Optimal coordination of plug-in hybrid electric vehicles: A stochastic market-based approach

• Published in: Journal of cleaner production 2021-10, Vol.321, p.128990, Article 128990
• Main Authors: Azad-Farsani, Ehsan, Abedini, Saeed, Sardou, Iman Goroohi
• Format: Article
• Language: English
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2021.128990

The existing models implemented to coordinate the plug-in electric vehicles (PEV) based on the price of electricity almost exclusively use the uniform price methods. These methods are based on price-taker mechanisms where prices are exogenous and unaffected by the PEVs activity. This standpoint, however, suffers from two major issues. First, the important indexes of network operation are neglected, e.g., power losses, network reliability, cost/benefit of PEVs operation. Second, coordination with distributed generations (DG) is not possible, efficiently. In this paper, a new viewpoint based on locational marginal price (LMP) is formulated for coordination of PEVs and DGs in electricity day-ahead markets. The LMPs at fleet and DG connected busses are used as a signal to motivate PEV and DG owners to participate in coordination problem. Also, coordination problem is inserted in the optimal operation problem of the network and an efficient algorithm based on Chaotic Crow Search algorithm (CCSA) is used to meet the optimal operation of the network. To make a bidirectional power flow, the vehicle-to-grid (V2G) is used as the PEVs; and, for modeling random behavior of loads, market price, and PEVs, a stochastic modeling framework based on PEM is implemented. The feasibility and satisfying performance of the proposed method has been tested on an 84-bus test system, and simulation results reveal promising results. •A market-based approach for coordination of PEV and DG in distribution networks.•Calculation of Locational marginal price at PEVs and DGs connected buses.•Application of chaotic crow search algorithm in coordination problem.•Modeling the uncertainty of PEVs mobility, load demand, and market price.