Stochastic Day-Ahead Scheduling of ElectricityGas Coupled Systems via Progressive Hedging

With the intensification of synergy between different energy systems, coupling between power and natural gas systems has been laboriously investigated. Here we apply Progressive Hedging, a scenario-based decomposition method, for efficient stochastic day-ahead scheduling in the dynamic electricity-gas coupled system. Combined with the iteration-free second-order cone relaxation method, a total of merely $\sim 60$ s is required to complete a 15-scenario stochastic optimization, in a medium-scale coupled network. Also, thanks to the algorithm’s support for parallel computing, the computation time can be further reduced to $\sim$35s with 4 workers running in parallel. Results yielded via Progressive Hedging have been compared with those via traditional stochastic programming and via Benders Decomposition. High accuracy in unit commitment and expected cost is proved to be restored after the introduction of this heuristic algorithm.