Energy management system scheduling optimization based on an improved generative adversarial network deep reinforcement learning algorithm - Chao, Weipeng; Shi, Yuanbo; Li, Yushuai; Liu, Meng; Leng, Xiaoling (Engineering applications of artificial intelligence, 2025)
Households, as electricity consumers, play a critical role in achieving the carbon peak and carbon neutrality goals. The development of smart grids provides technical support for the efficient integration and distribution of renewable energy, gradually extending to household users. This has led to higher demands for the stability of electricity supply to address the growing electricity demand and the uncertainties associated with renewable energy. To address this, this paper proposes an improved generative adversarial network and an enhanced deep reinforcement learning algorithm to improve the scheduling capability of the energy management system. First, we introduce an improved wasserstein generative adversarial network that combines stochastic differential equations and autocorrelation penalty terms with the generator. The experimental results demonstrate that the proposed method can generate high-quality time series data. The generated data were used to train our scheduling model, effectively enhancing its generalization capability. Secondly, We introduced the Minmax mechanism to address Q-value estimation bias by utilizing multiple Q-networks. This mechanism first divides the target Q-values into several groups, selects the maximum value from each group, and then takes the minimum among these maxima as the final target Q-value. We applied this mechanism to improve deep reinforcement learning algorithms based on multi-Q-value evaluation. Comparison experiments show that this improvement significantly enhances the algorithm’s performance, outperforming traditional algorithms in terms of convergence, volatility, and final rewards. The energy management system demonstrates stronger adaptability when handling uncertainties arising from renewable energy variations, ensuring reliable power supply and achieving balanced energy management. •Proposed an enhanced GAN capturing data nonlinearity and randomness.•Mitigated deep RL bias and optimized energy scheduling.•Proposed a new evaluation method to stabilize learning and improve reliability.•Proposed a scheduling optimization to enhance system stability under renewable energy.•Extensive experiments demonstrated the improvement’s superiority.