Two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties

This paper proposes a stochastic programming (SP) method for coordinated operation of distributed energy resources (DERs) in the unbalanced active distribution network (ADN) with diverse correlated uncertainties. First, the three-phase branch flow is modeled to characterize the unbalanced nature of...

Full description

Saved in:
Bibliographic Details
Main Authors: Leng, Ruoxuan, Li, Zhengmao, Xu, Yan
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
Subjects:
Online Access:https://hdl.handle.net/10356/169662
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-169662
record_format dspace
spelling sg-ntu-dr.10356-1696622023-07-28T15:39:44Z Two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties Leng, Ruoxuan Li, Zhengmao Xu, Yan School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Active Distribution Network Two-Stage Stochastic Programming This paper proposes a stochastic programming (SP) method for coordinated operation of distributed energy resources (DERs) in the unbalanced active distribution network (ADN) with diverse correlated uncertainties. First, the three-phase branch flow is modeled to characterize the unbalanced nature of the ADN, schedule DER for three phases, and derive a realistic DER allocation. Then, both active and reactive power resources are co-optimized for voltage regulation and power loss reduction. Second, the battery degradation is considered to model the aging cost for each charging or discharging event, leading to a more realistic cost estimation. Further, copula-based uncertainty modeling is applied to capture the correlations between renewable generation and power loads, and the two-stage SP method is then used to get final solutions. Finally, numerical case studies are conducted on an IEEE 34- bus three-phase ADN, verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power. Published version 2023-07-28T05:26:48Z 2023-07-28T05:26:48Z 2023 Journal Article Leng, R., Li, Z. & Xu, Y. (2023). Two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties. Journal of Modern Power Systems and Clean Energy, 11(1), 120-131. https://dx.doi.org/10.35833/MPCE.2022.000510 2196-5625 https://hdl.handle.net/10356/169662 10.35833/MPCE.2022.000510 2-s2.0-85148066171 1 11 120 131 en Journal of Modern Power Systems and Clean Energy © 2023 The Author(s). Published by IEEE. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Active Distribution Network
Two-Stage Stochastic Programming
spellingShingle Engineering::Electrical and electronic engineering
Active Distribution Network
Two-Stage Stochastic Programming
Leng, Ruoxuan
Li, Zhengmao
Xu, Yan
Two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties
description This paper proposes a stochastic programming (SP) method for coordinated operation of distributed energy resources (DERs) in the unbalanced active distribution network (ADN) with diverse correlated uncertainties. First, the three-phase branch flow is modeled to characterize the unbalanced nature of the ADN, schedule DER for three phases, and derive a realistic DER allocation. Then, both active and reactive power resources are co-optimized for voltage regulation and power loss reduction. Second, the battery degradation is considered to model the aging cost for each charging or discharging event, leading to a more realistic cost estimation. Further, copula-based uncertainty modeling is applied to capture the correlations between renewable generation and power loads, and the two-stage SP method is then used to get final solutions. Finally, numerical case studies are conducted on an IEEE 34- bus three-phase ADN, verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Leng, Ruoxuan
Li, Zhengmao
Xu, Yan
format Article
author Leng, Ruoxuan
Li, Zhengmao
Xu, Yan
author_sort Leng, Ruoxuan
title Two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties
title_short Two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties
title_full Two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties
title_fullStr Two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties
title_full_unstemmed Two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties
title_sort two-stage stochastic programming for coordinated operation of distributed energy resources in unbalanced active distribution networks with diverse correlated uncertainties
publishDate 2023
url https://hdl.handle.net/10356/169662
_version_ 1773551391818645504