When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers

Demand response (DR) has been known to play an important role in the electricity sector to balance supply and demand. To this end, the DR baseline is a key factor in a successful DR program since it influences the incentive allocation mechanism and customer participation. Previous studies have investigated baseline accuracy and bias for large, industrial and commercial customers. However, the analysis of baseline performance for residential customers has received less attention. In this paper, we analyze DR baselines for residential customers. Our analysis goes beyond accuracy and bias by understanding the impact of baselines on all stakeholders' profit. Using our customer models, we successfully show how customer participation changes depending on the incentive actually received. We found that, in general, bias is more relevant than accuracy for determining which baseline provides the highest profit to stakeholders. Consequently, this result provides a valuable insight into designing effective DR incentive schemes.

[1]  A. Rosenfeld,et al.  An exploratory analysis of California residential customer response to critical peak pricing of electricity , 2007 .

[2]  Sanem Sergici,et al.  The Impact of Informational Feedback on Energy Consumption -- A Survey of the Experimental Evidence , 2009 .

[3]  Karl Aberer,et al.  Electricity load forecasting for residential customers: Exploiting aggregation and correlation between households , 2013, 2013 Sustainable Internet and ICT for Sustainability (SustainIT).

[4]  M. Chapa,et al.  An economic dispatch algorithm for cogeneration systems , 2004 .

[5]  Karl Aberer,et al.  Matching demand with supply in the smart grid using agent-based multiunit auction , 2013, 2013 Fifth International Conference on Communication Systems and Networks (COMSNETS).

[6]  Michael Johnson,et al.  StepGreen.org: Increasing Energy Saving Behaviors via Social Networks , 2010, ICWSM.

[7]  Karl Aberer,et al.  Effective consumption scheduling for demand-side management in the smart grid using non-uniform participation rate , 2013, 2013 Sustainable Internet and ICT for Sustainability (SustainIT).

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

[9]  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.

[10]  Sascha Ossowski,et al.  Smart consumer load balancing: state of the art and an empirical evaluation in the Spanish electricity market , 2012, Artificial Intelligence Review.

[11]  Sarvapali D. Ramchurn,et al.  Putting the 'smarts' into the smart grid , 2012, Commun. ACM.

[12]  Pierre Desprairies,et al.  World Energy Outlook , 1977 .

[13]  Na Li,et al.  Optimal demand response based on utility maximization in power networks , 2011, 2011 IEEE Power and Energy Society General Meeting.

[14]  Hongbin Sun,et al.  Study on wind-EV complementation in transmission grid side , 2011, 2011 IEEE Power and Energy Society General Meeting.

[15]  Srinivasan Keshav,et al.  The impact of electricity pricing schemes on storage adoption in Ontario , 2012, 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy).

[16]  Marco Levorato,et al.  Residential Demand Response Using Reinforcement Learning , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[17]  Sarvapali D. Ramchurn,et al.  Theoretical and Practical Foundations of Large-Scale Agent-Based Micro-Storage in the Smart Grid , 2011, J. Artif. Intell. Res..

[18]  Prashant J. Shenoy,et al.  Scaling distributed energy storage for grid peak reduction , 2013, e-Energy '13.

[19]  Sarvapali D. Ramchurn,et al.  Agent-based control for decentralised demand side management in the smart grid , 2011, AAMAS.

[20]  Sila Kiliccote,et al.  Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory Title Estimating Demand Response Load Impacts : Evaluation of Baseline Load Models for Non-Residential Buildings in California Permalink , 2008 .

[21]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[22]  P. S. Nagendra Rao Combined Heat and Power Economic Dispatch: A Direct Solution , 2006 .

[23]  Helmut Krcmar,et al.  Engaging energy saving through motivation-specific social comparison , 2011, CHI Extended Abstracts.

[24]  Jean-Yves Le Boudec,et al.  A Demand-Response Calculus with Perfect Batteries , 2012, MMB/DFT.

[25]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[26]  Philip M. Johnson,et al.  The Kukui Cup: A Dorm Energy Competition Focused on Sustainable Behavior Change and Energy Literacy , 2011, 2011 44th Hawaii International Conference on System Sciences.