Design and Implementation of Device-level Load Dynamic Adjustment System for Electric Vehicles

Abstract

The development of electric vehicles is an important means to deal with the energy crisis, relieve environmental pressure, and achieve sustainable development. The rapid growth of electric vehicle ownership brings great challenges to the planning and safe and stable operation of the power system. To ensure the stable operation of the power grid and meet the changing needs of users to the greatest extent in the case of extreme power shortage and demand response, it is necessary to dynamically adjust the power of charging piles according to the actual situation. At present, many operators cannot accurately and dynamically control the power of each charging pile in the station in the face of power limit demand, so they can only turn off some or all charging piles, which not only affects the charging experience of users but also leads to the decline of operators’ reputation and profits. To solve the above problems, this paper proposed a charging load dynamic adjustment system, which uses the TCN model based on similar days to predict the charging load. It proposed a load decomposition algorithm to control the device-level charging load. Experiments show that the load prediction model used in this system has higher accuracy than LSTM, and the load decomposition algorithm can reasonably allocate limited load resources, which means the system can meet the user’s charging demand to the greatest extent while meeting the power limit demand and improve the user experience.