LAUSR

The optimal scheduling of integrated energy system (IES) can improve energy efficiency and economic operation. However, the existing scheduling methods cannot accurately handle the dynamic changes of supply and demand sides in the electricity-gas-heat IES thanks to their power uncertainties. To tackle this problem, an optimal dispatch framework based on the asynchronous advantage actor-critic (A3C) method of IES is proposed. Firstly, we describe the dispatch problem of IES with multiple uncertainties as Markov decision process (MDP) according to the corresponding mathematical models and constraints. Then, the dispatch framework based on A3C is developed to optimize the control decision for supply and demand sides by asynchronous learning of agents and to reduce the relevance of parameters update of neural networks by the multi-agent utilization of the central processing unit (CPU) multi-threading function. Finally, our proposed methods are verified by the simulations. Compared with the previous optimization algorithms, the training time of the proposed methods is shortened by 37% and 30%, and the daily average operating cost is reduced by 3%, 5.2% and 8.7%.