Evolutionary Multi-Objective Day-Ahead Thermal Generation Scheduling in Uncertain Environment

This paper addresses day-ahead thermal generation scheduling as a realistic multi-objective optimization problem in an uncertain environment considering system operation cost, emission cost and reliability as the multiple objectives. The uncertainties occurring due to unit outage and load forecast error are incorporated using loss of load probability (LOLP) and expected unserved energy (EUE) reliability indices. For solving the above-mentioned scheduling problem, a multi-objective generation scheduling algorithm (MOGSA) is proposed in this paper. Three case studies are performed on large scale test systems considering two different bi-objective optimization models and a three-objective optimization model that may be chosen by the system operator according to his/her own preference. The simulation results demonstrate the advantages of solving the thermal generation scheduling problem as a realistic multi-objective optimization problem in an uncertain environment. Finally the authors suggest a systematic procedure for the system operators to choose a single solution for the thermal generation scheduling problem.