On Distributed PV Hosting Capacity Estimation, Sensitivity Study and Improvement

Fei Ding; Barry Mather

doi:10.1109/TSTE.2016.2640239

On Distributed PV Hosting Capacity Estimation, Sensitivity Study and Improvement

Fei Ding, Barry Mather

Power Systems Engineering

Research output: Contribution to journal › Article › peer-review

232 Scopus Citations

Abstract

This paper first studies the estimated distributed photovoltaic (PV) hosting capacities of 17 utility distribution feeders using the Monte Carlo simulation based stochastic analysis, and then analyzes the sensitivity of PV hosting capacity to both feeder and PV system characteristics. Furthermore, an active distribution network management approach is proposed to maximize PV hosting capacity by optimally switching capacitors, adjusting voltage regulator taps, managing controllable branch switches, and controlling smart PV inverters. The approach is formulated as a mixed-integer nonlinear optimization problem and a genetic algorithm is developed to obtain the solution. Multiple simulation cases are studied and the effectiveness of the proposed approach on increasing PV hosting capacity is demonstrated.

Original language	American English
Article number	7784836
Pages (from-to)	1010-1020
Number of pages	11
Journal	IEEE Transactions on Sustainable Energy
Volume	8
Issue number	3
DOIs	https://doi.org/10.1109/TSTE.2016.2640239 https://doi.org/10.1109/TSTE.2016.2640239
State	Published - Jul 2017

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

NREL Publication Number

NREL/JA-5D00-67598

Keywords

distribution network management
genetic algorithm
hosting capacity
network reconfiguration
Photovoltaic
smart inverter
voltage improvement
voltage regulation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

Cite this

@article{b015c982ec9b47b09a768b6a87b74b4b,

title = "On Distributed PV Hosting Capacity Estimation, Sensitivity Study and Improvement",

abstract = "This paper first studies the estimated distributed photovoltaic (PV) hosting capacities of 17 utility distribution feeders using the Monte Carlo simulation based stochastic analysis, and then analyzes the sensitivity of PV hosting capacity to both feeder and PV system characteristics. Furthermore, an active distribution network management approach is proposed to maximize PV hosting capacity by optimally switching capacitors, adjusting voltage regulator taps, managing controllable branch switches, and controlling smart PV inverters. The approach is formulated as a mixed-integer nonlinear optimization problem and a genetic algorithm is developed to obtain the solution. Multiple simulation cases are studied and the effectiveness of the proposed approach on increasing PV hosting capacity is demonstrated.",

keywords = "distribution network management, genetic algorithm, hosting capacity, network reconfiguration, Photovoltaic, smart inverter, voltage improvement, voltage regulation",

author = "Fei Ding and Barry Mather",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.",

year = "2017",

month = jul,

doi = "10.1109/TSTE.2016.2640239",

language = "American English",

volume = "8",

pages = "1010--1020",

journal = "IEEE Transactions on Sustainable Energy",

issn = "1949-3029",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "3",

}

TY - JOUR

T1 - On Distributed PV Hosting Capacity Estimation, Sensitivity Study and Improvement

AU - Ding, Fei

AU - Mather, Barry

PY - 2017/7

Y1 - 2017/7

N2 - This paper first studies the estimated distributed photovoltaic (PV) hosting capacities of 17 utility distribution feeders using the Monte Carlo simulation based stochastic analysis, and then analyzes the sensitivity of PV hosting capacity to both feeder and PV system characteristics. Furthermore, an active distribution network management approach is proposed to maximize PV hosting capacity by optimally switching capacitors, adjusting voltage regulator taps, managing controllable branch switches, and controlling smart PV inverters. The approach is formulated as a mixed-integer nonlinear optimization problem and a genetic algorithm is developed to obtain the solution. Multiple simulation cases are studied and the effectiveness of the proposed approach on increasing PV hosting capacity is demonstrated.

AB - This paper first studies the estimated distributed photovoltaic (PV) hosting capacities of 17 utility distribution feeders using the Monte Carlo simulation based stochastic analysis, and then analyzes the sensitivity of PV hosting capacity to both feeder and PV system characteristics. Furthermore, an active distribution network management approach is proposed to maximize PV hosting capacity by optimally switching capacitors, adjusting voltage regulator taps, managing controllable branch switches, and controlling smart PV inverters. The approach is formulated as a mixed-integer nonlinear optimization problem and a genetic algorithm is developed to obtain the solution. Multiple simulation cases are studied and the effectiveness of the proposed approach on increasing PV hosting capacity is demonstrated.

KW - distribution network management

KW - genetic algorithm

KW - hosting capacity

KW - network reconfiguration

KW - Photovoltaic

KW - smart inverter

KW - voltage improvement

KW - voltage regulation

UR - http://www.scopus.com/inward/record.url?scp=85028807082&partnerID=8YFLogxK

U2 - 10.1109/TSTE.2016.2640239

DO - 10.1109/TSTE.2016.2640239

M3 - Article

AN - SCOPUS:85028807082

SN - 1949-3029

VL - 8

SP - 1010

EP - 1020

JO - IEEE Transactions on Sustainable Energy

JF - IEEE Transactions on Sustainable Energy

IS - 3

M1 - 7784836

ER -

On Distributed PV Hosting Capacity Estimation, Sensitivity Study and Improvement

Abstract

Bibliographical note

NREL Publication Number

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this