Iman Goroohi Sardou

Wind generation resources have already been subjected to a multitude of challenges to participate in competitive markets due to their production uncertainty. System flexibility, which is needed for reliable and secure operation of a power system with high penetrations of wind energy, may be provided by gas-fired generating (GFG) units. So, these units are considered as the key supporting assets for wind energy integration. In this paper, a biobjective stochastic self-scheduling strategy is proposed for coordinated operation of GFG units and wind farms owned by a generation company (GenCo), including objectives of profit maximization and financial risk minimization. Hybrid nondominated sorting genetic algorithm-II and mixed integer linear programming techniques are employed to solve the proposed biobjective problem, providing a set of Pareto solutions. Additionally, a fuzzy decision making approach is proposed to choose the most preferred solution among the obtained Pareto solutions based on the level of risk seeking of the GenCo. The uncertainties of forecasting errors of wind power generation and natural gas and electricity market prices, and the contingencies of the GFG units are modeled in the proposed stochastic framework. A Monte Carlo simulation-based validation (MCSV) approach is employed to verify the efficiency of the proposed strategy in different case studies. The results of the MCSV approach throughout 10 000 real world scenarios demonstrate that the scheduling plan procured by the proposed hybrid stochastic strategy is generally more optimal than that of the conventional stochastic and deterministic ones.