Day-Ahead Scheduling of Wind-Hydro Balancing Group Operation to Maximize Expected Revenue Considering Wind Power Output Uncertainty

Abstract

To use energy generated in wind farms (WFs), which contain uncertainty in their output, as a primary power source in a power system, a sophisticated balancing operation scheme is required. This study focuses on a balancing group (BG) scheme combining WFs and a variable-speed pumped-storage hydro generator (PSHG), which has a large capacity to compensate for the WF output. The proposed BG operational scheduling approach aims to maximize the expected revenue obtained in the day-ahead power market under the uncertainty in WF output and market price by considering the operational constraints of the PSHG, i.e., the water storage capacity and frequency of operation for switching pump-up/-down. In such a BG scheme, it is crucial to consider time-varying and time-dependent uncertainties in WF output to manage PSHG capacity constraints, as well as to derive a reasonable plan in practical computation time. The proposed BG operational scheduling scheme derives a set of WF output scenarios that represents the heterogeneous and time-dependent characteristics of real-world WF output behavior from probability density distributions derived by a cutting-edge prediction approach and implements the expected revenue maximization problem with scenario-based chance constraints of water storage transition by introducing computationally effective iterative optimization algorithm based on surrogate functions. Simulation results suggest that the proposed scheme provides an effective BG operation by considering the uncertainty in the WF output.

Keywords

• Balancing group operation
• wind power
• pumped storage hydropower generation
• probability density prediction
• vine copula
• expectation optimization