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Integrated distribution expansion planning considering stochastic renewable energy resources and electric vehicles

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  • Fan, Vivienne Hui
  • Dong, Zhaoyang
  • Meng, Ke

Abstract

Renewable energy resources and transport electrification have become essential components of modern and future distribution planning. Despite the impetus of economic and environmental benefits, the uncertainty brought in poses challenges. In this paper, we propose an integrated expansion planning framework based on a multiobjective mixed-integer nonlinear program. The aim is to minimize the net present value of investments considering feeder routing, substation alterations and construction while maximizing the utilization of proposed charging stations. Distributed generation and load uncertainties, recast in two-stage stochastic programming, are tackled with a scenario generation and reduction technique using a probabilistic approach with Kantorovich metrics. The final number of the scenarios are validated with an alternative clustering method. A flow-based location–allocation theory with user equilibrium traffic assignment model is exploited to site charging stations. The sizing problem is determined using continuous-time Markov chain modeling. The proposed framework is solved with a multiobjective Tchebycheff decomposition-based evolutionary algorithm and tested on a modified 54 bus distribution network and 25 transportation node system. Numerical results demonstrate the capability of the proposed method. Distribution planning authorities can benefit from the presented approach to make intertemporal investment decisions while maintaining the quality of system performance.

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  • Fan, Vivienne Hui & Dong, Zhaoyang & Meng, Ke, 2020. "Integrated distribution expansion planning considering stochastic renewable energy resources and electric vehicles," Applied Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920312125
    DOI: 10.1016/j.apenergy.2020.115720
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    Cited by:

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    2. Hyeon Woo & Yongju Son & Jintae Cho & Sungyun Choi, 2022. "Stochastic Second-Order Conic Programming for Optimal Sizing of Distributed Generator Units and Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    3. Abdullah M. Shaheen & Ehab E. Elattar & Nadia A. Nagem & Asmaa F. Nasef, 2023. "Allocation of PV Systems with Volt/Var Control Based on Automatic Voltage Regulators in Active Distribution Networks," Sustainability, MDPI, vol. 15(21), pages 1-23, November.
    4. Tabar, Vahid Sohrabi & Banazadeh, Hamidreza & Tostado-Véliz, Marcos & Jordehi, Ahmad Rezaee & Nasir, Mohammad & Jurado, Francisco, 2022. "Stochastic multi-stage multi-objective expansion of renewable resources and electrical energy storage units in distribution systems considering crypto-currency miners and responsive loads," Renewable Energy, Elsevier, vol. 198(C), pages 1131-1147.
    5. Zhichun Yang & Gang Han & Fan Yang & Yu Shen & Yu Liu & Huaidong Min & Zhiqiang Zhou & Bin Zhou & Wei Hu & Yang Lei, 2023. "A Distribution Network Planning Method Considering the Distributed Energy Resource Flexibility of Virtual Power Plants," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    6. Mohseni-Bonab, Seyed Masoud & Kamwa, Innocent & Rabiee, Abbas & Chung, C.Y., 2022. "Stochastic optimal transmission Switching: A novel approach to enhance power grid security margins through vulnerability mitigation under renewables uncertainties," Applied Energy, Elsevier, vol. 305(C).
    7. Li, Chengzhou & Wang, Ningling & Wang, Zhuo & Dou, Xiaoxiao & Zhang, Yumeng & Yang, Zhiping & Maréchal, François & Wang, Ligang & Yang, Yongping, 2022. "Energy hub-based optimal planning framework for user-level integrated energy systems: Considering synergistic effects under multiple uncertainties," Applied Energy, Elsevier, vol. 307(C).

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