Stochastic power management strategy for optimal day-ahead scheduling of wind-HESS considering wind power generation and market price uncertainties

Abstract

Wind power uncertainty is one of the problems in large-scale wind farms (WFs) integration to the electrical network. In the restructured markets, WFs have to participate in the day-ahead (DA) and the real-time (RT) markets. Due to the intermittent nature of wind speed, the system operator may impose penalty to them because of their uncertainties and deviations from the announced schedule in the DA market. The use of energy storage systems (ESSs) is a practical solution for WFs power management. This paper proposes a stochastic framework for power management of a wind-hybrid ESS (HESS) to maximize the DA market profit through bidding a scheduled power, based on a mixed-integer linear programming. In the scenario-based model of DA optimal scheduling, the wind power generation and DA market price uncertainties are considered in the wind-HESS power management and the scenarios are generated using Monte-Carlo method and roulette wheel. Thus, the DA optimal power bidding strategy and real-time operational management are incorporated to submit power to DA market with no deviation, while keeping the state of charge of HESS in a safely range. Simulation results prove increase in the profit obtained from the modified version of limited min–max wind power management method.