Bi-Level Optimization Scheduling Strategy for PIES Considering Uncertainties of Price-Based Demand Response

The asymmetry-induced uncertainty in both sources and loads is a crucial and continuously spotlighted issue within modern power systems. Applying optimization scheduling method to deal with this asymmetry is a feasible solution. Accordingly, this paper proposes a bi-level park-level integrated energy system (PIES) optimization strategy considering uncertainties of price-based load demand response (PLDR). Firstly, a model for characterizing the uncertainties of the PLDR is developed based on fuzzy theory. Secondly, a bi-level two-stage PIES optimization model that includes multiple device models is established. In the first stage, the dynamic pricing optimization is carried out with the aim of maximizing user satisfaction. In the second stage, the PIES scheduling strategy optimization is performed with the aim of minimizing the operation costs of PIES. Finally, multiple scenarios are set to conduct comparative validation, which demonstrates that the proposed method not only improves the renewable energy integration capacity of the system, optimizes the load profiles, and enhances the economic and low-carbon performance, but also increases user satisfaction, thus providing a reference for the dispatch and operation of the park-level integrated energy system.