Risk‐constrained offering strategies for a large‐scale price‐maker electric vehicle demand aggregator

In this study, the problem of an electric vehicle (EV) aggregator participating in a three‐settlement pool‐based market is presented. In addition to energy procurement, it is assumed that EVs can sell electricity back to the markets. In order to obtain optimised solutions, the aggregator is consider...

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Vydáno v:	IET smart grid Ročník 3; číslo 6; s. 860 - 869
Hlavní autoři:	Hossein Abbasi, Mohammad, Taki, Mehrdad, Rajabi, Amin, Li, Li, Zhang, Jiangfeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Durham The Institution of Engineering and Technology 01.12.2020 John Wiley & Sons, Inc Wiley
Témata:	24-hour market horizon Algorithms balancing markets Bids Constraints Convergence day-ahead market electric vehicle aggregator Electric vehicles Electricity Energy prices energy procurement Evolutionary algorithms evolutionary metaheuristic algorithm Genetic algorithms Heuristic methods Integer programming intractability large-scale price-maker electric vehicle demand aggregator Linear programming Market prices mixed-integer linear programming problem nonlinear programming problem optimisation optimised solutions power generation economics power markets price-energy bids price-maker agent pricing purchasing energy Reagents Renewable resources risk-constrained offering strategies stochastic problem three-settlement pool-based market traditional mathematical algorithms Variables
ISSN:	2515-2947, 2515-2947
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Abstract	In this study, the problem of an electric vehicle (EV) aggregator participating in a three‐settlement pool‐based market is presented. In addition to energy procurement, it is assumed that EVs can sell electricity back to the markets. In order to obtain optimised solutions, the aggregator is considered as a price‐maker agent who tries to minimise the cost of purchasing energy from the markets by offering price‐energy bids in the day‐ahead market and only energy bids in both adjustment and balancing markets. Since the problem is heavily constrained by equality constraints, the number of binary variables for a 24‐hour market horizon is too large which leads to intractability when solved by traditional mathematical algorithms like the interior point. Therefore, an evolutionary metaheuristic algorithm based on genetic algorithms (GAs) is proposed to deal with the intractability. In this regard, first, the stochastic problem is formulated as a mixed‐integer linear programming problem, and as a non‐linear programming problem to be solved by CPLEX and GA, respectively. The former is used to ensure that the GA is tuned properly, and helps to avoid converging to local extremums. Furthermore, the solutions of the two formulations are compared in simulations to demonstrate GA could be faster in obtaining better results.
AbstractList	In this study, the problem of an electric vehicle (EV) aggregator participating in a three‐settlement pool‐based market is presented. In addition to energy procurement, it is assumed that EVs can sell electricity back to the markets. In order to obtain optimised solutions, the aggregator is considered as a price‐maker agent who tries to minimise the cost of purchasing energy from the markets by offering price‐energy bids in the day‐ahead market and only energy bids in both adjustment and balancing markets. Since the problem is heavily constrained by equality constraints, the number of binary variables for a 24‐hour market horizon is too large which leads to intractability when solved by traditional mathematical algorithms like the interior point. Therefore, an evolutionary metaheuristic algorithm based on genetic algorithms (GAs) is proposed to deal with the intractability. In this regard, first, the stochastic problem is formulated as a mixed‐integer linear programming problem, and as a non‐linear programming problem to be solved by CPLEX and GA, respectively. The former is used to ensure that the GA is tuned properly, and helps to avoid converging to local extremums. Furthermore, the solutions of the two formulations are compared in simulations to demonstrate GA could be faster in obtaining better results.
Author	Taki, Mehrdad Li, Li Hossein Abbasi, Mohammad Zhang, Jiangfeng Rajabi, Amin
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Cites_doi	10.1109/TPWRS.2013.2274673 10.1016/j.energy.2019.04.048 10.1109/TPWRS.2011.2172005 10.1145/321127.321128 10.1109/TPWRS.2015.2405084 10.1109/JPROC.2010.2066250 10.1109/TSTE.2015.2498521 10.1109/TSG.2012.2186642 10.1109/TSG.2015.2494371 10.1109/TPWRS.2012.2221750 10.1109/TPWRS.2015.2418335 10.1109/TSG.2013.2259270 10.1109/TPWRS.2013.2258690 10.1109/TII.2019.2932107 10.1109/TSG.2016.2598851 10.1109/ICEMS.2017.8056318 10.1023/A:1022602019183 10.1109/TPWRS.2011.2119335 10.1016/j.apenergy.2017.12.036 10.1109/TPWRS.2014.2363159 10.1016/j.engappai.2018.03.022 10.1109/TPWRS.2013.2252633 10.1109/TPWRS.2014.2330375 10.1109/TSG.2015.2472597
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Snippet	In this study, the problem of an electric vehicle (EV) aggregator participating in a three‐settlement pool‐based market is presented. In addition to energy... In this study, the problem of an electric vehicle (EV) aggregator participating in a three-settlement pool-based market is presented. In addition to energy...
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SubjectTerms	24-hour market horizon Algorithms balancing markets Bids Constraints Convergence day-ahead market electric vehicle aggregator Electric vehicles Electricity Energy prices energy procurement Evolutionary algorithms evolutionary metaheuristic algorithm Genetic algorithms Heuristic methods Integer programming intractability large-scale price-maker electric vehicle demand aggregator Linear programming Market prices mixed-integer linear programming problem nonlinear programming problem optimisation optimised solutions power generation economics power markets price-energy bids price-maker agent pricing purchasing energy Reagents Renewable resources risk-constrained offering strategies stochastic problem three-settlement pool-based market traditional mathematical algorithms Variables
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Title	Risk‐constrained offering strategies for a large‐scale price‐maker electric vehicle demand aggregator
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