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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

Postal Subscription Code 80-972

2018 Impact Factor: 1.701

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Front. Energy    2018, Vol. 12 Issue (4) : 582-590    https://doi.org/10.1007/s11708-018-0592-9
RESEARCH ARTICLE
A comprehensive energy solution for households employing a micro combined cooling, heating and power generation system
Huayi ZHANG1, Can ZHANG2, Fushuan WEN3(), Yan XU4
1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; State Grid Nanjing Power Supply Company, Nanjing 210019, China
2. State Grid Nanjing Power Supply Company, Nanjing 210019, China
3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
4. School of Electrical and Electronic Engineering, Changsha University of Science and Technology, Changsha 410114, China
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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     
Corresponding Author(s): Fushuan WEN   
Just Accepted Date: 27 September 2018   Online First Date: 03 December 2018    Issue Date: 21 December 2018
 Cite this article:   
Huayi ZHANG,Can ZHANG,Fushuan WEN, et al. A comprehensive energy solution for households employing a micro combined cooling, heating and power generation system[J]. Front. Energy, 2018, 12(4): 582-590.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-018-0592-9
https://academic.hep.com.cn/fie/EN/Y2018/V12/I4/582
Fig.1  The framework of a HEH
PMV +3 +2 +1 0 –1 –2 –3
Comfort level Hot Warm Slightly warm Neutral Slightly cool Cool Cold
Tab.1  PMV and thermal comfort
Fig.2  Time-of-use tariffs in a day
Fig.3  Hot water demands in a day
Fig.4  Demand of inflexible electricity loads in a day
Flexible demand Rate power/kW Run time/min Operating period
Washer (WS) 0.5 45 7 am–11 am
Dishwasher (DW) 1.28 75 12 pm–18 pm
Dryer (DY) 0.9 45 7 am–12 pm
Iron (IR) 1.3 15 19 pm–22 pm
Cleaner (CL) 1.1 60 8 am–12 pm
Tab.2  Parameters of flexible electricity loads
hCCHPmin /kW pCCHP min/kW PEC min/kW ηe/% ZEC κgas/(kWh?m–3)
0 0 0 30 3 9.78
hCCHP max/kW pCCHP max/kW PEC max/kW ηh/% ZAC
3 1.8 1 50 0.7
Tab.3  Parameters of the mCCHP
R/(ºC?kW–1) θws min/ºC Cw/(kWh?(kg·ºC)–1) θcw/ºC Δθin/ºC
18 40 1.1667×10-3 10 0.5
ρw/(kg?m–3) θws max/ºC Cair/(kWh? ºC–1) Vw/L Δθws/ºC
103 50 0.525 150 0.5
Tab.4  Parameters of thermal resistance, temperature, specific heat capacity, water storage volume, and water density
Fig.5  Temperature control results, storage water tank heating power, air heating power, and output heating power of the mCCHP in a typical winter day
Fig.6  Temperature control results, storage water tank heating power, air cooling power, and output heating power of the mCCHP in a typical summer day
Γ ε/% Cost (CNY) Relative change in the optimal value of the robust optimization model/%
Winter 0 58.09 20.1665 0
4 27.01 20.6581 2.4377
8 7.65 21.056 4.4108
12 1.24 21.3511 5.8741
Summer 0 58.09 16.0039 0
4 27.01 16.5237 3.25
8 7.65 16.7977 4.96
12 1.24 16.9353 5.82
Tab.5  Robust optimization results in a typical winter day and a typical summer day
1 JRifkin. The Third Industrial Revolution: How Lateral Power Is Transforming Energy, the Economy, and the World. New York: Palgrave MacMillan, 2011
2 TKrause, G Andersson, KFröhlich, AVaccaro. 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 MGeidl, G Koeppel, PFavre-Perrod, BKlockl, GAndersson, KFrohlich. Energy hubs for the future. IEEE Power & Energy Magazine, 2007, 5(1): 24–30 doi:10.1109/MPAE.2007.264850
4 NBadea. Design for Micro-combined Cooling, Heating and Power Systems: Stirling Engines and Renewable Power Systems. London: Springer, 2014
5 DXie, Y Lu, JSun, C HGu, J LYu. Optimal operation of network-connected combined heat and powers for customer profit maximization. Energies, 2016, 9(6): 442 doi:10.3390/en9060442
6 DXie, Y Lu, JSun, CGu, G Li. 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 P WDu, N Lu. Appliance commitment for household load scheduling. IEEE Transactions on Smart Grid, 2011, 2(2): 411–419 doi:10.1109/TSG.2011.2140344
8 CChen, J Wang, SKishore. 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 DZhang, N Shah, L GPapageorgiou. 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 M CBozchalui, S AHashmi, HHassen, C ACanizares, KBhattacharya. 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 MRastegar, M Fotuhi-Firuzabad, MLehtonen. 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 MTasdighi, H Ghasemi, ARahimi-Kian. 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 FBrahman, M Honarmand, SJadid. 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,
15 MHouwing, R R Negenborn, B De Schutter. Demand response with micro-CHP systems. Proceedings of the IEEE, 2011, 99(1): 200–213 doi:10.1109/JPROC.2010.2053831
16 NLu. 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 J HWang, Z Q Zhai, Y Y Jing, C F Zhang. 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 PFanger. Thermal Comfort. Copenhagen: Danish Technical Press, 1970
19 PHe. 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 A LSoyster. 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 DBertsimas, M Sim. The price of robustness. Operations Research, 2004, 52(1): 35–53 doi:10.1287/opre.1030.0065
23 DBertsimas, A Thiele. Robust and data-driven optimization: modern decision-making under uncertainty. 2012–10–15,
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