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基于碳排放流的柔性配电网低碳规划方法
Low-Carbon Planning of Flexible Distribution Network Based on Carbon Emission Flow
随着分布式电源（distributed generation，DG）的规模化发展，利用智能软开关（soft open point，SOP）可以实现对配电网潮流的灵活控制，使柔性配电网（flexible distribution network，FDN）调节潮流的能力进一步增强。建立了一种基于改进碳排放流理论的柔性配电网低碳规划模型。首先，分析柔性配电网的碳排放特点，提出了改进的柔性配电网碳排放流计算方法；然后，考虑DG和SOP的特性，基于改进的碳排放流理论建立了柔性配电网低碳规划模型；进一步，基于二阶锥规划方法求解所提模型，得到柔性配电网的最优低碳规划方案，并结合碳排放流理论得到全网的碳排放流分布；最后，在改进的IEEE 33节点配电系统上对所提模型进行分析与验证，结果表明，对DG和SOP进行合理的协调规划，可以有效提升系统运行的经济性，减少碳排放。
With the large-scale development of distributed generation （DG）， flexible control of power flow in a distribution network can be realized using a soft open point （SOP）， which further enhances the ability of a flexible distribution network （FDN） to adjust the power flow. In this study， a low-carbon planning model of an FDN based on an improved carbon emission flow theory is established. First， the carbon emission characteristics of the FDN are analyzed and an improved method for carbon emission flow calculation for the FDN is proposed. Considering the characteristics of DG and SOP， a low-carbon planning model of the FDN is established based on the improved carbon emission flow theory. Furthermore， based on second-order cone programming， the proposed model is solved to obtain an optimal low-carbon planning scheme for the FDN， and the carbon flow distribution of the entire network is obtained by combining the carbon emission flow theory. Finally， the proposed model is analyzed and verified using an improved IEEE 33 node distribution system. The results indicate that reasonable coordinated planning of DG and SOP can effectively improve the economy of system operation and reduce carbon emissions.
柔性配电网（FDN） / 低碳规划 / 碳排放流 / 分布式电源（DG） / 智能软开关（SOP）
flexible distribution network （FDN） / low-carbon planning / carbon emission flow / distributed generation （DG） / soft open point（SOP）
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In the context of low-carbon distribution network, this paper proposes a mixed-integer second-order cone programming model for active distribution networks considering carbon emissions and flexible loads, with the investment strategy with minimum total cost. Considering the uncertainty of renewable energy, load and energy price, a scenario clustering method based on K-means is proposed. The decision variables of the model are the replacement of overloaded lines, the construction of new energy and energy storage devices, and the construction of voltage control equipment such as voltage regulators and capacitor banks. The polynomial voltage-dependent flexible loads, network reconfiguration and carbon emission constraints are considered. Aiming at the non-convex nonlinear characteristics of the planning model, the virtual demand method is used to model the network reconstruction as a mixed integer linear programming form, and an improved second-order cone relaxation method based on Taylor expansion is proposed to solve the problem of traditional second-order cone relaxation caused by flexible load model. The model is tested with a 69-node system, and the results show that the proposed model not only has a lower overall planning cost, but also helps reduce carbon emissions. |
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