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. 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.
    2. 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.
    3. 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.
    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. 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).
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shree Om Bade & Olusegun Stanley Tomomewo & Michael Mann & Johannes Van der Watt & Hossein Salehfar, 2025. "Optimal Sizing and Techno-Economic Evaluation of a Utility-Scale Wind–Solar–Battery Hybrid Plant Considering Weather Uncertainties, as Well as Policy and Economic Incentives, Using Multi-Objective Opt," Energies, MDPI, vol. 18(13), pages 1-39, July.

    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. Yasemin Merzifonluoglu & Eray Uzgoren, 2018. "Photovoltaic power plant design considering multiple uncertainties and risk," Annals of Operations Research, Springer, vol. 262(1), pages 153-184, March.
    2. Selin Kocaman, Ayse & Abad, Carlos & Troy, Tara J. & Tim Huh, Woonghee & Modi, Vijay, 2016. "A stochastic model for a macroscale hybrid renewable energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 688-703.
    3. 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.
    4. Harri Aaltonen & Seppo Sierla & Rakshith Subramanya & Valeriy Vyatkin, 2021. "A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage," Energies, MDPI, vol. 14(17), pages 1-20, September.
    5. Dirk Johan van Vuuren & Annlizé L. Marnewick & Jan Harm C. Pretorius, 2021. "A Financial Evaluation of a Multiple Inclination, Rooftop-Mounted, Photovoltaic System Where Structured Tariffs Apply: A Case Study of a South African Shopping Centre," Energies, MDPI, vol. 14(6), pages 1-26, March.
    6. 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.
    7. Askarzadeh, Alireza & Rezazadeh, Alireza, 2013. "Artificial bee swarm optimization algorithm for parameters identification of solar cell models," Applied Energy, Elsevier, vol. 102(C), pages 943-949.
    8. Maleki, Akbar & Ameri, Mehran & Keynia, Farshid, 2015. "Scrutiny of multifarious particle swarm optimization for finding the optimal size of a PV/wind/battery hybrid system," Renewable Energy, Elsevier, vol. 80(C), pages 552-563.
    9. 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.
    10. 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.
    11. Salameh, Tareq & Ghenai, Chaouki & Merabet, Adel & Alkasrawi, Malek, 2020. "Techno-economical optimization of an integrated stand-alone hybrid solar PV tracking and diesel generator power system in Khorfakkan, United Arab Emirates," Energy, Elsevier, vol. 190(C).
    12. Magdalena Tutak & Jarosław Brodny & Peter Bindzár, 2021. "Assessing the Level of Energy and Climate Sustainability in the European Union Countries in the Context of the European Green Deal Strategy and Agenda 2030," Energies, MDPI, vol. 14(6), pages 1-32, March.
    13. 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.
    14. 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.
    15. Ahsan, Syed M. & Khan, Hassan A. & Hassan, Naveed-ul & Arif, Syed M. & Lie, Tek-Tjing, 2020. "Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts," Applied Energy, Elsevier, vol. 273(C).
    16. Sheng Zhang & Yifu Yang & Chengdi Ding & Zhongquan Miao, 2023. "The Impact of International Relations Patterns on China’s Energy Security Supply, Demand, and Sustainable Development: An Exploration of Oil Demand and Sustainability Goals," Sustainability, MDPI, vol. 15(17), pages 1-12, August.
    17. Chen, Jun & Garcia, Humberto E., 2016. "Economic optimization of operations for hybrid energy systems under variable markets," Applied Energy, Elsevier, vol. 177(C), pages 11-24.
    18. Chang-Yong Lee & Dongju Lee, 2014. "Determination of initial temperature in fast simulated annealing," Computational Optimization and Applications, Springer, vol. 58(2), pages 503-522, June.
    19. Ahmed Al Ameri & Aouchenni Ounissa & Cristian Nichita & Aouzellag Djamal, 2017. "Power Loss Analysis for Wind Power Grid Integration Based on Weibull Distribution," Energies, MDPI, vol. 10(4), pages 1-16, April.
    20. 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.

    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.