Robust stochastic optimal dispatching of integrated electricity-gas-heat systems with improved integrated demand response

In recent years, integrated energy systems with deep coupling of power, natural gas, and heat energy have gradually attracted extensive attention. The increasing issue of uncertainty in the generation and load of an integrated electric-gas-heat system (IEGHS) is a growing prominent problem. The effective implementation of demand response (DR) programs is an important way to solve this problem in the IEGHS. In this paper, a robust stochastic optimal dispatching method for an IEGHS with integrated DR (IDR) under multiple uncertainties is proposed. A robust adjustable uncertainty set is adopted to deal with the uncertainty of wind power. The Wasserstein generative adversarial network based on gradient normalization is proposed to generate load-side demand scenarios. Furthermore, an improved IDR model that considers the peak-valley difference cost of the electricity-gas-heat load is proposed. Finally, the simulation analysis successfully demonstrates the efficacy of the proposed model. By utilizing this approach, the system can obtain a scheduling scheme with the lowest operating cost even under worst-case scenarios.