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A Solution to Unit Commitment Problem Using Evolutionary Algorithm With Environmental Constraints

Jeba M Shalin, M. Godfrey Bhonz, R. Femi

Abstract


This project presents an Evolutionary Programming based Ant Colony Search Algorithm (ACSA) approach to solve the unit commitment (UC) problem. This ACS algorithm is a relatively new metaheuristic for solving hard combinatorial optimization problems. It is a population- based approach that uses exploitation of positive feedback, distributed computation as well as constructive greedy heuristic. Positive feedback is for fast discovery of good solutions, distributed computation avoids early convergence, and the greedy heuristic helps find adequate solutions in the early stages of the search process. The ACSA was inspired from natural behaviour of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. The UC problem solved using the proposed approach is subject to real power balance, real power operating limits of generating units, spinning reserve, start-up cost and minimum up and down time and environmental constraints. The proposed approach determines the search space of multi-stage scheduling followed by considering the unit transition related constraints during the process of state transition. The project describes the proposed approach and presents test results on a 4 unit test system that demonstrates its effectiveness in solving the UC problem.

Keywords: Ant colony search algorithm, distributed cooperative agents, optimization and unit commitment


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References


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