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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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    3. Ferreira, Willian M. & Meneghini, Ivan R. & Brandao, Danilo I. & Guimarães, Frederico G., 2020. "Preference cone based multi-objective evolutionary algorithm applied to optimal management of distributed energy resources in microgrids," Applied Energy, Elsevier, vol. 274(C).

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