An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles. (February 2020)

• Record Type: Journal Article
• Authors: Liu, Jun; Chen, Chong; Liu, Zhenling; Jermsittiparsert, Kittisak; Ghadimi, Noradin
• Is Part Of: Journal of energy storage. Volume 27(2020)
• Publication Date: 2020-02
• Subjects: Social welfare of owners of electric vehicles -- Intelligent parking lot -- Optimal bidding curve -- Power price policy -- IGDT technique -- Energy management and business; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2019.101057
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: Hydrogen storage-based intelligent parking lot (IPL) of electric vehicles is studied. Information gap decision theory (IGDT) is proposed to model price uncertainty. Risk-averse, risk-neutral, and risk-taker strategies are obtained based on IGDT. Optimal bidding strategy of IPL is obtained to submit the power market. Demand response program is used to manage peak load. Abstract: In a near future, electric vehicles (EVs) will constitute considerable part of transportation systems due to their important aspects such as being environment friendly. To manage high number of EVs, developing hydrogen storage-based intelligent parking lots (IPLs) can help power system operators to overcome caused problems by high penetration of EVs. In this work, a new method is applied to get optimal management of IPLs in an uncertain environment and provide optimal bidding curves to take part in power market. The main purpose of this work is to get optimal bidding curves with considering power price uncertainty and optimal operation of IPLs. To model uncertainty of power price in the power market and develop optimal bidding curve, the opportunity, deterministic and robustness functions of the information gap decision theory (IGDT) technique has been developed. Obtained results has been presented in three strategies namely risk-taker, risk-neutral, and risk-averse corresponding to opportunity, deterministic, and robustness functions of the IGDT technique. In order to demonstrate the effects of demand response program (DRP), each strategy is optimized with and without DRP cases. The mixed-integer non-linear programming model is used to formulate the proposed problem which is solved using the GAMS optimization software under DICOPT solver.