IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p13939-d954220.html
   My bibliography  Save this article

Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids

Author

Listed:
  • Muqing Wu

    (Beijing Laboratory of Advanced Information Networks and Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Qingsu He

    (Beijing Laboratory of Advanced Information Networks and Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China
    State Grid Gansu Elect Power Co., Lanzhou 730060, China)

  • Yuping Liu

    (State Grid Gansu Elect Power Co., Lanzhou 730060, China)

  • Ziqiang Zhang

    (State Grid Gansu Elect Power Co., Lanzhou 730060, China)

  • Zhongwen Shi

    (State Grid Gansu Elect Power Co., Lanzhou 730060, China)

  • Yifan He

    (University of California, Santa Cruz, CA 95064, USA)

Abstract

Given the vitality of the renewable-energy grid market, the optimal allocation of clean energy is crucial. An optimal dispatching method for source–load coordination of renewable-energy grid is proposed. An improved K-means clustering algorithm is used to preprocess the source data and historical load data. A support vector machine is used to predict the cluster of renewable-energy grid resources and load data, and typical scenarios are selected from the prediction results. Taking typical scenarios as a representative, the probability distribution of wind power output is accurately obtained. An optimization model of the total operation cost of the renewable-energy grid is established. The experimental results show that the algorithm reduces the error between the predicted value and the actual value. Our method can improve the real-time prediction accuracy of the renewable-energy grid system and increase the economic benefits of the renewable energy grid.

Suggested Citation

  • Muqing Wu & Qingsu He & Yuping Liu & Ziqiang Zhang & Zhongwen Shi & Yifan He, 2022. "Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13939-:d:954220
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/13939/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/13939/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Kim, Donghun & Wang, Zhe & Brugger, James & Blum, David & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization," Applied Energy, Elsevier, vol. 321(C).
    3. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    4. Julie Waldron & Lucelia Rodrigues & Mark Gillott & Sophie Naylor & Rob Shipman, 2022. "The Role of Electric Vehicle Charging Technologies in the Decarbonisation of the Energy Grid," Energies, MDPI, vol. 15(7), pages 1-25, March.
    5. Yilmaz, Hasan Ümitcan & Kimbrough, Steven O. & van Dinther, Clemens & Keles, Dogan, 2022. "Power-to-gas: Decarbonization of the European electricity system with synthetic methane," Applied Energy, Elsevier, vol. 323(C).
    6. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    7. Ihab Taleb & Guillaume Guerard & Frédéric Fauberteau & Nga Nguyen, 2022. "A Flexible Deep Learning Method for Energy Forecasting," Energies, MDPI, vol. 15(11), pages 1-16, May.
    8. Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
    9. Rosero, D.G. & Díaz, N.L. & Trujillo, C.L., 2021. "Cloud and machine learning experiments applied to the energy management in a microgrid cluster," Applied Energy, Elsevier, vol. 304(C).
    10. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    11. Horak, Daniel & Hainoun, Ali & Neugebauer, Georg & Stoeglehner, Gernot, 2022. "A review of spatio-temporal urban energy system modeling for urban decarbonization strategy formulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(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. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    2. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    3. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    4. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    5. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    6. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    7. Soleimanzade, Mohammad Amin & Kumar, Amit & Sadrzadeh, Mohtada, 2022. "Novel data-driven energy management of a hybrid photovoltaic-reverse osmosis desalination system using deep reinforcement learning," Applied Energy, Elsevier, vol. 317(C).
    8. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    9. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    10. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    11. Pranav Nair & Vinay Vakharia & Himanshu Borade & Milind Shah & Vishal Wankhede, 2023. "Predicting Li-Ion Battery Remaining Useful Life: An XDFM-Driven Approach with Explainable AI," Energies, MDPI, vol. 16(15), pages 1-19, July.
    12. Yuriy Zhukovskiy & Pavel Tsvetkov & Aleksandra Buldysko & Yana Malkova & Antonina Stoianova & Anastasia Koshenkova, 2021. "Scenario Modeling of Sustainable Development of Energy Supply in the Arctic," Resources, MDPI, vol. 10(12), pages 1-25, December.
    13. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    14. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
    15. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    16. Norouzi, Mohammadali & Aghaei, Jamshid & Niknam, Taher & Alipour, Mohammadali & Pirouzi, Sasan & Lehtonen, Matti, 2023. "Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting," Applied Energy, Elsevier, vol. 348(C).
    17. Shahbeig, Hossein & Nosrati, Mohsen, 2020. "Pyrolysis of municipal sewage sludge for bioenergy production: Thermo-kinetic studies, evolved gas analysis, and techno-socio-economic assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    18. Emrani, Anisa & Berrada, Asmae & Bakhouya, Mohamed, 2022. "Optimal sizing and deployment of gravity energy storage system in hybrid PV-Wind power plant," Renewable Energy, Elsevier, vol. 183(C), pages 12-27.
    19. Germán Arana-Landín & Naiara Uriarte-Gallastegi & Beñat Landeta-Manzano & Iker Laskurain-Iturbe, 2023. "The Contribution of Lean Management—Industry 4.0 Technologies to Improving Energy Efficiency," Energies, MDPI, vol. 16(5), pages 1-19, February.
    20. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).

    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:jsusta:v:14:y:2022:i:21:p:13939-:d:954220. 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.