Residential electricity pricing using time-varying and non-time-varying scenarios: an application of game theory

Residential electricity pricing using time-varying and non-time-varying scenarios: an application of game theory

:The aim of this work is to analyze and describe the interaction between a residential consumer and the power network. With the growth of power systems and the advent of new energy sources, such as solar energy, it seems to be more essential to investigate how the network and the consumer can interact with each other to achieve more financial benefits. To do that, a static game is defined considering the fact that there is a direct relationship between the amount of load shifted by the consumer and the incentive offered by the network. It is concluded that the Nash equilibrium of this game is when the consumer decides to cooperate with the network during non-peak hours. Finally, a simple optimization problem is defined in which both the consumer and power network try to achieve better financial benefit considering the fact that in the real world the total load of a typical residential consumer can be divided into the flexible and inflexible parts. A time-varying pricing scenario as well as time-of-use and constant pricing scenarios is used. It is concluded that the more convenient scenario for the consumer is the time-of-use scenario, whereas the power network would prefer to use a dynamic one as it leads to more financial benefit.

___

  • [1] Mielczarski W, Michalik G, Widjaja M, editors. Bidding Strategies in Electricity Markets. Proceedings of the 21st IEEE International Conference on Power Industry Computer Applications. New York, NY, USA: IEEE, 1999.
  • [2] Ferrero R, Shahidehpour S, Ramesh V. Transaction analysis in deregulated power systems using game theory. IEEE T Power Syst 1997; 12: 1340-1347.
  • [3] Yu Z, Sparrow F, Morin T, Nderitu G, editors. A Stackelberg price leadership model with application to deregulated electricity markets. In: IEEE Power Engineering Society Winter Meeting; 2000. pp. 1814-1819.
  • [4] Gans W, Alberini A, Longo A. Smart meter devices and the effect of feedback on residential electricity consumption: evidence from a natural experiment in Northern Ireland. Energ Econ 2012; 36: 729-743.
  • [5] Gyamfi S, Krumdieck S, Urmee T. Residential peak electricity demand response—Highlights of some behavioural issues. Renew Sust Energ Rev 2013; 25: 71-77.
  • [6] Salies E. Real-time pricing when some consumers resist in saving electricity. Energ Policy 2013; 59: 843-849.
  • [7] Zugno M, Morales JM, Pinson P, Madsen H. A bilevel model for electricity retailers participation in a demand response market environment. Energ Econ 2012; 36: 182-197.
  • [8] Doostizadeh M, Ghasemi H. A day-ahead electricity pricing model based on smart metering and demand-side management. Energy 2012; 46: 221-230.
  • [9] Fong WK, Matsumoto H, Lun YF, Kimura R. Energy savings potential of the summer Time concept in different regions of Japan from the perspective of household lighting. J Asian Archit Build 2007; 6: 371-378.
  • [10] Kotchen MJ, Grant LE. Does daylight saving time save energy? Evidence from a natural experiment in Indiana. Rev Econ Stat 2011; 93: 1172-1185.
  • [11] Shimoda Y, Asahi T, Taniguchi A, Mizuno M. Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model. Energy 2007; 32: 1617-1633.
  • [12] Bartusch C, Wallin F, Odlare M, Vassileva I, Wester L. Introducing a demand-based electricity distribution tariff in the residential sector: demand response and customer perception. Energ Policy 2011; 39: 5008-5025.
  • [13] White LV, Lloyd B, Wakes SJ. Are Feed-in tariffs suitable for promoting solar PV in New Zealand cities? Energ Policy 2013; 60: 167-178.
  • [14] Kavousian A, Rajagopal R, Fischer M. Determinants of residential electricity consumption: using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants’ behavior. Energy 2013; 55: 184-194.
  • [15] Faruqui A, Malko JR. The residential demand for electricity by time-of-use: a survey of twelve experiments with peak load pricing. Energy 1983; 8: 781-795.
  • [16] Friedman LS. The importance of marginal cost electricity pricing to the success of greenhouse gas reduction programs. Energ Policy 2011; 39: 7347-7360.
  • [17] He Y, Wang B, Wang J, Xiong W, Xia T. Residential demand response behavior analysis based on Monte Carlo simulation: the case of Yinchuan in China. Energy 2012; 47: 230-236.
  • [18] Torriti J. Price-based demand side management: assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy. Energy 2012; 44: 576-583.
  • [19] Allcott H. Rethinking real-time electricity pricing. Resour Energy Econ 2011; 33: 820-842.
  • [20] Lin B, Liu X. Electricity tariff reform and rebound effect of residential electricity consumption in China. Energy 2013; 59: 240-247.
  • [21] D¨utschke E, Paetz A. Dynamic electricity pricing—Which programs do consumers prefer? Energ Policy 2013; 59: 226-234.
  • [22] Nelson T, Orton F. A new approach to congestion pricing in electricity markets: Improving user pays pricing incentives. Energ Econ 2013; 40: 1-7.
  • [23] Gilbraith N, Powers SE. Residential demand response reduces air pollutant emissions on peak electricity demand days in New York City. Energ Policy 2013; 59: 459-469.
  • [24] Hu Y, Li X, Li H, Yan J. Peak and off-peak operations of the air separation unit in oxy-coal combustion power generation systems. Appl Energ 2013; 112: 747-754.
  • [25] Sun C, Lin B. Reforming residential electricity tariff in China: block tariffs pricing approach. Energ Policy 2013; 60: 741-752.
  • [26] Berg SV. Causal responsibility and peak load pricing. Energ Econ 1982; 4: 246-250.
  • [27] Newsham GR, Bowker BG. The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: a review. Energ Policy 2010; 38: 3289-3296.
  • [28] Thorsnes P, Williams J, Lawson R. Consumer responses to time varying prices for electricity. Energ Policy 2012; 49: 552-561.
  • [29] Woo C, Li R, Shiu A, Horowitz I. Residential winter kWh responsiveness under optional time-varying pricing in British Columbia. Appl Energ 2013; 108: 288-297.
  • [30] Pettersen E, Philpott AB, Wallace SW. An electricity market game between consumers, retailers and network operators. Decis Support Syst 2005; 40: 427-438.
  • [31] Halvgaard R, Poulsen NK, Madsen H, Jorgensen JB, editors. Economic model predictive control for building climate control in a smart grid. In: IEEE PES Innovative Smart Grid Technologies Conference; 2012.