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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Shrnutí: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.
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ISSN:2515-2947
2515-2947
DOI:10.1049/iet-stg.2019.0210