Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity-gas coupled systems considering system uncertainty: An improved soft actor-critic approach

Due to uncertainties in renewable energy generation and load demands, traditional energy dispatch schemes for an integrated electricity-gas system (IEGS) considerably depend on explicit forecast mathematical models. In this study, a novel data-driven deep reinforcement learning method is applied to solve the IEGS dynamic dispatch problem with the targets of minimizing carbon emission and operating cost. Moreover, a flexible operation of carbon capture system and power-to-gas facility is proposed to attain low operating costs. The IEGS dynamic dispatch problem is formulated as a Markov game, and a soft actor-critic (SAC) algorithm is applied to learn the optimal dispatch solution. To improve training efficiency and convergence, prioritized experience replay (PER) is employed. In the simulation, the proposed PER-SAC algorithm compared with deep Q-network and SAC has fast and stable learning performance. In contrast to a modified sequential quadratic programming based on uncer-tainty prediction, the proposed method can reduce the target cost by 11.62% when the prediction error exceeds 10%. The computational time of scenario analysis solution on the same hardware platform is 4.58 times than that of training the PER-SAC method. Finally, the simulation results under different scenarios demonstrate that the PER-SAC-based dispatch strategy has satisfactory generalization and adaptability.