Reinforcement learning method for plug‐in electric vehicle bidding. Issue 4 (1st August 2019)

• Record Type: Journal Article
• Authors: Najafi, Soroush; Shafie‐khah, Miadreza; Siano, Pierluigi; Wei, Wei; Catalão, João P.S.
• Is Part Of: IET smart grid. Volume 2:Issue 4(2019)
• Page Start: 529
• Page End: 536
• Publication Date: 2019-08-01
• Subjects: electric vehicles -- power markets -- learning (artificial intelligence) -- multi‐agent systems; plug‐in electric vehicle -- novel multiagent method -- electric vehicle owners -- electricity market -- vehicle‐to‐grid capability -- charging cost -- EV owners -- aggregator role -- independent decision cores -- buying selling energy -- reinforcement learning algorithm -- q‐learning algorithm -- multiagent methods -- cost minimisation goal; B8110B Power system management, operation and economics -- B8520 Transportation -- C6170 Expert systems and other AI software and techniques -- C6170K Knowledge engineering techniques; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0297
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : This study proposes a novel multi‐agent method for electric vehicle (EV) owners who will take part in the electricity market. Each EV is considered as an agent, and all the EVs have vehicle‐to‐grid capability. These agents aim to minimise the charging cost and to increase the privacy of EV owners due to omitting the aggregator role in the system. Each agent has two independent decision cores for buying and selling energy. These cores are developed based on a reinforcement learning (RL) algorithm, i.e. Q‐learning algorithm, due to its high efficiency and appropriate performance in multi‐agent methods. Based on the proposed method, agents can buy and sell energy with the cost minimisation goal, while they should always have enough energy for the trip, considering the uncertain behaviours of EV owners. Numeric simulations on an illustrative example with one agent and a testing system with 500 agents demonstrate the effectiveness of the proposed method.