A comprehensive energy solution for households employing a micro combined cooling, heating and power generation system
Received date: 06 Mar 2018
Accepted date: 20 Jul 2018
Published date: 21 Dec 2018
Copyright
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.
Huayi ZHANG , Can ZHANG , Fushuan WEN , Yan XU . A comprehensive energy solution for households employing a micro combined cooling, heating and power generation system[J]. Frontiers in Energy, 2018 , 12(4) : 582 -590 . DOI: 10.1007/s11708-018-0592-9
1 |
Rifkin J. The Third Industrial Revolution: How Lateral Power Is Transforming Energy, the Economy, and the World. New York: Palgrave MacMillan, 2011
|
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): 15–27 doi:10.1109/JPROC.2010.2083610
|
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): 24–30 doi:10.1109/MPAE.2007.264850
|
4 |
Badea N. Design for Micro-combined Cooling, Heating and Power Systems: Stirling Engines and Renewable Power Systems. London: Springer, 2014
|
5 |
Xie D, Lu Y, Sun J, Gu C H, Yu J L. Optimal operation of network-connected combined heat and powers for customer profit maximization. Energies, 2016, 9(6): 442 doi:10.3390/en9060442
|
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: 3005–3015 doi:10.1109/ACCESS.2016.2580918
|
7 |
Du P W, Lu N. Appliance commitment for household load scheduling. IEEE Transactions on Smart Grid, 2011, 2(2): 411–419 doi:10.1109/TSG.2011.2140344
|
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): 2219–2228 doi:10.1109/TPWRS.2014.2307474
|
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: 209–222 doi:10.1016/j.enconman.2013.04.038
|
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): 1755–1766 doi:10.1109/TSG.2012.2212032
|
11 |
Rastegar M, Fotuhi-Firuzabad M, Lehtonen M. Home load management in a residential energy hub. Electric Power Systems Research, 2015, 119: 322–328 doi:10.1016/j.epsr.2014.10.011
|
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): 349–357 doi:10.1109/TSG.2013.2261829
|
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: 65–75 doi:10.1016/j.enbuild.2014.12.039
|
14 |
Energy Saving Advice Service. The benefits of micro-CHP. 2016–02–16, http://www.energysavingtrust.org.uk/domestic/micro-chp
|
15 |
Houwing M, Negenborn R R, De Schutter B. Demand response with micro-CHP systems. Proceedings of the IEEE, 2011, 99(1): 200–213 doi:10.1109/JPROC.2010.2053831
|
16 |
Lu N. An evaluation of the HVAC load potential for providing load balancing service. IEEE Transactions on Smart Grid, 2012, 3(3): 1263–1270 doi:10.1109/TSG.2012.2183649
|
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): 3668–3679 doi:10.1016/j.apenergy.2010.06.021
|
18 |
Fanger P. Thermal Comfort. Copenhagen: Danish Technical Press, 1970
|
19 |
He P. The study about indoor air conditioning based on PMV. Dissertation for the Master’s Degree.Chongqing: Chongqing University, 2010
|
20 |
ISO Standard 7730. Moderate thermal environment-determination of PMV and PPD indices and specification of the condition for thermal comfort, 1984
|
21 |
Soyster A L. Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 1973, 21(5): 1154–1157 doi:10.1287/opre.21.5.1154
|
22 |
Bertsimas D, Sim M. The price of robustness. Operations Research, 2004, 52(1): 35–53 doi:10.1287/opre.1030.0065
|
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.pdf
|
/
〈 | 〉 |