IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4399-d1469933.html
   My bibliography  Save this article

Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

Author

Listed:
  • Aras Ghafoor

    (Department of Electrical and Electronic Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK)

  • Jamal Aldahmashi

    (School of Engineering, Lancaster University, Lancaster LA1 4YW, UK
    Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73213, Saudi Arabia)

  • Judith Apsley

    (Department of Electrical and Electronic Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK)

  • Siniša Djurović

    (Department of Electrical and Electronic Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK)

  • Xiandong Ma

    (School of Engineering, Lancaster University, Lancaster LA1 4YW, UK)

  • Mohamed Benbouzid

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, France)

Abstract

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

Suggested Citation

  • Aras Ghafoor & Jamal Aldahmashi & Judith Apsley & Siniša Djurović & Xiandong Ma & Mohamed Benbouzid, 2024. "Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges," Energies, MDPI, vol. 17(17), pages 1-28, September.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4399-:d:1469933
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4399/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/17/4399/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Zhichun & Yang, Fan & Min, Huaidong & Tian, Hao & Hu, Wei & Liu, Jian & Eghbalian, Nasrin, 2023. "Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources," Energy, Elsevier, vol. 263(PA).
    2. Kou, Peng & Liang, Deliang & Wang, Chen & Wu, Zihao & Gao, Lin, 2020. "Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks," Applied Energy, Elsevier, vol. 264(C).
    3. Qiu, Dawei & Wang, Yi & Sun, Mingyang & Strbac, Goran, 2022. "Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 313(C).
    4. Oh, Seok Hwa & Yoon, Yong Tae & Kim, Seung Wan, 2020. "Online reconfiguration scheme of self-sufficient distribution network based on a reinforcement learning approach," Applied Energy, Elsevier, vol. 280(C).
    5. Grover-Silva, Etta & Heleno, Miguel & Mashayekh, Salman & Cardoso, Gonçalo & Girard, Robin & Kariniotakis, George, 2018. "A stochastic optimal power flow for scheduling flexible resources in microgrids operation," Applied Energy, Elsevier, vol. 229(C), pages 201-208.
    6. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(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. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    2. Oh, Seok Hwa & Yoon, Yong Tae & Kim, Seung Wan, 2020. "Online reconfiguration scheme of self-sufficient distribution network based on a reinforcement learning approach," Applied Energy, Elsevier, vol. 280(C).
    3. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    4. Vinay Kumar Jadoun & Nipun Sharma & Piyush Jha & Jayalakshmi N. S. & Hasmat Malik & Fausto Pedro Garcia Márquez, 2021. "Optimal Scheduling of Dynamic Pricing Based V2G and G2V Operation in Microgrid Using Improved Elephant Herding Optimization," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    5. Zhao, Yincheng & Zhang, Guozhou & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Meta-learning based voltage control strategy for emergency faults of active distribution networks," Applied Energy, Elsevier, vol. 349(C).
    6. Mohammad Javad Bordbari & Fuzhan Nasiri, 2024. "Networked Microgrids: A Review on Configuration, Operation, and Control Strategies," Energies, MDPI, vol. 17(3), pages 1-28, February.
    7. Li, J.Y. & Chen, J.J. & Wang, Y.X. & Chen, W.G., 2024. "Combining multi-step reconfiguration with many-objective reduction as iterative bi-level scheduling for stochastic distribution network," Energy, Elsevier, vol. 290(C).
    8. Lingling Hu & Junming Zhou & Feng Jiang & Guangming Xie & Jie Hu & Qinglie Mo, 2023. "Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization," Sustainability, MDPI, vol. 16(1), pages 1-25, December.
    9. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
    10. Yin, Linfei & Lu, Yuejiang, 2021. "Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources," Energy, Elsevier, vol. 226(C).
    11. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    12. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    13. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
    14. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
    15. Jiankai Gao & Yang Li & Bin Wang & Haibo Wu, 2023. "Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm," Energies, MDPI, vol. 16(7), pages 1-21, April.
    16. Cao, Di & Zhao, Junbo & Hu, Weihao & Ding, Fei & Yu, Nanpeng & Huang, Qi & Chen, Zhe, 2022. "Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    17. Se-Heon Lim & Sung-Guk Yoon, 2022. "Dynamic DNR and Solar PV Smart Inverter Control Scheme Using Heterogeneous Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 15(23), pages 1-18, December.
    18. Ibrahim Salem Jahan & Vojtech Blazek & Stanislav Misak & Vaclav Snasel & Lukas Prokop, 2022. "Forecasting of Power Quality Parameters Based on Meteorological Data in Small-Scale Household Off-Grid Systems," Energies, MDPI, vol. 15(14), pages 1-20, July.
    19. Jude Suchithra & Duane Robinson & Amin Rajabi, 2023. "Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    20. Phani Raghav, L. & Seshu Kumar, R. & Koteswara Raju, D. & Singh, Arvind R., 2022. "Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid," Applied Energy, Elsevier, vol. 306(PB).

    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:17:y:2024:i:17:p:4399-:d:1469933. 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.