Open Access Open Access  Restricted Access Subscription or Fee Access

A Conditional Demand Analysis Based AMR Metered Load Disaggregation: a Case Study


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iremos.v9i2.8384

Abstract


There is growing effort to harness the load flexibility potential from household loads. Nevertheless, the true flexibility potential has not yet been quantified properly. Although the load profiles of limited number of appliances can be attained through appliance level measuring units, the application of the approach to quantify the use profile of appliances for aggregated number of households is cost prohibitive.  This study applies a conditional demand analysis (CDA) technique on automatic meter reading (AMR) system metered hourly consumption data linked to detailed statistical survey data for thousands of households from different geographical location in Finland. The study principally assesses the applicability of the CDA technique in an effort to disaggregate household consumption to individual appliances. The results of the analysis include the share of major appliances from household hourly consumption on workdays and holidays of the four seasons of the year. The limitations of the application of the CDA method on hourly-metered consumption data are investigated. Furthermore a use case scenario is presented to demonstrate to what extent the disaggregated results can be useful for demand response programs. Finally, for a more accurate CDA analysis, the study recommends relevant questions to be included in the survey questioner with regard to specific appliances.
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords


Demand Response; Energy Consumption; Energy Efficiency; Load Disaggregation; Smart Meter

Full Text:

PDF


References


L. G. Swan , V. I. Ugursal, Modeling of end-use energy consumption in the residential sector: A review of modeling techniques, Renewable and Sustainable Energy Reviews, vol. 13 n. 8, October 2009, pp. 1819–1835
http://dx.doi.org/10.1016/j.rser.2008.09.033

K. C. Armel, A. Gupta, G. Shrimali, A. Albert, Is disaggregation the holy grail of energy efficiency? The case of electricity, Energy Policy, vol. 52 iss. C, January 2013, pp. 213-234.
http://dx.doi.org/10.1016/j.enpol.2012.08.062

S. Kelly, Do homes that are more energy efficient consume less energy?: A structural equation model of the English residential sector, Energy, vol. 36 iss. 9, September 2011, pp. 5610-5620.
http://dx.doi.org/10.1016/j.energy.2011.07.009

M. Parti, C. Parti, The total and appliance-specific conditional demand for electricity in the household sector, The Bell Journal of Economics, vol. 11 no. 1, Spring 1980, pp. 309-321.
http://dx.doi.org/10.2307/3003415

A. Kavousian, R. Rajagopal, M. Fischer, Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior, Energy, vol. 55, June 2013, pp. 184-194.
http://dx.doi.org/10.1016/j.energy.2013.03.086

M. Aydinalp-Koksal, V. IsmetUgursal, Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector, Applied Energy, vol. 85 iss. 4, April 2008, pp. 271-296.
http://dx.doi.org/10.1016/j.apenergy.2006.09.012

G. Lafrance, D. Perron, Evolution of residential electricity demand by end-use in Quebec 1979-1989: a conditional demand analysis, Energy Studies Review, vol. 6 no. 2, 1994, pp. 164-173.
http://dx.doi.org/10.15173/esr.v6i2.334

R. Stamminger, G. Broil, C. Pakula, H. Jungbecker, M. Braun, I. Rudenauer, C. Wendker, Synergy Potential of Smart Appliances, D2. 3 of WP 2 from the Smart-A project, 2008.

K. H. Tiedermann, Using conditional demand analysis to estimate residential energy use and energy savings, European Council for an Energy Efficient Economy Summer Study, Proceedings of the CDEEE, 2007.

Merkebu Zenebe Degefa, Energy Efficiency Analysis of Residential Electric End-Uses: Based on Statistical Survey and Hourly Metered Data, Masters Thesis, Aalto University, March 23, 2010.

MATLAB Statistics Toolbox, 2014. Available from (http://www.mathworks.se/help/stats/stepwisefit.html?refresh=true)

U.S. Deparment of Energy, Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them, February 2006.

OriolSementeTarrago, Demand response potential of residential load equipments, Masters Thesis, Aalto University, December 15, 2014.

Molina, C.E., Lehtonen, M., Degefa, M.Z., Heat gains, heating and cooling in nordic housing with electrical space heating, (2015) International Review of Electrical Engineering (IREE), 10 (5), pp. 599-606.
http://dx.doi.org/10.15866/iree.v10i5.7185

Gosálbez, I.G., Lehtonen, M., Stochastic genetic algorithm and its application as a demand control tool for houses with thermal energy storage systems, (2015) International Review on Modelling and Simulations (IREMOS), 8 (3), pp. 284-292.
http://dx.doi.org/10.15866/iremos.v8i3.6045

Malik, F.H., Ali, M., Lehtonen, M., Collaborative demand response optimization of electric vehicles and storage space heating for residential peak shaving, (2014) International Review of Electrical Engineering (IREE), 9 (6), pp. 1154-1161.
http://dx.doi.org/10.15866/iree.v9i6.4829

Ali, M., Koivisto, M., Lehtonen, M., Optimizing the DR control of electric storage space heating using LP approach, (2013) International Review on Modelling and Simulations (IREMOS), 6 (3), pp. 853-860.

Degefa, M.Z., Lehtonen, M., Stochastic characteristics of load profiling in distribution systems based on AMR measurements, (2013) International Review of Electrical Engineering (IREE), 8 (6), pp. 1833-1842.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize