Demand response evaluation and forecasting — Methods and results from the EcoGrid EU experiment

Abstract Understanding electricity consumers participating in new demand response schemes is important for investment decisions and the design and operation of electricity markets. Important metrics include peak response, time to peak response, energy delivered, ramping, and how the response changes with respect to external conditions. Such characteristics dictate the services DR is capable of offering, like primary frequency reserves, peak load shaving, and system balancing. In this paper, we develop methods to characterise price-responsive demand from the EcoGrid EU demonstration in a way that was bid into a real-time market. EcoGrid EU is a smart grid experiment with 1900 residential customers who are equipped with smart meters and automated devices reacting to five-minute electricity pricing. Customers are grouped and analysed according to the manufacturer that controlled devices. A number of advanced statistical models are used to show significant flexibility in the load, peaking at 27% for the best performing groups.

[1]  F. Wolak.  An Experimental Comparison of Critical Peak and Hourly Pricing: The PowerCentsDC Program* , 2010 .

[2]  Sebastian Pokutta,et al.  Strict linear prices in non-convex European day-ahead electricity markets , 2012, Optim. Methods Softw..

[3]  G.B. Shrestha,et al.  Congestion-driven transmission expansion in competitive power markets , 2004, IEEE Transactions on Power Systems.

[4]  J. Hausmann,et al.  A two-level electricity demand model: Evaluation of the connecticut time-of-day pricing test , 1979 .

[5]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[6]  Minghong Lin,et al.  Stochastic analysis of file-swarming systems , 2007, Perform. Evaluation.

[7]  Mohsen A. Jafari,et al.  A multi-scale adaptive model of residential energy demand , 2015 .

[8]  R. Tibshirani,et al.  A SIGNIFICANCE TEST FOR THE LASSO. , 2013, Annals of statistics.

[9]  Wei-Jen Lee,et al.  Multiregion Load Forecasting for System With Large Geographical Area , 2009, IEEE Transactions on Industry Applications.

[10]  James W. Taylor,et al.  Triple seasonal methods for short-term electricity demand forecasting , 2010, Eur. J. Oper. Res..

[11]  Yi Ding,et al.  Real-Time Market Concept Architecture for EcoGrid EU—A Prototype for European Smart Grids , 2013, IEEE Transactions on Smart Grid.

[12]  R. Weron.  Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach , 2006 .

[13]  H. Madsen,et al.  Controlling Electricity Consumption by Forecasting its Response to Varying Prices , 2013, IEEE Transactions on Power Systems.

[14]  Rob J Hyndman,et al.  Short-Term Load Forecasting Based on a Semi-Parametric Additive Model , 2012, IEEE Transactions on Power Systems.

[15]  Tao Hong,et al.  Short Term Electric Load Forecasting , 2012 .

[16]  Shi You,et al.  Indirect control for demand side management - A conceptual introduction , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[17]  H. Madsen,et al.  Benefits and challenges of electrical demand response: A critical review , 2014 .

[18]  Siem Jan Koopman,et al.  Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling , 2012, Comput. Stat. Data Anal..

[19]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[20]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[21]  Johanna L. Mathieu,et al.  Examining uncertainty in demand response baseline models and variability in automated responses to dynamic pricing , 2011, IEEE Conference on Decision and Control and European Control Conference.

[22]  Michael Baldea,et al.  Nonintrusive disaggregation of residential air-conditioning loads from sub-hourly smart meter data , 2014 .

[23]  N. Amjady.  Day-ahead price forecasting of electricity markets by a new fuzzy neural network , 2006, IEEE Transactions on Power Systems.

[24]  Sila Kiliccote,et al.  Statistical analysis of baseline load models for non-residential buildings , 2009 .

[25]  Thomas Bier,et al.  Smart Meter with non-intrusive load monitoring for use in Smart Homes , 2010, 2010 IEEE International Energy Conference.

[26]  Soumyadip Ghosh,et al.  Short‐term forecasting of the daily load curve for residential electricity usage in the Smart Grid , 2013 .

[27]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.