Optimizing Distributed Photovoltaic System Set Points Under Uncertainty

Kelsey Horowitz; Dheepak Krishnamurthy; Bryan Palmintier

doi:10.1109/PMAPS.2018.8440547

Optimizing Distributed Photovoltaic System Set Points Under Uncertainty

Kelsey Horowitz, Dheepak Krishnamurthy, Bryan Palmintier

Research output: Contribution to conference › Paper › peer-review

1 Scopus Citations

Abstract

We explore the potential for using dynamic programming (DP) and approximate dynamic programming (ADP) techniques to optimize the set points of distributed photovoltaic (DGPV) inverters under uncertainty about future DGPV deployment. We consider a case where a large $(\geq1\mathbf{MW})$ system is installed first, and growth in deployment of small rooftop systems is anticipated, but uncertain. We find that for a real feeder (EPRI's J1 feeder), a significant reduction in the number and severity of voltage violations can be expected when DP or ADP is used compared to selecting set points based on the current conditions (the traditional myopic approach). Additionally, we find that using a simple ADP algorithm, sampled backward induction, is more than twice as fast as DP with similar outcomes.

Original language	American English
Number of pages	6
DOIs	https://doi.org/10.1109/PMAPS.2018.8440547 https://doi.org/10.1109/PMAPS.2018.8440547
State	Published - 17 Aug 2018
Event	2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Boise, United States Duration: 24 Jun 2018 → 28 Jun 2018

Conference

Conference	2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018
Country/Territory	United States
City	Boise
Period	24/06/18 → 28/06/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

NREL Publication Number

NREL/CP-6A20-70690

Keywords

advanced inverter
approximate dynamic programming
distributed PV
distribution system
dynamic programming
optimization
power systems
PV inverter

UN SDGs

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

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Cite this

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abstract = "We explore the potential for using dynamic programming (DP) and approximate dynamic programming (ADP) techniques to optimize the set points of distributed photovoltaic (DGPV) inverters under uncertainty about future DGPV deployment. We consider a case where a large $(\geq1\mathbf{MW})$ system is installed first, and growth in deployment of small rooftop systems is anticipated, but uncertain. We find that for a real feeder (EPRI's J1 feeder), a significant reduction in the number and severity of voltage violations can be expected when DP or ADP is used compared to selecting set points based on the current conditions (the traditional myopic approach). Additionally, we find that using a simple ADP algorithm, sampled backward induction, is more than twice as fast as DP with similar outcomes.",

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AU - Palmintier, Bryan

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KW - approximate dynamic programming

KW - distributed PV

KW - distribution system

KW - dynamic programming

KW - optimization

KW - power systems

KW - PV inverter

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Optimizing Distributed Photovoltaic System Set Points Under Uncertainty

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

UN SDGs

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