ADN source-load-storage cooperative two-layer optimal allocation based on ICSQPSO algorithm

Smart grid technology continues to advance. Renewable energy sees extensive application. The traditional one-way passive distribution network is changing. It is evolving into a two-way interactive, multi-dimensional, and coordinated active distribution network (ADN). The stochastic and temporal natu...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 38866 - 22
Hlavní autoři:	Li, Xiaobang, Zhang, Kang, Zhao, Liying, Zhu, Liying, Lu, Zongqiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 06.11.2025 Nature Publishing Group Nature Portfolio
Témata:	639/4077/4079 639/4077/4082 639/4077/909 Active distribution network Algorithms Alternative energy sources Collaboration Collaborative planning Decision making Demand response Distributed power Economical analysis Energy resources Energy storage Humanities and Social Sciences ICSQPSO algorithm Integrated approach multidisciplinary Network management systems Operating costs Optimization Renewable energy Renewable resources Resource allocation Scheduling Science Science (multidisciplinary) Solar power ICSQPSO algorithm Demand response Economical analysis Active distribution network Collaborative planning Distributed power
ISSN:	2045-2322, 2045-2322
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Abstract	Smart grid technology continues to advance. Renewable energy sees extensive application. The traditional one-way passive distribution network is changing. It is evolving into a two-way interactive, multi-dimensional, and coordinated active distribution network (ADN). The stochastic and temporal nature of distributed generation (DG) output presents challenges. The volatility of loads also presents challenges. These factors pose significant challenges to ADN resource allocation and operation regulation. To address these challenges, a two-layer optimization method is proposed. This method focuses on ADN source-load-storage coordination. It incorporates demand response. The upper planning layer determines the near global optimum device siting and capacity setting scheme. This is within the active distribution network. The lower operation layer derives the near global optimum scheduling scheme for each flexible resource. This includes demand response loads. The two-layer planning problem has inherent complexity. To manage this complexity, an improved algorithm, called cuckoo search quantum behavioural particle swarm optimization (ICSQPSO), is introduced. The model’s validity is demonstrated through an example, indicating a potential reduction in operating costs by 49.7%, a decrease in total investment and operating costs by 37.1%, and a reduction in the wind curtailment rate by 0.4% and the solar power curtailment rate by 1.5%. The effectiveness of the model and algorithm is verified. This verification uses simulation with the IEEE33 algorithm..
AbstractList	Smart grid technology continues to advance. Renewable energy sees extensive application. The traditional one-way passive distribution network is changing. It is evolving into a two-way interactive, multi-dimensional, and coordinated active distribution network (ADN). The stochastic and temporal nature of distributed generation (DG) output presents challenges. The volatility of loads also presents challenges. These factors pose significant challenges to ADN resource allocation and operation regulation. To address these challenges, a two-layer optimization method is proposed. This method focuses on ADN source-load-storage coordination. It incorporates demand response. The upper planning layer determines the near global optimum device siting and capacity setting scheme. This is within the active distribution network. The lower operation layer derives the near global optimum scheduling scheme for each flexible resource. This includes demand response loads. The two-layer planning problem has inherent complexity. To manage this complexity, an improved algorithm, called cuckoo search quantum behavioural particle swarm optimization (ICSQPSO), is introduced. The model’s validity is demonstrated through an example, indicating a potential reduction in operating costs by 49.7%, a decrease in total investment and operating costs by 37.1%, and a reduction in the wind curtailment rate by 0.4% and the solar power curtailment rate by 1.5%. The effectiveness of the model and algorithm is verified. This verification uses simulation with the IEEE33 algorithm.. Abstract Smart grid technology continues to advance. Renewable energy sees extensive application. The traditional one-way passive distribution network is changing. It is evolving into a two-way interactive, multi-dimensional, and coordinated active distribution network (ADN). The stochastic and temporal nature of distributed generation (DG) output presents challenges. The volatility of loads also presents challenges. These factors pose significant challenges to ADN resource allocation and operation regulation. To address these challenges, a two-layer optimization method is proposed. This method focuses on ADN source-load-storage coordination. It incorporates demand response. The upper planning layer determines the near global optimum device siting and capacity setting scheme. This is within the active distribution network. The lower operation layer derives the near global optimum scheduling scheme for each flexible resource. This includes demand response loads. The two-layer planning problem has inherent complexity. To manage this complexity, an improved algorithm, called cuckoo search quantum behavioural particle swarm optimization (ICSQPSO), is introduced. The model’s validity is demonstrated through an example, indicating a potential reduction in operating costs by 49.7%, a decrease in total investment and operating costs by 37.1%, and a reduction in the wind curtailment rate by 0.4% and the solar power curtailment rate by 1.5%. The effectiveness of the model and algorithm is verified. This verification uses simulation with the IEEE33 algorithm.. Smart grid technology continues to advance. Renewable energy sees extensive application. The traditional one-way passive distribution network is changing. It is evolving into a two-way interactive, multi-dimensional, and coordinated active distribution network (ADN). The stochastic and temporal nature of distributed generation (DG) output presents challenges. The volatility of loads also presents challenges. These factors pose significant challenges to ADN resource allocation and operation regulation. To address these challenges, a two-layer optimization method is proposed. This method focuses on ADN source-load-storage coordination. It incorporates demand response. The upper planning layer determines the near global optimum device siting and capacity setting scheme. This is within the active distribution network. The lower operation layer derives the near global optimum scheduling scheme for each flexible resource. This includes demand response loads. The two-layer planning problem has inherent complexity. To manage this complexity, an improved algorithm, called cuckoo search quantum behavioural particle swarm optimization (ICSQPSO), is introduced. The model's validity is demonstrated through an example, indicating a potential reduction in operating costs by 49.7%, a decrease in total investment and operating costs by 37.1%, and a reduction in the wind curtailment rate by 0.4% and the solar power curtailment rate by 1.5%. The effectiveness of the model and algorithm is verified. This verification uses simulation with the IEEE33 algorithm..Smart grid technology continues to advance. Renewable energy sees extensive application. The traditional one-way passive distribution network is changing. It is evolving into a two-way interactive, multi-dimensional, and coordinated active distribution network (ADN). The stochastic and temporal nature of distributed generation (DG) output presents challenges. The volatility of loads also presents challenges. These factors pose significant challenges to ADN resource allocation and operation regulation. To address these challenges, a two-layer optimization method is proposed. This method focuses on ADN source-load-storage coordination. It incorporates demand response. The upper planning layer determines the near global optimum device siting and capacity setting scheme. This is within the active distribution network. The lower operation layer derives the near global optimum scheduling scheme for each flexible resource. This includes demand response loads. The two-layer planning problem has inherent complexity. To manage this complexity, an improved algorithm, called cuckoo search quantum behavioural particle swarm optimization (ICSQPSO), is introduced. The model's validity is demonstrated through an example, indicating a potential reduction in operating costs by 49.7%, a decrease in total investment and operating costs by 37.1%, and a reduction in the wind curtailment rate by 0.4% and the solar power curtailment rate by 1.5%. The effectiveness of the model and algorithm is verified. This verification uses simulation with the IEEE33 algorithm..
ArticleNumber	38866
Author	Lu, Zongqiang Li, Xiaobang Zhang, Kang Zhao, Liying Zhu, Liying
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Title	ADN source-load-storage cooperative two-layer optimal allocation based on ICSQPSO algorithm
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