Distributed generation planning in active distribution network considering demand side management and network reconfiguration

Shenxi Zhang, Haozhong Cheng, Dan Wang, Libo Zhang, Furong Li and Liangzhong Yao

Applied Energy, 2018, vol. 228, issue C, 1936 pages

Abstract: This paper proposes a novel distributed generation (DG) planning methodology in active distribution network considering both demand side management and network reconfiguration. The objective function of the planning model is to minimize the total cost over the planning horizon, including investment cost of DG, operation and management cost of DG, fuel cost of DG, active management cost of DG, and demand side management cost. The constraints contain not only traditional DG investment and electrical restrictions (for instance, limitation of DG penetration, constraint of nodal voltage, constraint of branch capacity, etc.), but also the various restrictions of active management measures including regulating the on-load tap changer of transformer, controlling the output power of DG, demand side management and network reconfiguration. It is a large-scale mixed integer nonlinear programming model, which cannot be effectively solved by a single algorithm. Based on the idea of decomposition and coordination, the planning model is converted to a three-layer programming model. A hybrid solving strategy is developed to solve the model, in which differential evolution algorithm is used to determine the type, location and capacity of DG, and tree structure encoding-partheno genetic algorithm and primal–dual interior point method are applied to simulate the operation of active distribution network and find out the optimal operation state for each scenario. Case studies are carried out on a 61-bus active distribution network in East China, and results show that the total cost over the planning horizon can be reduced about 3.8% when demand side management and network reconfiguration are considered.

Keywords: Active distribution network; Distributed generation; Active management; Three-layer programming; Hybrid solving strategy (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (30)

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DOI: 10.1016/j.apenergy.2018.07.054

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