Definitions of Demand Flexibility for Aggregate Residential Loads

Nowadays, enhanced knowledge of the nature of the electricity demand is achieved through the progressively increasing deployment of smart meters and advanced data analysis techniques. One of the major challenges is to exploit this knowledge to support the introduction of strategies to modify the demand according to relevant objectives to be achieved, like users' participation in demand response programmes. A key point for facing this challenge is to characterize the demand flexibility. In spite of many discussions about the concept of flexibility, the few mathematical definitions of flexibility available do not address the variation in time of the overall demand aggregation. This paper starts from the analysis of time-variable patterns of aggregate residential customers, ending up with suitable definitions of expected flexibility for aggregate demand. These definitions are based on assessing positive and negative pattern variations and are identified from the analysis of the collective behavior of the aggregate users. A set of results is shown for different numbers of aggregate customers, by considering different values of the averaging time step for load pattern representation.

[1]  Brian Vad Mathiesen,et al.  Comparative analyses of seven technologies to facilitate the integration of fluctuating renewable energy sources , 2009 .

[2]  Tyrone L. Vincent,et al.  Aggregate Flexibility of Thermostatically Controlled Loads , 2015, IEEE Transactions on Power Systems.

[3]  L. Brown,et al.  Interval Estimation for a Binomial Proportion , 2001 .

[4]  Hui Li,et al.  Increasing the Flexibility of Combined Heat and Power for Wind Power Integration in China: Modeling and Implications , 2015, IEEE Transactions on Power Systems.

[5]  Philip Powell,et al.  Towards a definition of flexibility: in search of the Holy Grail? , 2000 .

[6]  E. Lannoye,et al.  Evaluation of Power System Flexibility , 2012, IEEE Transactions on Power Systems.

[7]  I. Kockar,et al.  Agent-based modeling of the demand-side flexibility , 2011, 2011 IEEE Power and Energy Society General Meeting.

[8]  G. Andersson,et al.  On operational flexibility in power systems , 2012, 2012 IEEE Power and Energy Society General Meeting.

[9]  B. John Oommen,et al.  On the estimation of independent binomial random variables using occurrence and sequential information , 2007, Pattern Recognit..

[10]  Dan Wang,et al.  Performance evaluation of controlling thermostatically controlled appliances as virtual generators using comfort-constrained state-queueing models , 2014 .

[11]  R. Newcombe Two-sided confidence intervals for the single proportion: comparison of seven methods. , 1998, Statistics in medicine.

[12]  Gianfranco Chicco,et al.  Effect of aggregation level and sampling time on load variation profile — A statistical analysis , 2014, MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference.

[13]  H. Rubin,et al.  Estimation of binomial parameters when both n, p are unknown , 2005 .

[14]  Enrico Carpaneto,et al.  Probabilistic characterisation of the aggregated residential load patterns , 2008 .

[15]  Regina Lamedica,et al.  A bottom-up approach to residential load modeling , 1994 .

[16]  Gianfranco Chicco,et al.  A probabilistic approach to study the load variations in aggregated residential load patterns , 2014, 2014 Power Systems Computation Conference.

[17]  R. Belhomme,et al.  Evaluating and planning flexibility in sustainable power systems , 2013, 2013 IEEE Power & Energy Society General Meeting.

[18]  E. B. Wilson Probable Inference, the Law of Succession, and Statistical Inference , 1927 .

[19]  Anna Scaglione,et al.  Reduced-Order Load Models for Large Populations of Flexible Appliances , 2015, IEEE Transactions on Power Systems.

[20]  Gerard Ledwich,et al.  Demand Response for Residential Appliances via Customer Reward Scheme , 2014, IEEE Transactions on Smart Grid.

[21]  Matti Lehtonen,et al.  Benefits of Demand Response on Operation of Distribution Networks: A Case Study , 2016, IEEE Systems Journal.

[22]  Ross Baldick,et al.  On energy delivery to delay-averse flexible loads: Optimal algorithm, consumer value and network level impacts , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[23]  Achim Dobermann,et al.  An algorithm for spatially constrained classification of categorical and continuous soil properties , 2006 .

[24]  G. Chicco,et al.  Characterisation of the aggregated load patterns for extraurban residential customer groups , 2004, Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521).

[25]  D. S. Kirschen,et al.  Flexibility from the demand side , 2012, 2012 IEEE Power and Energy Society General Meeting.

[26]  Johanna L. Mathieu,et al.  Uncertainty in the flexibility of aggregations of demand response resources , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[27]  Sangtae Ha,et al.  Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility , 2012, IEEE Journal on Selected Areas in Communications.

[28]  A. Agresti An introduction to categorical data analysis , 1997 .

[29]  Yu Guan,et al.  A generalized score confidence interval for a binomial proportion , 2012 .

[30]  François Béland,et al.  Correspondence analysis is a useful tool to uncover the relationships among categorical variables. , 2010, Journal of clinical epidemiology.

[31]  I. Barany,et al.  Central limit theorems for Gaussian polytopes , 2006 .

[32]  Wei Zhang,et al.  Aggregate model for heterogeneous thermostatically controlled loads with demand response , 2012, 2012 IEEE Power and Energy Society General Meeting.

[33]  S. Oren,et al.  Large-Scale Integration of Deferrable Demand and Renewable Energy Sources , 2014, IEEE Transactions on Power Systems.

[34]  G. Strbac,et al.  Decentralized Participation of Flexible Demand in Electricity Markets—Part I: Market Mechanism , 2013, IEEE Transactions on Power Systems.

[35]  R. Burdine,et al.  Categorical data analysis in experimental biology. , 2010, Developmental biology.