With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%.
Demand response technologies can achieve the objective of optimizing resource allocation and ensuring efficient operation of the smart grid by motivating the energy users to change their power usage behavior. However, the increasing uncertainty of smart grid environment brings great challenges to the development of demand response technique. In this study, we build a long short-term memory (LSTM) network as a load forecasting model to predict user load data in order to obtain accurate consumption behavior of energy users. Then, we utilize a Stackelberg game model based on the load forecasting model to dynamically optimize the electricity prices set by power suppliers at different times, enhancing the efficiency of demand response between users and suppliers. Extensive simulation experiments demonstrate that the LSTM-based load forecasting model achieves an accuracy of up to 96.37% in predicting user load demand. And the game model reduces the overall expenditure of users by 30% compared with the general pricing model.
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