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Multi-objective adaptive Clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty
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DOI：10.1016/j.ijepes.2013.04.024.png)
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This paper presents a new multi objective optimization approach based on Adaptive Clonal Selection Algorithm (ACSA) to solve complex Environmental/Economic Dispatch (EED) problem of thermal generators in power system. The proposed methodology also incorporates the power demand equality constraint and ensures various operating constraint limits while solving EED problem. In this algorithm an adaptive Clonal selection principle with non-dominated sorting technique and crowding distance has been used to find and manage Pareto-optimal set. Clonal selection principle is one of the models used to incorporate the behavior of the artificial immune system. The biological principles of clone generation, proliferation and maturation are mimicked and incorporated into this algorithm. To show the effectiveness of the proposed Multi Objective Adaptive Clonal Selection Algorithm (MOACSA) in solving EED problem two types of test systems have been considered with various objectives. These includes an IEEE 30-bus 6 unit test system and an 82-bus 10 unit Indian utility real life power system network for solving EED problem without and with load uncertainty. Simulation results are compared by implementation of three other standard algorithms such as Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Differential Evaluation (MODE) methods. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Adaptive Clonal selection algorithm
Multi-objective optimization
Load uncertainty
Pareto front
Economic emission dispatch
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