Day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage

Abstract

• Day-ahead and real-time market bidding and scheduling strategy for wind power participation. • Shared energy storage is used to reduce the real-time market deviation penalty of wind power. • Analyze the influence of deviation penalty coefficient on wind power bidding. • Analyze the impact of energy storage capacity on the income of wind farms and energy storage operator. In order to reduce the impact of wind power output and electricity price uncertainty on the income of wind power participating in the electricity market, this paper proposes a day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage. In the first stage, considering the uncertainty of wind power output and electricity price, aiming at the maximum income of wind farms in the day-ahead market, the optimal bidding power of each wind farm in the day-ahead market is obtained by using quantum genetic algorithm. In the second stage, based on the day-ahead market winning bid volume and actual output, by coordinating each wind farm to use the charging and discharging services of the shared energy storage power station, the net income of multiple wind farms in the day-ahead and real-time market is maximized. Case studies are carried out with three wind farms and one shared energy storage operator in this paper. Compared with the modes that wind farms not configuring energy storage or configuring energy storage independently, it is verified that the shared energy storage can significantly reduce the deviation penalty of wind farms in the real-time market and improve wind farm income.