IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i11p1755-d1664047.html
   My bibliography  Save this article

Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration

Author

Listed:
  • Yufeng Wang

    (School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China)

  • Haining Ji

    (School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
    Hunan Engineering Laboratory for Microelectronics, Optoelectronics and System on a Chip, Xiangtan University, Xiangtan 411105, China)

  • Runteng Luo

    (School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China)

  • Bin Liu

    (School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China
    Hunan Engineering Laboratory for Microelectronics, Optoelectronics and System on a Chip, Xiangtan University, Xiangtan 411105, China)

  • Yongzi Wu

    (School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China)

Abstract

With the progressive advancement of the energy transition strategy, wind–solar energy complementary power generation has emerged as a pivotal component in the global transition towards a sustainable, low-carbon energy future. To address the inherent challenges of intermittent renewable energy generation, this paper proposes a comprehensive energy optimization strategy that integrates coordinated wind–solar power dispatch with strategic battery storage capacity allocation. Through the development of a linear programming model for the wind–solar–storage hybrid system, incorporating critical operational constraints including load demand, an optimization solution was implemented using the Artificial Fish Swarm Algorithm (AFSA). This computational approach enabled the determination of an optimal scheme for the coordinated operation of wind, solar, and storage components within the integrated energy system. Based on the case study analysis, the AFSA optimization algorithm achieves a 1.07% reduction in total power generation costs compared to the traditional Simulated Annealing (SA) approach. Comparative analysis reveals that the integrated grid-connected operation mode exhibits superior economic performance over the standalone storage microgrid system. Additionally, we conducted a further analysis of the key factors contributing to the enhancement of economic benefits. The strategy proposed in this paper significantly enhances power supply stability, reduces overall costs and promotes the large-scale application of green energy.

Suggested Citation

  • Yufeng Wang & Haining Ji & Runteng Luo & Bin Liu & Yongzi Wu, 2025. "Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration," Mathematics, MDPI, vol. 13(11), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1755-:d:1664047
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/11/1755/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/11/1755/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    2. Xun-Gui Li & Xia Wei, 2008. "An Improved Genetic Algorithm-Simulated Annealing Hybrid Algorithm for the Optimization of Multiple Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(8), pages 1031-1049, August.
    3. Ekren, Orhan & Ekren, Banu Y., 2010. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing," Applied Energy, Elsevier, vol. 87(2), pages 592-598, February.
    4. Calise, Francesco & Cappiello, Francesco Liberato & Cimmino, Luca & Dentice d’Accadia, Massimo & Vicidomini, Maria, 2023. "Renewable smart energy network: A thermoeconomic comparison between conventional lithium-ion batteries and reversible solid oxide fuel cells," Renewable Energy, Elsevier, vol. 214(C), pages 74-95.
    5. Debin Fang & Shanshan Shi & Qian Yu, 2018. "Evaluation of Sustainable Energy Security and an Empirical Analysis of China," Sustainability, MDPI, vol. 10(5), pages 1-23, May.
    6. Sepúlveda-Mora, Sergio B. & Hegedus, Steven, 2021. "Making the case for time-of-use electric rates to boost the value of battery storage in commercial buildings with grid connected PV systems," Energy, Elsevier, vol. 218(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. Asma Khalil Alkhamis & Manar Hosny, 2023. "A Multi-Objective Simulated Annealing Local Search Algorithm in Memetic CENSGA: Application to Vaccination Allocation for Influenza," Sustainability, MDPI, vol. 15(21), pages 1-37, October.
    2. T. Fowe & I. Nouiri & B. Ibrahim & H. Karambiri & J. Paturel, 2015. "OPTIWAM: An Intelligent Tool for Optimizing Irrigation Water Management in Coupled Reservoir–Groundwater Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3841-3861, August.
    3. Felipe, Ángel & Ortuño, M. Teresa & Righini, Giovanni & Tirado, Gregorio, 2014. "A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 111-128.
    4. Junbai Pan & Kun Lv & Shurong Yu & Dian Fu, 2022. "What Mechanisms Do Financial Marketization and China’s Fiscal Decentralization Have on Regional Energy Intensity? Evidence Based on Spatial Spillover and Panel Threshold Effects Perspectives," IJERPH, MDPI, vol. 19(9), pages 1-27, May.
    5. Busiswe Skosana & Mukwanga W. Siti & Nsilulu T. Mbungu & Sonu Kumar & Willy Mulumba, 2023. "An Evaluation of Potential Strategies in Renewable Energy Systems and Their Importance for South Africa—A Review," Energies, MDPI, vol. 16(22), pages 1-27, November.
    6. Tao Xu & He Meng & Jie Zhu & Wei Wei & He Zhao & Han Yang & Zijin Li & Yuhan Wu, 2021. "Optimal Capacity Allocation of Energy Storage in Distribution Networks Considering Active/Reactive Coordination," Energies, MDPI, vol. 14(6), pages 1-24, March.
    7. Wissem, Zghal & Gueorgui, Kantchev & Hédi, Kchaou, 2012. "Modeling and technical–economic optimization of an autonomous photovoltaic system," Energy, Elsevier, vol. 37(1), pages 263-272.
    8. Tao Bai & Lianzhou Wu & Jian-xia Chang & Qiang Huang, 2015. "Multi-Objective Optimal Operation Model of Cascade Reservoirs and Its Application on Water and Sediment Regulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2751-2770, June.
    9. Adeinat, Hamza & Pazhani, Subramanian & Mendoza, Abraham & Ventura, Jose A., 2022. "Coordination of pricing and inventory replenishment decisions in a supply chain with multiple geographically dispersed retailers," International Journal of Production Economics, Elsevier, vol. 248(C).
    10. Abdulrasheed Zakari & Jurij Toplak & Luka Martin Tomažič, 2022. "Exploring the Relationship between Energy and Food Security in Africa with Instrumental Variables Analysis," Energies, MDPI, vol. 15(15), pages 1-14, July.
    11. Mudasser, Muhammad & Yiridoe, Emmanuel K. & Corscadden, Kenneth, 2015. "Cost-benefit analysis of grid-connected wind–biogas hybrid energy production, by turbine capacity and site," Renewable Energy, Elsevier, vol. 80(C), pages 573-582.
    12. S.-C. Horng & S.-Y. Lin, 2009. "Ordinal Optimization of G/G/1/K Polling Systems with k-Limited Service Discipline," Journal of Optimization Theory and Applications, Springer, vol. 140(2), pages 213-231, February.
    13. Pavlos S. Georgilakis, 2020. "Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Researc," Energies, MDPI, vol. 13(1), pages 1-37, January.
    14. Hossein Heirani & Naser Bagheri Moghaddam & Sina Labbafi & Seyedali Sina, 2022. "A Business Model for Developing Distributed Photovoltaic Systems in Iran," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    15. Zhao, Wenjuan & Lin, Bin & Wang, Hao & Wang, Faze & Asghar, Muhammad Imran & Wang, Jun & Zhu, Bin & Lund, Peter, 2024. "A half-metallic heterostructure fuel cell with high performance," Renewable Energy, Elsevier, vol. 232(C).
    16. Zhang, Yin & Qian, Tong & Tang, Wenhu, 2022. "Buildings-to-distribution-network integration considering power transformer loading capability and distribution network reconfiguration," Energy, Elsevier, vol. 244(PB).
    17. Koray Altintas & Ozalp Vayvay & Sinan Apak & Emine Cobanoglu, 2020. "An Extended GRA Method Integrated with Fuzzy AHP to Construct a Multidimensional Index for Ranking Overall Energy Sustainability Performances," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    18. Samer Hanoun & Asim Bhatti & Doug Creighton & Saeid Nahavandi & Phillip Crothers & Celeste Gloria Esparza, 2016. "Target coverage in camera networks for manufacturing workplaces," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1221-1235, December.
    19. Banna, Hasanul & Alam, Ashraful & Chen, Xihui Haviour & Alam, Ahmed W., 2023. "Energy security and economic stability: The role of inflation and war," Energy Economics, Elsevier, vol. 126(C).
    20. Kaabeche, A. & Belhamel, M. & Ibtiouen, R., 2011. "Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system," Energy, Elsevier, vol. 36(2), pages 1214-1222.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jmathe:v:13:y:2025:i:11:p:1755-:d:1664047. 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.