Realistic and Transparent Optimum Scheduling Strategy for Hybrid Power System

Abstract

This paper addresses the transparent and realistic optimum day-ahead (DA) scheduling for a hybrid power system by explicitly considering the uncertainties. The basic components of the hybrid power system include conventional thermal generators, wind farm, and solar photovoltaic (PV) modules. A set of batteries is available for energy storage and/or discharge. The most critical problem in operating a wind farm or solar PV module is that these renewable energy resources cannot be dispatched in the same manner as conventional plants, because they involve climatic factors such as wind velocity and solar irradiation. This paper proposes the optimal scheduling strategy taking into account the impact of uncertainties in wind, solar PV, and load forecasts, and provides the best-fit DA schedule by minimizing both DA and real-time adjustment costs including the revenue from renewable energy certificates. This strategy consists of a genetic algorithm (GA)-based scheduling and a two-point estimate-based probabilistic real-time optimal power flow. The simulation for the IEEE 30-bus system with the GA and two-point estimate method, and the GA and Monte Carlo simulation have been obtained to test the effectiveness of the proposed scheduling strategy.