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The impact of wind power forecast uncertainty has been amplified by the deepening wind power penetration. To guarantee system security and reliability, sufficient dispatchable generation and transmission capacities have to be reserved. Currently, research has been carried out to improve system operational performance by optimizing schedules considering uncertainty. However, most methods are designed to cover a given risk level of uncertainty, which is determined ex ante. With the increase in wind power capacity, defining the risk level a priori without considering the unit commitment (UC) may limit the scheduling efficiency. Essentially, there is no absolute standard for acceptable risk exposure. Instead, there is a tradeoff between the risk and the cost of reserve capacity. Therefore, it is necessary to develop a tool to enable a flexible and comprehensive consideration of the risk level. In this paper, by combining chance constrained programming and goal programming, a novel model based on chance constrained goal programming is proposed to optimize the risk adjustable UC problem. To facilitate an efficient solution, the proposed model is transferred into a tractable mixed integer linear programming problem by a deterministic equivalent and piecewise linearization. Case studies are performed on an IEEE 118-bus system to illustrate the effectiveness and efficiency of the model.
Wang et al. (Tue,) studied this question.