Improving photovoltaic hosting capacity of distribution networks with coordinated inverter control: A case study of the EPRI J1 feeder

Dhaval Dalal; Madhura Sondharangalla; Rajapandian Ayyanar; Anamitra Pal

doi:10.1049/rpg2.13181

journal article Open Access Jan 01, 2025

Improving photovoltaic hosting capacity of distribution networks with coordinated inverter control: A case study of the EPRI J1 feeder

Dhaval Dalal

Madhura Sondharangalla Rajapandian Ayyanar Anamitra Pal

IET Renewable Power Generation Vol. 19 No. 1 · Institution of Engineering and Technology (IET)

View at Publisher Save 10.1049/rpg2.13181

Abstract

Abstract
Adding photovoltaic (PV) systems in distribution networks, while desirable for reducing the carbon footprint, can lead to voltage violations under high solar‐low load conditions. The inability of traditional volt‐VAr control in eliminating all the violations is also well‐known. This article presents a novel coordinated inverter control methodology that leverages system‐wide situational awareness to significantly improve hosting capacity (HC). The methodology employs a real‐time voltage‐reactive power (VQ) sensitivity matrix in an iterative linear optimizer to calculate the minimum reactive power intervention from PV inverters needed for mitigating over‐voltage without resorting to active power curtailing or requiring step voltage regulator setting changes. The algorithm is validated using the EPRI J1 feeder under an extensive set of realistic use cases and is shown to provide 3x improvement in HC under all scenarios.

Topics

No keywords indexed for this article. Browse by subject →

References

43

[1]

International Energy Agency (IEA): Global energy and climate model.Tech. Rep. IEA Paris(2022).https://www.iea.org/reports/global‐energy‐and‐climate‐model

[2]

International Energy Agency (IEA): Tracking solar PV.Tech. Rep. IEA Paris(2023).https://www.iea.org/energy‐system/renewables/solar‐pv

[3]

National Renewable Energy Laboratory (NREL): The solar futures study. Report No. NREL/FS‐6A20‐80826.NREL Golden CO(2021).https://www.osti.gov/biblio/1820105

[4]

SolarPower Europe: Global market outlook for solar power 2023–2027.https://www.solarpowereurope.org/insights/outlooks/global‐market‐outlook‐for‐solar‐power‐2023‐2027/detail#global‐solar‐market‐update‐2000‐2022

[5]

10.1109/access.2019.2952569

[6]

IEEE:IEEE standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces.IEEE Std 1547‐2018 (Revision of IEEE Std 1547‐2003) pp.1–138.IEEE Piscataway(2018)

[7]

Interstate Renewable Energy Council: IEEE 1547‐2018 Adoption Tracker.https://irecusa.org/resources/ieee‐1547‐2018‐adoption‐tracker/

[8]

10.1109/tste.2016.2640239

[9]

10.1049/pe:20020101

[10]

10.1109/jphotov.2011.2174821

[11]

Electric Power Research Institute: Hosting capacity process guidebook: 2022 edition.https://www.epri.com/research/programs/108271/results/3002024682

[12]

10.1016/j.ijepes.2021.106928

[13]

Reno M.J. Coogan K. Grijalva S. Broderick R.J. Quiroz J.E.:Pv interconnection risk analysis through distribution system impact signatures and feeder zones. In:2014 IEEE PES General Meeting Conference & Exposition pp.1–5.IEEE Piscataway(2014) 10.1109/pesgm.2014.6939028

[14]

10.1016/j.epsr.2019.106130

[15]

Dalal D. Pal A. Ayyanar R.:Analyzing cross‐phase effects of reactive power intervention on distribution voltage control. In:2024 IEEE Power and Energy Society General Meeting (PESGM).IEEE Piscataway(2024).https://arxiv.org/abs/2311.14553 10.1109/pesgm51994.2024.10689150

[16]

10.1109/jsyst.2020.2982467

[17]

10.1109/tpwrs.2020.3007997

[18]

Horowitz K.A.W. Ding F. Mather B.A. Palmintier B.S.:The cost of distribution system upgrades to accommodate increasing penetrations of distributed photovoltaic systems on real feeders in the United States. Tech. Rep. NREL/TP‐6A20‐70710.NREL Golden CO(2018).https://www.osti.gov/biblio/1432760 10.2172/1432760

[19]

10.1016/j.compeleceng.2023.108872

[20]

10.1016/j.rser.2016.05.016

[21]

Savasci A. Inaolaji A. Paudyal S.:Distribution grid optimal power flow with adaptive volt‐var droop of smart inverters. In:2021 IEEE Industry Applications Society Annual Meeting (IAS) pp.1–8.IEEE Piscataway(2021) 10.1109/ias48185.2021.9677119

[22]

10.1109/tpwrd.2022.3160375

[23]

10.1109/tpwrd.2020.2965107

[24]

McEntee C. Mulcahy D. Wang J. Zhu X. Lu N.:A VSM‐based DER dispatch MINLP for Volt‐VAR control in unbalanced power distribution systems. In:2019 IEEE Power and Energy Society General Meeting (PESGM) pp.1–5.IEEE Piscataway(2019) 10.1109/pesgm40551.2019.8973721

[25]

10.1109/tsg.2020.3030311

[26]

10.1109/jsyst.2021.3071998

[27]

10.1109/tsg.2019.2950120

[28]

10.1109/tsg.2017.2648779

[29]

10.1109/tsg.2022.3185975

[30]

Alizadeh S.M. Ozansoy C. Alpcan T.:The impact of X/R ratio on voltage stability in a distribution network penetrated by wind farms. In:2016 Australasian Universities Power Engineering Conference (AUPEC) pp.1–6.IEEE Piscataway(2016) 10.1109/aupec.2016.7749289

[31]

10.3390/en10111896

[32]

Electric Power Research Institute: EPRI test circuits J1 feeder OpenDSS code.https://sourceforge.net/p/electricdss/code/HEAD/tree/trunk/Distrib/EPRITestCircuits/epri_dpv/J1/

[33]

Kim I. Harley R.G.:A study on the effect of the high‐penetration photovoltaic system on an increase in overvoltage of distribution feeders. In:2015 North American Power Symposium (NAPS) pp.1–4.IEEE Piscataway(2015) 10.1109/naps.2015.7335204

[34]

Pecan Street Research Institute: The Dataport Database.https://pecanstreet.org/dataport/

[35]

Electric Power Research Institute: OpenDSS Reference Guide(2020).https://sourceforge.net/p/electricdss/code/HEAD/tree/trunk/Distrib/Doc/OpenDSSManual.pdf

[36]

10.1109/tsg.2018.2869367

[37]

Azimian B. Biswas R.S. Pal A. Tong L.:Time synchronized state estimation for incompletely observed distribution systems using deep learning considering realistic measurement noise. In:2021 IEEE Power & Energy Society General Meeting (PESGM) pp.1–5.IEEE Piscataway(2021) 10.1109/pesgm46819.2021.9637858

[38]

10.1109/tim.2022.3167722

[39]

Azimian B. Biswas R.S. Pal A.:Cyber‐physical power systems: challenges and solutions by ai/ml big data blockchain iot and information theory paradigms. In:Application of AI and Machine Learning Algorithms in Power System State Estimation.Wiley‐IEEE Press Piscataway(2023)

[40]

Azimian B. "Analytical verification of performance of deep neural network based time‐synchronized distribution system state estimation" J. Mod. Power Syst. Clean Energy (2024)

[41]

Alrushoud A. McEntee C. Lu N.:A zonal Volt/VAR control mechanism for high PV penetration distribution systems. In:2021 IEEE Power and Energy Society General Meeting (PESGM) pp.1–5.IEEE Piscataway(2021) 10.1109/pesgm46819.2021.9637841

[42]

10.1007/978-3-030-68928-5

[43]

Free Software Foundation: GNU linear programming kit.https://www.gnu.org/software/glpk/

Metrics

5

Citations

43

References

Details

Published: Jan 01, 2025
Vol/Issue: 19(1)
License: View

Authors

D

Dhaval Dalal

School of Electrical, Computer, and Energy Engineering Arizona State University (ASU) Tempe, Arizona USA

M

Madhura Sondharangalla

School of Electrical, Computer, and Energy Engineering Arizona State University (ASU) Tempe, Arizona USA

R

Rajapandian Ayyanar

School of Electrical, Computer, and Energy Engineering Arizona State University (ASU) Tempe, Arizona USA

A

Anamitra Pal

School of Electrical, Computer, and Energy Engineering Arizona State University (ASU) Tempe, Arizona USA

Funding

Solar Energy Technologies Office Award: DE‐EE0009355

Cite This Article

Dhaval Dalal, Madhura Sondharangalla, Rajapandian Ayyanar, et al. (2025). Improving photovoltaic hosting capacity of distribution networks with coordinated inverter control: A case study of the EPRI J1 feeder. IET Renewable Power Generation, 19(1). https://doi.org/10.1049/rpg2.13181

Improving photovoltaic hosting capacity of distribution networks with coordinated inverter control: A case study of the EPRI J1 feeder

You May Also Like