Conditional value-at-credibility for random fuzzy wind power in demand response integrated multi-period economic emission dispatch

As wind energy increasingly penetrates into power systems, new challenges arise for the dispatcher to keep the systems reliable under uncertain circumstances. To solve this problem, a conditional value-at-credibility (CVaC) model is proposed in this paper for hedging random fuzzy wind power in demand response integrated multi-period economic emission dispatch (MEED). In the model, a Gaussian distribution-based probability measure is presented to assess wind randomness with wind farm wake effects. Whilst a Cauchy distribution-based credibility measure is derived to assess wind fuzziness. After that, the wind power is deemed as a random fuzzy variable, and the objective function with respect to CVaC is developed to balance the risk and the profit of MEED with the wind farm's integration. In addition, this paper presents an incentive-based demand response that incorporates a peak-flat-valley period partition mechanism into the time of use strategy to find the optimal incentive in shedding peak load. A novel global optimization algorithm is then established to solve the proposed optimization model. Case studies prove the feasibility and effectiveness of the proposed model in solving MEED with uncertain wind power by providing the optimal trade-off solution between economy and security.