Applications of thermostatically controlled loads for demand response with the proliferation of variable renewable energy
Meng SONG1, Wei SUN2()
1. Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China 2. Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
More flexibility is desirable with the proliferation of variable renewable resources for balancing supply and demand in power systems.Thermostatically controlled loads (TCLs) attract tremendous attentions because of their specific thermal inertia capability in demand response (DR) programs. To effectively manage numerous and distributed TCLs, intermediate coordinators, e.g., aggregators, as a bridge between end users and dispatch operators are required to model and control TCLs for serving the grid. Specifically, intermediate coordinators get the access to fundamental models and response modes of TCLs, make control strategies, and distribute control signals to TCLs according the requirements of dispatch operators. On the other hand, intermediate coordinators also provide dispatch models that characterize the external characteristics of TCLs to dispatch operators for scheduling different resources. In this paper, the bottom-up key technologies of TCLs in DR programs based on the current research have been reviewed and compared, including fundamental models, response modes, control strategies, dispatch models and dispatch strategies of TCLs, as well as challenges and opportunities in future work.
H Wu, M Shahidehpour, A Alabdulwahab, et al.. Demand response exchange in the stochastic day-ahead scheduling with variable renewable generation. IEEE Transactions on Sustainable Energy, 2015, 6(2): 516–525 https://doi.org/10.1109/TSTE.2015.2390639
2
I Sajjad, G Chicco, R Napoli. Definitions of demand flexibility for aggregate residential loads. IEEE Transactions on Smart Grid, 2016, 7(6): 2633–2643 https://doi.org/10.1109/TSG.2016.2522961
3
A Radaideh, U Vaidya, V Ajjarapu. Sequential set-point control for heterogeneous thermostatically controlled loads through an extended Markov chain abstraction. IEEE Transactions on Smart Grid, 2019, 10(1): 116–127 https://doi.org/10.1109/TSG.2017.2732949
4
P Kohlhepp, H Harb, H Wolisz, et al.. Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: a review of international field studies. Renewable & Sustainable Energy Reviews, 2019, 101: 527–547 https://doi.org/10.1016/j.rser.2018.09.045
5
X Xu, Z Yan, M Shahidehpour, et al.. Data-driven risk-averse two-stage optimal stochastic scheduling of energy and reserve with correlated wind power. IEEE Transactions on Sustainable Energy, 2020, 11(1): 436–447 https://doi.org/10.1109/TSTE.2019.2894693
6
O Erdinc, A Tascikaraoglu, N Paterakis, et al.. End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts. IEEE Transactions on Smart Grid, 2017, 8(1): 362–372 https://doi.org/10.1109/TSG.2016.2556619
7
Y J Kim, L K Norford, J L Kirtley. Modeling and analysis of a variable speed heat pump for frequency regulation through direct load control. IEEE Transactions on Power Systems, 2015, 30(1): 397–408 https://doi.org/10.1109/TPWRS.2014.2319310
8
M Song, C Gao, H Yan, et al.. Thermal battery modeling of inverter air conditioning for demand response. IEEE Transactions on Smart Grid, 2018, 9(6): 5522–5534 https://doi.org/10.1109/TSG.2017.2689820
9
H Hao, D Wu, J Lian, et al.. Optimal coordination of building loads and energy storage for power grid and end user services. IEEE Transactions on Smart Grid, 2018, 9(5): 4335–4345 https://doi.org/10.1109/TSG.2017.2655083
10
A Pahwa, C W Brice. Modeling and system identification of residential air conditionning load. IEEE Transactions on Power Apparatus and Systems, 1985, 104(6): 1418–1425 https://doi.org/10.1109/TPAS.1985.319247
11
M Song, C Gao, W Su. Modeling and controlling of air-conditioning load for demand response applications. Automation of Electric Power Systems, 2016, 40(14): 158–167 (in Chinese)
12
A Molina, A Gabaldon, J A Fuentes, et al.. Implementation and assessment of physically based electrical load models: application to direct load control residential programmes. IEE Proceedings. Generation, Transmission and Distribution, 2003, 150(1): 61–66 https://doi.org/10.1049/ip-gtd:20020750
13
S Iacovella, F Ruelens, P Vingerhoets, et al.. Cluster control of heterogeneous thermostatically controlled loads using tracer devices. IEEE Transactions on Smart Grid, 2017, 8(2): 528–536 https://doi.org/10.1109/TSG.2015.2483506
14
H Hao, C D Corbin, K Kalsi, et al.. Transactive control of commercial buildings for demand response. IEEE Transactions on Power Systems, 2017, 32(1): 774–783 https://doi.org/10.1109/TPWRS.2016.2559485
15
X Wu, J He, Y Xu, et al.. Hierarchical control of residential HVAC units for primary frequency regulation. IEEE Transactions on Smart Grid, 2018, 9(4): 3844–3856 https://doi.org/10.1109/TSG.2017.2766880
16
M Song, C Gao, M Shahidehpour, et al.. Impact of uncertain parameters on TCL power capacity calculation via HDMR for generating power pulses. IEEE Transactions on Smart Grid, 2019, 10(3): 3112–3124 https://doi.org/10.1109/TSG.2018.2817925
17
C H Wai, M Beaudin, H Zareipour, et al.. Cooling devices in demand response: a comparison of control methods. IEEE Transactions on Smart Grid, 2015, 6(1): 249–260 https://doi.org/10.1109/TSG.2014.2358579
18
N A Sinitsyn, S Kundu, S Backhaus. Safe protocols for generating power pulses with heterogeneous populations of thermostatically controlled loads. Energy Conversion and Management, 2013, 67: 297–308 https://doi.org/10.1016/j.enconman.2012.11.021
19
N Lu, Y Zhang. Design considerations of a centralized load controller using thermostatically controlled appliances for continuous regulation reserves. IEEE Transactions on Smart Grid, 2013, 4(2): 914–921 https://doi.org/10.1109/TSG.2012.2222944
20
J Hu, J Cao, T Yong, et al.. Demand response load following of source and load systems. IEEE Transactions on Control Systems Technology, 2017, 25(5): 1586–1598 https://doi.org/10.1109/TCST.2016.2615087
21
Y J Kim, E Fuentes, L K Norford. Experimental study of grid frequency regulation ancillary service of a variable speed heat pump. IEEE Transactions on Power Systems, 2016, 31(4): 3090–3099 https://doi.org/10.1109/TPWRS.2015.2472497
22
M Song, C W Cao, M Shahidehpour, et al.. Multi-time-scale modeling and parameter estimation of TCLs for smoothing out wind power generation variability. IEEE Transactions on Sustainable Energy, 2019, 10(1): 105–118 https://doi.org/10.1109/TSTE.2018.2826540
23
R Malhame, C Y Chong. Electric load model synthesis by diffusion approximation of a high-order hybrid-state stochastic system. IEEE Transactions on Automatic Control, 1985, 30(9): 854–860 https://doi.org/10.1109/TAC.1985.1104071
24
S H Tindemans, V Trovato, G Strbac. Decentralized control of thermostatic loads for flexible demand response. IEEE Transactions on Control Systems Technology, 2015, 23(5): 1685–1700 https://doi.org/10.1109/TCST.2014.2381163
25
S Bashash, H K Fathy. Modeling and control of aggregate air conditioning loads for robust renewable power management. IEEE Transactions on Control Systems Technology, 2013, 21(4): 1318–1327 https://doi.org/10.1109/TCST.2012.2204261
26
M Kamgarpour, C Ellen, S E Z Soudjani, et al.. Modeling options for demand side participation of thermostatically controlled loads. In: Bulk Power System Dynamics and Control- IX Optimization, Security and Control of the Emerging Power Grid (IREP), Rethymno, Greece, 2013
27
M Song, C Gao, M Shahidehpour, et al.. State space modeling and control of aggregated TCLs for regulation services in power grids. IEEE Transactions on Smart Grid, 2019, 10(4): 4095–4106 https://doi.org/10.1109/TSG.2018.2849321
28
M Song, C Cao, J Yang, et al.. Novel aggregate control model of air conditioning loads for fast regulation service. IET Generation, Transmission & Distribution, 2017, 11(17): 4391–4401 https://doi.org/10.1049/iet-gtd.2017.0496
29
M Liu, Y Shi. Model predictive control of aggregated heterogeneous second-order thermostatically controlled loads for ancillary services. IEEE Transactions on Power Systems, 2016, 31(3): 1963–1971 https://doi.org/10.1109/TPWRS.2015.2457428
30
J Hu, J Cao, M Z Q Chen, et al.. Load following of multiple heterogeneous TCL aggregators by centralized control. IEEE Transactions on Power Systems, 2017, 32(4): 3157–3167 https://doi.org/10.1109/TPWRS.2016.2626315
31
M Liu, Y Shi, X Liu. Distributed MPC of aggregated heterogeneous thermostatically controlled loads in smart grid. IEEE Transactions on Industrial Electronics, 2016, 63(2): 1120–1129 https://doi.org/10.1109/TIE.2015.2492946
32
J L Mathieu, S Koch, D S Callaway. State estimation and control of electric loads to manage real-time energy imbalance. IEEE Transactions on Power Systems, 2013, 28(1): 430–440 https://doi.org/10.1109/TPWRS.2012.2204074
33
N Lu. An evaluation of the HVAC load potential for providing load balancing service. IEEE Transactions on Smart Grid, 2012, 3(3): 1263–1270 https://doi.org/10.1109/TSG.2012.2183649
34
S Esmaeil Zadeh Soudjani, A. Abate Aggregation and control of populations of thermostatically controlled loads by formal abstractions. IEEE Transactions on Control Systems Technology, 2015, 23(3): 975–990 https://doi.org/10.1109/TCST.2014.2358844
35
C Li, Y Chen, F Luo, et al.. Real-time decision making model for thermostatically controlled load aggregators by natural aggregation algorithm. In: IEEE International Conference on Energy Internet, Beijing, China, 2017 https://doi.org/10.1109/ICEI.2017.56
36
A Radaideh, U Vaidya, V Ajjarapu. Sequential set-point control for heterogeneous thermostatically controlled loads through an extended markov chain abstraction. IEEE Transactions on Smart Grid, 2019, 10(1): 116–127 https://doi.org/10.1109/TSG.2017.2732949
37
B P Bhattarai, I D de Cerio Mendaza, K S Myers, et al.. Optimum aggregation and control of spatially distributed flexible resources in smart grid. IEEE Transactions on Smart Grid, 2018, 9(5): 5311–5322 https://doi.org/10.1109/TSG.2017.2686873
38
S Bashash, H K Fathy. Modeling and control insights into demand-side energy management through setpoint control of thermostatic loads. In: Proceedings of the 2011 American Control Conference, San Francisco, CA, USA, 2011 https://doi.org/10.1109/ACC.2011.5990939
39
H Hao, B M Sanandaji, K Poolla, et al.. Aggregate flexibility of thermostatically controlled loads. IEEE Transactions on Power Systems, 2015, 30(1): 189–198 https://doi.org/10.1109/TPWRS.2014.2328865
40
F Ruelens, B J Claessens, S Vandael, et al.. Residential demand response applications using batch reinforcement learning. IEEE Transactions on Smart Grid, 2017, 8(5): 2149–2159 https://doi.org/10.1109/TSG.2016.2517211
41
E Vrettos, C Ziras, G Andersson. Fast and reliable primary frequency reserves from refrigerators with decentralized stochastic control. IEEE Transactions on Power Systems, 2017, 32(4): 2924–2941 https://doi.org/10.1109/TPWRS.2016.2630601
42
A Molina-Garcia, F Bouffard, D S Kirschen. Decentralized demand-side contribution to primary frequency control. IEEE Transactions on Power Systems, 2011, 26(1): 411–419 https://doi.org/10.1109/TPWRS.2010.2048223
43
D Guo, W Zhang, G Yan, et al.. Decentralized control of aggregated loads for demand response. In: 2013 American Control Conference (ACC), Washington D.C., USA, 2013 https://doi.org/10.1109/ACC.2013.6580875
44
K Meng, D Wang, Z Y Dong, et al.. Distributed control of thermostatically controlled loads in distribution network with high penetration of solar PV. CSEE Journal of Power and Energy Systems., 2017, 3(1): 53–62 https://doi.org/10.17775/CSEEJPES.2017.0008
45
N Saker, M Petit, J C Vannier, et al.. Demand side management of electrical water heaters and evaluation of the cold load pick-up characteristics. In: 2011 IEEE Trondheim PowerTech, Trondheim, Norway, 2011 https://doi.org/10.1109/PTC.2011.6019312
46
C Perfumo, J Braslavsky, J K Ward. A sensitivity analysis of the dynamics of a population of thermostatically-controlled loads. In: 2013 Australasian Universities Power Engineering Conference (AUPEC), Hobart, TAS, Australia, 2013 https://doi.org/10.1109/AUPEC.2013.6725373
47
M Gilvanejad, H Askarian Abyaneh, K Mazlumi. Estimation of cold-load pickup occurrence rate in distribution systems. IEEE Transactions on Power Delivery, 2013, 28(2): 1138–1147 https://doi.org/10.1109/TPWRD.2012.2231967
48
D Wang, N Lu, W Miao, et al.. Performance evaluation of controlling thermostatically controlled appliances as virtual generators using comfort-constrained state-queueing models. IET Generation Transmission & Distribution, 2014, 8(4): 591–599 https://doi.org/10.1049/iet-gtd.2013.0093
49
N Mahdavi, J H Braslavsky, C Perfumo. Mapping the effect of ambient temperature on the power demand of populations of air conditioners. IEEE Transactions on Smart Grid, 2018, 9(3): 1540–1550 https://doi.org/10.1109/TSG.2016.2592522
50
N Ruiz, I Cobelo, J Oyarzabal. A direct load control model for virtual power plant management. IEEE Transactions on Power Systems, 2009, 24(2): 959–966 https://doi.org/10.1109/TPWRS.2009.2016607
51
V Trovato, S H Tindemans, G Strbac. The leaky storage model for optimal multi-service allocation of thermostatic loads. IET Generation, Transmission & Distribution, 2016, 10(3): 585–593 https://doi.org/10.1049/iet-gtd.2015.0168
52
V Trovato, S H Tindemans, G Strbac. Security constrained economic dispatch with flexible thermostatically controlled loads. In: IEEE PES Innovative Smart Grid Technologies, Istanbul, Turkey, 2014 https://doi.org/10.1109/ISGTEurope.2014.7028766
53
M Song, C Gao, J Yang, et al.. Energy storage modeling of inverter air conditioning for output optimizing of wind generation in the electricity market. CSEE Journal of Power & Energy Systems, 2018, 4(3): 305–315 https://doi.org/10.17775/CSEEJPES.2016.01480
54
F Luo, Z Y Dong, K Meng, et al.. An operational planning framework for large-scale thermostatically controlled load dispatch. IEEE Transactions on Industrial Informatics, 2017, 13(1): 217–227 https://doi.org/10.1109/TII.2016.2515086
55
L Zhao, W Zhang, H Hao, et al.. A geometric approach to aggregate flexibility modeling of thermostatically controlled loads. IEEE Transactions on Power Systems, 2017, 32(6): 4721–4731 https://doi.org/10.1109/TPWRS.2017.2674699
56
J L Mathieu, M Kamgarpour, J Lygeros, et al.. Arbitraging intraday wholesale energy market prices with aggregations of thermostatic loads. IEEE Transactions on Power Systems, 2015, 30(2): 763–772 https://doi.org/10.1109/TPWRS.2014.2335158
57
C N Kurucz, D Brandt, S Sim. A linear programming model for reducing system peak through customer load control programs. IEEE Transactions on Power Systems, 1996, 11(4): 1817–1824 https://doi.org/10.1109/59.544648
58
H T Yang, K Y Huang. Direct load control using fuzzy dynamic programming. IEE Proceedings–Generation, Transmission and Distribution, 1999, 146(3): 294–300 https://doi.org/10.1049/ip-gtd:19990317
59
T F Lee, M Y Cho, Y C Hsiao, et al.. Optimization and implementation of a load control scheduler using relaxed dynamic programming for large air conditioner loads. IEEE Transactions on Power Systems, 2008, 23(2): 691–702 https://doi.org/10.1109/TPWRS.2008.919311
60
C M Chu, T L Jong, Y W Huang. Mitigating DLC constraints of air-conditioning loads using a group-DLC method. In: 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, USA, 2007 https://doi.org/10.1109/PES.2007.385626
61
Y Wen, W Li, G Huang, et al.. Frequency dynamics constrained unit commitment with battery energy storage. IEEE Transactions on Power Systems, 2016, 31(6): 5115–5125 https://doi.org/10.1109/TPWRS.2016.2521882
62
Y Ning, X Li, X Ma, et al.. Optimal schedule strategy of battery energy storage systems for peak load shifting based on interior point method. In: 2016 12th World Congress on Intelligent Control and Automation (WCICA), Guilin, China, 2016 https://doi.org/10.1109/WCICA.2016.7578532
63
N Li, C Uckun, E M Constantinescu, et al.. Flexible operation of batteries in power system scheduling with renewable energy. IEEE Transactions on Sustainable Energy, 2016, 7(2): 685–696 https://doi.org/10.1109/TSTE.2015.2497470
64
J M Lujano-Rojas, R Dufo-Lopez, J L Bernal-Agustin, et al.. Optimizing daily operation of battery energy storage systems under real-time pricing schemes. IEEE Transactions on Smart Grid, 2017, 8(1): 316–330 https://doi.org/10.1109/TSG.2016.2602268
65
G Hutchison, D Giaouris, S Gadoue, et al.. Non-invasive identification of turbo-generator parameters from actual transient network data. IET-Generation Transmission & Distribution, 2015, 9(11): 1129–1136 https://doi.org/10.1049/iet-gtd.2014.0481
66
N Mahdavi, J H Braslavsky, M M Seron, et al.. Model predictive control of distributed air-conditioning loads to compensate fluctuations in solar powe. IEEE Transactions on Smart Grid, 2017, 8(6): 3055–3065 https://doi.org/10.1109/TSG.2017.2717447
67
J L Mathieu, D S Callaway. State estimation and control of heterogeneous thermostatically controlled loads for load following. In: 2012 45th Hawaii International Conference on System Sciences, Maui, HI, USA, 2012 https://doi.org/10.1109/HICSS.2012.545
68
M Vanouni, N Lu. A reward allocation mechanism for thermostatically controlled loads participating in intra-hour ancillary services. IEEE Transactions on Smart Grid, 2018, 9(5): 4209–4219 https://doi.org/10.1109/TSG.2017.2652981
69
Y K Renani, M Ehsan, M Shahidehpour. Optimal transactive market operations with distribution system operators. IEEE Transactions on Smart Grid, 2018, 9(6): 6692–6701 https://doi.org/10.1109/TSG.2017.2718546
70
D Gregoratti, J Matamoros. Distributed energy trading: the multiple-microgrid case. Industrial Electronics IEEE Transactions on Industrial Electronics, 2015, 62(4): 2551–2559 https://doi.org/10.1109/TIE.2014.2352592
71
Z Liu, Q Wu, M Shahidehpour, et al.. Transactive real-time electric vehicle charging management for commercial buildings with PV on-site generation. IEEE Transactions on Smart Grid, 2019, 10(5): 4939–4950 https://doi.org/10.1109/TSG.2018.2871171
