IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i10p2328-d1392915.html
   My bibliography  Save this article

Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution

Author

Listed:
  • Dong Liang

    (State Grid Gansu Electric Power Company Linxia Power Supply Company, Linxia 731100, China)

  • Long Ma

    (State Grid Gansu Electric Power Company Linxia Power Supply Company, Linxia 731100, China)

  • Peng Wang

    (State Grid Gansu Electric Power Company Linxia Power Supply Company, Linxia 731100, China)

  • Yuanxia Li

    (State Grid Gansu Electric Power Company Linxia Power Supply Company, Linxia 731100, China)

  • Yiping Luo

    (State Key Laboratory of Eco-Hydraulic in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, China)

Abstract

The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To incorporate the stochastic characteristics of solar radiation into the sizing process, a data-driven stochastic modeling method for solar radiation is proposed. The method involves two layers of stochastic processes that capture the intraday variation and daily evolution of solar radiation. First, the clearness index (CI) is introduced to describe the radiation intensity at different times. Then, the daily clearness state (DCS) is proposed, based on the statistical indicators of the intraday CI. The Markov model is used to describe the stochastic evolutionary characteristics between different DCSs. The probabilistic distribution of the CI under different DCS is obtained based on the diffusion kernel density estimation (DKDE), which is used for the stochastic generation of the CI at various times of the day. Finally, the radiation profile required for the optimal design is obtained by the stochastic generation of the DCS sequences and the intraday clearness index under corresponding states. A case study of an off-grid solar-powered HES is provided to illustrate this methodology.

Suggested Citation

  • Dong Liang & Long Ma & Peng Wang & Yuanxia Li & Yiping Luo, 2024. "Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution," Energies, MDPI, vol. 17(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2328-:d:1392915
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/10/2328/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/10/2328/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Zhang, Xiongwen, 2014. "A statistical approach for sub-hourly solar radiation reconstruction," Renewable Energy, Elsevier, vol. 71(C), pages 307-314.
    3. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    4. Chen, Zhang & Yiliang, Xie & Hongxia, Zhang & Yujie, Gu & Xiongwen, Zhang, 2023. "Optimal design and performance assessment for a solar powered electricity, heating and hydrogen integrated energy system," Energy, Elsevier, vol. 262(PA).
    5. Li, Mengyu & Zhang, Xiongwen & Li, Guojun & Jiang, Chaoyang, 2016. "A feasibility study of microgrids for reducing energy use and GHG emissions in an industrial application," Applied Energy, Elsevier, vol. 176(C), pages 138-148.
    6. Schuster, Christian Stefano, 2020. "Analytical framework for the assessment and modelling of multi-junction solar cells in the outdoors," Renewable Energy, Elsevier, vol. 152(C), pages 1367-1379.
    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. Nazim Hajiyev & Klaudia Smoląg & Ali Abbasov & Valeriy Prasolov, 2020. "Energy War Strategies: The 21st Century Experience," Energies, MDPI, vol. 13(21), pages 1-15, November.
    2. Zhang, Xi & Geng, Yong & Shao, Shuai & Wilson, Jeffrey & Song, Xiaoqian & You, Wei, 2020. "China’s non-fossil energy development and its 2030 CO2 reduction targets: The role of urbanization," Applied Energy, Elsevier, vol. 261(C).
    3. Alharbi, Abdullah G. & Fathy, Ahmed & Rezk, Hegazy & Abdelkareem, Mohammad Ali & Olabi, A.G., 2023. "An efficient war strategy optimization reconfiguration method for improving the PV array generated power," Energy, Elsevier, vol. 283(C).
    4. Sun, Wei & Harrison, Gareth P., 2019. "Wind-solar complementarity and effective use of distribution network capacity," Applied Energy, Elsevier, vol. 247(C), pages 89-101.
    5. Wang, Yongli & Guo, Lu & Wang, Yanan & Zhang, Yunfei & Zhang, Siwen & Liu, Zeqiang & Xing, Juntai & Liu, Ximei, 2024. "Bi-level programming optimization method of rural integrated energy system based on coupling coordination degree of energy equipment," Energy, Elsevier, vol. 298(C).
    6. Ahmad Abuelrub & Osama Saadeh & Hussein M. K. Al-Masri, 2018. "Scenario Aggregation-Based Grid-Connected Photovoltaic Plant Design," Sustainability, MDPI, vol. 10(4), pages 1-13, April.
    7. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    8. Chen, Yizhong & He, Li & Li, Jing, 2017. "Stochastic dominant-subordinate-interactive scheduling optimization for interconnected microgrids with considering wind-photovoltaic-based distributed generations under uncertainty," Energy, Elsevier, vol. 130(C), pages 581-598.
    9. Morshed, Mohammad Javad & Hmida, Jalel Ben & Fekih, Afef, 2018. "A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems," Applied Energy, Elsevier, vol. 211(C), pages 1136-1149.
    10. Bustos, Cristian & Watts, David, 2017. "Novel methodology for microgrids in isolated communities: Electricity cost-coverage trade-off with 3-stage technology mix, dispatch & configuration optimizations," Applied Energy, Elsevier, vol. 195(C), pages 204-221.
    11. Ying Chen & Krystel K. Castillo-Villar & Bing Dong, 2021. "Stochastic control of a micro-grid using battery energy storage in solar-powered buildings," Annals of Operations Research, Springer, vol. 303(1), pages 197-216, August.
    12. Li, Yanbin & Zhang, Feng & Li, Yun & Wang, Yuwei, 2021. "An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties," Energy, Elsevier, vol. 223(C).
    13. Kinnon, Michael Mac & Razeghi, Ghazal & Samuelsen, Scott, 2021. "The role of fuel cells in port microgrids to support sustainable goods movement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    14. Wei, Wei & Liu, Feng & Wang, Jianhui & Chen, Laijun & Mei, Shengwei & Yuan, Tiejiang, 2016. "Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants," Applied Energy, Elsevier, vol. 183(C), pages 674-684.
    15. Su, Gang & Zhang, Shuangyang & Hu, Mengru & Yao, Wanxiang & Li, Ziwei & Xi, Yue, 2022. "The modified layer-by-layer weakening solar radiation models based on relative humidity and air quality index," Energy, Elsevier, vol. 239(PE).
    16. Yang, Wenjun & Guo, Jia & Vartosh, Aris, 2022. "Optimal economic-emission planning of multi-energy systems integrated electric vehicles with modified group search optimization," Applied Energy, Elsevier, vol. 311(C).
    17. 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).
    18. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    19. Tan, Hong & Yan, Wei & Ren, Zhouyang & Wang, Qiujie & Mohamed, Mohamed A., 2022. "Distributionally robust operation for integrated rural energy systems with broiler houses," Energy, Elsevier, vol. 254(PC).
    20. Izadbakhsh, Maziar & Gandomkar, Majid & Rezvani, Alireza & Ahmadi, Abdollah, 2015. "Short-term resource scheduling of a renewable energy based micro grid," Renewable Energy, Elsevier, vol. 75(C), pages 598-606.

    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:10:p:2328-:d:1392915. 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.