SCUC Considering Loads and Wind Power Forecasting Uncertainties using Binary Gray Wolf Optimization Method

Recently, the renewable resources such as wind farms assumed more attraction due to their features of being clean, no dependency to any type of fuel and having a low marginal cost. The output power of wind units is dependent on the wind speed which has a volatile and intermittent nature. This fact confronts the solution of unit commitment problem with some challenges when a huge amount of wind resources is penetrated and considerable uncertainties are included in the problem. Moreover, the demand of the system has some volatility in comparison with forecasted values. This kind of volatility and stochastic nature is another source of uncertainty in the power system. In this paper, thermal and wind units are incorporated and the optimization problem is solved by the employment of proper probability distribution function and the Monte Carlo simulation approach for dealing with uncertainties. Afterwards, the optimization problem is solved by the use of the binary form of gray wolf optimization algorithm and the minimized total cost will be obtained. Ultimately, the unit commitment schedule and optimal generation of each unit are determined and the optimization results are compared with the solution of genetic algorithm and particle swarm algorithm.