Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids

Large-scale introduction of new energy could effectively alleviate energy shortage and environmental pollution. However, the uncertainty of wind and solar energy brings serious random disturbance problems to microgrids. This paper proposes lightweight actor-critic generative adversarial networks based on ensemble empirical mode decomposition and evolutionary strategy for increasing the robustness and adaptability of microgrids. Firstly, to improve the training speed and stability of generative adversarial networks, the complex power data is properly decomposed into more regular and simpler sub-data by the ensemble empirical mode decomposition; the generative adversarial networks are optimized by the evolutionary strategy with a set of different loss functions. Secondly, fully connecting the generative adversarial networks with the actor-critic framework, the lightweight actor-critic generative adversarial networks can realize dynamic learning in the random environment and store the sample for online training by the empirical replay mechanism. Thirdly, the multi-path lightweight method is proposed to reduce the consumption of time and storage resources of lightweight actor-critic generative adversarial networks. Eventually, the lightweight actor-critic generative adversarial networks are compared with comparison algorithms in two-area and real-life four-area systems. Case study results reveal that lightweight actor-critic generative adversarial networks have better dynamic performance, online learning capabilities, and higher control performance with lower economic costs.