IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i18p5606-d630793.html
   My bibliography  Save this article

The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers

Author

Listed:
  • Jaemin Park

    (School of Electrical Engineering, Inha University, Incheon 22212, Korea)

  • Haesung Jo

    (School of Electrical Engineering, Inha University, Incheon 22212, Korea)

  • Insu Kim

    (School of Electrical Engineering, Inha University, Incheon 22212, Korea)

Abstract

Distributed generation (DG) using renewable energy sources is of widespread interest. For example, modern centralized conventional fossil fuel power generation commonly adds DG using renewable energy resources to the grid. Therefore, in these changes, it is necessary to optimize renewable energy systems to increase energy efficiency and reduce emissions. In previous studies, meta-heuristic algorithms were used to optimize DG location and capacity, but different types of DG systems and integrated energy hub conditions were not considered. Determining the most effective DG type for an integrated energy hub is critical. Accordingly, this study presented a methodology for selecting the most cost-efficient DG for metropolitan residential customers of energy hubs. In this paper, we model energy hubs for residential customers and the most cost-efficient DG type using MATLAB and HOMER software, considering microturbine (MT), photovoltaic (PV), wind turbine, and fuel cell (FC) power sources. For this purpose, the energy hub was modeled as a combined cooling heat and power (CCHP) system and selected a specific metropolitan area as a testbed (Atlanta, USA). For practical simulation, the total active power of the Atlanta community was measured by multiplying the average load profile data of residential houses collected by open energy information (OpenEI). The first case study showed that optimal-blast MTs without absorption chillers (AbCs) were the most cost-efficient compared to other optimal-blast DG systems without AbCs. Additional second case studies for optimal and full-blast MTs with AbCs were performed to verify the results for energy consumption, costs, and emissions savings. As a result, full-blast MTs with AbCs comprise the most cost-efficient DG type in the CCHP system for metropolitan residential customers, reducing energy consumption, cost, and emissions.

Suggested Citation

  • Jaemin Park & Haesung Jo & Insu Kim, 2021. "The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers," Energies, MDPI, vol. 14(18), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5606-:d:630793
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/18/5606/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/18/5606/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sovacool, Benjamin K., 2008. "Valuing the greenhouse gas emissions from nuclear power: A critical survey," Energy Policy, Elsevier, vol. 36(8), pages 2940-2953, August.
    2. Guozheng Li & Rui Wang & Tao Zhang & Mengjun Ming, 2018. "Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g," Energies, MDPI, vol. 11(4), pages 1-26, March.
    3. Kim, Insu, 2018. "Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method," Energy, Elsevier, vol. 150(C), pages 642-652.
    4. Haesung Jo & Jaemin Park & Insu Kim, 2021. "Environmentally Constrained Optimal Dispatch Method for Combined Cooling, Heating, and Power Systems Using Two-Stage Optimization," Energies, MDPI, vol. 14(14), pages 1-20, July.
    5. Beopsoo Kim & Nikita Rusetskii & Haesung Jo & Insu Kim, 2021. "The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method," Energies, MDPI, vol. 14(2), pages 1-18, January.
    6. Tsikalakis, A.G. & Hatziargyriou, N.D., 2007. "Environmental benefits of distributed generation with and without emissions trading," Energy Policy, Elsevier, vol. 35(6), pages 3395-3409, June.
    7. Donghyeon Lee & Seungwan Son & Insu Kim, 2021. "Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization," Energies, MDPI, vol. 14(11), pages 1-19, 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. Ghodusinejad, Mohammad Hasan & Lavasani, Zahra & Yousefi, Hossein, 2023. "A combined decision-making framework for techno-enviro-economic assessment of a commercial CCHP system," Energy, Elsevier, vol. 276(C).
    2. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.

    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. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
    2. Tsai, Bi-Huei & Chang, Chih-Jen & Chang, Chun-Hsien, 2016. "Elucidating the consumption and CO2 emissions of fossil fuels and low-carbon energy in the United States using Lotka–Volterra models," Energy, Elsevier, vol. 100(C), pages 416-424.
    3. Jānis Krūmiņš & Māris Kļaviņš, 2023. "Investigating the Potential of Nuclear Energy in Achieving a Carbon-Free Energy Future," Energies, MDPI, vol. 16(9), pages 1-31, April.
    4. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    5. Ivan Faiella & Luciano Lavecchia, 2012. "Costs and benefits of relaunching nuclear energy in Italy," Questioni di Economia e Finanza (Occasional Papers) 114, Bank of Italy, Economic Research and International Relations Area.
    6. Bi-Huei Tsai & Yao-Min Huang, 2023. "Comparing the Substitution of Nuclear Energy or Renewable Energy for Fossil Fuels between the United States and Africa," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    7. Merad, Faycel & Labar, Hocine & Samira KELAIAIA, Mounia & Necaibia, Salah & Djelailia, Okba, 2019. "A maximum power control based on flexible collector applied to concentrator solar power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 315-331.
    8. Valentine, Scott, 2010. "Braking wind in Australia: A critical evaluation of the renewable energy target," Energy Policy, Elsevier, vol. 38(7), pages 3668-3675, July.
    9. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    10. World Bank, 2012. "Air Transport and Energy Efficiency," World Bank Publications - Reports 16805, The World Bank Group.
    11. Halliki Kreinin, 2021. "The divergent narratives and strategies of unions in times of social-ecological crises: fracking and the UK energy sector," Transfer: European Review of Labour and Research, , vol. 27(4), pages 453-468, November.
    12. Alkan, Ömer & Albayrak, Özlem Karadağ, 2020. "Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA," Renewable Energy, Elsevier, vol. 162(C), pages 712-726.
    13. Mahmud, Nasif & Zahedi, A., 2016. "Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 582-595.
    14. Yazdanie, Mashael & Densing, Martin & Wokaun, Alexander, 2016. "The role of decentralized generation and storage technologies in future energy systems planning for a rural agglomeration in Switzerland," Energy Policy, Elsevier, vol. 96(C), pages 432-445.
    15. Linares, Pedro & Conchado, Adela, 2013. "The economics of new nuclear power plants in liberalized electricity markets," Energy Economics, Elsevier, vol. 40(S1), pages 119-125.
    16. Treyer, Karin & Bauer, Christian & Simons, Andrew, 2014. "Human health impacts in the life cycle of future European electricity generation," Energy Policy, Elsevier, vol. 74(S1), pages 31-44.
    17. Shoeibi, Shahin & Rahbar, Nader & Esfahlani, Ahad Abedini & Kargarsharifabad, Hadi, 2021. "Energy matrices, exergoeconomic and enviroeconomic analysis of air-cooled and water-cooled solar still: Experimental investigation and numerical simulation," Renewable Energy, Elsevier, vol. 171(C), pages 227-244.
    18. Saadon, Syamimi & Gaillard, Leon & Menezo, Christophe & Giroux-Julien, Stéphanie, 2020. "Exergy, exergoeconomic and enviroeconomic analysis of a building integrated semi-transparent photovoltaic/thermal (BISTPV/T) by natural ventilation," Renewable Energy, Elsevier, vol. 150(C), pages 981-989.
    19. Alshammari, Yousef M. & Sarathy, S. Mani, 2017. "Achieving 80% greenhouse gas reduction target in Saudi Arabia under low and medium oil prices," Energy Policy, Elsevier, vol. 101(C), pages 502-511.
    20. Basu, Ashoke Kumar & Chowdhury, S.P. & Chowdhury, S. & Paul, S., 2011. "Microgrids: Energy management by strategic deployment of DERs—A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4348-4356.

    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:14:y:2021:i:18:p:5606-:d:630793. 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.