Robust stochastic optimal dispatching of integrated electricity-gas-heat system considering generation-network-load uncertainties

Integrated energy systems with the deep coupling of electric power, natural gas, and heat have attracted much interest lately. In the modeling of such an integrated electric -gas -heat system (IEGHS), there is the problem of generation-load uncertainty. The literature has not yet addressed the uncertainties related to pipeline parameters and buried temperature in a natural gas network. To fill this gap, a robust stochastic optimal dispatching model has been proposed in this paper, which can effectively solve the IEGHS dispatching problem under multiple uncertainties. A robust adjustable uncertain set is adopted to deal with the uncertainties of wind power generation and pipeline parameters. The Wasserstein generative adversarial network based on gradient normalization is proposed to generate load -side demand scenarios. Then, a column and constraint generation algorithm is adopted to solve the proposed model. The simulation analysis successfully demonstrates the efficacy of the proposed model and algorithm. By utilizing this approach, the system can obtain a scheduling scheme with the lowest operating cost even under worst -case scenarios.