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Optimal thermostatically-controlled residential demand response for retail electric providers

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In the face of the considerable volatility in electricity market prices, retail electric providers (REPs) occupy the difficult position between the utility and the end-user. They purchase electricity either through long-term contracts, in the day-ahead or spot markets, or self-generate. This electricity is then sold to the end-user at a fixed price. Thus, REPs bear the financial burden of electricity price uncertainty. In a market where prices can increase by orders of magnitude in as little as 15 min, single spikes have driven some REPs to bankruptcy. To mitigate the negative effects of market volatility, REPs can employ demand response (DR), shifting consumers’ electricity load from periods of high prices to lower-priced time periods. While DR comes in many forms, we focus on thermostatically-controlled residential DR, made possible through internet-connected thermostats. The timing of residential heating and cooling load makes it a particularly financially advantageous target for DR. We present a dynamic programming (DP) formulation designed to optimally schedule DR events to maximize their savings for the REP. We also consider the effects of price uncertainty on the savings through the use of stochastic dynamic programming (SDP). Our case study examines the profitability of thermostatically-controlled DR, specifically of air-conditioning (A/C) load, in the Electricity Reliability Council of Texas (ERCOT). We find that optimal management of thermostatically-controlled DR can generate significant savings to the REP, potentially improving their profit margins as much as 25%. However, we also find that few events generate most savings, necessitating precise scheduling facilitated by our formulation. Electricity price uncertainty marginally reduces these expected savings, but optimally-scheduled DR still offers REPs an effective method of mitigating risk.

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  1. Baniasadi, A., Habibi, D., Bass, O., Masoum, M.A.S.: Optimal real-time residential thermal energy management for peak-load shifting with experimental verification. IEEE Trans. Smart Grid 10(5), 5587–5599 (2019).

    Article  Google Scholar 

  2. Boroumand, R.H., Goutte, S., Porcher, S., Porcher, T.: Hedging strategies in energy markets: The case of electricity retailers. Energy Economics 51, 503–509 (2015).

  3. Burger, M., Klar, B., Müller, A., Schindlmayr, G.: A spot market model for pricing derivatives in electricity markets. Quantitative Finance 4(1), 109–122 (2004).

  4. Carrion, M., Arroyo, J.M., Conejo, A.J.: A bilevel stochastic programming approach for retailer futures market trading. IEEE Trans. Power Syst. 24(3), 1446–1456 (2009).

    Article  Google Scholar 

  5. Chanpiwat, P., Gabriel, S.A., Moglen, R.L., Siemann, M.J.: Using Cluster Analysis and Dynamic Programming for Demand Response Applied to Electricity Load in Residential Homes. ASME J. Eng. Sustain. Buildings Cities 1(1) (2020).

  6. Chen, W., Wang, X., Petersen, J., Tyagi, R., Black, J.: Optimal scheduling of demand response events for electric utilities. IEEE Trans. Smart Grid 4(4), 2309–2319 (2013).

    Article  Google Scholar 

  7. Commission, F.E.R., et al.: A national assessment of demand response potential. prepared by The Brattle Group, Freeman Sullivan, & Co, and Global Energy Partners (2009)

  8. Conejo, A.J., Morales, J.M., Baringo, L.: Real-time demand response model. IEEE Trans. Smart Grid 1(3), 236–242 (2010).

    Article  Google Scholar 

  9. EIA, U.: Electric power annual 2017. US Energy Information Administration (2018)

  10. Electricity Reliability council Of Texas: ERCOT market information. Accessed: 2018-01-01

  11. Gabriel, S.A., Conejo, A.J., Plazas, M.A., Balakrishnan, S.: Optimal price and quantity determination for retail electric power contracts. IEEE Trans. Power Syst. 21(1), 180–187 (2006).

    Article  Google Scholar 

  12. Gabriel, S.A., Ferudun Genc, M., Balakrishnan, S.: A simulation approach to balancing annual risk and reward in retail electrical power markets. IEEE Trans. Power Syst. 17(4), 1050–1057 (2002).

    Article  Google Scholar 

  13. Gabriel, S.A., Kiet, S., Balakrishnan, S.: A mixed integer stochastic optimization model for settlement risk in retail electric power markets. Netw. Spatial Econ. 4(4), 323–345 (2004).

    Article  MATH  Google Scholar 

  14. Gabriel, S.A., Sahakij, P., Balakrishnan, S.: Optimal retailer load estimates using stochastic dynamic programming. J. Energy Eng. 130(1), 1–17 (2004).

    Article  Google Scholar 

  15. Ghazvini, M.A.F., Lipari, G., Pau, M., Ponci, F., Monti, A., Soares, J., Castro, R., Vale, Z.: Congestion management in active distribution networks through demand response implementation. Sustain. Energy Grids Netw. 17, 100185 (2019).

  16. Imani, M.H., Niknejad, P., Barzegaran, M.: The impact of customers’ participation level and various incentive values on implementing emergency demand response program in microgrid operation. Int. J. Electr. Power Energy Syst. 96, 114–125 (2018).

  17. Jin, M., Feng, W., Marnay, C., Spanos, C.: Microgrid to enable optimal distributed energy retail and end-user demand response. Appl. Energy 210, 1321–1335 (2018).

  18. Kirby, B.J.: Load response fundamentally matches power system reliability requirements. In: 2007 IEEE Power Engineering Society General Meeting, pp. 1–6 (2007).

  19. Lui, T.J., Stirling, W., Marcy, H.O.: Get smart. IEEE Power Energy Mag. 8(3), 66–78 (2010).

    Article  Google Scholar 

  20. Mahmoudi, N., Saha, T.K., Eghbal, M.: A new demand response scheme for electricity retailers. Electr. Power Syst. Res. 108, 144–152 (2014).

  21. Mallette, M., Venkataramanan, G.: Financial incentives to encourage demand response participation by plug-in hybrid electric vehicle owners. In: 2010 IEEE Energy Conversion Congress and Exposition, pp. 4278–4284 (2010).

  22. Moradi, M.H., Reisi, A.R., Hosseinian, S.M.: An optimal collaborative congestion management based on implementing dr. IEEE Trans. Smart Grid 9(5), 5323–5334 (2018).

    Article  Google Scholar 

  23. Moslehi, K., Kumar, R.: Smart grid - a reliability perspective. In: 2010 Innovative Smart Grid Technologies (ISGT), pp. 1–8 (2010).

  24. National climate data center: Climate data online. Accessed: 2018-01-01

  25. Nguyen, M.Y., Nguyen, D.M.: A new framework of demand response for household customers based on advanced metering infrastructure under smart grids. Electr. Power Components Syst. 44(2), 165–171 (2016).

  26. Park, L., Lee, C., Kim, J., Mohaisen, A., Cho, S.: Two-stage iot device scheduling with dynamic programming for energy internet systems. IEEE Int. Things J. 6(5), 8782–8791 (2019).

    Article  Google Scholar 

  27. Qdr, Q.: Benefits of demand response in electricity markets and recommendations for achieving them. US Dept. Energy, Washington, DC, USA, Tech. Rep (2006)

  28. Qureshi, M.U., Girault, A., Mauger, M., Grijalva, S.: Implementation of home energy management system with optimal load scheduling based on real-time electricity pricing models. In: 2017 IEEE 7th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), pp. 134–139 (2017).

  29. Schaperow, J.R., Gabriel, S.A., Siemann, M., Crawford, J.: A simulation-based model for optimal demand response load shifting: a case study for the texas power market. J. Energy Markets 12, 53–80 (2019).

  30. Siddiqui, O.: Assessment of achievable potential from energy efficiency and demand response programs in the us (2010–2030), pp. 2010–2030. Electric Power and Research Institute, January pp (2009)

  31. Siemann, M.: Performance and applications of residential building energy grey-box models. Ph.D. thesis, University of Maryland, College Park, MD 20742 (2013)

  32. Tan, Z., Yang, P., Nehorai, A.: An optimal and distributed demand response strategy with electric vehicles in the smart grid. IEEE Trans. Smart Grid 5(2), 861–869 (2014).

    Article  Google Scholar 

  33. Vardakas, J.S., Zorba, N., Verikoukis, C.V.: A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun. Surveys Tutorials 17(1), 152–178 (2015).

    Article  Google Scholar 

  34. Wang, D., Wu, R., Li, X., Lai, C.S., Wu, X., Wei, J., Xu, Y., Wu, W., Lai, L.L.: Two-stage optimal scheduling of air conditioning resources with high photovoltaic penetrations. J Clean Prod 241, 118407 (2019).

  35. Wang, J., Chen, X., Xie, J., Xu, S., Yu, K., Gan, L.: Dynamic control strategy of residential air conditionings considering environmental and behavioral uncertainties. Appl. Energy 250, 1312–1320 (2019).

  36. Wang, L., Peng, H., Zhu, H., Shen, L.: A survey of approximate dynamic programming. In: 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2, pp. 396–399 (2009).

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Correspondence to Rachel L. Moglen.

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The work presented in this paper was supported by the Maryland Industrial Partnerships (MIPS) program and Whisker Labs under Grant MIPS No. 5905. The authors would like to thank the anonymous referees for their valuable comments.

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Moglen, R.L., Chanpiwat, P., Gabriel, S.A. et al. Optimal thermostatically-controlled residential demand response for retail electric providers. Energy Syst 14, 641–661 (2023).

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