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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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 38866 - 22
Main Authors: Li, Xiaobang, Zhang, Kang, Zhao, Liying, Zhu, Liying, Lu, Zongqiang
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 06.11.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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
Online Access:Get full text
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Summary: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..
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-22641-8