Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load Uncertainties

The increasing prevalence of renewable energy sources and the heightened uncertainty in load demands within active distribution networks (ADNs) have led to more fluctuations in power flow and voltage levels during operational periods. In light of these challenges, this paper proposes a robust optimi...

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Main Authors: R. Ding, L. Cheng, X. Wang, Y. Yao, H. Xu, E. Zhao
Format: Article
Language:English
Published: Spolecnost pro radioelektronicke inzenyrstvi 2025-04-01
Series:Radioengineering
Subjects:
Online Access:https://www.radioeng.cz/fulltexts/2025/25_01_0037_0049.pdf
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author R. Ding
L. Cheng
X. Wang
Y. Yao
H. Xu
E. Zhao
author_facet R. Ding
L. Cheng
X. Wang
Y. Yao
H. Xu
E. Zhao
author_sort R. Ding
collection DOAJ
description The increasing prevalence of renewable energy sources and the heightened uncertainty in load demands within active distribution networks (ADNs) have led to more fluctuations in power flow and voltage levels during operational periods. In light of these challenges, this paper proposes a robust optimization framework specifically designed for ADNs, which carefully balances system secu¬rity, economic efficiency, and operational flexibility with multiple types of regulation resources. Firstly, a compre¬hensive regulation methodology is employed to integrate a variety of dispatchable resources. Secondly, the proposed model accounts for the inherent uncertainties related to load demand and the output of renewable energy genera¬tion by using the robust optimization (RO) technique. The proposed robust operational model for ADNs aims to min¬imizing power losses within the network and reducing voltage deviations, thereby improving overall network performance and reliability. Thirdly, the proposed model is linearized and reformulated as a convex optimization problem utilizing second-order cone relaxation techniques, and a relaxed cooperative co-evolution algorithm is im¬plemented to solve it efficiently. Numerical results across various scenarios indicate that, compared to the conven¬tional model without regulation resources, the proposed robust optimization model with multiple types of regulation resources can reduce voltage fluctuations by 89.6% and network losses by 12.9%. The proposed algorithm demon-strates better computational performance compared to conventional methods.
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issn 1210-2512
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publishDate 2025-04-01
publisher Spolecnost pro radioelektronicke inzenyrstvi
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spelling doaj-art-0c6cf4826c7f4b0aa1d5c7feba5a06312025-08-20T02:12:14ZengSpolecnost pro radioelektronicke inzenyrstviRadioengineering1210-25122025-04-013413749Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load UncertaintiesR. DingL. ChengX. WangY. YaoH. XuE. ZhaoThe increasing prevalence of renewable energy sources and the heightened uncertainty in load demands within active distribution networks (ADNs) have led to more fluctuations in power flow and voltage levels during operational periods. In light of these challenges, this paper proposes a robust optimization framework specifically designed for ADNs, which carefully balances system secu¬rity, economic efficiency, and operational flexibility with multiple types of regulation resources. Firstly, a compre¬hensive regulation methodology is employed to integrate a variety of dispatchable resources. Secondly, the proposed model accounts for the inherent uncertainties related to load demand and the output of renewable energy genera¬tion by using the robust optimization (RO) technique. The proposed robust operational model for ADNs aims to min¬imizing power losses within the network and reducing voltage deviations, thereby improving overall network performance and reliability. Thirdly, the proposed model is linearized and reformulated as a convex optimization problem utilizing second-order cone relaxation techniques, and a relaxed cooperative co-evolution algorithm is im¬plemented to solve it efficiently. Numerical results across various scenarios indicate that, compared to the conven¬tional model without regulation resources, the proposed robust optimization model with multiple types of regulation resources can reduce voltage fluctuations by 89.6% and network losses by 12.9%. The proposed algorithm demon-strates better computational performance compared to conventional methods.https://www.radioeng.cz/fulltexts/2025/25_01_0037_0049.pdfactive distribution networksrenewable energy integrationdispatch resourcessource and load uncertaintiessecond-order cone relaxation techniques
spellingShingle R. Ding
L. Cheng
X. Wang
Y. Yao
H. Xu
E. Zhao
Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load Uncertainties
Radioengineering
active distribution networks
renewable energy integration
dispatch resources
source and load uncertainties
second-order cone relaxation techniques
title Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load Uncertainties
title_full Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load Uncertainties
title_fullStr Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load Uncertainties
title_full_unstemmed Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load Uncertainties
title_short Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load Uncertainties
title_sort robust optimal operation method for active distribution networks with multiple types of regulation resources considering source and load uncertainties
topic active distribution networks
renewable energy integration
dispatch resources
source and load uncertainties
second-order cone relaxation techniques
url https://www.radioeng.cz/fulltexts/2025/25_01_0037_0049.pdf
work_keys_str_mv AT rding robustoptimaloperationmethodforactivedistributionnetworkswithmultipletypesofregulationresourcesconsideringsourceandloaduncertainties
AT lcheng robustoptimaloperationmethodforactivedistributionnetworkswithmultipletypesofregulationresourcesconsideringsourceandloaduncertainties
AT xwang robustoptimaloperationmethodforactivedistributionnetworkswithmultipletypesofregulationresourcesconsideringsourceandloaduncertainties
AT yyao robustoptimaloperationmethodforactivedistributionnetworkswithmultipletypesofregulationresourcesconsideringsourceandloaduncertainties
AT hxu robustoptimaloperationmethodforactivedistributionnetworkswithmultipletypesofregulationresourcesconsideringsourceandloaduncertainties
AT ezhao robustoptimaloperationmethodforactivedistributionnetworkswithmultipletypesofregulationresourcesconsideringsourceandloaduncertainties