Abstract—Economic Load Dispatch is an integral part of power system generation planning and it is of utmost importance for the electrical utilities and power engineers to explore this area in short and long term planning scenarios. Load demand requirements subjected to economic feasible solutions matching voltage profile, power demand, minimization of losses, voltage stability and improve the capacity of the system is the need of the hour. Optimization techniques based on evolutionary computing, artificial intelligence, search method finds their applications in the area of economic load dispatch planning to reach global optimal solution for this multi-decision, multi-objective combinatorial problem subjected to different constraints. In this paper, Differential Evolution based algorithm has been proposed to solve economic dispatch problem. Unlike other heuristic algorithms, Differential Evolution possesses a flexible and well-balanced mutation operator to enhance and adapt the global and fine tune local search. The Differential Evolution algorithm starts by initialization in first iteration. The next step is mutation where addition, subtraction and multiplication are done to achieve target population from donor population starting with initial count. Mutation operator in general works well within bandwidth of 0 to 1. Similarly, crossover is benchmarked in this slab to promote the promising results. Exponential cross over is chosen for recombination. Recombination results in trial version of generated population vector for next generation. The suggested technique is tested on IEEE 25 bus system. Test results are compared with other techniques presented in literature. Test results appeals for further investigation of differential evolution in active load dispatch problem.

Index Terms—differential evolution (DE), Unit commitment (UC), economic dispatch (ED)

Cite: Navpreet Singh Tung and Sandeep Chakravorty, "Active Power Dispatch Planning Using Differential Evolution Algorithm," Journal of Industrial and Intelligent Information, Vol. 2, No. 3, pp. 200-204, September 2014. doi: 10.12720/jiii.2.3.200-204