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The increasing number of gas-fired units has significantly intensified thecoupling between power and gas networks. Traditionally, the nonlinearity andnonconvexity in gas flow equations, together with renewable-inducedstochasticity, result in a computationally expensive model for unit commitmentin electricity-gas coupled integrated energy systems (IES). To acceleratestochastic day-ahead scheduling, we applied and modified Progressive Hedging(PH), a heuristic approach that can be computed in parallel to yieldscenario-independent unit commitment. By applying a termination and enumerationtechnique, the modified PH algorithm saves considerable computational time,especially when the unit production prices are similar for all generators, andwhen the scale of IES is large. Moreover, an adapted second-order conerelaxation (SOCR) is utilized to tackle the nonconvex gas flow equation. Casestudies are performed on the IEEE 24-bus system/Belgium 20-node gas system andthe IEEE 118-bus system/Belgium 20-node gas system. The computationalefficiency when employing PH is 188 times that of commercial software, evenoutperforming Benders Decomposition. Meanwhile, the gap between the PHalgorithm and the benchmark is less than 0.01% in both IES systems, whichproves that the solution produced by PH reaches acceptable optimality in thisstochastic UC problem.

Stochastic Unit Commitment in Electricity-Gas Coupled Integrated Energy Systems based on Modified Progressive Hedging