Potential of Active Demand Reduction With Residential Wet Appliances: A Case Study for Belgium

Two problems are tackled in this paper: determining the active demand reduction potential of wet appliances and making time series estimates from project data. The former is an application of the latter. Household groups representative to the average population are defined by applying expectation maximization clustering to a representative measurement set (n = 1363). Attitudes toward active demand are found by conducting a survey (n = 418). Project data (n = 58) containing wet appliance measurements are scaled up by adapting the clustering algorithm, spreading the electricity demand of the wet appliances over the clusters. The potential for active demand reduction with wet appliances is 4% of the total residential power demand, assuming that 29% of the households take part. The potential is in the order of magnitude of the power reserves, but does not fulfill availability and response time requirements.

[1]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[2]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[3]  M. Sforna,et al.  Data mining in a power company customer database , 2000 .

[4]  Ian Witten,et al.  Data Mining , 2000 .

[5]  D. Kirschen Demand-side view of electricity markets , 2003 .

[6]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[7]  G. Chicco,et al.  Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.

[8]  C. Senabre,et al.  Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps , 2006, IEEE Transactions on Power Systems.

[9]  Torgeir Ericson Short-term electricity demand response , 2007 .

[10]  N.D. Hatziargyriou,et al.  Two-Stage Pattern Recognition of Load Curves for Classification of Electricity Customers , 2007, IEEE Transactions on Power Systems.

[11]  C. Senabre,et al.  Methods for customer and demand response policies selection in new electricity markets , 2007 .

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

[13]  Olof M. Jarvegren,et al.  Pacific Northwest GridWise™ Testbed Demonstration Projects; Part I. Olympic Peninsula Project , 2008 .

[14]  J. Torriti,et al.  Demand response experience in Europe: Policies, programmes and implementation , 2010 .

[15]  Katherine Hamilton,et al.  Taking Demand Response to the Next Level , 2010, IEEE Power and Energy Magazine.

[16]  G. Coke,et al.  Random effects mixture models for clustering electrical load series , 2010 .

[17]  T Joseph Lui,et al.  Get Smart , 2010, IEEE Power and Energy Magazine.

[18]  Hanne Sæle,et al.  Demand Response From Household Customers: Experiences From a Pilot Study in Norway , 2011, IEEE Transactions on Smart Grid.

[19]  Jin-ho Kim,et al.  Common failures of demand response , 2011 .

[20]  I. Vassileva,et al.  Introducing a demand-based electricity distribution tariff in the residential sector: Demand response and customer perception , 2011 .

[21]  Gianfranco Chicco,et al.  Overview and performance assessment of the clustering methods for electrical load pattern grouping , 2012 .

[22]  J. Torriti,et al.  Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy , 2012 .

[23]  Koen Vanthournout,et al.  LINEAR breakthrough project: Large-scale implementation of smart grid technologies in distribution grids , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[24]  G. Deconinck,et al.  Customer sampling in a smart grid pilot , 2012, 2012 IEEE Power and Energy Society General Meeting.

[25]  Jie Lu,et al.  A New Index and Classification Approach for Load Pattern Analysis of Large Electricity Customers , 2012, IEEE Transactions on Power Systems.

[26]  Geert Deconinck,et al.  Residential Electrical Load Model Based on Mixture Model Clustering and Markov Models , 2013, IEEE Transactions on Industrial Informatics.

[27]  Nico Keyaerts,et al.  Shift, not drift : towards active demand response and beyond , 2013 .

[28]  Lieven De Marez,et al.  Towards More Energy Efficient Domestic Appliances? Measuring the Perception of Households on Smart Appliances , 2013 .

[29]  Marnix C. Vlot,et al.  Economical Regulation Power Through Load Shifting With Smart Energy Appliances , 2013, IEEE Transactions on Smart Grid.

[30]  C. H. Antunes,et al.  Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions , 2014 .

[31]  Saifur Rahman,et al.  Load Profiles of Selected Major Household Appliances and Their Demand Response Opportunities , 2014, IEEE Transactions on Smart Grid.

[32]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[33]  R. Belmans,et al.  Impact of residential demand response on power system operation: A Belgian case study , 2014 .