IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1135-d1599625.html
   My bibliography  Save this article

Strategies of a Wind–Solar–Storage System in Jiangxi Province Using the LEAP–NEMO Framework for Achieving Carbon Peaking Goals

Author

Listed:
  • Yao Xiao

    (College of Electrical Engineering, Hunan Mechanical and Electrical Polytechnic, Changsha 410073, China)

  • Caixia Yang

    (College of Electrical Engineering, Hunan Mechanical and Electrical Polytechnic, Changsha 410073, China)

  • Tao Chen

    (College of Electrical Engineering, Hunan Mechanical and Electrical Polytechnic, Changsha 410073, China)

  • Mingze Lei

    (Electrical and Computer Engineering Research Unit, Faculty of Engineering, Mahasarakham University, Maha Sarakham 44150, Thailand)

  • Supannika Wattana

    (Electrical and Computer Engineering Research Unit, Faculty of Engineering, Mahasarakham University, Maha Sarakham 44150, Thailand)

  • Buncha Wattana

    (Electrical and Computer Engineering Research Unit, Faculty of Engineering, Mahasarakham University, Maha Sarakham 44150, Thailand)

Abstract

Jiangxi Province relies heavily on thermal power and energy imports but is rich in natural resources, particularly lithium. This study explores strategies for advancing wind–solar–storage systems to help Jiangxi transition to a low-carbon energy structure. Using LEAP and NEMO models, four scenarios are examined: the reference (REF) scenario, new energy storage policy scenario (NPS), high wind–solar power capacity scenario (HWSS), and comprehensive optimization scenario (COS). Key findings show that the COS and HWSS offer significant advantages over the REF scenario and NPS in terms of energy storage efficiency, carbon emission reduction, and cost savings. By 2035, under the COS, wind and solar power share rises to 48%, reducing coal use by 5.9 million tons and electricity imports by 40.0 TWh compared to the REF scenario. Battery storage utilization increases by 1499.8 GWh, nearly four times that of the REF scenario. This scenario also cuts CO 2 emissions by 16.8% and lowers cumulative social costs by 5.19 billion USD, delivering optimal economic efficiency. The study also identifies challenges such as high investment costs, underdeveloped business models, and low resource utilization, and recommends setting higher targets, implementing flexible solutions, promoting market reforms, and increasing R&D efforts, among other measures.

Suggested Citation

  • Yao Xiao & Caixia Yang & Tao Chen & Mingze Lei & Supannika Wattana & Buncha Wattana, 2025. "Strategies of a Wind–Solar–Storage System in Jiangxi Province Using the LEAP–NEMO Framework for Achieving Carbon Peaking Goals," Energies, MDPI, vol. 18(5), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1135-:d:1599625
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1135/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/5/1135/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    2. Zhou, Xiaoxin & Yi, Jun & Song, Ruihua & Yang, Xiaoyu & Li, Yan & Tang, Haiyan, 2010. "An overview of power transmission systems in China," Energy, Elsevier, vol. 35(11), pages 4302-4312.
    3. Wambui, Valentine & Njoka, Francis & Muguthu, Joseph & Ndwali, Patrick, 2022. "Scenario analysis of electricity pathways in Kenya using Low Emissions Analysis Platform and the Next Energy Modeling system for optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    4. Stanley, Andrew P.J. & King, Jennifer, 2022. "Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant," Applied Energy, Elsevier, vol. 317(C).
    5. Gangopadhyay, Anasuya & Seshadri, Ashwin K. & Patil, Balachandra, 2024. "Wind-solar-storage trade-offs in a decarbonizing electricity system," Applied Energy, Elsevier, vol. 353(PA).
    6. Pin Chen & Xiyue Wang & Zexia Yang & Changfeng Shi, 2024. "Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration," Energies, MDPI, vol. 17(23), pages 1-20, November.
    7. Yang, Zhixue & Ren, Zhouyang & Li, Hui & Sun, Zhiyuan & Feng, Jianbing & Xia, Weiyi, 2024. "A multi-stage stochastic dispatching method for electricity‑hydrogen integrated energy systems driven by model and data," Applied Energy, Elsevier, vol. 371(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. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Mujammil Asdhiyoga Rahmanta & Rahmat Adiprasetya Al Hasibi & Handrea Bernando Tambunan & Ruly & Agussalim Syamsuddin & Indra Ardhanayudha Aditya & Benny Susanto, 2024. "Towards a Net Zero-Emission Electricity Generation System by Optimizing Renewable Energy Sources and Nuclear Power Plant," Energies, MDPI, vol. 17(8), pages 1-22, April.
    3. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    4. Yanshan Yu & Jin Yang & Bin Chen, 2012. "The Smart Grids in China—A Review," Energies, MDPI, vol. 5(5), pages 1-18, May.
    5. 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).
    6. Visser, L.R. & AlSkaif, T.A. & Khurram, A. & Kleissl, J. & van Sark, W.G.H.J.M., 2024. "Probabilistic solar power forecasting: An economic and technical evaluation of an optimal market bidding strategy," Applied Energy, Elsevier, vol. 370(C).
    7. Muhammad Aslam & Jae-Myeong Lee & Hyung-Seung Kim & Seung-Jae Lee & Sugwon Hong, 2019. "Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study," Energies, MDPI, vol. 13(1), pages 1-15, December.
    8. Wolfram Rozas & Rafael Pastor-Vargas & Angel Miguel García-Vico & José Carpio, 2023. "Consumption–Production Profile Categorization in Energy Communities," Energies, MDPI, vol. 16(19), pages 1-27, October.
    9. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.
    10. Wen, Xin & Heinisch, Verena & Müller, Jonas & Sasse, Jan-Philipp & Trutnevyte, Evelina, 2023. "Comparison of statistical and optimization models for projecting future PV installations at a sub-national scale," Energy, Elsevier, vol. 285(C).
    11. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    12. Nakıp, Mert & Çopur, Onur & Biyik, Emrah & Güzeliş, Cüneyt, 2023. "Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network," Applied Energy, Elsevier, vol. 340(C).
    13. Guo, Zheng & Ma, Linwei & Liu, Pei & Jones, Ian & Li, Zheng, 2016. "A multi-regional modelling and optimization approach to China's power generation and transmission planning," Energy, Elsevier, vol. 116(P2), pages 1348-1359.
    14. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.
    15. Adoum Abdoulaye, Mahamat & Waita, Sebastian & Wabuge Wekesa, Cyrus & Mwabora, Julius Mwakondo, 2024. "Optimal sizing of an off-grid and grid-connected hybrid photovoltaic-wind system with battery and fuel cell storage system: A techno-economic, environmental, and social assessment," Applied Energy, Elsevier, vol. 365(C).
    16. Abhnil Amtesh Prasad & Merlinde Kay, 2021. "Prediction of Solar Power Using Near-Real Time Satellite Data," Energies, MDPI, vol. 14(18), pages 1-20, September.
    17. Phathutshedzo Mpfumali & Caston Sigauke & Alphonce Bere & Sophie Mulaudzi, 2019. "Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data," Energies, MDPI, vol. 12(18), pages 1-28, September.
    18. Herrerias, M.J. & Cuadros, A. & Orts, V., 2013. "Energy intensity and investment ownership across Chinese provinces," Energy Economics, Elsevier, vol. 36(C), pages 286-298.
    19. Ma, Chao & Xu, Ximeng & Pang, Xiulan & Li, Xiaofeng & Zhang, Pengfei & Liu, Lu, 2024. "Scenario-based ultra-short-term rolling optimal operation of a photovoltaic-energy storage system under forecast uncertainty," Applied Energy, Elsevier, vol. 356(C).
    20. Wang, Hongye & Su, Bin & Mu, Hailin & Li, Nan & Jiang, Bo & Kong, Xue, 2019. "Optimization of electricity generation and interprovincial trading strategies in Southern China," Energy, Elsevier, vol. 174(C), pages 696-707.

    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:5:p:1135-:d:1599625. 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.