Optimal Operation of Wind-thermal generation using differential evolution

Abstract

Numerous optimization paradigms have been developed for power system optimization tasks till date but none has found the level of acceptance which is being received by evolutionary soft computing methods. Traditional methods are found to be inefficient for complex practical problems with equality and inequality constraints therefore the complexity of the task reveals the necessity for development of efficient algorithms to accurately locate the optimum solution. The present paper proposes to solve complex constrained optimization problems using differential evolution (DE) with multiple mutation strategies. The role of control parameters and mutation strategies of DE algorithm in achieving the global best result is critically explored.The depleting reserves of fossil fuel and growing concern about environmental protection dictates the integration of renewable power resources into the power grid. Including wind power with the conventional power has become very popular in recent years due to the rapid development of technology in this field. Modeling of wind-thermal system is required to find the optimal wind generator capacity that can be integrated into the existing system such that all operating constraints are satisfied. The developed algorithm is tested on a standard test system taking into consideration the wind uncertainty and ramp-rate limits of thermal power units. The results clearly demonstrate the effectiveness of the proposed method in finding feasible and efficient globally optimal solutions