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Dynamic distributed generation dispatch strategy for lowering the cost of building energy

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  • Flores, Robert J.
  • Shaffer, Brendan P.
  • Brouwer, Jacob

Abstract

The practicality of any particular distributed generation installation depends upon its ability to reduce overall energy costs. A simple yet effective economic dispatch strategy with the goal to use distributed generation to minimize the cost of building energy is developed in this work. The strategy is designed to reduce the individual utility, operations and maintenance charges that increase the cost of energy. Various electric rate structures are modeled in detail and applied with the economic dispatch strategy to simulate meeting various measured building demand dynamics for heat and power. Using the economic dispatch strategy, various modes of operation, such as electric or thermal load following, peak shaving, peak shifting, or base-load operation are simulated. The economic dispatch strategy is compared to more traditional dispatch strategies to demonstrate its effectiveness in reducing total energy costs.

Suggested Citation

  • Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2014. "Dynamic distributed generation dispatch strategy for lowering the cost of building energy," Applied Energy, Elsevier, vol. 123(C), pages 196-208.
    • Handle: RePEc:eee:appene:v:123:y:2014:i:c:p:196-208
    DOI: 10.1016/j.apenergy.2014.02.028
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    Cited by:

    1. Jafari-Marandi, Ruholla & Hu, Mengqi & Omitaomu, OluFemi A., 2016. "A distributed decision framework for building clusters with different heterogeneity settings," Applied Energy, Elsevier, vol. 165(C), pages 393-404.
    2. Hu, Mengqi, 2015. "A data-driven feed-forward decision framework for building clusters operation under uncertainty," Applied Energy, Elsevier, vol. 141(C), pages 229-237.
    3. Cho, Heejin & Smith, Amanda D. & Mago, Pedro, 2014. "Combined cooling, heating and power: A review of performance improvement and optimization," Applied Energy, Elsevier, vol. 136(C), pages 168-185.
    4. Salas, V. & Suponthana, W. & Salas, R.A., 2015. "Overview of the off-grid photovoltaic diesel batteries systems with AC loads," Applied Energy, Elsevier, vol. 157(C), pages 195-216.
    5. Mbuli, N. & Ngaha, W.S., 2022. "A survey of big bang big crunch optimisation in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    6. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.
    7. Flores, Robert J. & Brouwer, Jacob, 2018. "Optimal design of a distributed energy resource system that economically reduces carbon emissions," Applied Energy, Elsevier, vol. 232(C), pages 119-138.
    8. Xiaohua Song & Yun Long & Zhongfu Tan & Xubei Zhang & Leming Li, 2016. "The Optimization of Distributed Photovoltaic Comprehensive Efficiency Based on the Construction of Regional Integrated Energy Management System in China," Sustainability, MDPI, vol. 8(11), pages 1-19, November.
    9. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2016. "Electricity costs for an electric vehicle fueling station with Level 3 charging," Applied Energy, Elsevier, vol. 169(C), pages 813-830.
    10. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2017. "Electricity costs for a Level 3 electric vehicle fueling station integrated with a building," Applied Energy, Elsevier, vol. 191(C), pages 367-384.
    11. Gao, Penghui & Li, Wangliang & Cheng, Yongpan & Tong, YenWah & Dai, Yanjun & Wang, Ruzhu, 2014. "Thermodynamic performance assessment of CCHP system driven by different composition gas," Applied Energy, Elsevier, vol. 136(C), pages 599-610.

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