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Frontiers in Energy

, Volume 12, Issue 4, pp 582–590 | Cite as

A comprehensive energy solution for households employing a micro combined cooling, heating and power generation system

  • Huayi Zhang
  • Can Zhang
  • Fushuan WenEmail author
  • Yan Xu
Research Article
  • 9 Downloads

Abstract

In recent years, micro combined cooling, heating and power generation (mCCHP) systems have attracted much attention in the energy demand side sector. The input energy of a mCCHP system is natural gas, while the outputs include heating, cooling and electricity energy. The mCCHP system is deemed as a possible solution for households with multiple energy demands. Given this background, a mCCHP based comprehensive energy solution for households is proposed in this paper. First, the mathematical model of a home energy hub (HEH) is presented to describe the inputs, outputs, conversion and consumption process of multiple energies in households. Then, electrical loads and thermal demands are classified and modeled in detail, and the coordination and complementation between electricity and natural gas are studied. Afterwards, the concept of thermal comfort is introduced and a robust optimization model for HEH is developed considering electricity price uncertainties. Finally, a household using a mCCHP as the energy conversion device is studied. The simulation results show that the comprehensive energy solution proposed in this work can realize multiple kinds of energy supplies for households with the minimized total energy cost.

Keywords

energy hub micro combined cooling heating and power generation (mCCHP) thermal comfort robust optimization 

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Notes

Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (Grant No. 51477151), and National Key Research and Development Program of China (Basic Research Class) (No. 2017YFB0903000).

References

  1. 1.
    Rifkin J. The Third Industrial Revolution: How Lateral Power Is Transforming Energy, the Economy, and the World. New York: Palgrave MacMillan, 2011Google Scholar
  2. 2.
    Krause T, Andersson G, Fröhlich K, Vaccaro A. Multiple-energy carriers, modeling of production, delivery, and consumption. Proceedings of the IEEE, 2011, 99(1): 15–27CrossRefGoogle Scholar
  3. 3.
    Geidl M, Koeppel G, Favre-Perrod P, Klockl B, Andersson G, Frohlich K. Energy hubs for the future. IEEE Power & Energy Magazine, 2007, 5(1): 24–30CrossRefGoogle Scholar
  4. 4.
    Badea N. Design for Micro-combined Cooling, Heating and Power Systems: Stirling Engines and Renewable Power Systems. London: Springer, 2014Google Scholar
  5. 5.
    Xie D, Lu Y, Sun J, Gu C H, Yu J L. Optimal operation of networkconnected combined heat and powers for customer profit maximization. Energies, 2016, 9(6): 442CrossRefGoogle Scholar
  6. 6.
    Xie D, Lu Y, Sun J, Gu C, Li G. Optimal operation of a combined heat and power system considering real-time energy prices. IEEE Access: Practical Innovations, Open Solutions, 2016, 4: 3005–3015CrossRefGoogle Scholar
  7. 7.
    Du P W, Lu N. Appliance commitment for household load scheduling. IEEE Transactions on Smart Grid, 2011, 2(2): 411–419CrossRefGoogle Scholar
  8. 8.
    Chen C, Wang J, Kishore S. A distributed direct load control approach for large-scale residential demand response. IEEE Transactions on Power Systems, 2014, 29(5): 2219–2228CrossRefGoogle Scholar
  9. 9.
    Zhang D, Shah N, Papageorgiou L G. Efficient energy consumption and operation management in a smart building with microgrid. Energy Conversion and Management, 2013, 74: 209–222CrossRefGoogle Scholar
  10. 10.
    Bozchalui M C, Hashmi S A, Hassen H, Canizares C A, Bhattacharya K. Optimal operation of residential energy hubs in smart grids. IEEE Transactions on Smart Grid, 2012, 3(4): 1755–1766CrossRefGoogle Scholar
  11. 11.
    Rastegar M, Fotuhi-Firuzabad M, Lehtonen M. Home load management in a residential energy hub. Electric Power Systems Research, 2015, 119: 322–328CrossRefGoogle Scholar
  12. 12.
    Tasdighi M, Ghasemi H, Rahimi-Kian A. Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling. IEEE Transactions on Smart Grid, 2014, 5 (1): 349–357Google Scholar
  13. 13.
    Brahman F, Honarmand M, Jadid S. Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system. Energy and Building, 2015, 90: 65–75CrossRefGoogle Scholar
  14. 14.
    Energy Saving Advice Service. The benefits of micro-CHP. 2016–02–16, http://www.energysavingtrust.org.uk/domestic/micro-chpGoogle Scholar
  15. 15.
    Houwing M, Negenborn R R, De Schutter B. Demand response with micro-CHP systems. Proceedings of the IEEE, 2011, 99(1): 200–213CrossRefGoogle Scholar
  16. 16.
    Lu N. An evaluation of the HVAC load potential for providing load balancing service. IEEE Transactions on Smart Grid, 2012, 3(3): 1263–1270CrossRefGoogle Scholar
  17. 17.
    Wang J H, Zhai Z Q, Jing Y Y, Zhang C F. Particle swarm optimization for redundant building cooling heating and power system. Applied Energy, 2010, 87(12): 3668–3679CrossRefGoogle Scholar
  18. 18.
    Fanger P. Thermal Comfort. Copenhagen: Danish Technical Press, 1970Google Scholar
  19. 19.
    He P. The study about indoor air conditioning based on PMV. Dissertation for the Master’s Degree. Chongqing: Chongqing University, 2010Google Scholar
  20. 20.
    ISO Standard 7730. Moderate thermal environment-determination of PMV and PPD indices and specification of the condition for thermal comfort, 1984Google Scholar
  21. 21.
    Soyster A L. Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 1973, 21(5): 1154–1157CrossRefzbMATHGoogle Scholar
  22. 22.
    Bertsimas D, Sim M. The price of robustness. Operations Research, 2004, 52(1): 35–53MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Bertsimas D, Thiele A. Robust and data-driven optimization: modern decision-making under uncertainty. 2012–10–15, http://web.mit.edu/dbertsim/www/papers/Robust%20Optimization/Robust%20and%20data-driven%20optimization-%20modern% 20decision-making%20under%20uncertainty.pdfGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electrical EngineeringZhejiang UniversityHangzhouChina
  2. 2.State Grid Nanjing Power Supply CompanyNanjingChina
  3. 3.School of Electrical and Electronic EngineeringChangsha University of Science and TechnologyChangshaChina

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