With the increasing integration of wind power and demand response into power system, the complex uncertainties from supply-side and demand-side have brought great challenges to daily operation of the power system. It is essential for enabling a comprehensive and adequate consideration of the manifold uncertainties in the grid. In this study, information granule chance constraint goal programming (IGCCGP) is proposed by a combination of chance constraint goal programming and information granule to provide a uniform representation of the multifaceted uncertainties. Leveraging on IGCCGP, the reserve constraints and branch flow constraints for accommodating the complex uncertainties in the grid are developed and the economic dispatch model considering the combined uncertainties from both supply-side and demand-side is established. To accelerate model solutions without losing much of model accuracy, the deviation in the IGCCGP is transformed to a deterministic equivalent and the optimal schedule is obtained by a mixed linear integer programming. Finally, a computational study is illustrated in the IEEE 39-bus system to verify the efficiency of the proposed method.

With the increasing penetration of wind power and demand response integrated into the grid, the combined uncertainties from wind power and demand response have been a challenging concern for system operators. It is necessary to develop an approach to accommodate the combined uncertainties in the source side and load side. In this paper, the fuzzy stochastic conditional value-at-risk criterions are proposed as the risk measure of the combination of both wind power uncertainty and demand response uncertainty. To improve the computational tractability without sacrificing the accuracy, the fuzzy stochastic chance-constrained goal programming is proposed to transfer the fuzzy stochastic conditional value-at-risk to a deterministic equivalent. The operational risk of forecast error under fuzzy stochastic conditional value-at-risk assessment is represented by the shortage of reserve resource, which can be further divided into the load-shedding risk and the wind curtailment risk. To identify different priority levels for the different objective functions, the three-stage day-ahead unit commitment model is proposed through preemptive goal programming, in which the reliability requirement has the priority over the economic operation. Finally, a case simulation is performed on the IEEE 39-bus system to verify the effectiveness and efficiency of the proposed model.

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