Two-Stage Multi-Objective Unit Commitment Optimization Under Hybrid Uncertainties

Unit commitment, as one of the most important control processes in power systems, has been studied extensively in the past decades. Usually, the goal of unit commitment is to reduce as much production cost as possible while guaranteeing the power supply operated with a high reliability. However, system operators encounter increasing difficulties to achieve an optimal scheduling due to the challenges in coping with uncertainties that exist in both supply and demand sides. This study develops a day-ahead two-stage multi-objective unit commitment model which optimizes both the supply reliability and the total cost with environmental concerns of thermal generation systems. To tackle the manifold uncertainties of unit commitment in a more comprehensive and realistic manner, stochastic and fuzzy set theories are utilized simultaneously, and a unified reliability measurement is then introduced to evaluate the system reliability under the uncertainties of both sudden unit outage and unforeseen load fluctuation. In addition, a cumulative probabilistic method is proposed to address the spinning reserve optimization during the scheduling. To solve this complicated model, a multi-objective particle swarm optimization algorithm is developed. Finally, a series of experiments were performed to demonstrate the effectiveness of this research; we also justify its feasibility on test systems with generation uncertainty.