Unit Commitment Optimization Using Gradient-Genetic Algorithm and Fuzzy Logic Approaches

Abstract

The development of the industry and the gradual increase of the population are the main factors for which the consumption of electricity increases. In order to establish a good exploitation of the electrical grid, it is necessary to solve technical and economic problems. This can only be done through the resolution of unit commitment problem (UCP). The decisions are which units to commit at each time period and at what level to generate power meeting the electricity demand. Therefore, in a robust unit commitment problem, first stage commitment decisions are made to anticipate the worst case realization of demand uncertainty and minimize operation cost under such scenarios. Unit Commitment Problem allows optimizing the combination of the production units’ states and determining their production planning in order to satisfy the expected consumption with minimal cost during a specified period which varies usually from 24 h to 1 week. However, each production unit has some constraints that make this problem complex, combinatorial and nonlinear. In this chapter, we have proposed two strategies applied to an IEEE electrical network 14 buses to solve the UCP in general and in particular to find the optimized combination scheduling of the produced power for each unit production. The First strategy is based on a hybrid optimization method, Gradient-Genetic algorithm, and the second one relies on a Fuzzy logic approach. Throughout these two strategies, we arrived to develop an optimized scheduling plan of the generated power allowing a better exploitation of the production cost in order to bring the total operating cost to possible minimum when it’s subjected to a series of constraints. A comparison was made to test the performances of the proposed strategies and to prove their effectiveness in solving Unit Commitment problems.