Unit Commitment with Ancillary Services in a Day-Ahead Power Market

This paper integrates Discrete Particle Swarm Optimization (DPSO) and Sequential Quadratic Programming (SQP) to propose a DPSO-SQP method for solving unit commitment problems ancillary services. Through analysis of services, including Automatic Generation Control (AGC), Real Spinning Reserve (RSR), Supplemental (SR), the cost model was developed. With requirements energy balance, operating constraints considered, DPSO-PSO used calculate supply each source, associated AGC, RSR, SR, day-ahead power market calculated. A study case using real data from thermal units Taipower Company (TPC) Independent Power Producers (IPPs) demonstrated effective results “summer” “non-summer” seasons, as classified by TPC two charging rates. According test cases in this research, costs without services non-summer summer seasons are higher than those with The simulation also compared Genetic Algorithm (GA), Evolutionary (EP), (PSO), Simulated Annealing (SA). shows effectiveness enhanced sorting efficiency, probability reaching global optimum.