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A HYBRID APPROACH BASED ON ANN AND PSO FOR PROFICIENT SOLVING OF UNIT COMMITMENT PROBLEM

This paper presents a new hybrid approach to solve the short-term unit commitment problem using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO). The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. PSO, which happens to be a Global Optimization technique for solving Unit Commitment Problem, operates on a system, which is designed to encode each unit’s operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all the units according to their initial status (“flat start”). Here the parents are obtained from a pre-defined set of solution’s i.e. each and every solution is adjusted to meet the requirements. Then, a random decommitment is carried out with respect to the unit’s minimum down times. The network is trained by a numerous possible combinations of demand and their corresponding optimal generation schedule, which can be determined by the PSO algorithm. For the purpose of training, BP algorithm, one of the most widely used training algorithms is utilized for our approach. After the process of training, given any demands of a time horizon, the network effectively gives a schedule of unit’s commitment which will satisfy the demands of all the periods with minimum total cost. A thermal Power System in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different power systems consist of 10, 26, 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the IPSO and other conventional methods like Dynamic Programming (DP), Legrangian Relaxation (LR) in reaching proper unit commitment.