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


Unit commitment, mutually of the foremost vital control processes in power systems, has been studied extensively in the past decades. Typically, the goal of unit commitment is to scale back as a lot of production cost as doable whereas guaranteeing the facility offer operated with a high reliability. But, system operators encounter increasing difficulties to attain an optimal scheduling thanks to the challenges in coping with uncertainties that exist in both supply and demand sides. This study develops on a daily basis-ahead two-stage multi-objective unit commitment model that optimizes both the provision reliability and the entire value with environmental issues of thermal generation systems. To tackle the manifold uncertainties of unit commitment during a more comprehensive and realistic manner, stochastic and fuzzy set theories are used simultaneously, and a unified reliability measurement is then introduced to evaluate the system reliability beneath the uncertainties of both sudden unit outage and unforeseen load fluctuation. Additionally, a cumulative probabilistic technique is proposed to deal with the spinning reserve optimization during the scheduling. To solve this difficult 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 tend to also justify its feasibility on check systems with generation uncertainty.

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