Exploiting advanced genetic algorithm technique in optimal scheduling of pumped storage hydropower plant and wind farms in unit commitment program

K. Naidoo*, F. Coetzee, J. Potgieter

Department of Electrical and Mining Engineering, College of Science, Engineering & Technology, University of South Africa, Pretoria, South Africa


Unit commitment problem has great importance in power system operation planning. Recently, with the restructuring process in power systems, and concern about economic and ecological issues, a need for efficient and green energy production with renewable resources such as wind power plants has risen. Wind energy does not impose any charge for its owners; but on the other hand, due to a variable and stochastic nature of wind speed, wind farm's generation changes, accordingly. Because of uncertainty in predicting wind power, even for short time, use of pumped storage hydropower plants alongside wind resources has been proposed to achieve higher maneuver power in units operation and benefit of energy exchange in power market. In this paper, a powerful advanced genetic algorithm is applied to solve common unit commitment problem at the presence of wind and pumped storage hydropower plants. The objective function of the optimization problem is maximizing the sum of electrical energy generation benefit of various power plants in the day-ahead power pool market, considering all operational limits. Proposed advanced genetic algorithm and its formulation with a coding procedure of unknown variable in a chromosome are explained and then, the numerical studies are performed on a typical test system under power pool market conditions, which its generation system consists of 10 thermal units, 1 wind farm, and 1 PSH power plants. Finally, the simulation results and the effectiveness of the proposed algorithm are evaluated.


Unit commitment, Optimal scheduling, Thermal units, Wind farms, Pumped storage hydropower plant, Advanced genetic algorithm technique

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Article history

Received 1 November 2018, Received in revised form 9 January 2019, Accepted 22 January 2019

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Naidoo N, Coetzee F, and Potgieter J (2019). Exploiting advanced genetic algorithm technique in optimal scheduling of pumped storage hydropower plant and wind farms in unit commitment program. Annals of Electrical and Electronic Engineering, 2(2): 6-13

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