Distributionally robust risk-resistant synergistic restoration strategy for integrated electricity and gas system with high-fidelity gas dynamic modeling

Abstract

A synergistic restoration strategy offers a promising solution for expediting restoration of integrated electricity and gas system (IEGS) following blackouts, particularly under the increasing energy interdependence between these two subsystems. However, achieving synergistic restoration is challenging in 1) distinct operational characteristics between electric and gas flow rates, leading to difficulties in accurately modeling the restoration processes with significantly varying system states; 2) potential risks posed by uncertainties, such as fluctuating renewable energy and random contingencies, undermining the effectiveness of restoration strategy. To address these challenges, this paper proposes a novel distributionally robust risk-resistant synergistic restoration strategy for IEGS. Firstly, to accurately capture the dynamics of slow gas flow relative to instantaneous power flow, we propose a high-fidelity linear dynamic gas flow model with multiparametric disaggregation technique, demonstrating satisfactory accuracy in restoration scenarios. Then, the risks by uncertainties of renewable energy and contingencies are handled within distributionally robust framework. Moreover, the IEGS restoration problem is reformulated as a two-stage robust optimization problem by convex conservative approximation and strong duality theory, which is solved by a nested column-and-constraint generation algorithm. Finally, simulation results validate the effectiveness of the proposed restoration strategy, showing the improved restoration efficiency, security, and risk-resistant performance.