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doi: 10.3934/jimo.2020112

Air-Conditioner Group Power Control Optimization for PV integrated Micro-grid Peak-shaving

a. 

Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha 5825, Qatar

b. 

ICube Laboratory, Université de Strasbourg–CNRS, Strasbourg 67000, France

* Corresponding author: Zhaohui Cen

Received  December 2019 Revised  March 2020 Published  June 2020

Heating, Ventilation, and Air-Condition (HVAC) systems are considered to be one of the essential applications for modern human life comfort. Due to global warming and population growth, the demand for such HVAC applications will continue to increase, especially in arid areas countries like the Arabian Gulf region. HVAC systems' energy consumption is very high and accounts for up to 70% of the total load consumption in some rapidly growing GCC countries such as Qatar. Additionally, the local extremely hot weather conditions usually lead to typical power demand peak issues that require adequate mitigation measures to ensure grid stability. In this paper, a novel control scheme for a combined group of Air-Conditioners is proposed as a peak-shaving strategy to address high power demand issues for Photo-Voltaic(PV)-integrated micro-grid applications. Using the local daily ambient temperature as input, the AC group control optimization is formulated as a Mixed-Integer Quadratic Programming (MIQP) problem. Under an acceptable range of indoor temperatures, the units in the same AC group are coordinately controlled to generate desired power consumption performance that is capable of shaving load peaks for both power consumption and PV generation. Finally, various simulations are performed that demonstrate the effectiveness of the proposed control strategy.

Citation: Mohammed Al-Azba, Zhaohui Cen, Yves Remond, Said Ahzi. Air-Conditioner Group Power Control Optimization for PV integrated Micro-grid Peak-shaving. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2020112
References:
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H. LüL. JiaS. Kong and Z. Zhang, Predictive functional control based on fuzzy ts model for hvac systems temperature control, Journal of Control Theory and Applications, 5 (2007), 94-98.   Google Scholar

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X. MaE. D. McCormack and Y. Wang, Processing commercial global positioning system data to develop a web-based truck performance measures program, Transportation Research Record, 2246 (2011), 92-100.  doi: 10.3141/2246-12.  Google Scholar

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[16]

E. RodriguesR. GodinaE. PouresmaeilJ. Ferreira and J. Catalão, Domestic appliances energy optimization with model predictive control, Energy Conversion and Management, 142 (2017), 402-413.   Google Scholar

[17]

M. SongC. Gao and W. Su, Modeling and controlling of air-conditioning load for demand response applications, Autom Electr Power Syst, 40 (2016), 158-167.   Google Scholar

[18]

M. SongC. GaoH. Yan and J. Yang, Thermal battery modeling of inverter air conditioning for demand response, IEEE Transactions on Smart Grid, 9 (2017), 5522-5534.  doi: 10.1109/TSG.2017.2689820.  Google Scholar

[19]

X.-C. XiA.-N. Poo and S.-K. Chou, Support vector regression model predictive control on a hvac plant, Control Engineering Practice, 15 (2007), 897-908.  doi: 10.1016/j.conengprac.2006.10.010.  Google Scholar

[20]

G. XiaD. Zhuang and G. Ding, Thermal management solution for enclosed controller used in inverter air conditioner based on heat pipe heat sink, International Journal of Refrigeration, 99 (2019), 69-79.  doi: 10.1016/j.ijrefrig.2018.12.020.  Google Scholar

[21]

Q. Zhang, Q. Guo and Y. Yu, Research on the load characteristics of inverter and constant speed air conditioner and the influence on distribution network, in 2016 China International Conference on Electricity Distribution (CICED), IEEE, 2016, 1–4. doi: 10.1109/CICED.2016.7575908.  Google Scholar

show all references

References:
[1]

M. Al-Azba, Z. Cen, Y. Remond and S. Ahzi, An optimal air-conditioner on-off control scheme under extremely hot weather conditions, Energies, 13 (2020), 1021. doi: 10.3390/en13051021.  Google Scholar

[2]

G. ChaudharyP. ShrivastavaM. Alam and Y. Rafat, Performance optimization and development of an efficient solar photovoltaic based inverter air conditioning system, Smart Science, 6 (2018), 188-196.  doi: 10.1080/23080477.2018.1437322.  Google Scholar

[3]

M. Di Felice, L. Piroddi, A. Leva and A. Boer, Adaptive temperature control of a household refrigerator, in 2009 American Control Conference, IEEE, 2009,889–894. doi: 10.1109/ACC.2009.5159862.  Google Scholar

[4]

J. Dong, Stochastic and optimal distributed control for energy optimization and spatially invariant systems. Google Scholar

[5]

J. Dong, S. M. Djouadi, T. Kuruganti and M. M. Olama, Augmented optimal control for buildings under high penetration of solar photovoltaic generation, in 2017 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 2017, 2158–2163. doi: 10.1109/CCTA.2017.8062772.  Google Scholar

[6]

J. Dong, M. M. Olama, T. Kuruganti, J. Nutaro, Y. Xue, I. Sharma and S. M. Djouadi, Adaptive building load control to enable high penetration of solar photovoltaic generation, in 2017 IEEE Power & Energy Society General Meeting, IEEE, 2017, 1–5. doi: 10.1109/PESGM.2017.8274533.  Google Scholar

[7]

M. S. Elliott, Decentralized Model Predictive Control of a Multiple Evaporator HVAC System, PhD thesis, Texas A & M University, 2010. Google Scholar

[8]

R. Godina, E. M. Rodrigues, E. Pouresmaeil and J. P. Catalão, Home hvac energy management and optimization with model predictive control, in 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I & CPS Europe), IEEE, 2017, 1–5. doi: 10.1109/EEEIC.2017.7977766.  Google Scholar

[9]

R. GodinaE. M. RodriguesE. Pouresmaeil and J. P. Catalão, Optimal residential model predictive control energy management performance with pv microgeneration, Computers & Operations Research, 96 (2018), 143-156.  doi: 10.1016/j.cor.2017.12.003.  Google Scholar

[10]

R. Godina, E. M. Rodrigues, M. Shafie-khah, E. Pouresmaeil and J. P. Catalão, Energy optimization strategy with model predictive control and demand response, in 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I & CPS Europe), IEEE, 2017, 1–5. doi: 10.1109/EEEIC.2017.7977767.  Google Scholar

[11]

G. Huang, Model predictive control of vav zone thermal systems concerning bi-linearity and gain nonlinearity, Control Engineering Practice, 19 (2011), 700-710.  doi: 10.1016/j.conengprac.2011.03.005.  Google Scholar

[12]

N. JinD. L. DanilovP. M. Van den Hof and M. Donkers, Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis, International Journal of Energy Research, 42 (2018), 2417-2430.  doi: 10.1002/er.4022.  Google Scholar

[13]

H. LüL. JiaS. Kong and Z. Zhang, Predictive functional control based on fuzzy ts model for hvac systems temperature control, Journal of Control Theory and Applications, 5 (2007), 94-98.   Google Scholar

[14]

X. MaE. D. McCormack and Y. Wang, Processing commercial global positioning system data to develop a web-based truck performance measures program, Transportation Research Record, 2246 (2011), 92-100.  doi: 10.3141/2246-12.  Google Scholar

[15]

J. Rehrl and M. Horn, Temperature control for hvac systems based on exact linearization and model predictive control, in 2011 IEEE International Conference on Control Applications (CCA), IEEE, 2011, 1119–1124. doi: 10.1109/CCA.2011.6044437.  Google Scholar

[16]

E. RodriguesR. GodinaE. PouresmaeilJ. Ferreira and J. Catalão, Domestic appliances energy optimization with model predictive control, Energy Conversion and Management, 142 (2017), 402-413.   Google Scholar

[17]

M. SongC. Gao and W. Su, Modeling and controlling of air-conditioning load for demand response applications, Autom Electr Power Syst, 40 (2016), 158-167.   Google Scholar

[18]

M. SongC. GaoH. Yan and J. Yang, Thermal battery modeling of inverter air conditioning for demand response, IEEE Transactions on Smart Grid, 9 (2017), 5522-5534.  doi: 10.1109/TSG.2017.2689820.  Google Scholar

[19]

X.-C. XiA.-N. Poo and S.-K. Chou, Support vector regression model predictive control on a hvac plant, Control Engineering Practice, 15 (2007), 897-908.  doi: 10.1016/j.conengprac.2006.10.010.  Google Scholar

[20]

G. XiaD. Zhuang and G. Ding, Thermal management solution for enclosed controller used in inverter air conditioner based on heat pipe heat sink, International Journal of Refrigeration, 99 (2019), 69-79.  doi: 10.1016/j.ijrefrig.2018.12.020.  Google Scholar

[21]

Q. Zhang, Q. Guo and Y. Yu, Research on the load characteristics of inverter and constant speed air conditioner and the influence on distribution network, in 2016 China International Conference on Electricity Distribution (CICED), IEEE, 2016, 1–4. doi: 10.1109/CICED.2016.7575908.  Google Scholar

Figure 1.  Baseline on-off AC control temperature profile
Figure 2.  AC Group Control ICT hardware infrastructure diagram
Figure 3.  Flowchart for AC group control program
Figure 4.  Outdoor Temperature in One day measured in Qatar
Figure 5.  On-Off Control Power profile subjected to different time delay
Figure 6.  Indoor Temperature Control profile Comparison
Figure 7.  Indoor temperature profiles of load-side peak shaving (The different curves are for the considered 40 AC units)
Figure 8.  Individual AC power control logic of load-side peak shaving
Figure 9.  Load-side shaving by AC group control
Figure 10.  Indoor temperature profiles under binary Mode
Figure 11.  Individual AC power control logic for PV peak shaving scenario under binary mode
Figure 12.  PV side Peak-shaving by AC group control under binary Mode
Figure 13.  Indoor temperature profiles under Ternary Mode (0-1-2)
Figure 14.  Individual AC power control logic under Ternary Mode (0-1-2)
Figure 15.  PV side Peak-shaving by AC group control with Ternary Mode (0-1-2)
Table 1.  House thermal model parameters definition
Parameter Definition
$ {T}_{indoor} $ Indoor temperature of the house
$ {T}_{outdoor} $ Outdoor temperature of the house
$ {{\dot{Q}}_{d}} $ Heat flow from outdoor to the house
$ {{\dot{Q}}_{e}} $ Cooling Energy by AC system
$ R $ Thermal resistance from outdoor to the house
$ m $ Mass of the indoor air
$ {{C}_{p}} $ Heat capacities of the room air
Parameter Definition
$ {T}_{indoor} $ Indoor temperature of the house
$ {T}_{outdoor} $ Outdoor temperature of the house
$ {{\dot{Q}}_{d}} $ Heat flow from outdoor to the house
$ {{\dot{Q}}_{e}} $ Cooling Energy by AC system
$ R $ Thermal resistance from outdoor to the house
$ m $ Mass of the indoor air
$ {{C}_{p}} $ Heat capacities of the room air
Table 2.  Parameters values of the thermal model and optimization
Parameter Value Parameter Value
A -2.00123e-4 $ J_{SW} $ 2
B 4.4028e-6 Cp($ J/Kg^oC $) 1005
E 0.002*$ T_{ref} $ $ m(kg) $ 222
R($ ^oC/W $) 0.022 $ Q $ 300
Parameter Value Parameter Value
A -2.00123e-4 $ J_{SW} $ 2
B 4.4028e-6 Cp($ J/Kg^oC $) 1005
E 0.002*$ T_{ref} $ $ m(kg) $ 222
R($ ^oC/W $) 0.022 $ Q $ 300
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