Low Carbon Planning Optimization Model of Active Distribution Network Considering Flexible Load

In the context of low carbon distribution network, this paper proposes a mixed integer second-order cone programming model for active distribution network considering carbon emission and flexible load. The objective is to provide the investment strategy with the minimum total cost under the premise of satisfying network operation constraints and CO2 emission ceiling. Considering the uncertainty of new energy, load and energy price, a scenario clustering method based on K-means was proposed. The decision variables of the model include replacing overload lines, investing in new energy and energy storage devices, and investing in voltage control devices such as voltage regulators and capacitor banks. The polynomial voltage dependent flexible load, network reconstruction and carbon emission limit constraints are considered. Aiming at the nonconvex nonlinear characteristics of the programming model, the network reconstruction was modeled as a mixed integer linear programming form by using the virtual demand method, and an improved second-order cone relaxation method based on Taylor expansion was proposed to solve the problem of the traditional second-order cone relaxation caused by the flexible load model. Finally, the model is solved under the framework of two-stage stochastic programming. The model was tested by a 69-node system, and the results show that the proposed model not only has a lower total planning cost, but also contributes to reducing carbon emissions.