Research on Demand Response of Electric Vehicle Agents Based on Multi-Layer Machine Learning Algorithm

作者：Lin, Jianping; Dong, Ping^*; Liu, Mingbo; Huang, Xuewei; Deng, Wenli

来源：IEEE Access, 2020, 8: 224224-224234.

DOI：10.1109/ACCESS.2020.3042235

• Logic gates
• Machine learning algorithms
• Load management
• Predictive models
• Data models
• Training
• Power grids
• Multi-layer machine learning
• optimal bidding
• demand response
• electric vehicle
• service provider

Charging of large-scale electric vehicles (EVs) will have a serious impact on the power grid. In the environment of power market, the orderly scheduling of EVs through agents is an effective way to solve this problem. Due to the uncertainty of EV travels, how to participate and profit in demand response has become urgent. In this paper, a set of optimal bidding method for agents participating in the market is proposed. Firstly, because the EV service provider cannot collect the historical travel data of all the owners in the whole region, a multi-layer machine learning algorithm is used to simulate the travel behavior of EV in this paper. Secondly, based on the demand response rules of the current electricity market, the optimal bidding model for EV service providers is proposed. In addition, considering the offset between the predicted and the actual electricity price, the model is designed based on interval optimization. Finally, the behavior model and the bidding method are applied to IEEE30 bus system and a provincial practical system. It is shown that the proposed method can bring greater benefits to EV service providers.