Research on optimal scheduling and source-network-load correlation matching of integrated energy system considering uncertainty

To fully leverage system flexibility and demand-side resources, ‘source-load interaction’ is regarded as the future trend for integrated energy systems (IES). However, uncertainties existing on both sides of the source and load cannot be ignored in the scheduling of IES. To reduce system operating costs and carbon emissions and improve the utilization of renewable energy in IES operation with uncertainty. This paper proposes an optimal scheduling method considering the uncertainty. Firstly, Clayton-Copula Latin hypercube sampling method and enhanced Wasserstein deep convolution generative adversarial network with gradient penalty (WDCGAN-GP) are employed to generate source-load scenarios, capture source-load correlations, and simulate extreme scenarios. Secondly, the influence of the coupling model of low-carbon economic equipment and thermal power units, integrated demand response (IDR) and source-load correlation on the low-carbon and economic performance of PIES are analyzed in depth. Finally, the causality analysis of economic change caused by correlation is conducted. The results show that the proposed model and strategy improve the system flexibility. Furthermore, The enhancement of source-load correlation matching degree will also improve the utilization rate of renewable energy and system low-carbon economic benefits.