IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i2p280-d1319545.html
   My bibliography  Save this article

Neural Network Algorithm with Reinforcement Learning for Microgrid Techno-Economic Optimization

Author

Listed:
  • Hassan M. Hussein Farh

    (Electrical Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia)

Abstract

Hybrid energy systems (HESs) are gaining prominence as a practical solution for powering remote and rural areas, overcoming limitations of conventional energy generation methods, and offering a blend of technical and economic benefits. This study focuses on optimizing the sizes of an autonomous microgrid/HES in the Kingdom of Saudi Arabia, incorporating solar photovoltaic energy, wind turbine generators, batteries, and a diesel generator. The innovative reinforcement learning neural network algorithm (RLNNA) is applied to minimize the annualized system cost (ASC) and enhance system reliability, utilizing hourly wind speed, solar irradiance, and load behavior data throughout the year. This study validates RLNNA against five other metaheuristic/soft-computing approaches, demonstrating RLNNA’s superior performance in achieving the lowest ASC at USD 1,219,744. This outperforms SDO and PSO, which yield an ASC of USD 1,222,098.2, and MRFO, resulting in an ASC of USD 1,222,098.4, while maintaining a loss of power supply probability (LPSP) of 0%. RLNNA exhibits faster convergence to the global solution than other algorithms, including PSO, MRFO, and SDO, while MRFO, PSO, and SDO show the ability to converge to the optimal global solution. This study concludes by emphasizing RLNNA’s effectiveness in optimizing HES sizing, contributing valuable insights for off-grid energy systems in remote regions.

Suggested Citation

  • Hassan M. Hussein Farh, 2024. "Neural Network Algorithm with Reinforcement Learning for Microgrid Techno-Economic Optimization," Mathematics, MDPI, vol. 12(2), pages 1-24, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:280-:d:1319545
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/2/280/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/2/280/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ali Hadi Abdulwahid & Muna Al-Razgan & Hassan Falah Fakhruldeen & Meryelem Tania Churampi Arellano & Vedran Mrzljak & Saber Arabi Nowdeh & Mohammad Jafar Hadidian Moghaddam, 2023. "Stochastic Multi-Objective Scheduling of a Hybrid System in a Distribution Network Using a Mathematical Optimization Algorithm Considering Generation and Demand Uncertainties," Mathematics, MDPI, vol. 11(18), pages 1-30, September.
    2. Kvasov, Dmitri E. & Mukhametzhanov, Marat S., 2018. "Metaheuristic vs. deterministic global optimization algorithms: The univariate case," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 245-259.
    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. Sergey S. Ketkov & Oleg A. Prokopyev & Lisa M. Maillart, 2023. "Planning of life-depleting preventive maintenance activities with replacements," Annals of Operations Research, Springer, vol. 324(1), pages 1461-1483, May.
    2. Linas Stripinis & Remigijus Paulavičius, 2023. "Novel Algorithm for Linearly Constrained Derivative Free Global Optimization of Lipschitz Functions," Mathematics, MDPI, vol. 11(13), pages 1-19, June.
    3. Alisher Askarov & Vladimir Rudnik & Nikolay Ruban & Pavel Radko & Pavel Ilyushin & Aleksey Suvorov, 2024. "Enhanced Virtual Synchronous Generator with Angular Frequency Deviation Feedforward and Energy Recovery Control for Energy Storage System," Mathematics, MDPI, vol. 12(17), pages 1-26, August.
    4. Konstantin Barkalov & Irek Gubaydullin & Evgeny Kozinov & Ilya Lebedev & Roza Faskhutdinova & Azamat Faskhutdinov & Leniza Enikeeva, 2022. "On Solving the Problem of Finding Kinetic Parameters of Catalytic Isomerization of the Pentane-Hexane Fraction Using a Parallel Global Search Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-13, October.
    5. Yan Liang & Xianzhi Hu & Gang Hu & Wanting Dou, 2022. "An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems," Mathematics, MDPI, vol. 10(22), pages 1-33, November.
    6. Yong Wang & Kunzhao Wang & Gaige Wang, 2022. "Neural Network Algorithm with Dropout Using Elite Selection," Mathematics, MDPI, vol. 10(11), pages 1-17, May.
    7. Nihuan Liao & Zhihong Hu & Vedran Mrzljak & Saber Arabi Nowdeh, 2024. "Stochastic Techno-Economic Optimization of Hybrid Energy System with Photovoltaic, Wind, and Hydrokinetic Resources Integrated with Electric and Thermal Storage Using Improved Fire Hawk Optimization," Sustainability, MDPI, vol. 16(16), pages 1-30, August.
    8. Umesh Balande & Deepti Shrimankar, 2019. "SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems," Mathematics, MDPI, vol. 7(3), pages 1-26, March.
    9. Juan Li & Dan-dan Xiao & Hong Lei & Ting Zhang & Tian Tian, 2020. "Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location," Mathematics, MDPI, vol. 8(2), pages 1-32, January.
    10. R. Cavoretto & A. Rossi & M. S. Mukhametzhanov & Ya. D. Sergeyev, 2021. "On the search of the shape parameter in radial basis functions using univariate global optimization methods," Journal of Global Optimization, Springer, vol. 79(2), pages 305-327, February.
    11. Jannatul Ferdous & Farid Bensebaa & Abbas S. Milani & Kasun Hewage & Pankaj Bhowmik & Nathan Pelletier, 2024. "Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assess," Sustainability, MDPI, vol. 16(7), pages 1-21, March.
    12. Konstantin Barkalov & Ilya Lebedev & Marina Usova & Daria Romanova & Daniil Ryazanov & Sergei Strijhak, 2022. "Optimization of Turbulence Model Parameters Using the Global Search Method Combined with Machine Learning," Mathematics, MDPI, vol. 10(15), pages 1-20, July.
    13. Ziadi, Raouf & Bencherif-Madani, Abdelatif & Ellaia, Rachid, 2020. "A deterministic method for continuous global optimization using a dense curve," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 62-91.

    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:12:y:2024:i:2:p:280-:d:1319545. 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.