Abstract
With the rapid development of artificial intelligence, adopting advanced deep reinforcement learning (DRL) methodologies to solve the optimisation problem in power systems has become more effective. This study proposes a novel energy control method associated with DRL to solve the economical optimisation problems in an integrated energy system with wind power and power-to-gas technology. To consider the randomness of wind power and the flexibility of upper-level energy prices, the economical optimisation problem is formulated as a finite Markov decision-making process. Cycling decay learning rate deep deterministic policy gradient (CDLR-DDPG) algorithm is proposed to obtain the optimal operation strategy. A comparison among different benchmark methods is provided to demonstrate the superiority of CDLR-DDPG algorithm in optimising an economical problem for the considered system.
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