This paper presents an improved local search method using bee colony optimization (ILS-BCO) to solve an economic dispatch (ED) problem with smooth cost function characteristic. The proposed ILS-BCO algorithm is an integration of lambda iteration and bee colony optimization (CLI-BCO) combined with golden section search and bee colony optimization (CGS-BCO). To show its effectiveness, the ILS-BCO was applied to test two systems consisting of either 6 or 15 power generating units. Results confirm that the proposed ILS-BCO approach is capable of obtaining rapid convergence and a high quality solution efficiently.
Słowa kluczowe: Bee Colony Optimization, Lambda Iteration, Golden Section Search, Economic Dispatch.
W artykule zaproponowano metodę rozwoiązywania problemu ekonomicznego rozsyłu energii z uwzględnieniem kosztów. Wykorzystano metodę optymalizacji opartą na algorytmach rojowych. Metodę przetestowano na dwóch systemach złożonych z 6 lub 15 jednostek generatorów.
Keywords: algorytmy rojowe, ekonomiczny rozsył energii, iteracja lambda.
The operating cost of a power plant mainly depends on the fuel cost of generators which is minimized via economic dispatch. The Economic Dispatch (ED) problem is one of the fundamental issues in power system operation. The main objective is to reduce the cost of energy production taking into account transmission losses while satisfying equality and inequality constraints. The rational distribution of economic load between running units can lead to significant cost savings making it important to research the economic dispatch problem. Several classical methods, such as the lambda iteration method , quadratic programming , the gradient method , dynamic programming , linear programming , and nonlinear programming  have been applied to solve ED problems. However, these methods are not feasible in practical power systems owing to the non-linear characteristics of the generators. Solutions can be limited to achieving a local optimum which leads to less desirable performance. In addition, these methods often use approximations to limit complexity. Recently, a number of researchers have used meta-heuristic optimization techniques, which are unlike conventional mathematical techniques, to solve ED problems in power systems. Different meta-heuristic approaches have proved to be effective with promising performance, such as a Genetic Algorithm (GA) -. Such methods have been inspired by the Darwinian law of optimal survival of a species, Particle Swarm Optimization (PSO) - inspired by the social behavior of bird raising or fish production, Ant Colony Optimization (ACO) - inspired by food habits in an ant colony, and by Tabu Search (TS)  as a way to build a better foundation from prior knowledge. This latter method records previous answers and forbids the new solution to converge at the same point for different input data. Other methods to be used include the Cuckoo Search Algorithm (CSA) [1 [...]
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