Digital twin based multi-objective energy management strategy for energy internet

Energy management problem (EMP) has been a widely researched topic in optimal operation of Energy Internet (EI). However, the rapid growth in energy network scale and penetration of distributed renewable generations (DRGs) bring new challenges to energy management. Therefore, a digital twin (DT) based parallel energy management strategy is proposed for the large-scale EI which consists of We-energy (WE). Firstly, a parallel energy management framework is proposed. By establishing this triple parallel structure, states of energy networks can be observed realtimely, which enables flexible responses to fluctuations of DRGs and energy plug-and-play. Abandoned renewable energy is taken into account in the optimization model, which promotes the utilization of renewable energy. Then, a multi-timescale optimization strategy is proposed to handle different timescales of multi-energy networks. Furthermore, for better obtaining and processing information and avoiding dimensional curse, a DT based deep Q-learning algorithm (DQN) is proposed. Eventually, compared with the traditional benefit consensus based strategy, the simulation verifies the effectiveness of the DT based parallel energy management strategy.