DATA-DRIVEN THREE-STAGE SCHEDULING METHOD FOR POWER-HEAT-GAS GRID BASED ON WIND POWER UNCERTAINTY

摘要

A data-driven three-stage scheduling method for a power-heat-gas grid based on wind power uncertainty. The method comprises the following steps: S1: performing initialization; S2: establishing a deterministic power-heat-gas coordination optimization scheduling model; S3: establishing a data-driven distributed robust scheduling optimization model employing mixed norms; S4: resolving a main problem of economic scheduling; S5: verifying convergence of a wind power uncertainty sub-problem: if the sub-problem converges, performing step S6; if not, performing step S4, and using a CCG algorithm to add a constraint to the main problem of economic scheduling; and S6: verifying the convergence of a gas grid operation constraint sub-problem: if the sub-problem converges, ending the calculation, and acquiring an optimal solution; if not, performing step S4, and adding a Benders cut set constraint to the main problem of economic scheduling. Under operational constraints of a power grid, a heat grid, and a gas grid, the scheduling method rationally arranges output power of each unit, leverages an energy storage device, and responds to the uncertainty of wind power, thereby improving the economic result of system operation.