EnergyCoupon: A Case Study on Incentive-based Demand Response in Smart Grid

In competitive electricity market systems such as Texas, Load Serving Entities (LSEs) purchase energy in a wholesale market and then sell it in a retail market. In wholesale market, LSEs see dynamic real-time prices, whereas in retail market, LSEs typically provide flat rate contracts to end-consumers. An intuitive idea to induce savings for LSEs is to shift loads from high-price hours to low-price hours of the wholesale market by incentive-based demand response. We design and implement such a system, called EnergyCoupon, to provide coupon incentives to users and collect their responses. We run the experiment over household participants through the summer of 2016. The experimental results suggest behavioral changes in energy consumption can be achieved, which could be beneficial to both end users and LSEs. This paper presents the system set up, key algorithms, as well as experimental results analysis.

[1]  Judd B. Kessler,et al.  The Welfare Effects of Nudges: A Case Study of Energy Use Social Comparisons , 2019, American Economic Journal: Applied Economics.

[2]  H. Madsen,et al.  Forecasting Electricity Spot Prices Accounting for Wind Power Predictions , 2013, IEEE Transactions on Sustainable Energy.

[3]  Le Xie,et al.  Coupon Incentive-Based Demand Response: Theory and Case Study , 2013, IEEE Transactions on Power Systems.

[4]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[5]  Yang Weng,et al.  Probabilistic baseline estimation via Gaussian process , 2015, 2015 IEEE Power & Energy Society General Meeting.

[6]  H. Chao Demand response in wholesale electricity markets: the choice of customer baseline , 2011 .

[7]  Le Xie,et al.  Analysis of coupon incentive-based demand response with bounded consumer rationality , 2014, 2014 North American Power Symposium (NAPS).

[8]  Vijay Subramanian,et al.  Energy Coupon , 2015, SIGMETRICS.

[9]  B. Prabhakar,et al.  An Incentive Mechanism for Decongesting the Roads : A Pilot Program in Bangalore , 2009 .

[10]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[11]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[12]  Pei Zhang,et al.  Demo abstract: saving energy in smart commercial buildings through social gaming , 2013, UbiComp.

[13]  M. Shahidehpour,et al.  A Hybrid Model for Day-Ahead Price Forecasting , 2010, IEEE Transactions on Power Systems.

[14]  Hamidou Tembine,et al.  Electricity Demand Shaping via Randomized Rewards : A Mean Field Game Approach , 2013 .