Solution to Economic – Emission Load Dispatch by Cultural Algorithm Combined With Local Search: Case Study

Abstract

Economic-emission load dispatch uses the fuel cost variables and gas emission in a minimized way to obtain an optimal operation in generation units in a power plant, guaranteeing the supply of demand. The first variable is definitive to ensure business continuity and the second to comply with environmental legislation and no degradation of the environment. This paper analyzes the use of a new computational optimization algorithm based on the cultural algorithm (CA), improved with local search techniques simulated annealing and Tabu search, using data from a real power plant with 10 generators and the system of the IEEE with 13 generating units. The application has two options of operation: the classic one, which operates with all generators seeking to minimize the cost and emission meeting the specified demand; and the controlled one, which turns off the generators that have the highest incremental fuel cost but guaranteeing the demand and reducing the emission of gases. Simulations were performed on the six possible options in this application. The results obtained were compared with each other and with the results of other techniques reported in the literature. The local search that improved the CA and the new way of updating topographic knowledge allowed the results to be better than those found by other metaheuristics that solved the same problem of the real plant and the IEEE system.