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Optimal allocation and adaptive VAR control of PV-DG in distribution networks

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  • Fu, Xueqian
  • Chen, Haoyong
  • Cai, Runqing
  • Yang, Ping

Abstract

The development of distributed generation (DG) has brought new challenges to power networks. One of them that catches extensive attention is the voltage regulation problem of distribution networks caused by DG. Optimal allocation of DG in distribution networks is another well-known problem being widely investigated. This paper proposes a new method for the optimal allocation of photovoltaic distributed generation (PV-DG) considering the non-dispatchable characteristics of PV units. An adaptive reactive power control model is introduced in PV-DG allocation as to balance the trade-off between the improvement of voltage quality and the minimization of power loss in a distribution network integrated with PV-DG units. The optimal allocation problem is formulated as a chance-constrained stochastic programming (CCSP) model for dealing with the randomness of solar power energy. A novel algorithm combining the multi-objective particle swarm optimization (MOPSO) with support vector machines (SVM) is proposed to find the Pareto front consisting of a set of possible solutions. The Pareto solutions are further evaluated using the weighted rank sum ratio (WRSR) method to help the decision-maker obtain the desired solution. Simulation results on a 33-bus radial distribution system show that the optimal allocation method can fully take into account the time-variant characteristics and probability distribution of PV-DG, and obtain the best allocation scheme.

Suggested Citation

  • Fu, Xueqian & Chen, Haoyong & Cai, Runqing & Yang, Ping, 2015. "Optimal allocation and adaptive VAR control of PV-DG in distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 173-182.
    • Handle: RePEc:eee:appene:v:137:y:2015:i:c:p:173-182
    DOI: 10.1016/j.apenergy.2014.10.012
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    14. Fu, Xueqian & Li, Gengyin & Zhang, Xiurong & Qiao, Zheng, 2018. "Failure probability estimation of the gas supply using a data-driven model in an integrated energy system," Applied Energy, Elsevier, vol. 232(C), pages 704-714.
    15. Abeysinghe, Sathsara & Wu, Jianzhong & Sooriyabandara, Mahesh & Abeysekera, Muditha & Xu, Tao & Wang, Chengshan, 2018. "Topological properties of medium voltage electricity distribution networks," Applied Energy, Elsevier, vol. 210(C), pages 1101-1112.
    16. Wu, Qiyan & Zhang, Xiaoling & Sun, Jingwei & Ma, Zhifei & Zhou, Chen, 2016. "Locked post-fossil consumption of urban decentralized solar photovoltaic energy: A case study of an on-grid photovoltaic power supply community in Nanjing, China," Applied Energy, Elsevier, vol. 172(C), pages 1-11.
    17. Wang, Licheng & Yan, Ruifeng & Saha, Tapan Kumar, 2019. "Voltage regulation challenges with unbalanced PV integration in low voltage distribution systems and the corresponding solution," Applied Energy, Elsevier, vol. 256(C).
    18. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Zhang, Xiurong & Wang, Li, 2017. "Estimation of the failure probability of an integrated energy system based on the first order reliability method," Energy, Elsevier, vol. 134(C), pages 1068-1078.
    19. Fu, Xueqian & Zhang, Xiurong & Qiao, Zheng & Li, Gengyin, 2019. "Estimating the failure probability in an integrated energy system considering correlations among failure patterns," Energy, Elsevier, vol. 178(C), pages 656-666.
    20. Zhang, Lu & Shen, Chen & Chen, Ying & Huang, Shaowei & Tang, Wei, 2018. "Coordinated allocation of distributed generation, capacitor banks and soft open points in active distribution networks considering dispatching results," Applied Energy, Elsevier, vol. 231(C), pages 1122-1131.

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