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

Author

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  • 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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
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    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.

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