IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p8933-d984455.html
   My bibliography  Save this article

Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid

Author

Listed:
  • Asad Ali

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Muhammad Salman Fakhar

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
    National Transmission and Despatch Company (NTDC), Lahore 54000, Pakistan)

  • Syed Abdul Rahman Kashif

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Ghulam Abbas

    (Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan)

  • Irfan Ahmad Khan

    (Clean and Resilient Energy Systems (CARES) Lab, Electrical and Computer Engineering Department, Texas A&M University, Galveston, TX 77553, USA)

  • Akhtar Rasool

    (Department of Electrical Engineering, University of Botswana, Gaborone, Botswana)

  • Nasim Ullah

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

Abstract

In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor’s living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper.

Suggested Citation

  • Asad Ali & Muhammad Salman Fakhar & Syed Abdul Rahman Kashif & Ghulam Abbas & Irfan Ahmad Khan & Akhtar Rasool & Nasim Ullah, 2022. "Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid," Energies, MDPI, vol. 15(23), pages 1-31, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8933-:d:984455
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/8933/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/23/8933/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rathi, Sambhu Singh & Vermaak, Claire, 2018. "Rural electrification, gender and the labor market: A cross-country study of India and South Africa," World Development, Elsevier, vol. 109(C), pages 346-359.
    2. Nandi, Sanjoy Kumar & Ghosh, Himangshu Ranjan, 2010. "Prospect of wind–PV-battery hybrid power system as an alternative to grid extension in Bangladesh," Energy, Elsevier, vol. 35(7), pages 3040-3047.
    3. Georgiou, Giorgos S. & Christodoulides, Paul & Kalogirou, Soteris A., 2020. "Optimizing the energy storage schedule of a battery in a PV grid-connected nZEB using linear programming," Energy, Elsevier, vol. 208(C).
    4. Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael, 2021. "Mixed-integer linear programming based optimization strategies for renewable energy communities," Energy, Elsevier, vol. 237(C).
    5. Moretti, Luca & Astolfi, Marco & Vergara, Claudio & Macchi, Ennio & Pérez-Arriaga, Josè Ignacio & Manzolini, Giampaolo, 2019. "A design and dispatch optimization algorithm based on mixed integer linear programming for rural electrification," Applied Energy, Elsevier, vol. 233, pages 1104-1121.
    6. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    7. Dalton, G.J. & Lockington, D.A. & Baldock, T.E., 2009. "Feasibility analysis of renewable energy supply options for a grid-connected large hotel," Renewable Energy, Elsevier, vol. 34(4), pages 955-964.
    8. Margaret Amutha, W. & Rajini, V., 2015. "Techno-economic evaluation of various hybrid power systems for rural telecom," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 553-561.
    9. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
    10. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    11. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    12. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
    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. Bahramara, S. & Moghaddam, M. Parsa & Haghifam, M.R., 2016. "Optimal planning of hybrid renewable energy systems using HOMER: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 609-620.
    2. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    3. 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.
    4. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    5. William López-Castrillón & Héctor H. Sepúlveda & Cristian Mattar, 2021. "Off-Grid Hybrid Electrical Generation Systems in Remote Communities: Trends and Characteristics in Sustainability Solutions," Sustainability, MDPI, vol. 13(11), pages 1-29, May.
    6. Diab, Fahd & Lan, Hai & Ali, Salwa, 2016. "Novel comparison study between the hybrid renewable energy systems on land and on ship," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 452-463.
    7. Yilmaz, Saban & Dincer, Furkan, 2017. "Optimal design of hybrid PV-Diesel-Battery systems for isolated lands: A case study for Kilis, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 344-352.
    8. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Pasquale, Stefano, 2019. "Fuel cell based hybrid renewable energy systems for off-grid telecom stations: Data analysis and system optimization," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Fazelpour, Farivar & Soltani, Nima & Rosen, Marc A., 2014. "Feasibility of satisfying electrical energy needs with hybrid systems for a medium-size hotel on Kish Island, Iran," Energy, Elsevier, vol. 73(C), pages 856-865.
    10. Olatomiwa, Lanre & Mekhilef, Saad & Ismail, M.S. & Moghavvemi, M., 2016. "Energy management strategies in hybrid renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 821-835.
    11. Hua, Jian & Shiu, Hong-Gwo, 2018. "Sustainable development of renewable energy on Wangan Island, Taiwan," Utilities Policy, Elsevier, vol. 55(C), pages 200-208.
    12. Chauhan, Anurag & Saini, R.P., 2016. "Techno-economic optimization based approach for energy management of a stand-alone integrated renewable energy system for remote areas of India," Energy, Elsevier, vol. 94(C), pages 138-156.
    13. William E., Lilley & Luke J., Reedman & Liam D., Wagner & Colin F., Alie & Anthony R., Szatow, 2012. "An economic evaluation of the potential for distributed energy in Australia," Energy Policy, Elsevier, vol. 51(C), pages 277-289.
    14. Rezk, Hegazy & Sayed, Enas Taha & Al-Dhaifallah, Mujahed & Obaid, M. & El-Sayed, Abou Hashema M. & Abdelkareem, Mohammad Ali & Olabi, A.G., 2019. "Fuel cell as an effective energy storage in reverse osmosis desalination plant powered by photovoltaic system," Energy, Elsevier, vol. 175(C), pages 423-433.
    15. Mo, Qiu & Liu, Fang, 2020. "Modeling and optimization for distributed microgrid based on Modelica language," Applied Energy, Elsevier, vol. 279(C).
    16. Fahd Diab & Hai Lan & Lijun Zhang & Salwa Ali, 2015. "An Environmentally-Friendly Tourist Village in Egypt Based on a Hybrid Renewable Energy System––Part One: What Is the Optimum City?," Energies, MDPI, vol. 8(7), pages 1-19, July.
    17. Li, Chong & Ge, Xinfeng & Zheng, Yuan & Xu, Chang & Ren, Yan & Song, Chenguang & Yang, Chunxia, 2013. "Techno-economic feasibility study of autonomous hybrid wind/PV/battery power system for a household in Urumqi, China," Energy, Elsevier, vol. 55(C), pages 263-272.
    18. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    19. Soheil Mohseni & Alan C. Brent, 2022. "A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
    20. Pollini, Nicolò, 2022. "Topology optimization of wind farm layouts," Renewable Energy, Elsevier, vol. 195(C), pages 1015-1027.

    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:15:y:2022:i:23:p:8933-:d:984455. 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.