IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i14p3676-d1699721.html
   My bibliography  Save this article

Optimization of Photovoltaic and Battery Storage Sizing in a DC Microgrid Using LSTM Networks Based on Load Forecasting

Author

Listed:
  • Süleyman Emre Eyimaya

    (Department of Electronics and Automation, TUSAS-Kazan Vocational School, Gazi University, Ankara 06560, Turkey)

  • Necmi Altin

    (Department of Electrical-Electronics Engineering, Faculty of Technology, Gazi University, Ankara 06560, Turkey
    Electrical Engineering Department, Molinaroli College of Engineering and Computing, University of South Carolina (USC), Columbia, SC 29208, USA)

  • Adel Nasiri

    (Electrical Engineering Department, Molinaroli College of Engineering and Computing, University of South Carolina (USC), Columbia, SC 29208, USA)

Abstract

This study presents an optimization approach for sizing photovoltaic (PV) and battery energy storage systems (BESSs) within a DC microgrid, aiming to enhance cost-effectiveness, energy reliability, and environmental sustainability. PV generation is modeled based on environmental parameters such as solar irradiance and ambient temperature, while battery charging and discharging operations are managed according to real-time demand. A simulation framework is developed in MATLAB 2021b to analyze PV output, battery state of charge (SOC), and grid energy exchange. For demand-side management, the Long Short-Term Memory (LSTM) deep learning model is employed to forecast future load profiles using historical consumption data. Moreover, a Multi-Layer Perceptron (MLP) neural network is designed for comparison purposes. The dynamic load prediction, provided by LSTM in particular, improves system responsiveness and efficiency compared to MLP. Simulation results indicate that optimal sizing of PV and storage units significantly reduces energy costs and dependency on the main grid for both forecasting methods; however, the LSTM-based approach consistently achieves higher annual savings, self-sufficiency, and Net Present Value (NPV) than the MLP-based approach. The proposed method supports the design of more resilient and sustainable DC microgrids through data-driven forecasting and system-level optimization, with LSTM-based forecasting offering the greatest benefits.

Suggested Citation

  • Süleyman Emre Eyimaya & Necmi Altin & Adel Nasiri, 2025. "Optimization of Photovoltaic and Battery Storage Sizing in a DC Microgrid Using LSTM Networks Based on Load Forecasting," Energies, MDPI, vol. 18(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3676-:d:1699721
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/14/3676/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/14/3676/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
    2. Aqib Khan & Mathieu Bressel & Arnaud Davigny & Dhaker Abbes & Belkacem Ould Bouamama, 2025. "Comprehensive Review of Hybrid Energy Systems: Challenges, Applications, and Optimization Strategies," Energies, MDPI, vol. 18(10), pages 1-34, May.
    3. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Ehsan Sedgh Gooya, 2021. "Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends," Energies, MDPI, vol. 14(14), pages 1-45, July.
    4. Adefarati, T. & Bansal, R.C., 2017. "Reliability and economic assessment of a microgrid power system with the integration of renewable energy resources," Applied Energy, Elsevier, vol. 206(C), pages 911-933.
    5. Necmi Altin & Süleyman Emre Eyimaya & Adel Nasiri, 2023. "Multi-Agent-Based Controller for Microgrids: An Overview and Case Study," Energies, MDPI, vol. 16(5), pages 1-18, March.
    6. Okoye, Chiemeka Onyeka & Solyalı, Oğuz, 2017. "Optimal sizing of stand-alone photovoltaic systems in residential buildings," Energy, Elsevier, vol. 126(C), pages 573-584.
    7. Ahmed A. Zaki Diab & Ali M. El-Rifaie & Magdy M. Zaky & Mohamed A. Tolba, 2022. "Optimal Sizing of Stand-Alone Microgrids Based on Recent Metaheuristic Algorithms," Mathematics, MDPI, vol. 10(1), pages 1-25, January.
    8. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    9. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    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. Rômulo de Oliveira Azevêdo & Paulo Rotela Junior & Luiz Célio Souza Rocha & Gianfranco Chicco & Giancarlo Aquila & Rogério Santana Peruchi, 2020. "Identification and Analysis of Impact Factors on the Economic Feasibility of Photovoltaic Energy Investments," Sustainability, MDPI, vol. 12(17), pages 1-40, September.
    2. Martin Henke & Getu Hailu, 2020. "Thermal Management of Stationary Battery Systems: A Literature Review," Energies, MDPI, vol. 13(16), pages 1-16, August.
    3. Ridha, Hussein Mohammed & Gomes, Chandima & Hazim, Hashim & Ahmadipour, Masoud, 2020. "Sizing and implementing off-grid stand-alone photovoltaic/battery systems based on multi-objective optimization and techno-economic (MADE) analysis," Energy, Elsevier, vol. 207(C).
    4. 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).
    5. Hector Beltran & Sam Harrison & Agustí Egea-Àlvarez & Lie Xu, 2020. "Techno-Economic Assessment of Energy Storage Technologies for Inertia Response and Frequency Support from Wind Farms," Energies, MDPI, vol. 13(13), pages 1-21, July.
    6. Miguel J. Prieto & Juan Á. Martínez & Rogelio Peón & Lourdes Á. Barcia & Fernando Nuño, 2017. "On the Convenience of Using Simulation Models to Optimize the Control Strategy of Molten-Salt Heat Storage Systems in Solar Thermal Power Plants," Energies, MDPI, vol. 10(7), pages 1-17, July.
    7. Emrani-Rahaghi, Pouria & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2023. "Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids," Applied Energy, Elsevier, vol. 349(C).
    8. Majumder, Suman & De, Krishnarti & Kumar, Praveen & Sengupta, Bodhisattva & Biswas, Pabitra Kumar, 2021. "Techno-commercial analysis of sustainable E-bus-based public transit systems: An Indian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    9. Ziad Ragab & Ehsan Pashajavid & Sumedha Rajakaruna, 2024. "Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors," Energies, MDPI, vol. 17(18), pages 1-20, September.
    10. Sun, Li & Sun, Wen & You, Fengqi, 2020. "Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias," Applied Energy, Elsevier, vol. 271(C).
    11. Wenxian Duan & Chuanxue Song & Silun Peng & Feng Xiao & Yulong Shao & Shixin Song, 2020. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 13(23), pages 1-19, December.
    12. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    13. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    14. Das, Kaushik & Kumar, Roushan & Krishna, Anurup, 2024. "Analyzing electric vehicle battery health performance using supervised machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    15. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    16. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    17. Dib, Ghady & Haberschill, Philippe & Rullière, Romuald & Revellin, Rémi, 2021. "Modelling small-scale trigenerative advanced adiabatic compressed air energy storage for building application," Energy, Elsevier, vol. 237(C).
    18. Guo, Cong & Xu, Yujie & Zhang, Xinjing & Guo, Huan & Zhou, Xuezhi & Liu, Chang & Qin, Wei & Li, Wen & Dou, Binlin & Chen, Haisheng, 2017. "Performance analysis of compressed air energy storage systems considering dynamic characteristics of compressed air storage," Energy, Elsevier, vol. 135(C), pages 876-888.
    19. Alexandru Ciocan & Cosmin Ungureanu & Alin Chitu & Elena Carcadea & George Darie, 2020. "Electrical Longboard for Everyday Urban Commuting," Sustainability, MDPI, vol. 12(19), pages 1-14, September.
    20. Ameen, Muhammad Tahir & Ma, Zhiwei & Smallbone, Andrew & Norman, Rose & Roskilly, Anthony Paul, 2023. "Demonstration system of pumped heat energy storage (PHES) and its round-trip efficiency," Applied Energy, Elsevier, vol. 333(C).

    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:jeners:v:18:y:2025:i:14:p:3676-:d:1699721. 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.