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Research Articles

Robust provision of demand response from thermostatically controllable loads using lagrangian relaxation

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Pages 1918-1933 | Received 06 Jan 2022, Accepted 20 Jul 2023, Published online: 05 Aug 2023

References

  • Australian Energy Market Operator (AEMO) (n.d.). National electricity market (NEM) data dashboard. https://aemo.com.au/en/energy-systems/electricity/national-electricity-market-nem/data-nem/data-dashboard-nem.
  • Australian Institute of Refrigeration Air Conditioning and Heating (AIRAH) (n.d.). DA09 air conditioning load estimation and psychrometrics. Retrieved January 26, 2023, from https://www.airah.org.au/DA_manuals/DA_Manuals/Manuals.aspx.
  • Australian Renewable Energy Agency (2019). Demand response RERT trial year 1 report. Retrieved December 23, 2019, from https://arena.gov.au/assets/2019/03/demand-response-rert-trial-year-1-report.pdf.
  • Bashash, S., & Fathy, H. K. (2013, July). Modeling and control of aggregate air conditioning loads for robust renewable power management. IEEE Transactions on Control Systems Technology, 21(4), 1318–1327. https://doi.org/10.1109/TCST.2012.2204261
  • Bemporad, A., & Morari, M. (1999). Robust model predictive control: A survey. In A. Garulli & A. Tesi (Eds.), Robustness in identification and control (pp. 207–226). Springer London.
  • Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. S. (2009). Robust optimization. Princeton University Press.
  • Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. https://doi.org/10.1287/opre.1030.0065
  • Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
  • Bureau of Meteorology (BoM), Australia (n.d.). Bureau forecast accuracy. Retrieved April 27, 2020, from http://www.bom.gov.au/inside/forecast-accuracy.shtml.
  • Burger, E. M., & Moura, S. J. (2017, May). Generation following with thermostatically controlled loads via alternating direction method of multipliers sharing algorithm. Electric Power Systems Research, 146, 141–160. https://doi.org/10.1016/j.epsr.2016.12.001
  • Callaway, D. S. (2009). Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy. Energy Conversion and Management, 50(5), 1389–1400. https://doi.org/10.1016/j.enconman.2008.12.012
  • Callaway, D. S., & Hiskens, I. A. (2011, January). Achieving controllability of electric loads. Proceedings of the IEEE, 99(1), 184–199. https://doi.org/10.1109/JPROC.2010.2081652
  • Camponogara, E., Jia, D., Krogh, B. H., & Talukdar, S. (2002). Distributed model predictive control. IEEE Control Systems Magazine, 22(1), 44–52. https://doi.org/10.1109/37.980246
  • Chen, C., Wang, J., & Kishore, S. (2014). A distributed direct load control approach for large-scale residential demand response. IEEE Transactions on Power Systems, 29(5), 2219–2228. https://doi.org/10.1109/TPWRS.2014.2307474
  • Conejo, A., Castillo, E., Minguez, R., & Garcia-Bertrand, R. (2006). Decomposition in nonlinear programming. In Decomposition techniques in mathematical programming: Engineering and science applications (pp. 187–242). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-27686-6_5
  • Department of Environment and Energy Australia (2019). Consultation paper: ‘Smart’ demand response capabilities for selected appliances. Retrieved December 23, 2019, from https://www.energyrating.gov.au/document/consultation-paper-smart-demand-response-capabilities-selected-appliances.
  • Diekerhof, M., Peterssen, F., & Monti, A. (2018). Hierarchical distributed robust optimization for demand response services. IEEE Transactions on Smart Grid, 9(6), 6018–6029. https://doi.org/10.1109/TSG.2017.2701821
  • Energex (2021). Home energy management systems. https://www.energex.com.au/home/control-your-energy/smarter-energy/home-energy-management-systems.
  • Energex (2022). PeakSmart events. https://www.energex.com.au/home/control-your-energy/managing-electricity-demand/peak-demand/peaksmart-events.
  • Erdinc, O., Tascikaraoglu, A., Paterakis, N. G., & Catalao, J. P. S. (2019, February). Novel incentive mechanism for end-users enrolled in DLC-based demand response programs within stochastic planning context. IEEE Transactions on Industrial Electronics, 66(2), 1476–1487. https://doi.org/10.1109/TIE.2018.2811403
  • Goulart, P. J., Kerrigan, E. C., & Maciejowski, J. M. (2006). Optimization over state feedback policies for robust control with constraints. Automatica, 42(4), 523–533. https://doi.org/10.1016/j.automatica.2005.08.023
  • Gurobi Optimization, L. L. C. (2021). Gurobi optimizer reference manual. http://www.gurobi.com.
  • Halvgaard, R., Vandenberghe, L., Poulsen, N. K., Madsen, H., & J. B. Jørgensen (2016). Distributed model predictive control for smart energy systems. IEEE Transactions on Smart Grid, 7(3), 1675–1682. https://doi.org/10.1109/TSG.2016.2526077
  • Hu, Q., Li, F., Fang, X., & Bai, L. (2018, January). A framework of residential demand aggregation with financial incentives. IEEE Transactions on Smart Grid, 9(1), 497–505. https://doi.org/10.1109/TSG.2016.2631083
  • Hui, H., Ding, Y., & Zheng, M. (2019). Equivalent modeling of inverter air conditioners for providing frequency regulation service. IEEE Transactions on Industrial Electronics, 66(2), 1413–1423. https://doi.org/10.1109/TIE.2018.2831192
  • Kabalci, Y. (2016). A survey on smart metering and smart grid communication. Renewable and Sustainable Energy Reviews, 57, 302–318. https://doi.org/10.1016/j.rser.2015.12.114
  • Katipamula, S., & Lu, N. (2006). Evaluation of residential HVAC control strategies for demand response programs. ASHRAE Transactions, 112(1), 535–546.
  • Larsen, G. K. H., van Foreest, N. D., & Scherpen, J. M. A. (2013, June). Distributed control of the power supply-demand balance. IEEE Transactions on Smart Grid, 4(2), 828–836. https://doi.org/10.1109/TSG.2013.2242907
  • Liu, M., & Shi, Y. (2014). Distributed model predictive control of thermostatically controlled appliances for providing balancing service. In 53rd IEEE conference on decision and control (pp. 4850–4855). IEEE.
  • Liu, M., & Shi, Y. (2016a). Model predictive control for thermostatically controlled appliances providing balancing service. IEEE Transactions on Control Systems Technology, 24(6), 2082–2093. https://doi.org/10.1109/TCST.2016.2535400
  • Liu, M., & Shi, Y. (2016b). Model predictive control of aggregated heterogeneous second-order thermostatically controlled loads for ancillary services. IEEE Transactions on Power Systems, 31(3), 1963–1971. https://doi.org/10.1109/TPWRS.2015.2457428
  • Löfberg, J. (2004). YALMIP: A toolbox for modeling and optimization in MATLAB. In Proceedings of the IEEE international symposium on computer-aided control system design (pp. 284–289). IEEE. https://doi.org/10.1109/cacsd.2004.1393890
  • Lork, C., Li, W. T., Qin, Y., Zhou, Y., Yuen, C., Tushar, W., & Saha, T. K. (2020). An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management. Applied Energy, 276, Article 115426. https://doi.org/10.1016/j.apenergy.2020.115426
  • Lu, N. (2012). An evaluation of the HVAC load potential for providing load balancing service. IEEE Transactions on Smart Grid, 3(3), 1263–1270. https://doi.org/10.1109/TSG.2012.2183649
  • Maasoumy, M., Razmara, M., Shahbakhti, M., & Vincentelli, A. S. (2014). Handling model uncertainty in model predictive control for energy efficient buildings. Energy and Buildings, 77, 377–392. https://doi.org/10.1016/j.enbuild.2014.03.057
  • Mahdavi, N., & Braslavsky, J. H. (2020, September). Modelling and control of ensembles of variable-speed air conditioning loads for demand response. IEEE Transactions on Smart Grid, 11(5), 4249–4260. https://doi.org/10.1109/TSG.2020.2991835
  • Mahdavi, N., Braslavsky, J. H., Seron, M. M., & West, S. R. (2017, November). Model predictive control of distributed air-conditioning loads to compensate fluctuations in solar power. IEEE Transactions on Smart Grid, 8(6), 3055–3065. https://doi.org/10.1109/TSG.2017.2717447
  • Mathieu, J. L., Koch, S., & Callaway, D. S. (2013). State estimation and control of electric loads to manage real-time energy imbalance. IEEE Transactions on Power Systems, 28(1), 430–440. https://doi.org/10.1109/TPWRS.2012.2204074
  • Molina, A., Gabaldon, A., Fuentes, J. A., & Alvarez, C. (2003). Implementation and assessment of physically based electrical load models: Application to direct load control residential programmes. IEE Proceedings-Generation, Transmission and Distribution, 150(1), 61–66. https://doi.org/10.1049/ip-gtd:20020750
  • Molzahn, D. K., Dörfler, F., Sandberg, H., Low, S. H., Chakrabarti, S., Baldick, R., & Lavaei, J. (2017, November). A survey of distributed optimization and control algorithms for electric power systems. IEEE Transactions on Smart Grid, 8(6), 2941–2962. https://doi.org/10.1109/TSG.2017.2720471
  • Oldewurtel, F., C. N. Jones, Parisio, A., & Morari, M. (2014, May). Stochastic model predictive control for building climate control. IEEE Transactions on Control Systems Technology, 22(3), 1198–1205. https://doi.org/10.1109/TCST.2013.2272178
  • OpenADR Alliance (n.d.). OpenADR 2.0 demand response program implementation guide. Retrieved May 12, 2020, from https://www.openadr.org/assets/openadr_drprogramguide_v1.0.pdf.
  • Palensky, P., & Dietrich, D. (2011, August). Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Transactions on Industrial Informatics, 7(3), 381–388. https://doi.org/10.1109/TII.2011.2158841
  • Papadaskalopoulos, D., & Strbac, G. (2013, November). Decentralized participation of flexible demand in electricity markets–Part I: Market mechanism. IEEE Transactions on Power Systems, 28(4), 3658–3666. https://doi.org/10.1109/TPWRS.2013.2245686
  • Pillitteri, V., & Brewer, T. (2014). Guidelines for smart grid cybersecurity (NIST Interagency/Internal Report (NISTIR)). National Institute of Standards and Technology, Gaithersburg, MD. https://doi.org/10.6028/NIST.IR.7628r1
  • PJM (2022). Markets & operations. https://www.pjm.com/markets-and-operations.aspx.
  • Scattolini, R. (2009). Architectures for distributed and hierarchical model predictive control–a review. Journal of Process Control, 19(5), 723–731. https://doi.org/10.1016/j.jprocont.2009.02.003
  • School of Earth and Environmental Sciences, The University of Queensland (n.d.). UQ weatherstations. Retrieved August 20, 2020, from https://bit.ly/2PXuc2n.
  • Scokaert, P. O. M., & Mayne, D. Q. (1998, August). Min-max feedback model predictive control for constrained linear systems. IEEE Transactions on Automatic Control, 43(8), 1136–1142. https://doi.org/10.1109/9.704989
  • Thomas, E., Sharma, R., & Nazarathy, Y. (2019, March). Towards demand side management control using household specific Markovian models. Automatica, 101, 450–457. https://doi.org/10.1016/j.automatica.2018.11.057
  • Tindemans, S. H., Trovato, V., & Strbac, G. (2015). Decentralized control of thermostatic loads for flexible demand response. IEEE Transactions on Control Systems Technology, 23(5), 1685–1700. https://doi.org/10.1109/TCST.2014.2381163.
  • Vrettos, E., Oldewurtel, F., & Andersson, G. (2016). Robust energy-constrained frequency reserves from aggregations of commercial buildings. IEEE Transactions on Power Systems, 31(6), 4272–4285. https://doi.org/10.1109/TPWRS.2015.2511541
  • Zhang, W., Lian, J., Chang, C. Y., & Kalsi, K. (2013, November). Aggregated modeling and control of air conditioning loads for demand response. IEEE Transactions on Power Systems, 28(4), 4655–4664. https://doi.org/10.1109/TPWRS.2013.2266121