Co-scheduling of a renewable-driven gas-electricity system integrated with hydrogen storage and network topology optimization: A novel risk-aware uncertainty management framework

The co-scheduling of integrated gas–electricity systems (IGESs) has become increasingly important due to the growing penetration of gas-fired power plants and wind generation. However, uncertainties in key operational parameters (namely wind generation and electricity/gas demands) along with the associated risks, pose significant challenges to the reliable and economic operation of IGESs. Uncertainties refer to forecast errors in these parameters, while risks arise from the undesirable operational conditions created by such deviations. To address these challenges, a novel hybrid co-scheduling framework integrating information gap decision theory (IGDT) and stochastic programming is proposed. Because stochastic programming is inherently risk-neutral, the p-robust approach is incorporated to explicitly control risk associated with wind and electrical load uncertainties by minimizing relative regret (worst-case cost). Meanwhile, the IGDT method adopts a risk-averse strategy to maximize the tolerable uncertainty radius of gas demand and generate conservative yet reliable operational decisions. To enhance IGES flexibility, the framework incorporates emerging technologies: on the electricity side, a hydrogen storage system and network topology optimization are employed to facilitate wind power integration; on the gas side, a gas storage system and pipeline line-pack capability are considered to secure gas supply. The proposed framework is tested on an IEEE benchmark IGES. Simulation results demonstrate that the coordinated use of emerging technologies reduces the total operational cost, alleviates line congestion, and lowers the locational marginal electricity price. Furthermore, the proposed hybrid model improves system robustness against uncertainties with a small increase in total cost.