IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v309y2022ics0306261921016664.html
   My bibliography  Save this article

A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids

Author

Listed:
  • Li, Shenglin
  • Zhu, Jizhong
  • Dong, Hanjiang
  • Zhu, Haohao
  • Fan, Junwei

Abstract

The rise of microgrids provides an effective solution to the problem of local consumption of renewable energy sources. However, the power fluctuations are the crucial issue for the widespread adoption of the grid-connected microgrid with renewable energy sources. We aim to economically and locally solve the problem of grid-connected power fluctuations of microgrid. In this paper, a novel rolling optimization strategy considering grid-connected power fluctuations smoothing for microgrids is provided. Firstly, the mathematical model of the microgrid is described, which contains the grid-connected power limits and supercapacitor-battery hybrid energy storage system. Then, a priority-based smoothing method of power fluctuations is proposed for the first time. As a flexible load, the heating/cooling load is used as virtual energy storage to participate in power regulation. Finally, the rolling optimization strategy integrated with the energy trigger mechanism is designed to achieve the operation optimization. The objectives of this paper are to help the microgrids improve grid-connection friendliness and minimize the daily operating costs that include fluctuation penalties. Simulation results on different scenarios for a grid-connected microgrid show that the novel rolling optimization strategy has better performance in any scenario. Compared with the traditional strategy, the total operating costs of the proposed strategy can save 5.67% for a given scenario. We can conclude that the proposed rolling optimization strategy can effectively reduce cost payment and meet the requirement of grid-connected power fluctuations smoothing.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016664
    DOI: 10.1016/j.apenergy.2021.118441
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921016664
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.118441?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
    2. 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).
    3. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    4. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Jiang, Tao & Yu, Xiaodan, 2017. "Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system," Applied Energy, Elsevier, vol. 194(C), pages 386-398.
    5. Peng, Xiaosheng & Wang, Hongyu & Lang, Jianxun & Li, Wenze & Xu, Qiyou & Zhang, Zuowei & Cai, Tao & Duan, Shanxu & Liu, Fangjie & Li, Chaoshun, 2021. "EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning," Energy, Elsevier, vol. 220(C).
    6. Li, Zhuochao & Zhang, Haoran & Meng, Jing & Long, Yin & Yan, Yamin & Li, Meixuan & Huang, Zhongliang & Liang, Yongtu, 2020. "Reducing carbon footprint of deep-sea oil and gas field exploitation by optimization for Floating Production Storage and Offloading," Applied Energy, Elsevier, vol. 261(C).
    7. 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).
    8. Ghorbani, Narges & Kasaeian, Alibakhsh & Toopshekan, Ashkan & Bahrami, Leyli & Maghami, Amin, 2018. "Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability," Energy, Elsevier, vol. 154(C), pages 581-591.
    9. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pavić, Ivan & Čović, Nikolina & Pandžić, Hrvoje, 2022. "PV–battery-hydrogen plant: Cutting green hydrogen costs through multi-market positioning," Applied Energy, Elsevier, vol. 328(C).
    2. Zhao, Xudong & Wang, Yibo & Liu, Chuang & Cai, Guowei & Ge, Weichun & Zhou, Jianing & Wang, Dongzhe, 2024. "Low carbon scheduling method of electric power system considering energy-intensive load regulation of electrofused magnesium and wind powerfluctuation stabilization," Applied Energy, Elsevier, vol. 357(C).
    3. Wei Wei & Li Ye & Yi Fang & Yingchun Wang & Xi Chen & Zhenhua Li, 2023. "Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    4. Liu, Shan & Yan, Jie & Yan, Yamin & Zhang, Haoran & Zhang, Jing & Liu, Yongqian & Han, Shuang, 2024. "Joint operation of mobile battery, power system, and transportation system for improving the renewable energy penetration rate," Applied Energy, Elsevier, vol. 357(C).
    5. Yuanyuan He & Luxin Wan & Manli Zhang & Huijuan Zhao, 2022. "Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    6. Manduleli Alfred Mquqwana & Senthil Krishnamurthy, 2024. "Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty," Energies, MDPI, vol. 17(2), pages 1-21, January.
    7. Nie, Yonghui & Qiu, Yu & Yang, Annan & Zhao, Yan, 2024. "Risk-limiting dispatching strategy considering demand response in multi-energy microgrids," Applied Energy, Elsevier, vol. 353(PA).

    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. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. Zhou, Yuan & Wang, Jiangjiang & Li, Yuxin & Wei, Changqi, 2023. "A collaborative management strategy for multi-objective optimization of sustainable distributed energy system considering cloud energy storage," Energy, Elsevier, vol. 280(C).
    3. 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).
    4. Gui, Yonghao & Wei, Baoze & Li, Mingshen & Guerrero, Josep M. & Vasquez, Juan C., 2018. "Passivity-based coordinated control for islanded AC microgrid," Applied Energy, Elsevier, vol. 229(C), pages 551-561.
    5. Cheng, Meng & Sami, Saif Sabah & Wu, Jianzhong, 2017. "Benefits of using virtual energy storage system for power system frequency response," Applied Energy, Elsevier, vol. 194(C), pages 376-385.
    6. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    7. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    8. Jihed Hmad & Azeddine Houari & Allal El Moubarek Bouzid & Abdelhakim Saim & Hafedh Trabelsi, 2023. "A Review on Mode Transition Strategies between Grid-Connected and Standalone Operation of Voltage Source Inverters-Based Microgrids," Energies, MDPI, vol. 16(13), pages 1-41, June.
    9. 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).
    10. Fichter, Tobias & Soria, Rafael & Szklo, Alexandre & Schaeffer, Roberto & Lucena, Andre F.P., 2017. "Assessing the potential role of concentrated solar power (CSP) for the northeast power system of Brazil using a detailed power system model," Energy, Elsevier, vol. 121(C), pages 695-715.
    11. Zhou, Jianguo & Xu, Zhongtian, 2023. "Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China," Renewable Energy, Elsevier, vol. 202(C), pages 1110-1137.
    12. Abdul K Hamid & Nsilulu T Mbungu & A. Elnady & Ramesh C Bansal & Ali A Ismail & Mohammad A AlShabi, 2023. "A systematic review of grid-connected photovoltaic and photovoltaic/thermal systems: Benefits, challenges and mitigation," Energy & Environment, , vol. 34(7), pages 2775-2814, November.
    13. Vorushylo, Inna & Keatley, Patrick & Shah, Nikhilkumar & Green, Richard & Hewitt, Neil, 2018. "How heat pumps and thermal energy storage can be used to manage wind power: A study of Ireland," Energy, Elsevier, vol. 157(C), pages 539-549.
    14. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    15. Shi, Jie & Wang, Luhao & Lee, Wei-Jen & Cheng, Xingong & Zong, Xiju, 2019. "Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction," Applied Energy, Elsevier, vol. 256(C).
    16. Faisal Mumtaz & Kashif Imran & Abdullah Abusorrah & Syed Basit Ali Bukhari, 2022. "Harmonic Content-Based Protection Method for Microgrids via 1-Dimensional Recursive Median Filtering Algorithm," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    17. Segurado, R. & Madeira, J.F.A. & Costa, M. & Duić, N. & Carvalho, M.G., 2016. "Optimization of a wind powered desalination and pumped hydro storage system," Applied Energy, Elsevier, vol. 177(C), pages 487-499.
    18. C. B. Sivaparthipan & Lydia J. Gnanasigamani & Ruchi Agrawal & Bakri Hossain Awaji & P. Sathyaprakash & Mustafa Musa Jaber & Awais Khan Jumani, 2023. "Internet of things enabled privacy-conserving health record virtual sharing using jungle computing," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-26, July.
    19. Polleux, Louis & Guerassimoff, Gilles & Marmorat, Jean-Paul & Sandoval-Moreno, John & Schuhler, Thierry, 2022. "An overview of the challenges of solar power integration in isolated industrial microgrids with reliability constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    20. Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Kevin Tomsovic, 2022. "An MILP-Based Distributed Energy Management for Coordination of Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-20, September.

    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:eee:appene:v:309:y:2022:i:c:s0306261921016664. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.