Stochastic Day-Ahead Generation Scheduling With Pumped-Storage Stations and Wind Power Integrated

With more and more uncertain wind power generation integrated in power systems, it is significant to enhance the resilience of generation scheduling to avoid imbalance charges. This paper proposes a stochastic day-ahead generation scheduling (SDAGS) with pumped-storage (PS) stations and wind power (WP) integrated in power systems to tackle the variability of wind power for the purpose of reliability and economy of system operation. Considering the uncertainties of load and wind power generation, Latin hypercube sampling with Cholesky decomposition (LIIS-CD) is utilized to generate several scenarios. Multi-objective group search optimizer with adaptive covariance and Levy flights (MGSO-ACL) is applied to optimize the SDAGS over 24-hour period, aiming at reaching a compromise between the minimization of expectation and variance of total cost of the SDAGS. Furthermore, a decision making method based on evidential reasoning (ER) approach is utilized to determine a final optimal solution considering expected carbon dioxide emission and expected polluted gas emission. Simulation studies are conducted on two different power systems with PS stations and WP integrated to verify the efficiency of the SDAGS.