Robust Scheduling Algorithm for Virtual Power Plants in Distributed Multi-Energy Systems Based on Generative Adversarial Reinforcement Learning

Abstract

In the scheduling of distributed multi-energy virtual power plants, this paper proposed a robust scheduling method based on Wasserstein Generative Adversarial Network with Reinforcement Learning (WGAN-RL) to address the vulnerability of scheduling strategies caused by renewable energy output fluctuations and load uncertainties. This method defined the state and action space based on physical constraints and embedded hard operating rules. Then, it designed a conditional Wasserstein GAN to generate the worst-case perturbation scenario that approximates the real distribution support boundary and covers high-risk areas, based on weather and load forecasts. On this basis, it used Proximal Policy Optimization (PPO) to train the scheduling policy in an environment with dynamically injected extreme perturbations, and improved the convergence stability by pruning probability ratios and GAE. Finally, it introduced a rolling time-domain online scheduling and a weekly fine-tuning mechanism of WGAN to achieve long-term adaptability under perturbation distribution drift. Experiments show that, in terms of economics, with a 70% renewable energy penetration rate, the average daily dispatch cost is 2680 USD ± 150 USD, and the curtailment rate is 9.6% ± 1.1%. Regarding robustness, under a perturbation of 0.7 output standard deviation, the dispatch feasibility rate remains at 90.1% ± 2.1%, and the number of strategy collapses is controlled at 9.9 ± 2.1. In terms of real-time performance, the single-step inference time is only 4.2 ms ± 0.3 ms, and the training convergence steps are only 823. This research provides a deployable, adaptive, and engineering-feasible technical path for robust dispatch of virtual power plants under high uncertainty environments.