The effective evaluation of the connectable capacity of renewable energy plays a vital role in the development of a sustainable distribution network. Quantifying the capacity while considering network security and the local renewable accommodation policy is a challenge. This paper proposes a scenario-based bi-level mathematical model using a Bayesian integrated optimization method to evaluate and quantify the connectable capacity of distributed wind generation in distribution networks, which effectively integrates the characteristics of wind power and the local accommodation policy. The constraint on the generation curtailment ratio (CR) is innovatively designed to represent the renewable accommodation policy and integrated with network security constraints to coordinatively quantify the capacity. The model is solved by the Bayesian integrated optimization method. The regression-based algorithm greatly reduces the complexity of alternating iteration and improves the calculation efficiency. Practical cases are used to verify the effectiveness of the proposed method. Results indicate that the method is more efficient than traditional optimization algorithms, and CR integration ensures that the connectable capacity fits local renewable energy development policies well.

With the aggravation of the energy crisis and the enhancement of people’s awareness of environmental protection, distributed energy in the distribution network is growing rapidly in recent years. In this case, the concept of a virtual power plant (VPP) is proposed. VPP aggregates the capacity of many diverse DERs. This paper mainly focuses on formulating the market trading strategy of VPP and the operation strategy of internal resources. Firstly, the basic concepts of VPP are introduced. Secondly, to compare with the proposed model, the traditional deterministic model is introduced. Finally, the robust optimization model is proposed, the objective function is to maximize the profit, the operation conditions of internal resources, and the rules of VPP and market as constraints. The results show that the proposed model can be used as an effective decision-making tool for VPP.

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