An Evolutionary Programming Algorithm for Solving Unit Commitment Problem in Smart Grid Environment
Keywords:
Unit Commitment, Evolutionary Programming, Simulated Annealing, Legrangian Relaxation, Dynamic ProgrammingAbstract
This paper presents a new approach to solving the unit commitment problem using Evolutionary Programming Algorithm (EPA) in smart grid environment. 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. This paper proposes distributed sources which includes electric vehicles and distributed generation. EPA, which happens to be a Global Optimisation 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 recommitment is carried out with respect to the unit’s minimum down times. The Neyveli Thermal Power Station (NTPS) Unit - II in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different power systems consists of IEEE 10, 26, 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the EPA and other conventional methods like Dynamic Programming, Legrangian Relaxation and Simulated Annealing and Tabu Search in reaching proper unit commitment.
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