A Minimal Incentive-Based Demand Response Program With Self Reported Baseline Mechanism

In this paper, we propose a novel incentive based Demand Response (DR) program with a self reported baseline mechanism. The System Operator (SO) managing the DR program recruits consumers or aggregators of DR resources. The recruited consumers are required to only report their baseline, which is the minimal information necessary for any DR program. During a DR event, a set of consumers, from this pool of recruited consumers, are randomly selected. The consumers are selected such that the required load reduction is delivered. The selected consumers, who reduce their load, are rewarded for their services and other recruited consumers, who deviate from their reported baseline, are penalized. The randomization in selection and penalty ensure that the baseline inflation is controlled. We also justify that the selection probability can be simultaneously used to control SO’s cost. This allows the SO to design the mechanism such that its cost is almost optimal when there are no recruitment costs or at least significantly reduced otherwise. Finally, we also show that the proposed method of self-reported baseline outperforms other baseline estimation methods commonly used in practice.

[1]  Clifford Grimm Evaluating Baselines for Demand Response Programs , 2008 .

[2]  P. Charpentier,et al.  Statistical Estimation of the Residential Baseline , 2016, IEEE Transactions on Power Systems.

[3]  M.D. Anderson,et al.  Dynamic pricing [of electricity] , 2000, IEEE Potentials.

[4]  John Keene,et al.  Demand Response Compensation in Organized Wholesale Energy Markets Technical Conference , 2010 .

[5]  Pravin Varaiya,et al.  Mechanism Design for Demand Response Programs , 2017, IEEE Transactions on Smart Grid.

[6]  Giambattista Gruosso,et al.  Limiting gaming opportunities on incentive-based demand response programs , 2018, Applied Energy.

[7]  H. Chao,et al.  Incentive effects of paying demand response in wholesale electricity markets , 2013 .

[8]  Sila Kiliccote,et al.  Estimating Demand Response Load Impacts: Evaluation of BaselineLoad Models for Non-Residential Buildings in California , 2008 .

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

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

[11]  Mark O'Malley,et al.  Challenges and barriers to demand response deployment and evaluation , 2015 .

[12]  Rui Xu,et al.  A Cluster-Based Method for Calculating Baselines for Residential Loads , 2016, IEEE Transactions on Smart Grid.

[13]  G. Hamoud,et al.  Assessment of transmission congestion cost and locational marginal pricing in a competitive electricity market , 2004, IEEE Transactions on Power Systems.

[14]  Fredy Ruiz,et al.  Rational consumer decisions in a peak time rebate program , 2017, ArXiv.

[15]  Giorgio Rizzoni,et al.  Residential Demand Response: Dynamic Energy Management and Time-Varying Electricity Pricing , 2016, IEEE Transactions on Power Systems.

[16]  Sila Kiliccote,et al.  Installation and Commissioning Automated Demand Response Systems , 2008 .

[17]  Lang Tong,et al.  Probabilistic Forecasting of Real-Time LMP and Network Congestion , 2015, IEEE Transactions on Power Systems.

[18]  Ross Baldick,et al.  Dynamic Demand Response Controller Based on Real-Time Retail Price for Residential Buildings , 2014, IEEE Transactions on Smart Grid.

[19]  Fei Wang,et al.  Synchronous Pattern Matching Principle-Based Residential Demand Response Baseline Estimation: Mechanism Analysis and Approach Description , 2018, IEEE Transactions on Smart Grid.

[20]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[21]  P. Khargonekar,et al.  Distributed control of flexible demand using proportional allocation mechanism in a smart grid: Game theoretic interaction and price of anarchy , 2017 .

[22]  Elizabeth Doris,et al.  Government Program Briefing: Smart Metering , 2011 .

[23]  Nanpeng Yu,et al.  Forecast load impact from demand response resources , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[24]  H. Chao Price-Responsive Demand Management for a Smart Grid World , 2010 .

[25]  Johanna L. Mathieu,et al.  Residential Demand Response program design: Engineering and economic perspectives , 2013, 2013 10th International Conference on the European Energy Market (EEM).

[26]  David White,et al.  LMP Electricity Markets: Market Operations, Market Power, and Value for Consumers , 2006 .

[27]  Nadia Oudjane,et al.  Analysis and Implementation of an Hourly Billing Mechanism for Demand Response Management , 2017, IEEE Transactions on Smart Grid.

[28]  S. Borenstein,et al.  Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets , 2002 .

[29]  Ahmad Faruqui,et al.  Dynamic Pricing and Its Discontents , 2011 .

[30]  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.

[31]  Karl Aberer,et al.  When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers , 2014, IEEE Transactions on Smart Grid.

[32]  A. Fuels,et al.  Electric power monthly , 1992 .

[33]  F. Wolak Residential Customer Response to Real-time Pricing: The Anaheim Critical Peak Pricing Experiment , 2007 .