Optimal Scheduling of PSHPPs and Wind Farms in Unit Commitment Program Using Advanced Genetic Algorithm Technique

Unit commitment problem has a great importance in power system operation planning. Recently, with 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 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 Hydro Power 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, powerful advanced genetic algorithm is applied to solve common unit commitment problem at the presence of wind and pumped storage hydro power plants. Objective function of 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 coding procedure of unknown variable in chromosome is 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 PSHpower plants. Finally, the simulation results and effectiveness of proposed algorithm are evaluated.