Treffer: ADN source-load-storage cooperative two-layer optimal allocation based on ICSQPSO algorithm.
Title:
ADN source-load-storage cooperative two-layer optimal allocation based on ICSQPSO algorithm.
Authors:
Li X; Hebei Petroleum University of Technology, Chengde, 067000, Hebei, China., Zhang K; Hebei Petroleum University of Technology, Chengde, 067000, Hebei, China. forkangkang@126.com., Zhao L; Hebei Petroleum University of Technology, Chengde, 067000, Hebei, China., Zhu L; Hebei Petroleum University of Technology, Chengde, 067000, Hebei, China., Lu Z; Hebei Petroleum University of Technology, Chengde, 067000, Hebei, China.
Source:
Scientific reports [Sci Rep] 2025 Nov 06; Vol. 15 (1), pp. 38866. Date of Electronic Publication: 2025 Nov 06.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
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Ahmadisourenabadi, B., Marzband, M., Hosseini-Hemati, S., Sadati, S. M. B. & Rastgou, A. Quantifying and enabling the resiliency of a microgrid considering electric vehicles using a bayesian network risk assessment. Energy 308, 133036 (2024). (PMID: 10.1016/j.energy.2024.133036)
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Cheng, T. et al. Dynamic multiobjective optimal dispatch of distribution network considering time of use–based demand response. J. Energy Eng. 148, 4021068 (2022). (PMID: 10.1061/(ASCE)EY.1943-7897.0000808)
Mi, Y. et al. Multi-timescale optimal dispatching strategy for coordinated source-grid-load-storage interaction in active distribution networks based on second-order cone planning. Energies 16, 1356 (2023). (PMID: 10.3390/en16031356)
Zhou, Y. & Zhang, J. Three-layer day-ahead scheduling for active distribution network by considering multiple stakeholders. Energy 207, 118263 (2020). (PMID: 10.1016/j.energy.2020.118263)
Ma, X., Wu, H. & Yuan, Y. Robust dispatching model of active distribution network considering PV time-varying spatial correlation. Front. Energy Res. 10, (2023).
Xie, R. et al. Bi-level two-stage stochastic optimization for wind energy transmission networks encompassing active distribution networks. Energy Rep. 6, 370–378 (2020). (PMID: 10.1016/j.egyr.2020.11.228)
Qiu, H., Gu, W. & You, F. Bilayer distributed optimization for robust microgrid dispatch with coupled individual-collective profits. IEEE Trans. Sustain. Energy 12, 1525–1538 (2021). (PMID: 10.1109/TSTE.2021.3053559)
Liu, J., Zeng, P. P., Xing, H., Li, Y. & Wu, Q. Hierarchical duality-based planning of transmission networks coordinating active distribution network operation. Energy 213, 118488 (2020). (PMID: 10.1016/j.energy.2020.118488)
Wang, M., Cao, Z. & Li, Y. Quantum‐behaved particle swarm optimization based on concentration selection probability assignment weights for power system economic dispatch. https://doi.org/10.1002/tee.24015 .
Huang, X., Xie, Z. & Huang, X. Fault location of distribution network base on improved cuckoo search algorithm. IEEE Access 8, 2272–2283 (2020). (PMID: 10.1109/ACCESS.2019.2962276)
Magzoub, M. A. & Alquthami, T. Optimal design of automatic generation control based on simulated annealing in interconnected two-area power system using hybrid PID—fuzzy control. Energies 15, 1540 (2022). (PMID: 10.3390/en15041540)
Selvakumar, A. & Thanushkodi, K. A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans. Power Syst. 22, 42–51 (2007). (PMID: 10.1109/TPWRS.2006.889132)
Wang, C., Liu, C., Zhou, X. & Zhang, G. Flexibility-based expansion planning of active distribution networks considering optimal operation of multi-community integrated energy systems. Energy 307, 132601 (2024). (PMID: 10.1016/j.energy.2024.132601)
Lu, Y. et al. Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load. Energy 271, 127087 (2023). (PMID: 10.1016/j.energy.2023.127087)
Zhu, B., Zhang, L. & Li, G. Identification of vulnerable transmission lines in power system based on game theory. IEEE Access 12, 29607–29616 (2024). (PMID: 10.1109/ACCESS.2024.3368629)
Salari, A., Zeinali, M. & Marzband, M. Model-free reinforcement learning-based energy management for plug-in electric vehicles in a cooperative multi-agent home microgrid with consideration of travel behavior. Energy 288, 129725 (2024). (PMID: 10.1016/j.energy.2023.129725)
Hossain, J. et al. Optimal peak-shaving for dynamic demand response in smart malaysian commercial buildings utilizing an efficient PV-BES system. Sustain. Cities Soc. 101, 105107 (2024). (PMID: 10.1016/j.scs.2023.105107)
Li, Y., Feng, B., Wang, B. & Sun, S. Joint planning of distributed generations and energy storage in active distribution networks: A bi-level programming approach. Energy 245, 123226 (2022). (PMID: 10.1016/j.energy.2022.123226)
Jonban, M. S., Romeral, L., Marzband, M. & Abusorrah, A. Intelligent fault tolerant energy management system using first-price sealed-bid algorithm for microgrids. Sustain. Energy Grids Netw. 38, 101309 (2024). (PMID: 10.1016/j.segan.2024.101309)
Jiang, Z., Yu, Q., Xiong, Y., Li, L. & Liu, Y. Regional active distribution network planning study based on robust optimization. Energy Rep. 7, 314–319 (2021). (PMID: 10.1016/j.egyr.2021.06.050)
Deshmukh, S. et al. Impact assessment of electric vehicles integration and optimal charging schemes under uncertainty: a case study of Qatar. IEEE Access 12, 131350–131371 (2024). (PMID: 10.1109/ACCESS.2024.3458410)
Ahmadisourenabadi, B., Marzband, M., Hosseini-Hemati, S., Sadati, S. M. B. & Rastgou, A. Quantifying and enabling the resiliency of a microgrid considering electric vehicles using a bayesian network risk assessment. Energy 308, 133036 (2024). (PMID: 10.1016/j.energy.2024.133036)
Moradi-Sepahvand, M., Amraee, T., Aminifar, F. & Akbari, A. Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies. Int. J. Electr. Power Energy Syst. 147, 108859 (2023). (PMID: 10.1016/j.ijepes.2022.108859)
Ibrahim, K., Sirjani, R. & Shareef, H. Performance assessment of pareto and non-pareto approaches for the optimal allocation of DG and DSTATCOM in the distribution system. Teh. Vjesn. 27, 1654–1661 (2020).
Koutsoukis, N. C. & Georgilakis, P. S. A multistage distribution network planning method considering distributed generation active management and demand response. https://doi.org/10.1049/rpg2.12325 .
Ding, T. et al. Multi-period active distribution network planning using multi-stage stochastic programming and nested decomposition by SDDIP. IEEE Trans. Power Syst. 36, 2281–2292 (2021). (PMID: 10.1109/TPWRS.2020.3032830)
Alhendi, A., Al-Sumaiti, A. S., Marzband, M., Kumar, R. & Diab, A. A. Z. Short-term load and price forecasting using artificial neural network with enhanced markov chain for ISO new england. Energy Rep. 9, 4799–4815 (2023). (PMID: 10.1016/j.egyr.2023.03.116)
Hu, Y. X. et al. Economic dispatch of active distribution network considering admissible region of net load based on new injection shift factors. Int. J. Electr. Power Energy Syst. 145, 108641 (2023). (PMID: 10.1016/j.ijepes.2022.108641)
Cheng, T. et al. Dynamic multiobjective optimal dispatch of distribution network considering time of use–based demand response. J. Energy Eng. 148, 4021068 (2022). (PMID: 10.1061/(ASCE)EY.1943-7897.0000808)
Mi, Y. et al. Multi-timescale optimal dispatching strategy for coordinated source-grid-load-storage interaction in active distribution networks based on second-order cone planning. Energies 16, 1356 (2023). (PMID: 10.3390/en16031356)
Zhou, Y. & Zhang, J. Three-layer day-ahead scheduling for active distribution network by considering multiple stakeholders. Energy 207, 118263 (2020). (PMID: 10.1016/j.energy.2020.118263)
Ma, X., Wu, H. & Yuan, Y. Robust dispatching model of active distribution network considering PV time-varying spatial correlation. Front. Energy Res. 10, (2023).
Xie, R. et al. Bi-level two-stage stochastic optimization for wind energy transmission networks encompassing active distribution networks. Energy Rep. 6, 370–378 (2020). (PMID: 10.1016/j.egyr.2020.11.228)
Qiu, H., Gu, W. & You, F. Bilayer distributed optimization for robust microgrid dispatch with coupled individual-collective profits. IEEE Trans. Sustain. Energy 12, 1525–1538 (2021). (PMID: 10.1109/TSTE.2021.3053559)
Liu, J., Zeng, P. P., Xing, H., Li, Y. & Wu, Q. Hierarchical duality-based planning of transmission networks coordinating active distribution network operation. Energy 213, 118488 (2020). (PMID: 10.1016/j.energy.2020.118488)
Wang, M., Cao, Z. & Li, Y. Quantum‐behaved particle swarm optimization based on concentration selection probability assignment weights for power system economic dispatch. https://doi.org/10.1002/tee.24015 .
Huang, X., Xie, Z. & Huang, X. Fault location of distribution network base on improved cuckoo search algorithm. IEEE Access 8, 2272–2283 (2020). (PMID: 10.1109/ACCESS.2019.2962276)
Magzoub, M. A. & Alquthami, T. Optimal design of automatic generation control based on simulated annealing in interconnected two-area power system using hybrid PID—fuzzy control. Energies 15, 1540 (2022). (PMID: 10.3390/en15041540)
Selvakumar, A. & Thanushkodi, K. A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans. Power Syst. 22, 42–51 (2007). (PMID: 10.1109/TPWRS.2006.889132)
Wang, C., Liu, C., Zhou, X. & Zhang, G. Flexibility-based expansion planning of active distribution networks considering optimal operation of multi-community integrated energy systems. Energy 307, 132601 (2024). (PMID: 10.1016/j.energy.2024.132601)
Lu, Y. et al. Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load. Energy 271, 127087 (2023). (PMID: 10.1016/j.energy.2023.127087)
Zhu, B., Zhang, L. & Li, G. Identification of vulnerable transmission lines in power system based on game theory. IEEE Access 12, 29607–29616 (2024). (PMID: 10.1109/ACCESS.2024.3368629)
Contributed Indexing:
Keywords: Active distribution network; Collaborative planning; Demand response; Distributed power; Economical analysis; ICSQPSO algorithm
Entry Date(s):
Date Created: 20251106 Latest Revision: 20251109
Update Code:
20251109
PubMed Central ID:
PMC12592411
DOI:
10.1038/s41598-025-22641-8
PMID:
41198713
Database:
MEDLINE
Weitere Informationen
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..
(© 2025. The Author(s).)
Declarations. Competing interests: The authors declare no competing interests.