Intraday Aggregated Optimal Scheduling Strategy for Adjustable Loads in Industrial Parks

Abstract

Industrial parks contain numerous adjustable loads that can be aggregated into high-quality virtual power plants, supporting the stable operation of power systems with high penetration of renewable energy. However, the diverse control characteristics of adjustable loads, coupled with the uncertainty in electricity consumption during production, present significant challenges to achieving precise power regulation. To address these challenges, adjustable loads are classified and modeled, and an intraday aggregation optimization scheduling model for industrial park-level adjustable loads is developed. The uncertainties associated with uncontrollable loads and distributed photovoltaics (PVs) are considered. Consequently, an intraday two-stage optimal scheduling strategy for adjustable loads in industrial parks is proposed. In the first stage, the scheduling strategy for discretely adjustable loads under deterministic conditions is solved. In the second stage, a distributionally robust optimization algorithm is used to solve the scheduling strategy for continuously adjustable loads under uncertainty. Simulation results show that the two-stage optimal scheduling strategy can achieve precise dynamic optimization of virtual power plants at a relatively low cost, and it exhibits good robustness.