Wind power has achieved great development in Northern China, but abundant wind power is dissipated, rather than utilized, due to inflexible electricity production of combined heat and power (CHP) units. In this paper, an integrated CHP system consisting of CHP units, wind power plants, and condensing power plants is investigated to decouple the power and heat production on both the power supply side and heat supply side, by incorporating electrical energy storage (EES) and thermal energy storage (TES). Then the integrated CHP system dispatch (ICHPSD) model is formulated to reach the target of reducing wind power curtailment and primary energy consumption. Finally, the feasibility and effectiveness of the proposed ICHPSD model are verified by the six-bus system, and the simulation results show that EES has a better effect on wind power integration than TES. The annual net benefits by incorporating EES and TES increase with increasing wind penetration, but they gradually approach saturation. Introducing both EES and TES can largely increase the amount of wind power integration and improve the operation efficiency of the system.

This paper aims at specifying the optimal allocation of a vanadium redox flow battery (VRB) energy storage system (ESS) for maintaining power balance of active distribution networks for wind power applications. Correspondingly, an optimal allocation approach for the VRB ESS was proposed. Different with the previous researches, the dynamic efficiency and life of VRB are considered in the proposed mathematical framework. Also, this proposed mathematical framework made a comprehensive assessment of the total benefits including the consumption of wind power, the reduced load interruption, greenhouse gas emission, network loss, and the capital cost of VRB ESS. These economic indicators are used to evaluate the penetration of VRB ESS and the economy of ADNs can achieve to the optimal with the designed VRB ESS. Finally, the proposed optimal allocation approach was validated and tested by a modified IEEE 33‐bus system and IEEE 123 node system. Test results have demonstrated the effectiveness of the proposed optimal allocation approach associated with the power flow operation characteristics of active distribution networks.

With the penetration of distributed generators (DGs), operation planning studies are essential in maintaining and operating a reliable and secure power system. Appropriate siting and sizing of DGs could lead to many positive effects forthe distribution system concerned, such as the reduced total costs associated with DGs, reduced network losses, and improved voltage profiles and enhanced power-supply reliability. In this paper, expected load interruption cost is used as the assessment of operation risk in distribution systems, which is assessed by the point estimate method (PEM). In light with the costs of system operation planning, a novel mathematical model of chance constrained programming (CCP) framework for optimal siting and sizing of DGs in distribution systems is proposed considering the uncertainties of DGs. And then, a hybrid genetic algorithm (HGA), which combines the GA with traditional optimization methods, is employed to solve the proposed CCP model. Finally,the feasibility and effectiveness of the proposed CCP model are verified by the modified IEEE 30-bus system, and the test results have demonstrated that this proposed CCP model is more reasonable to determine the siting and sizing of DGs compared with traditional CCP model.

In allusion to the prediction for the failure rate of transmission lines under multiple external environmental factors, a comprehensive prediction method for the failure rate of transmission lines based on an multi‐dimensional cloud model and Cholesky decomposition was proposed in this study. According to the historical data in meteorological early warning system and icing forecasting system, the mutual correlation degrees of these multiple external environmental factors are got by the grey relational algorithm and the comprehensive prediction model for the failure rate of transmission lines was established based on an multi‐dimensional cloud model and Cholesky decomposition. In light with the external environmental factors in the next year, the failure rate of transmission lines was predicted by this proposed method. The test results based on actual data in Guizhou Power Grid show that this proposed comprehensive prediction method for the failure rate of transmission lines has high accuracy as the prediction error is about 2.05% and a much low computational burden as it only takes 30.25 s which is highly suitable for practical applications.