- -

Residential end-uses disaggregation and demand response evaluation using integral transforms

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Residential end-uses disaggregation and demand response evaluation using integral transforms

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Gabaldón Marín, A. es_ES
dc.contributor.author Molina, R. es_ES
dc.contributor.author Marin-Parra, A. es_ES
dc.contributor.author Valero, S. es_ES
dc.contributor.author Álvarez, Carlos es_ES
dc.date.accessioned 2019-05-06T20:02:17Z
dc.date.available 2019-05-06T20:02:17Z
dc.date.issued 2017 es_ES
dc.identifier.issn 2196-5625 es_ES
dc.identifier.uri http://hdl.handle.net/10251/119998
dc.description.abstract [EN] Demand response is a basic tool used to develop modern power systems and electricity markets. Residential and commercial segments account for 40%-50% of the overall electricity demand. These segments need to overcome major obstacles before they can be included in a demand response portfolio. The objective of this paper is to tackle some of the technical barriers and explain how the potential of enabling technology (smart meters) can be harnessed, to evaluate the potential of customers for demand response (end-uses and their behaviors) and, moreover, to validate customers' effective response to market prices or system events by means of non-intrusive methods. A tool based on the Hilbert transform is improved herein to identify and characterize the most suitable loads for the aforesaid purpose, whereby important characteristics such as cycling frequency, power level and pulse width are identified. The proposed methodology allows the filtering of aggregated load according to the amplitudes of elemental loads, independently of the frequency of their behaviors that could be altered by internal or external inputs such as weather or demand response. In this way, the assessment and verification of customer response can be improved by solving the problem of load aggregation with the help of integral transforms. es_ES
dc.description.sponsorship This work has been supported by Spanish Government (Ministerio de Economia, Industria y Competitividad) and EU FEDER fund (No. ENE2013-48574-C2-2-P&1-P, No. ENE2015-70032-REDT). es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Journal of Modern Power Systems and Clean Energy es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Demand response es_ES
dc.subject Hilbert transform es_ES
dc.subject Load monitoring es_ES
dc.subject Instantaneous frequency es_ES
dc.subject Aggregation es_ES
dc.subject Smart meters es_ES
dc.subject.classification INGENIERIA ELECTRICA es_ES
dc.title Residential end-uses disaggregation and demand response evaluation using integral transforms es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s40565-016-0258-8 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//ENE2013-48574-C2-1-P/ES/HERRAMIENTAS DE ANALISIS PARA LA EVALUACION Y GESTION DE LA PARTICIPACION DE LA RESPUESTA DE LA DEMANDA EN LA PROVISION DE SERVICIOS COMPLEMENTARIOS EN SISTEMAS ELECTRICOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//ENE2015-70032-REDT/ES/RED TEMATICA EN RECURSOS ENERGETICOS DISTRIBUIDOS Y DE DEMANDA PARA EL DESARROLLO DEL HORIZONTE ENERGETICO 2050/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica es_ES
dc.description.bibliographicCitation Gabaldón Marín, A.; Molina, R.; Marin-Parra, A.; Valero, S.; Álvarez, C. (2017). Residential end-uses disaggregation and demand response evaluation using integral transforms. Journal of Modern Power Systems and Clean Energy. 5(1):91-104. https://doi.org/10.1007/s40565-016-0258-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1007/s40565-016-0258-8 es_ES
dc.description.upvformatpinicio 91 es_ES
dc.description.upvformatpfin 104 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 5 es_ES
dc.description.issue 1 es_ES
dc.relation.pasarela S\352435 es_ES
dc.contributor.funder Ministerio de Economía y Empresa es_ES
dc.description.references Chardon A, Almén O, Lewis PE (2009) Demand response: a decisive breakthrough for Europe. Capgemini, Enerdata. https://www.capgemini.com/resources/demand_response_a_decisive_breakthrough_for_europe es_ES
dc.description.references Faruqui A, Harris D, Hledick R (2010) Unlocking the €53 billion savings from smart meters in the EU: how increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy 38(10):6222–6231 es_ES
dc.description.references Federal Energy Regulatory Commission (FERC) (2015) Assessment of demand response and advanced metering, staff report. http://www.ferc.gov/legal/staff-reports/2015/demand-response.pdf es_ES
dc.description.references FERC Order 745 (December 2011) & 719 (December 2009). http://www.ferc.gov/legal/maj-ord-reg.asp es_ES
dc.description.references European Commission (2011) Energy efficiency plan 2011, COM (2011) 109 final. http://eur-lex.europa.eu/LexUriServ es_ES
dc.description.references Joitn Research Center (JRC), European Commission (2010) Energy Service Companies market in Europe, status report 2010. http://publications.jrc.ec.europa.eu/repository/handle/111111111/15108 es_ES
dc.description.references European Commission (2011) Impact assessment accompanying document “Energy Efficiency Plan 2011”, SEC (2011) 277 final. http://www.eurosfaire.prd.fr/7pc/bibliotheque/consulter.php?id=2357 es_ES
dc.description.references European Environment Agency (EEA) (2012) Final energy consumption by sector and fuel. http://www.eea.europa.eu/data-and-maps/indicators/final-energy-consumption-by-sector-8/assessment-2 es_ES
dc.description.references Piette MA, Watson D, Motegi N et al (2007) Automated critical peak pricing field tests: 2006 pilot program description and results. California Energy Commission, PIER Energy Systems Integration Research Program: CEC-500-03-026 es_ES
dc.description.references Gomatom K, Holmes C, Kresta D (2013) Non-intrusive load monitoring. https://www.e3tnw.org es_ES
dc.description.references Dimetrosky S, Parkinson K, Lieb N (2013) Residential lighting evaluation protocol, Report NREl/SR-7A30-53827. National Renewable Energy Laboratory es_ES
dc.description.references Zeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook. IEEE Trans Consum Electron 57(1):76–84 es_ES
dc.description.references Liang J, Simon K, Kendall G et al (2010) Load signature study part I: basic concept, structure and methodology. IEEE Trans Power Deliv 25(2):551–560 es_ES
dc.description.references Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891 es_ES
dc.description.references Powers J, Margossian B, Smith B (1991) Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data. IEEE Comput Appl Power 4(2):42–47 es_ES
dc.description.references Farinaccio L, Zmeureanu R (1999) Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy Build 30(3):245–259 es_ES
dc.description.references Marceau ML, Zmeureanu R (2000) Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings. Energy Convers Manag 41(13):1389–1403 es_ES
dc.description.references Baransju M, Voss J (2004) Genetic algorithm for pattern detection in NIALM systems. In: IEEE international conference on systems, man and cybernetics, 10–13 Oct, pp 3462–3468 es_ES
dc.description.references Baranski H, Voss J (2004) Detecting patterns of appliances from total load data using a dynamic programming approach. In: Fourth IEEE international conference on data mining (ICDM’04), 1–4 Nov, Brighton, UK, pp 327–330 es_ES
dc.description.references Sankara A (2015) Energy disaggregation in NIALM using hidden Markov models. Masters Theses, Missouri University of Science and Technology, Paper 7414 es_ES
dc.description.references Kolter J, Jaakkola T (2012) Approximate inference in additive factorial HMMs with application to energy disaggregation. J Mach Learn 22:1472–1482 es_ES
dc.description.references Leeb SB, Shaw SR, Kirtley SL (1995) Transient event detection in spectral envelope estimates for nonintrusive load monitoring. IEEE Trans Power Deliv 10(3):1200–1210 es_ES
dc.description.references Patel SN, Robertson T, Kientz JA et al (2007) At the flick of a switch: detecting and classifying unique electrical events on the residential power line. In: Conference on ubiquitous computing, pp 271–288 es_ES
dc.description.references Bonglifi R, Squartini S, Fagiani M et al (2015) Unsupervised algorithms for non-intrusive load monitoring: an up-to-date overview. In: EEEIC conference. doi: 10.1109/EEEIC.2015.7165334 es_ES
dc.description.references Browne TJ, Vittal V, Heydt GT et al (2008) A comparative assessment of two techniques for modal identification from power system measurements. IEEE Trans Power Syst 23(3):1408–1415 es_ES
dc.description.references Senroy N, Suryanarayanan S, Ribeiro PF (2007) An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality. IEEE Trans Power Syst 22(4):1843–1850 es_ES
dc.description.references Gabaldon A, Ortiz M, Molina R et al (2014) Disaggregation of electric loads of small customers through the application of the Hilbert transform. Energ Effic 7(4):711–728. doi: 10.1007/s12053-013-9250-6 es_ES
dc.description.references Kolter JZ, Johnson MJ (2011) REDD: a public data set for energy disaggregation research. http://101.96.10.59/people.csail.mit.edu/mattjj/papers/kddsust2011.pdf es_ES
dc.description.references Fibaro wall switches. http://www.fibaro.com/en/the-fibaro-system/ es_ES
dc.description.references IP-Symcon: innovative centre for the entire building automation. https://www.symcon.de/ es_ES
dc.description.references Energy Information Administration (2001) End-use consumption electricity 2001. http://www.eia.gov/emeu/recs/recs2001/enduse2001/enduse2001.html es_ES
dc.description.references Poularikas AD (1999) The Hilbert transform, handbook of formulas and tables for signal processing. CRC Press LLC, Boca Raton es_ES
dc.description.references Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proced R Soc Lond 454(1971):903–995 es_ES
dc.description.references Deering R, Kaiser JF (2005) The use of a masking signal to improve empirical mode decomposition. In: Proceedings IEEE international conference acoustic, speech, and signal processing, pp 485–488 es_ES
dc.description.references Liang J, Simon K, Kendall G, Cheng J et al (2010) Load signature study part II: disaggregation framework, simulation, and applications. IEEE Trans Power Deliv 25(2):561–569 es_ES
dc.description.references Load participation in ancillary services. http://www1.eere.energy.gov/analysis/pdfs es_ES
dc.description.references Gabaldón A, Guillamón A, Ruiz MC et al (2010) Development of a methodology for clustering electricity-price series to improve customer response initiatives. IET Gener Transm Distrib 4(6):706–715 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem