sample
More information2011 Probabilistic Assessment. 11JAN12 Interregional Coordination
The 2011 Probabilistic Assessment was a pilot study performed by SPP at NERC s request to voluntarily conduct an assessment to determine reliability indices using probabilistic methods. The majority of
More informationLOLE Fundamentals Loss of Load Expectation (LOLE) Fundamentals
Loss of Load Expectation (LOLE) Fundamentals March 13, 2014 Last material update: 03/12/2014 LOLE Agenda 2 Loss of Load Expectation (LOLE) Fundamentals Sections LOLE Background LOLE Study Connections to
More informationERCOT Public LTRA Probabilistic Reliability Assessment. Final Report
ERCOT Public 2016 LTRA Probabilistic Reliability Assessment Final Report November 21, 2016 Contents Summary... 1 Software Model Description... 3 Demand Modeling... 3 Controllable Capacity Demand Response
More informationRenewable Integration Impact Assessment (RIIA)
Renewable Integration Impact Assessment (RIIA) Overview Current progress and initial findings Next steps Planning Advisory Committee Jordan Bakke April 18, 18 Updated June 18, 18 Renewable Integration
More informationDetermining the Capacity Value of Wind: A Survey of Methods and Implementation
National Renewable Energy Laboratory Innovation for Our Energy Future A national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Determining the Capacity Value
More informationEnergy Storage Integration in Alberta s Energy Only Market. Kevin Dawson Director, Market Design Alberta Electric System Operator
Energy Storage Integration in Alberta s Energy Only Market Kevin Dawson Director, Market Design Alberta Electric System Operator Outline The AESO and Alberta s Wholesale Electricity Market About the AESO
More informationPacific Northwest Power Supply Adequacy Assessment for 2019 Final Report
Pacific Northwest Power Supply Adequacy Assessment for 2019 Final Report May 7, 2014 Council Document Number 2014-04 1 Executive Summary The Pacific Northwest s power supply is expected to be close to
More informationPan-Canadian Wind Integration Study (PCWIS)
GE Energy Consulting Pan-Canadian Wind Integration Study (PCWIS) Section 10: Wind Capacity Valuation Prepared for: Prepared by: Canadian Wind Energy Association (CanWEA) GE Energy Consulting October 14,
More informationReliability Modeling: Demand, Outage, Intermittent Generation, & Import
Reliability Modeling: Demand, Outage, Intermittent Generation, & Import Adequacy and Demand Curve Workgroup November 15, 2017 Draft for Discussion Outline Reliability modeling updates Question to WG Members:
More informationComparison of Installed Capacity (ICAP) & Unforced Capacity (UCAP) Capacity Value Calculation Methods. Eligibility WG Meeting #3 July 4, 2017
Comparison of Installed Capacity (ICAP) & Unforced Capacity (UCAP) Capacity Value Calculation Methods Eligibility WG Meeting #3 July 4, 2017 Overview The following materials provide an examination of the
More informationAssessment of Marginal and Long-term Surplus Power in Orissa A Case Study
1 Chandra 16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, 2010 103 Assessment of Marginal and Long-term in Orissa A Case Study Chandra Shekhar Reddy Atla, A.C. Mallik, Dr. Balaraman K and Dr.
More informationWind Workshop. Technical Characterization: Dependable Capacity & Firm Energy 10:00-10:30am
Wind Workshop Technical Characterization: Dependable Capacity & Firm Energy 10:00-10:30am Objective of this session: Understand BC Hydro s definitions and calculation methodology of dependable capacity
More information2010 Loss of Load Expectation Report PUBLISHED: 10/01/2010 LATEST REVISION: 10/08/2010
PUBLISHED: 10/01/2010 LATEST REVISION: 10/08/2010 Table of Contents Introduction... 2 Objective... 2 Background... 2 Study Assumptions... 3 Data discussion... 3 Topology... 3 Load... 3 Generation... 3
More informationNERC Probabilistic Assessments Overview & Future Improvements
NERC Probabilistic Assessments Overview & Future Improvements Noha Abdel-Karim, PhD. NERC IEEE LOLEWG Meeting July 31, 2015 Overview NERC Probabilistic Assessment (ProbA) reports extend the LTRA data with
More informationCONTRIBUTION OF ENERGY STORAGE AND DEMAND-SIDE RESPONSE TO SECURITY OF DISTRIBUTION NETWORKS
CONTRIBUTION OF ENERGY STORAGE AND DEMAND-SIDE RESPONSE TO SECURITY OF DISTRIBUTION NETWORKS Ioannis KONSTANTELOS i.konstantelos@ic.ac.uk Predrag DJAPIC p.djapic@ic.ac.uk Goran STRBAC g.strbac@ic.ac.uk
More informationAlberta Capacity Market
Alberta Capacity Market Comprehensive Market Design (CMD 1) Design Proposal Document Section 1: Overview of the Alberta Capacity Market Prepared by: Alberta Electric System Operator Date: January 26, 2018
More informationIncorporating Energy from Renewable Resources into Power System Planning
losure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized The bottom line As countries generate more energy from renewable sources, that energy must be taken
More informationEastern Wind Integration and Transmission Study
Eastern Wind Integration and Transmission Study NPCC Governmental/Regulatory Affairs Advisory Group February 23rd, 2010 Dave Corbus National Renewable Energy Lab 1 What is Needed to Integrate 20% Wind
More informationCapacity and Flexibility Needs under Higher Renewables
Capacity and Flexibility Needs under Higher Renewables Project Deliverable October 1, 2015 Arne Olson, Partner Ana Mileva, Senior Consultant Elaine Hart, Managing Consultant Defining today s planning problem
More informationNORTHWESTERN ENERGY 2018 ELECTRICITY RESOURCE PROCUREMENT PLAN STRAWMAN 1
NORTHWESTERN ENERGY 2018 ELECTRICITY RESOURCE PROCUREMENT PLAN CHAPTER 1. EXECUTIVE SUMMARY STRAWMAN 1 1. Load Service Requirements a. Peaking Capacity (Planning Reserve Margin) b. Dispatchable Capacity
More informationCalculation of Demand Curve Parameters
Calculation of Demand Curve Parameters Rationale 4.1 Resource adequacy standard 4.1.1 The resource adequacy standard announced by the Government of Alberta prescribes the minimum level of reliability to
More informationEvolution of the Grid in MISO Region. Jordan Bakke, David Duebner, Durgesh Manjure, Laura Rauch MIPSYCON November 7, 2017
Evolution of the Grid in MISO Region Jordan Bakke, David Duebner, Durgesh Manjure, Laura Rauch MIPSYCON November 7, 2017 1 MISO s mission is to ensure reliable delivery of low-cost energy through efficient,
More informationMethodology to Perform Long Term Assessments
Methodology to Perform Long Term Assessments MARCH 21, 2017 March 21, 2017 Public Page 1 Caution and Disclaimer The contents of these materials are for discussion and information purposes and are provided
More information4 Calculation of Demand Curve Parameters
4 Calculation of Demand Curve Parameters This section addresses the demand curve for the Alberta capacity market, including the calculations for the components of the demand curve. 4.1 Resource adequacy
More informationBackground: ERCOT studies in 80 s and 90 s using NARP (N Area Reliability Program): o Small model in which each node represented a major load center
A Direct High Speed Calculation Procedure For Determining LOLE, LOLH, and EUE For Fossil, Wind, and Solar Generation With A Suggested Procedure For Also Including Transmission Constraints a presentation
More informationRenewable Integration Impact Assessment Finding integration inflection points of increasing renewable energy. NCEP Webinar Sept.
Renewable Integration Impact Assessment Finding integration inflection points of increasing renewable energy NCEP Webinar Sept. 13 th, 18 Cumulative Renewable Additions (GW) Renewable energy is growing;
More informationCapacity Performance Training. June 24, 2015
Capacity Performance Training June 24, 2015 Training Objectives Provide Capacity Market Sellers with information necessary to participate in the Reliability Pricing Model (RPM) under a Capacity Performance
More informationMISO LOLE Modeling of Wind and Demand Response. Item-9b LOLE Best Practices Working Group July 26-27, 2012
MISO LOLE Modeling of Wind and Demand Response Item-9b LOLE Best Practices Working Group July 26-27, 2012 1 Overview Wind Capacity Modeling MISO performs a detailed analysis to determine what the capacity
More informationJune 5, 2018 MEMORANDUM. Council Members. John Fazio, Senior Systems Analyst. SUBJECT: Briefing on Adequacy Analysis and Report BACKGROUND:
James Yost Chair Idaho W. Bill Booth Idaho Guy Norman Washington Tom Karier Washington Jennifer Anders Vice Chair Montana Tim Baker Montana Ted Ferrioli Oregon Richard Devlin Oregon June 5, 208 MEMORANDUM
More informationIntegrating High Levels of Variable Renewable Energy Sources
Integrating High Levels of Variable Renewable Energy Sources Erik Ela EPRI Grid Ops and Planning eela@epri.com NYISO Environmental Advisory Council Troy, NY May 6, 2016 EPRI Grid Operations & Planning
More information2017 Long-term Outlook. Information Session July 25, 2017
2017 Long-term Outlook Information Session July 25, 2017 0 Welcome Overview of 2017 Long-term Outlook (2017 LTO) Questions will be answered at the end of the presentation Please use microphone when asking
More informationLONG-TERM SOLUTIONS FOR NEW YORK S CLEAN ENERGY FUTURE
Q U É B E C S H Y D R O P O W E R R E S O U R C E S P O W E R I N G T H E E M P I R E S TAT E LONG-TERM SOLUTIONS FOR NEW YORK S CLEAN ENERGY FUTURE Hydro-Québec, New York s energy partner for decades,
More informationAlberta Capacity Market
Alberta Capacity Market Comprehensive Market Design (CMD 1) Design Rationale Document Section 3: Calculation of Capacity Market Demand Parameters Prepared by: Alberta Electric System Operator Date: January
More informationRenewable Northwest. December 14, Via Electronic Mail. Public Utility Commission of Oregon Attn: Filing Center
Renewable Northwest Members Degrees American Wind Energy Association Atkins Bonneville Environmental Foundation Center for Energy Efficiency & Renewable Technologies Citizens' Utility Board of Oregon Climate
More informationAlberta Capacity Market
Alberta Capacity Market Comprehensive Market Design (CMD 1) Design Proposal Document Section 3: Calculation of Capacity Market Demand Parameters Prepared by: Alberta Electric System Operator Date: January
More informationBulk Power System Integration of Variable Generation - Program 173
Program Description Program Overview Environmentally driven regulations such as state-mandated renewable energy standards and federal air and water standards, along with improved economic viability for
More informationNERC Project. Increase Analytical Capabilities in the Probabilistic Domain
NERC Project Increase Analytical Capabilities in the Probabilistic Domain Noha Abdel-Karim, PhD. Senior Engineer for Reliability Assessment IEEE-LOLE Working Group Meeting July 21-22, 2016 About NERC International
More informationNPCC 2017 Ontario Interim Review of Resource Adequacy
NPCC 2017 Ontario Interim Review FOR THE PERIOD FROM 2018 TO 2020 APPROVED BY THE RCC ON DECEMBER 5, 2017 This page intentionally left blank. ii Document Change History Issue Reason for Issue Date 1.0
More informationProvided by the author(s) and University College Dublin Library in accordance with publisher policies. Please cite the published version when available. Title Impact of pumped storage on power systems
More informationDRAFT Version 1.2 of Installed Capacity Manual Attachment D For Discussion at Installed Capacity WG Meeting on January 27, 2005
Attachment D: Dependable Maximum Net Capability, Audit Forms, the Procedure to Adjust a Resource s Proven Maximum Production Capability and the Procedure to Weather Adjust DMNC Test Data 1.0 Introduction
More informationCONTENTS. Forward Executive Summary The Council s Resource Adequacy Standard Recent Adequacy Assessments... 8
CONTENTS Forward... 4 Executive Summary... 5 The Council s Resource Adequacy Standard... 7 Recent Adequacy Assessments... 8 2023 Resource Adequacy Assessment...10 Sensitivity Analysis...10 Monthly Analysis...12
More informationGovernance and Technical Demand Curve Parameters. May 4, 2018
Governance and Technical Demand Curve Parameters May 4, 2018 Outline Demand in energy and capacity markets Capacity Market Demand Curve What is it? Why is it required? Principles to the Alberta Demand
More informationCapacity Performance FAQ Response
PJM Interconnection September 15, 2014 This page is intentionally left blank. PJM 2014 www.pjm.com 2 P age Contents FAQ Response Introduction... 4 Capacity Products... 4 Methodology for Establishing Maximum
More informationNPCC 2016 Ontario Interim Review Of Resource Adequacy
Approved by the RCC December 6, 2016 NPCC 2016 Ontario Interim Review Of Resource Adequacy FOR THE PERIOD FROM 2017 TO 2020 DECEMBER 2016 [Type here] This page intentionally left blank. ii Document Change
More informationCMD Final Industry Stakeholder Comment Matrix
CMD Final Industry Stakeholder Comment Matrix The AESO invites stakeholders to provide comments on the final Comprehensive Market Design (CMD Final). All feedback (whether it be general or specific in
More informationBEFORE THE PUBLIC UTILITIES COMMISSION OF THE STATE OF CALIFORNIA
BEFORE THE PUBLIC UTILITIES COMMISSION OF THE STATE OF CALIFORNIA Order Instituting Rulemaking to Oversee ) the Resource Adequacy Program, Consider ) Program Refinements, and Establish Annual ) Rulemaking
More informationReliability Issues White Paper. Accommodating High Levels of Variable Generation
Reliability Issues White Paper Accommodating High Levels of Variable Generation April 7, 2008 Table of Contents Table of Contents Introduction...Error! Bookmark not defined. Introduction... 1 NERC s Mission...
More informationValue of PV at highpenetration
Value of PV at highpenetration Applied Project Final Report Bahram Emami August 218 Abstract:... 2 Introduction:... 3 Capacity Value:... 3 I. Capacity Value of PV System:... 3 A. First Approach Capacity
More informationCapacity Performance Training. March 16, 2015
Capacity Performance Training March 16, 2015 Training Objectives Provide Capacity Market Sellers with information necessary to participate in the Reliability Pricing Model (RPM) under a Capacity Performance
More information(This page is intentionally'
FORTISBC INC 2012-13 REVENUE REQUIREMENTS AND REVIEW OF ISP EXHIBIT C9-17 (This page is intentionally' left blank.) to ensure the reliability of the bulk power system The North American Electric Reliability
More information2016 Summer Reliability Assessment
Table of Contents Preface... 3 Overview... 5 FRCC... 6 MISO... 7 MRO-Manitoba Hydro... 8 MRO-SaskPower... 9 NPCC-Martimes... 10 NPCC-New England... 11 NPCC-Ontario... 13 NPCC- Québec... 14 PJM... 15 SERC...
More informationThe Economic Ramifications of Resource Adequacy White Paper
The Economic Ramifications of Resource Adequacy White Paper January 2013 Astrape Consulting For EISPC and NARUC Funded by the U.S. Department of Energy TABLE OF CONTENTS EXECUTIVE SUMMARY...1 I. HISTORY
More informationRELIABILITY AND SECURITY ISSUES OF MODERN ELECTRIC POWER SYSTEMS WITH HIGH PENETRATION OF RENEWABLE ENERGY SOURCES
RELIABILITY AND SECURITY ISSUES OF MODERN ELECTRIC POWER SYSTEMS WITH HIGH PENETRATION OF RENEWABLE ENERGY SOURCES Evangelos Dialynas Professor in the National Technical University of Athens Greece dialynas@power.ece.ntua.gr
More informationNPCC 2017 Québec Balancing Authority Area Comprehensive Review of Resource Adequacy
2017 Québec Balancing Authority Area Comprehensive Review of Resource Adequacy NPCC 2017 Québec Balancing Authority Area Comprehensive Review of Resource Adequacy Prepared by Planification et fiabilité
More informationExpanding Capacity Eligibility
Expanding Capacity Eligibility Zachary T. Smith Manager, Capacity Market Design ICAPWG/MIWG March 7 th, 2019 Agenda Background Installed Capacity Supplier Payment Structure Peak Load Windows Counting MWs
More informationEvaluating the Impact of Wind Power Uncertainty on Power System Adequacy
Evaluating the Impact of Wind Power Uncertainty on Power System Adequacy Esteban Gil Departamento de Ingeniería Eléctrica Universidad Técnica Federico Santa María Valparaíso, Chile esteban.gil@usm.cl Abstract
More informationCONTENTS. Forward Executive Summary The Council s Resource Adequacy Standard Recent Adequacy Assessments... 9
CONTENTS Forward... 4 Executive Summary... 5 The Council s Resource Adequacy Standard... 7 Recent Adequacy Assessments... 9 2021 Resource Adequacy Assessment...10 2022 Resource Adequacy Assessment...11
More informationRenewable Energy 35 (2010) 2761e2766. Contents lists available at ScienceDirect. Renewable Energy. journal homepage:
Renewable Energy (21) 2761e2766 Contents lists available at ScienceDirect Renewable Energy journal homepage: www.elsevier.com/locate/renene Capacity factor prediction and planning in the wind power generation
More information21,363 MW 22,774 MW ONTARIO ENERGY REPORT Q JULY SEPT 2014 ELECTRICITY. Electricity Highlights Third Quarter Ontario s Power Grid
ONTARIO ENERGY REPORT Q3 Y T ELECTRICITY Electricity Highlights Third Quarter Electricity Generation Output by Fuel Type (Q3) Nuclear Ontario Peak Demand (Q3) 21,363 MW 25.0 TWh 65.7% Hydro 8.8 TWh 23.1%
More informationNuclear Energy and Renewables: System Effects in Low carbon Electricity Systems
Nuclear Energy and Renewables: System Effects in Low carbon Electricity Systems Method comments to a NEA report Lennart Söder Professor in Electric Power Systems, KTH, lennart.soder@ee.kth.se 2012 12 20
More information2005 Integrated Electricity Plan. Resource Options Workshop #2 Planning Criteria March 09, 2005
2005 Integrated Electricity Plan Resource Options Workshop #2 Planning Criteria March 09, 2005 Agenda for Presentation Introduction Review industry reliability standards and regulatory Generation Reliability
More informationAPPENDIX I PLANNING RESERVE MARGIN STUDY
APPENDIX I PLANNING RESERVE MARGIN STUDY Introduction The planning reserve margin (), measured as a percentage of coincident system peak load, is a parameter used in resource planning to ensure there are
More informationUCAP Calculation Comparison
UCAP Calculation Comparison Dispatchable resources within PJM, NYISO, & MISO September 2017 Draft for Discussion Overview map British Columbia Alberta Manitoba Newfoundland Labrador Ontario Quebec PEI
More informationF L E X I B I L I T Y I N P O W E R S Y S T E M S
F L E X I B I L I T Y I N P O W E R S Y S T E M S Eamonn Lannoye 1 Electricity Research Centre, University College Dublin ABSTRACT Planning for variability in power systems is becoming more and more important
More informationEastern Interconnection Wind Integration & Transmission Study
Eastern Interconnection Wind Integration & Transmission Study Project Overview Prepared by: Robert Zavadil Enernex Presented by: Charlie Smith UWIG What is Needed to Integrate 20% Wind in the Eastern Interconnect?
More informationOn the Path to SunShot: Emerging Issues and Challenges in Integrating High Levels of Solar into the Electrical Generation and Transmission System
On the Path to SunShot: Emerging Issues and Challenges in Integrating High Levels of Solar into the Electrical Generation and Transmission System Paul Denholm, Kara Clark, and Matt O Connell National Renewable
More informationA New Method to Evaluate the Optimal Penetration Level of Wind Power
A New Method to Evaluate the Optimal Penetration Level of Wind Power Samer Sulaeman, Student Member, IEEE, Fares T. Alharbi, Student Member, IEEE, Mohammed Benidris, Member, IEEE, and Joydeep Mitra, Senior
More informationLOLE is expressed as hours per year with the usual target criteria being (0.1 days/year) or 1- day in 10-years MISO Tariff: Module E-1 - The
1 2 3 4 LOLE is expressed as hours per year with the usual target criteria being (0.1 days/year) or 1- day in 10-years MISO Tariff: Module E-1 - The Transmission Provider will calculate and post the Planning
More informationCapacity Credit of Wind Generation in South Africa. Final Report
Capacity Credit of Wind Generation in South Africa Final Report February 2011 Commissioned by: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH Office Pretoria Focal Area Energy & Climate
More informationSMALL GENERATOR ELIGIBILITY
SMALL GENERATOR ELIGIBILITY WHAT IS A MINIMUM SIZE THRESHOLD (MW) TO PARTICIPATE IN THE CAPACITY MARKET? Prepared for AESO Capacity Market Eligibility Working Group For Discussion Purposes Only November
More informationElectricity Supply. Monthly Energy Grid Output by Fuel Type (MWh)
For the first quarter of 2015, Ontario experienced overall demand that was typical for the province in winter, and strong generator output. Demand for Ontario electricity increased as a result of cold
More informationCapacity Market Technical Design Stakeholder Update Session
Capacity Market Technical Design Stakeholder Update Session Straw Alberta Market (SAM 3.0) Dec. 11, 2017 Session outline Time Agenda Item 1:30 1:35 p.m. Welcome and session overview: Matt Gray, AESO 1:35
More informationProduction Cost Modeling for High Levels of Photovoltaics Penetration
National Renewable Energy Laboratory Innovation for Our Energy Future A national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Production Cost Modeling for
More information2005 Integrated Electricity Plan. Provincial IEP Committee Meeting #2 Planning Criteria February 22/23, 2005
2005 Integrated Electricity Plan Provincial IEP Committee Meeting #2 Planning Criteria February 22/23, 2005 2005 IEP Objectives Means Objectives Ends (Fundamental) Objectives Agenda Items No net imports
More informationReliability and the Future of the Electricity Grid: A North American Bulk Power System Perspective
Reliability and the Future of the Electricity Grid: A North American Bulk Power System Perspective Mark Lauby, Senior Vice President and Chief Reliability Officer North American Electric Reliability Corporation
More informationUsing GE-MARS to estimate resource need for 33% RPS scenarios. January 2012
Using GE-MARS to estimate resource need for 33% RPS scenarios January 2012 Overview of methodology Uses GE-MARS, a loss-of-load probability (LOLP) model, to estimate the capacity needed to satisfy loss
More informationPJM Generation Adequacy Analysis: Technical Methods
PJM Generation Adequacy Analysis: Technical Methods Capacity Adequacy Planning Department PJM Interconnection, L.L.C. October 2003 Introduction Reliability requirements for a bulk power system are typically
More informationManual 21- Revision 13 Rules and Procedures for Determination of Generating Capability Changes
Manual 21- Revision 13 Rules and Procedures for Determination of Generating Capability Changes Jerry Bell Resource Adequacy Department Markets and Reliability Committee March 21 st, 2019 Manual 21 changes
More informationBEFORE THE PUBLIC UTILITIES COMMISSION OF THE STATE OF CALIFORNIA REPLY COMMENTS OF THE CALIFORNIA INDEPENDENT SYSTEM OPERATOR CORPORATION
BEFORE THE PUBLIC UTILITIES COMMISSION OF THE STATE OF CALIFORNIA Order Instituting Rulemaking To Continue Implementation and Administration, and Consider Further Development, of California Renewables
More informationWIND has been shown to be the fastest growing source
792 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 24, NO. 3, SEPTEMBER 2009 A Reliability Model of Large Wind Farms for Power System Adequacy Studies Ahmad Salehi Dobakhshari, Student Member, IEEE, and
More informationAlberta Capacity Market
Alberta Capacity Market Comprehensive Market Design (CMD 1) Design Proposal Document Section 4: Forward Capacity Auction Prepared by: Alberta Electric System Operator Date: January 26, 2018 Table of Contents
More informationNPCC 2015 Québec Balancing Authority Area Interim Review of Resource Adequacy
Québec Balancing Authority Area December 1, of Resource Adequacy NPCC Québec Balancing Authority Area of Resource Adequacy Prepared by Planification et fiabilité Direction Approvisionnement en électricité
More information