Shaik Affijulla and Sushil Chauhan
Economic load dispatch, Valve point loading , Gravitational artificial intelligence, Power system operation
Economic Load Dispatch is one of the major functions of modern Energy Management System (EMS), which determines the optimal real power settings of generating units with an objective of minimizing the total fuel cost. All industrial practice, the fuel cost of generators can be treated as a quadratic function of real power generation. In fact, valve point loading effect in thermal power plants calls discontinuity. The classical optimization methods require continuous differentiable objective functions; therefore they fail to provide global minima. The evolutionary computation methods can handle non-differential and non-convex objective functions and give global or near global optimum solutions. Evolutionary techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) are applied to economic dispatch problem widely. Similar to evolutionary computation, physical behaviour artificial intelligence called gravitational search intelligence is recently developed and has not been applied in many fields. Use of gravitational search intelligence not only avoids coding and monotonous decoding as prevalent transformations of GA and also results in less burden on parameter settings, population size and number of iterations; no memory requirement of solution as PSO. In this paper, gravitational intelligence is applied to solve economic load dispatch problem with valve point loading and Kron’s loss. Gravitational Search Optimization (GSA) algorithm is tested on a 3 and 6 unit test system. Results obtained shows that GS algorithm has a great potential in handling complex optimization problems and is capable of producing results in less time.
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