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

Author

Listed:
  • Zhang, Shenxi
  • Cheng, Haozhong
  • Wang, Dan
  • Zhang, Libo
  • Li, Furong
  • Yao, Liangzhong

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.

Suggested Citation

  • Zhang, Shenxi & Cheng, Haozhong & Wang, Dan & Zhang, Libo & Li, Furong & Yao, Liangzhong, 2018. "Distributed generation planning in active distribution network considering demand side management and network reconfiguration," Applied Energy, Elsevier, vol. 228(C), pages 1921-1936.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:1921-1936
    DOI: 10.1016/j.apenergy.2018.07.054
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    References listed on IDEAS

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    1. Mena, Rodrigo & Hennebel, Martin & Li, Yan-Fu & Zio, Enrico, 2014. "Self-adaptable hierarchical clustering analysis and differential evolution for optimal integration of renewable distributed generation," Applied Energy, Elsevier, vol. 133(C), pages 388-402.
    2. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
    3. Luo, Lizi & Gu, Wei & Zhang, Xiao-Ping & Cao, Ge & Wang, Weijun & Zhu, Gang & You, Dingjun & Wu, Zhi, 2018. "Optimal siting and sizing of distributed generation in distribution systems with PV solar farm utilized as STATCOM (PV-STATCOM)," Applied Energy, Elsevier, vol. 210(C), pages 1092-1100.
    4. Zhang, Shenxi & Cheng, Haozhong & Li, Ke & Tai, Nengling & Wang, Dan & Li, Furong, 2018. "Multi-objective distributed generation planning in distribution network considering correlations among uncertainties," Applied Energy, Elsevier, vol. 226(C), pages 743-755.
    5. Quadri, Imran Ahmad & Bhowmick, S. & Joshi, D., 2018. "A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems," Applied Energy, Elsevier, vol. 211(C), pages 1245-1260.
    6. Kabir, M.N. & Mishra, Y. & Bansal, R.C., 2016. "Probabilistic load flow for distribution systems with uncertain PV generation," Applied Energy, Elsevier, vol. 163(C), pages 343-351.
    7. Gharavi, H. & Ardehali, M.M. & Ghanbari-Tichi, S., 2015. "Imperial competitive algorithm optimization of fuzzy multi-objective design of a hybrid green power system with considerations for economics, reliability, and environmental emissions," Renewable Energy, Elsevier, vol. 78(C), pages 427-437.
    8. Calvillo, C.F. & Sánchez-Miralles, A. & Villar, J. & Martín, F., 2016. "Optimal planning and operation of aggregated distributed energy resources with market participation," Applied Energy, Elsevier, vol. 182(C), pages 340-357.
    9. Mokgonyana, Lesiba & Zhang, Jiangfeng & Li, Hailong & Hu, Yihua, 2017. "Optimal location and capacity planning for distributed generation with independent power production and self-generation," Applied Energy, Elsevier, vol. 188(C), pages 140-150.
    10. Wang, Chengshan & Song, Guanyu & Li, Peng & Ji, Haoran & Zhao, Jinli & Wu, Jianzhong, 2017. "Optimal siting and sizing of soft open points in active electrical distribution networks," Applied Energy, Elsevier, vol. 189(C), pages 301-309.
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