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Sensitivity analysis of volt-VAR optimization to data changes in distribution networks with distributed energy resources

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  • Mak, Davye
  • Choeum, Daranith
  • Choi, Dae-Hyun

Abstract

The Volt-VAR optimization (VVO) model is combined with distributed energy resources (e.g., solar photovoltaic (PV) systems and energy storage systems (ESSs)), advanced sensors and communication networks, as well as demand response programs for reliable and economical distribution grid operations. Unexpected changes in the data used for VVO can degrade the VVO performance. This study examines the impact of changes in VVO data on the optimal solutions of VVO (e.g., nodal voltage, real/reactive power flow at the substation, and total deviation of voltages from the nominal voltage). Using the perturbed Karush-Kuhn-Tucker conditions of the VVO formulation, we develop a linear matrix that evaluates the sensitivity of VVO with respect to change in various types of data—distribution line parameters, the predicted PV generation outputs, demand reduction, and load exponents in exponential load models. The proposed sensitivity matrix can be used by system operators as an analysis tool to quickly identify the data that influences the optimal VVO solution the most by accurately measuring and prioritizing sensitivities. The results of a simulation study using the developed sensitivity matrix are illustrated and verified in two distribution test systems (IEEE 33-node and 123-node systems) with an on-load tap changer (OLTC), capacitor banks (CBs), PV systems, and ESSs.

Suggested Citation

  • Mak, Davye & Choeum, Daranith & Choi, Dae-Hyun, 2020. "Sensitivity analysis of volt-VAR optimization to data changes in distribution networks with distributed energy resources," Applied Energy, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s0306261919320185
    DOI: 10.1016/j.apenergy.2019.114331
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    References listed on IDEAS

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    1. Wang, Xiaoxue & Wang, Chengshan & Xu, Tao & Guo, Lingxu & Li, Peng & Yu, Li & Meng, He, 2018. "Optimal voltage regulation for distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 210(C), pages 1027-1036.
    2. Zhang, Wenjie & Gandhi, Oktoviano & Quan, Hao & Rodríguez-Gallegos, Carlos D. & Srinivasan, Dipti, 2018. "A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination," Applied Energy, Elsevier, vol. 229(C), pages 96-110.
    3. Wang, Xiaoxue & Wang, Chengshan & Xu, Tao & Meng, He & Li, Peng & Yu, Li, 2018. "Distributed voltage control for active distribution networks based on distribution phasor measurement units," Applied Energy, Elsevier, vol. 229(C), pages 804-813.
    4. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Ding, Fei & Wu, Jianzhong, 2018. "A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks," Applied Energy, Elsevier, vol. 228(C), pages 2024-2036.
    5. E. Castillo & A. J. Conejo & C. Castillo & R. Mínguez & D. Ortigosa, 2006. "Perturbation Approach to Sensitivity Analysis in Mathematical Programming," Journal of Optimization Theory and Applications, Springer, vol. 128(1), pages 49-74, January.
    6. Ho-Yong Jeong & Jong-Chan Choi & Dong-Jun Won & Seon-Ju Ahn & Seung-il Moon, 2015. "Formulation and Analysis of an Approximate Expression for Voltage Sensitivity in Radial DC Distribution Systems," Energies, MDPI, vol. 8(9), pages 1-24, August.
    7. Manbachi, M. & Sadu, A. & Farhangi, H. & Monti, A. & Palizban, A. & Ponci, F. & Arzanpour, S., 2016. "Impact of EV penetration on Volt–VAR Optimization of distribution networks using real-time co-simulation monitoring platform," Applied Energy, Elsevier, vol. 169(C), pages 28-39.
    8. Choi, Dae-Hyun & Xie, Le, 2016. "A framework for sensitivity analysis of data errors on home energy management system," Energy, Elsevier, vol. 117(P1), pages 166-175.
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