The goal of a unit commitment optimization problem is to reduce the total generation cost as much as possible while satisfying future power demands. Thus, analysis must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory to evaluate the future uncertain loads. The objective of this research is to build a two-stage multi-objective fuzzy programming model based on 24-hour uncertain load forecasting. The first stage is a decision-making process on the interval data of the imprecise power loads, whereas the second stage pursues the optimization of the unit commitment scheduling, which can help find both optima simultaneously by maximizing power supply reliability and minimizing total generation cost. To define the supply reliability under uncertain forecasting, we propose a new concept of maximal blackout time during successful operation, which is based on the fuzzy credibility theory. Furthermore, as a solution approach to this model, an improved two-stage multi-objective particle swarm optimization algorithm is designed based on our previous studies. Finally, the performance of this algorithm is discussed in comparison with experimental results from several test systems.
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Energy Engineering and Power Technology