IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i8p2133-d1638975.html
   My bibliography  Save this article

An Adaptive Scheduling Method for Standalone Microgrids Based on Deep Q-Network and Particle Swarm Optimization

Author

Listed:
  • Borui Zhang

    (College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China)

  • Bo Liu

    (College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China)

Abstract

Standalone wind–solar–diesel–storage microgrids serve as a crucial solution for achieving energy self-sufficiency in remote and off-grid areas, such as rural regions and islands, where conventional power grids are unavailable. Addressing scheduling optimization challenges arising from the intermittent nature of renewable energy generation and the uncertainty of load demand, this paper proposes an adaptive optimization scheduling method (DQN-PSO) that integrates Deep Q-Network (DQN) with Particle Swarm Optimization (PSO). The proposed approach leverages DQN to assess the operational state of the microgrid and dynamically adjust the key parameters of PSO. Additionally, a multi-strategy switching mechanism, incorporating global search, local adjustment, and reliability enhancement, is introduced to jointly optimize both clean energy utilization and power supply reliability. Simulation results demonstrate that, under typical daily, high-volatility, and low-load scenarios, the proposed method improves clean energy utilization by 3.2%, 4.5%, and 10.9%, respectively, compared to conventional PSO algorithms while reducing power supply reliability risks to 0.70%, 1.04%, and 0.30%, respectively. These findings validate the strong adaptability of the proposed algorithm to dynamic environments. Further, a parameter sensitivity analysis underscores the significance of the dynamic adjustment mechanism.

Suggested Citation

  • Borui Zhang & Bo Liu, 2025. "An Adaptive Scheduling Method for Standalone Microgrids Based on Deep Q-Network and Particle Swarm Optimization," Energies, MDPI, vol. 18(8), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2133-:d:1638975
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/8/2133/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/8/2133/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuxin Wen & Peixiao Fan & Jia Hu & Song Ke & Fuzhang Wu & Xu Zhu, 2022. "An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    2. Haoqian Huang & Chao Jin & Hassan Zargarzadeh, 2021. "A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning," Complexity, Hindawi, vol. 2021, pages 1-13, December.
    3. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Zaman, Forhad & Guerrero, Josep M., 2019. "Energy scheduling of community microgrid with battery cost using particle swarm optimisation," Applied Energy, Elsevier, vol. 254(C).
    4. Bo Yang & Yulin Li & Wei Yao & Lin Jiang & Chuanke Zhang & Chao Duan & Yaxing Ren, 2023. "Optimization and Control of New Power Systems under the Dual Carbon Goals: Key Issues, Advanced Techniques, and Perspectives," Energies, MDPI, vol. 16(9), pages 1-4, May.
    5. Zhang, Zihang & Li, Jiayi & Lei, Zhenyu & Zhu, Qianyu & Cheng, Jiujun & Gao, Shangce, 2024. "Reinforcement learning-based particle swarm optimization for wind farm layout problems," Energy, Elsevier, vol. 313(C).
    6. Liang, Tao & Chai, Lulu & Cao, Xin & Tan, Jianxin & Jing, Yanwei & Lv, Liangnian, 2024. "Real-time optimization of large-scale hydrogen production systems using off-grid renewable energy: Scheduling strategy based on deep reinforcement learning," Renewable Energy, Elsevier, vol. 224(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Yuan & Wang, Jiangjiang & Dong, Fuxiang & Qin, Yanbo & Ma, Zherui & Ma, Yanpeng & Li, Jianqiang, 2021. "Novel flexibility evaluation of hybrid combined cooling, heating and power system with an improved operation strategy," Applied Energy, Elsevier, vol. 300(C).
    2. Lan, Penghang & Chen, She & Li, Qihang & Li, Kelin & Wang, Feng & Zhao, Yaoxun, 2024. "Intelligent hydrogen-ammonia combined energy storage system with deep reinforcement learning," Renewable Energy, Elsevier, vol. 237(PB).
    3. Shang, Yuwei & Wu, Wenchuan & Guo, Jianbo & Ma, Zhao & Sheng, Wanxing & Lv, Zhe & Fu, Chenran, 2020. "Stochastic dispatch of energy storage in microgrids: An augmented reinforcement learning approach," Applied Energy, Elsevier, vol. 261(C).
    4. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).
    5. Md Shafiul Alam & Fahad Saleh Al-Ismail & Mohammad Ali Abido, 2021. "PV/Wind-Integrated Low-Inertia System Frequency Control: PSO-Optimized Fractional-Order PI-Based SMES Approach," Sustainability, MDPI, vol. 13(14), pages 1-21, July.
    6. Prakash, K. & Ali, M. & Hossain, M A & Kumar, Nallapaneni Manoj & Islam, M.R. & Macana, C.A. & Chopra, Shauhrat S. & Pota, H.R., 2022. "Planning battery energy storage system in line with grid support parameters enables circular economy aligned ancillary services in low voltage networks," Renewable Energy, Elsevier, vol. 201(P1), pages 802-820.
    7. Feifeng Zheng & Zhaojie Wang & Ming Liu, 2022. "Overnight charging scheduling of battery electric buses with uncertain charging time," Operational Research, Springer, vol. 22(5), pages 4865-4903, November.
    8. Songkai Wang & Rong Jia & Xiaoyu Shi & Chang Luo & Yuan An & Qiang Huang & Pengcheng Guo & Xueyan Wang & Xuewen Lei, 2022. "Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)," Energies, MDPI, vol. 15(4), pages 1-18, February.
    9. Xuejun Li & Jiaxin Qian & Changhai Yang & Boyang Chen & Xiang Wang & Zongnan Jiang, 2024. "New Power System Planning and Evolution Path with Multi-Flexibility Resource Coordination," Energies, MDPI, vol. 17(1), pages 1-20, January.
    10. Collath, Nils & Cornejo, Martin & Engwerth, Veronika & Hesse, Holger & Jossen, Andreas, 2023. "Increasing the lifetime profitability of battery energy storage systems through aging aware operation," Applied Energy, Elsevier, vol. 348(C).
    11. Li, Shenglin & Zhu, Jizhong & Chen, Ziyu & Luo, Tengyan, 2021. "Double-layer energy management system based on energy sharing cloud for virtual residential microgrid," Applied Energy, Elsevier, vol. 282(PA).
    12. Philippe de Bekker & Sho Cremers & Sonam Norbu & David Flynn & Valentin Robu, 2023. "Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm," Energies, MDPI, vol. 16(5), pages 1-26, March.
    13. Qamar, Hafiz Ghulam Murtza & Guo, Xiaoqiang & Ahmad, Fareed, 2024. "Intelligent energy management system of hydrogen based microgrid empowered by AI optimization technique," Renewable Energy, Elsevier, vol. 237(PB).
    14. Li, Shenglin & Zhu, Jizhong & Dong, Hanjiang & Zhu, Haohao & Fan, Junwei, 2022. "A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids," Applied Energy, Elsevier, vol. 309(C).
    15. Zhang, Shulei & Jia, Runda & Pan, Hengxin & Cao, Yankai, 2023. "A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid," Applied Energy, Elsevier, vol. 348(C).
    16. Kumar, R. Seshu & Raghav, L. Phani & Raju, D. Koteswara & Singh, Arvind R., 2021. "Intelligent demand side management for optimal energy scheduling of grid connected microgrids," Applied Energy, Elsevier, vol. 285(C).
    17. Hafiz Abdul Muqeet & Hafiz Mudassir Munir & Haseeb Javed & Muhammad Shahzad & Mohsin Jamil & Josep M. Guerrero, 2021. "An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges," Energies, MDPI, vol. 14(20), pages 1-34, October.
    18. Zhou, Yuan & Wang, Jiangjiang & Yang, Mingxu & Xu, Hangwei, 2023. "Hybrid active and passive strategies for chance-constrained bilevel scheduling of community multi-energy system considering demand-side management and consumer psychology," Applied Energy, Elsevier, vol. 349(C).
    19. Yi, Yin & Zhou, Yun & Feng, Donghan & Yin, Wenhang & Li, Hengjie & Yang, Qingliu, 2024. "Stability control and analysis of hydrogen production using a multi-terminal DC EV charging system with PV," Renewable Energy, Elsevier, vol. 234(C).
    20. Yan, Zheping & Yan, Jinyu & Wu, Yifan & Cai, Sijia & Wang, Hongxing, 2023. "A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 55-86.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2133-:d:1638975. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.