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

IET Smart Grid

Jiangfeng Zhang¹  Mohammad Hossein Abbasi²  Mehrdad Taki²  Li Li³  Amin Rajabi³ 

[1] Clemson University;University of Qom;University of Technology;

关键词: power generation economics;    genetic algorithms;    pricing;    linear programming;    optimisation;    power markets;    integer programming;    24-hour market horizon;    intractability;    traditional mathematical algorithms;    evolutionary metaheuristic algorithm;    genetic algorithms;    stochastic problem;    mixed-integer linear programming problem;    nonlinear programming problem;    risk-constrained offering strategies;    large-scale price-maker electric vehicle demand aggregator;    electric vehicle aggregator;    ev;    three-settlement pool-based market;    energy procurement;    electricity;    optimised solutions;    price-maker agent;    purchasing energy;    price-energy bids;    day-ahead market;    balancing markets;   

DOI  :  10.1049/iet-stg.2019.0210

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown