Clonal Selection based Artificial Immune System to solve the Unit Commitment Problem in restructured electricity sectors

This paper addresses Clonal Selection based Artificial Immune System (AIS) for solving the non-linear combinatorial sub-problem of Profit Based Unit Commitment (PBUC) in restructured electricity markets. Here an objective is made to schedule the generators economically in order to maximize the profit of Generation Companies (GENCO's) based on forecasted information such as power demand and prices. In the proposed method, the objective function is represented as deemed antigen and the solutions obtained are deemed antibodies. If an antibody fits the antigen best, then this antibody is deemed the optimum solution. Encoding of continuous operating time shortened the code length and hence the searching speed of algorithm has been improved greatly. Simulations are carried out to evaluate the performance of the proposed technique. The results of 10 generating units - 24 hours IEEE test system are compared with the other existing methods like LR-GA and LR-EP. The test results shown that Artificial Immune algorithm having well global searching performance and is an efficient algorithm to solve Unit Commitment Problem in restructured electricity markets.