IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i21p8080-d958657.html
   My bibliography  Save this article

Fiscal- and Space-Constrained Energy Optimization Model for Hybrid Grid-Tied Solar Nanogrids

Author

Listed:
  • Muhammed Shahid

    (Department of Electronics Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan
    Department of Electronic Engineering, Dawood University of Engineering and Technology, Karachi 75270, Pakistan)

  • Rizwan Aslam Butt

    (Department of Telecommunication Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan)

  • Attaullah Khawaja

    (Department of Electrical Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan)

Abstract

Due to rising fossil fuel costs, electricity tariffs are also increasing. This is motivating users to install nanogrid systems to reduce their electricity bills using solar power. However, the two main constraints for a solar system installation are the initial financial investment cost and the availability of space for the installation of solar panels. Achieving greater electricity savings requires more panels and a larger energy storage system (ESS). However, a larger ESS also increases the electricity bill and reduces the available solar power due to higher charging power requirements. The increase in solar power leads to the need for more space for solar panel installation. Therefore, achieving the maximum electricity savings for a consumer unit requires an optimized number of solar panels and ESS size within the available financial budget and the available physical space. Thus, this study presents a fiscal- and space-constrained mixed-integer linear programming-based nanogrid system model (FS-MILP) designed to compute the optimal number of solar panels and ESS requirements, and the daily electricity unit consumption and savings. The proposed model is also validated through an OMNET++-based simulation using real-time solar irradiance and residential load values of one year for the city of Karachi, Pakistan. The investigation results show that a maximum of 1050 electricity units can be saved and exported to the main power grid within the maximum financial budget of PKR 1,000,000/-.

Suggested Citation

  • Muhammed Shahid & Rizwan Aslam Butt & Attaullah Khawaja, 2022. "Fiscal- and Space-Constrained Energy Optimization Model for Hybrid Grid-Tied Solar Nanogrids," Energies, MDPI, vol. 15(21), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8080-:d:958657
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/21/8080/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/21/8080/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mashayekh, Salman & Stadler, Michael & Cardoso, Gonçalo & Heleno, Miguel, 2017. "A mixed integer linear programming approach for optimal DER portfolio, sizing, and placement in multi-energy microgrids," Applied Energy, Elsevier, vol. 187(C), pages 154-168.
    2. Arrinda, M. & Berecibar, M. & Oyarbide, M. & Macicior, H. & Muxika, E. & Messagie, M., 2020. "Levelized cost of electricity calculation of the energy generation plant of a CO2 neutral micro-grid," Energy, Elsevier, vol. 208(C).
    3. Yerasimos Yerasimou & Marios Kynigos & Venizelos Efthymiou & George E. Georghiou, 2021. "Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings," Energies, MDPI, vol. 14(12), pages 1-19, June.
    4. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    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. Grover-Silva, Etta & Heleno, Miguel & Mashayekh, Salman & Cardoso, Gonçalo & Girard, Robin & Kariniotakis, George, 2018. "A stochastic optimal power flow for scheduling flexible resources in microgrids operation," Applied Energy, Elsevier, vol. 229(C), pages 201-208.
    2. Silverman, Rochelle E. & Flores, Robert J. & Brouwer, Jack, 2020. "Energy and economic assessment of distributed renewable gas and electricity generation in a small disadvantaged urban community," Applied Energy, Elsevier, vol. 280(C).
    3. Petrelli, Marina & Fioriti, Davide & Berizzi, Alberto & Bovo, Cristian & Poli, Davide, 2021. "A novel multi-objective method with online Pareto pruning for multi-year optimization of rural microgrids," Applied Energy, Elsevier, vol. 299(C).
    4. DiOrio, Nicholas & Denholm, Paul & Hobbs, William B., 2020. "A model for evaluating the configuration and dispatch of PV plus battery power plants," Applied Energy, Elsevier, vol. 262(C).
    5. Qiu, Jing & Zhao, Junhua & Yang, Hongming & Wang, Dongxiao & Dong, Zhao Yang, 2018. "Planning of solar photovoltaics, battery energy storage system and gas micro turbine for coupled micro energy grids," Applied Energy, Elsevier, vol. 219(C), pages 361-369.
    6. Scheller, Fabian & Bruckner, Thomas, 2019. "Energy system optimization at the municipal level: An analysis of modeling approaches and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 444-461.
    7. Rigo-Mariani, Rémy & Chea Wae, Sean Ooi & Mazzoni, Stefano & Romagnoli, Alessandro, 2020. "Comparison of optimization frameworks for the design of a multi-energy microgrid," Applied Energy, Elsevier, vol. 257(C).
    8. Nnaemeka Vincent Emodi & Taha Chaiechi & ABM Rabiul Alam Beg, 2018. "The impact of climate change on electricity demand in Australia," Energy & Environment, , vol. 29(7), pages 1263-1297, November.
    9. Liang, Zheming & Bian, Desong & Zhang, Xiaohu & Shi, Di & Diao, Ruisheng & Wang, Zhiwei, 2019. "Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market," Applied Energy, Elsevier, vol. 236(C), pages 916-926.
    10. Savelli, Iacopo & Cornélusse, Bertrand & Giannitrapani, Antonio & Paoletti, Simone & Vicino, Antonio, 2018. "A new approach to electricity market clearing with uniform purchase price and curtailable block orders," Applied Energy, Elsevier, vol. 226(C), pages 618-630.
    11. DeForest, Nicholas & MacDonald, Jason S. & Black, Douglas R., 2018. "Day ahead optimization of an electric vehicle fleet providing ancillary services in the Los Angeles Air Force Base vehicle-to-grid demonstration," Applied Energy, Elsevier, vol. 210(C), pages 987-1001.
    12. Pecenak, Zachary K. & Stadler, Michael & Mathiesen, Patrick & Fahy, Kelsey & Kleissl, Jan, 2020. "Robust design of microgrids using a hybrid minimum investment optimization," Applied Energy, Elsevier, vol. 276(C).
    13. Hemmati, S. & Ghaderi, S.F. & Ghazizadeh, M.S., 2018. "Sustainable energy hub design under uncertainty using Benders decomposition method," Energy, Elsevier, vol. 143(C), pages 1029-1047.
    14. Mateusz Andrychowicz, 2021. "RES and ES Integration in Combination with Distribution Grid Development Using MILP," Energies, MDPI, vol. 14(2), pages 1-19, January.
    15. Yang, Dongfeng & Jiang, Chao & Cai, Guowei & Yang, Deyou & Liu, Xiaojun, 2020. "Interval method based optimal planning of multi-energy microgrid with uncertain renewable generation and demand," Applied Energy, Elsevier, vol. 277(C).
    16. Heendeniya, Charitha Buddhika & Sumper, Andreas & Eicker, Ursula, 2020. "The multi-energy system co-planning of nearly zero-energy districts – Status-quo and future research potential," Applied Energy, Elsevier, vol. 267(C).
    17. Azimian, Mahdi & Amir, Vahid & Mohseni, Soheil & Brent, Alan C. & Bazmohammadi, Najmeh & Guerrero, Josep M., 2022. "Optimal Investment Planning of Bankable Multi-Carrier Microgrid Networks," Applied Energy, Elsevier, vol. 328(C).
    18. Wang, Fengjuan & Xu, Jiuping & Liu, Liying & Yin, Guangming & Wang, Jianhua & Yan, Jinyue, 2021. "Optimal design and operation of hybrid renewable energy system for drinking water treatment," Energy, Elsevier, vol. 219(C).
    19. Scheller, Fabian & Burgenmeister, Balthasar & Kondziella, Hendrik & Kühne, Stefan & Reichelt, David G. & Bruckner, Thomas, 2018. "Towards integrated multi-modal municipal energy systems: An actor-oriented optimization approach," Applied Energy, Elsevier, vol. 228(C), pages 2009-2023.
    20. Shaohao Xie & Fangguo Zhang & Huizhi Lin & Yangtong Tian, 2019. "A New Secure and Anonymous Metering Scheme for Smart Grid Communications," Energies, MDPI, vol. 12(24), pages 1-16, December.

    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:21:p:8080-:d:958657. 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.