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SMART energy homes and the smart grid : a framework for

intelligent energy management systems for residential

customers

Citation for published version (APA):

Asare-Bediako, B. (2014). SMART energy homes and the smart grid : a framework for intelligent energy

management systems for residential customers. Technische Universiteit Eindhoven.

https://doi.org/10.6100/IR781632

DOI:

10.6100/IR781632

Document status and date:

Published: 01/01/2014

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SMART Energy Homes

and the Smart Grid

A Framework for Intelligent Energy Management

Systems for Residential Customers

Ballard Asare-Bediako

SMART Energy Homes and the Smart Grid

Ballar

d Asar

e-Bediak

o

Invitation

You are cordially invited to

attend the public defense of

my Ph.D. dissertation entitled

SMART Energy Homes

and the Smart Grid

The defense will take place on

Thursday December 11, 2014

at 16:00 in the Auditorium

(Room 4) of Eindhoven

University of Technology.

After the defense you are also

invited to the reception which

will take place in the same

location.

Ballard Asare-Bediako

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SMART Energy Homes and the Smart Grid

A Framework for Intelligent Energy Management Systems for Residential Customers

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de rector magnificus, prof.dr.ir. C.J. van Duijn, voor een

commissie aangewezen door het College voor Promoties in het openbaar te verdedigen op donderdag 11 december 2014 om 16.00 uur

door

Ballard Asare-Bediako

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promotiecommissie is als volgt:

voorzitter: prof.dr.ir. A. C. P. M. Backx 1epromotor: prof.ir. W. L. Kling

2epromotor: prof.dr.ir. J. F. G. Cobben

leden: Univ.-Prof.Dr.-Ing. J. M. A. Myrzik (Technische Universität Dortmund) prof.dr.ir. J. Driesen (Katholieke Universiteit Leuven)

prof.dr.ir. J. L. Hurink (Universiteit Twente) prof.dr.ir. J. L. M. Hensen

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Elektromagnetische vermogenstechniek") research program. It is funded by Rijksdienst voor Ondernemend Nederland (RVO.nl), an agency of the Dutch Ministry of Economic Affairs.

Printed by Ipskamp drukkers, Enschede.

A catalogue record is available from the Eindhoven University of Technology Library. ISBN: 978-90-386-3730-3

Copyright c 2014 Ballard Asare-Bediako, Eindhoven, The Netherlands.

All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic, mechanical, including photocopy, recording, or any information storage and retrieval system, without the prior written permission of the copyright owner.

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Summary

This thesis investigates the expected changes in the energy supply systems and energy demand profiles for the residential sector. Investigations are carried out on residential energy consumption, energy conversion technologies and the impacts of energy management systems on the residential load profile. The thesis also presents a framework of concepts and technologies that enable Smart Grid applications at the residential environment.

Residential energy supply systems are influenced by two main factors. First is the penetration of new energy conversion technologies such as photovoltaic (PV) systems, micro combined heat and power (μCHP) units, heat pumps and electric vehicles in the residential sector. PV systems are one of the fastest growing energy supply technologies in the residential environment, attributable to the improved efficiency of PV modules and financial incentives offered by national governments, such as feed-in tariffs, capital subsidies and income tax credits. μCHP units are replacing conventional gas boilers in gas-connected houses to provide heat for space heating and domestic hot water, and also supply part of the electricity needs of the home.

The electrification of heating systems is expected to increase significantly for newly built residential areas in the Netherlands. Furthermore, electrification of mobility is taking off. The concurrent penetration of new conversion systems will significantly change the residential energy supply system. Secondly, residential neighborhoods are evolving in terms of building type and household composition. Analyses show that residential gas consumption is affected by the type of building, date of construction and the orientation, and age-group of occupants. The electricity demand is directly influenced by the composition of the household and the level of income. Families with young children are found to have high electricity consumption due to the frequent use of electrical appliances. Gas consumption is found to be high among the elderly due to their demands for more thermal comfort. Also, analyses indicate that a good mix of building types and residential groups could provide natural smoothing of the residential load profiles.

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The research focuses on the deployments of smart energy homes as important infrastructure for smart cities and the Smart Grid. Smart energy homes are equipped with home automation technologies to improve occupants’ comfort, health and safety, and provide savings on energy bills. They offer convenience to the occupants whereby many daily activities are fully automated or can be controlled from customers’ computers and smart phones. They play crucial roles in the development of smart cities by contributing to better living conditions, quality of functional space, minimization of greenhouse gas emissions, and stimulating economic development. Another important aspect is the seamless integration of smart energy homes into the Smart Grid framework. They can support the public grid by enabling Smart Grid applications. Smart energy homes are emerging among academic institutions as experimental laboratories for validating technology and systems and also as pilot demonstration projects to test efficient use of energy and Smart Grid interaction. The deployments of smart energy homes vary in purpose and functionality, yet results from pilot projects indicate promising prospects for large-scale implementations.

Furthermore, residential energy consumption continues to grow despite the enforcement of energy efficiency policies. The demand for more comfort, the use of more appliances and the lack of real-time or historical feedback on energy use to customers contribute to the increase in energy use. Home energy management systems (HEMS) employ automated technologies to manage and reduce residential energy use and cost, as well as make energy reductions through energy efficiency measures more visible to the customer, and extend Smart Grid applications to the home environment. HEMS facilitate the integration of residential generation to match users’ needs, and to support the reliability and robustness of the energy supply infrastructure. Their deployments are motivated by the need for more efficient operation of the power system, energy security, reduction in carbon footprints, and customer retention for the utilities.

Four main areas for HEMS applications are outlined in this thesis. First is the customer-based HEMS, where residential devices are managed to accommodate daily activities, preferences and needs of the residential customers without the influence of external parties. The second focuses on reduction of network peak loading and alleviation of network congestion through optimal control of flexible residential loads, storages and generation systems. The third is a market-oriented way of implementing HEMS, which is managing residential energy use in response to fluctuating energy prices. Finally, HEMS can be installed to inform customers on their energy use to prevent rebound effect (consuming more energy after implementation of energy-efficient measures), and to stay within the limits set by contractual agreements. Furthermore, barriers to large-scale introduction of HEMS technologies are investigated. Conservatism, cost and privacy are the major barriers to large scale implementation of HEMS. To address these challenges, five aspects - technological, economic, socio-cultural, structural and legal - have been outlined as crucial for sustainable deployments of HEMS technologies.

A sustainable HEMS should be robust, flexible, and capable of integrating the interest of the various stakeholders. This thesis proposes a multi-agent system (MAS) for home

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SUMMARY iii

energy management. The agent-based systems employ distributed intelligence to solve complex problems and facilitate the implementation of multiple control algorithms for the household. The proposed MAS architecture is hierarchical, comprising device agents for monitoring and control of devices, and a central agent who coordinates the activities of all other agents and determines the control objectives. The architecture is suited for present and near future Smart Grid applications such as the use of dynamic tariff systems, demand response programs, or households’ participation in a virtual power plant system. A co-simulation model of the MAS-based HEMS is tested using Java Agent Development Framework (JADE) and MATLAB software linked via TCP/IP protocol. The design of agents and control algorithms are implemented with JADE, while the residential devices are modelled with MATLAB software. Two control strategies are tested - a green optimization control algorithm which takes advantage of locally generated electricity, and a price-based control that integrates electricity price variations and the distribution network constraints.

Finally, laboratory demonstrations of two parts of home energy management are performed. The first part focuses on the extraction, processing and analysis of smart meter’s data for effective energy management. The second experiment investigates device-level monitoring and control using a mesh network of smart plugs, domestic appliances and a gateway (which also acts as coordinator) connected via ZigBee wireless communication protocol.

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Samenvatting

Dit proefschrift beschrijft het onderzoek naar de verwachte veranderingen in het energievoorzieningssysteem en de energievraag profielen van de huishoudelijke sector. Onderzoeken zijn uitgevoerd op het gebied van huishoudelijk energieverbruik, energieconversie technologieën en de impact van energiemanagement systemen op het huishoudelijke belastingprofiel. Het proefschrift presenteert ook een raamwerk van concepten en technologieën die Smart Grid toepassingen in de woonomgeving mogelijk maken.

Huishoudelijke energievoorzieningssystemen worden beïnvloed door twee belangrijke factoren. De eerste is de penetratie van nieuwe energieconversie technologieën zoals fotovoltaïsche (PV) systemen, micro warmtekrachtkoppeling (μWKK) eenheden, warmtepompen en elektrische voertuigen in de huishoudelijke sector. PV systemen zijn een van de snelst groeiende energie-opweksystemen in de woonomgeving, dit is te danken aan de verbeterde efficiëntie van PV-modules en de financiële prikkels die worden aangeboden door de nationale overheden, zoals teruglevertarieven, investeringssubsidies en inkomstenbelastingvoordeel. μWKK-eenheden vervangen conventionele gasboilers in huizen met een gasaansluiting, om warmte voor ruimteverwarming en warm water te leveren, en ook om in een deel van de elektriciteitsbehoefte te voorzien.

De elektrificatie van verwarmingssystemen zal naar verwachting aanzienlijk toenemen in nieuwbouwwijken in Nederland. Bovendien, neemt de elektrificatie van het vervoer ook sterk toe. De gelijktijdige penetratie van nieuwe conversiesystemen zal het huishoudelijke energievoorzieningssysteem aanzienlijk veranderen. Ten tweede, evolueren woonwijken ook in termen van type gebouwen en de samenstelling van het huishouden. Uit analyses blijkt dat de huishoudelijke gas vraag wordt beïnvloed door het type gebouw, het bouwjaar en de oriëntatie, en de leeftijdsgroep van de bewoners. Het elektriciteitsverbruik wordt direct beïnvloed door de samenstelling van het huishouden en het inkomensniveau. Gezinnen met jonge kinderen blijken een hoog elektriciteitsverbruik te hebben als gevolg van het veelvuldig gebruik van elektrische

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apparaten. Het gasverbruik blijkt hoog te zijn onder ouderen, als gevolg van hun vraag naar meer thermisch comfort. Ook blijkt uit analyses dat een goede mix van type gebouwen en samenstelling van huishoudens voor een natuurlijk afvlakking van de huishoudelijke belastingprofielen zorgt.

Dit onderzoek richt zich op de implementatie van zogenoemde ”smart energy homes” als belangrijke infrastructuur voor ”smart cities” en het ”Smart Grid”. Smart energy homes zijn uitgerust met huisautomatisering technologie om het comfort, de gezondheid en de veiligheid van de bewoners te verbeteren, en om te besparen op de energierekening. Ze bieden de bewoners gebruiksgemak, terwijl veel dagelijkse activiteiten volledig geautomatiseerd zijn of kunnen worden bediend vanaf de computers of de smart phones van de klanten. Ze spelen cruciale rollen in de ontwikkeling van smart cities door bij te dragen aan betere leefomstandigheden, de kwaliteit van de functionele ruimte, het minimaliseren van de uitstoot van broeikasgassen, en het stimuleren van economische ontwikkeling. Een ander belangrijk aspect is de naadloze integratie van smart energy homes in het Smart Grid raamwerk. Ze kunnen het publieke net ondersteunen door het mogelijk maken van Smart Grid toepassingen. Smart energy homes zijn in opkomst in academische kringen als experimentele laboratoria voor het valideren van de technologie en systemen, en ook als pilot demonstratieprojecten om efficiënt gebruik van energie en Smart Grid interactie te testen. De implementaties van smart energy homes variëren in doel en de functionaliteit, maar de resultaten van pilotprojecten geven veelbelovende perspectieven voor grootschalige implementaties.

Bovendien blijft het huishoudelijke energieverbruik groeien, ondanks een beleid gericht op efficiënt gebruik van energie. De vraag naar meer comfort, het gebruik van meer apparatuur en het gebrek aan real-time of historische feedback aan klanten over hun energieverbruik, draagt bij aan de toename van energieverbruik. Home Energy Management Systems (HEMS) gebruiken geautomatiseerde technologieën om zowel het huishoudelijke energieverbruik en de kosten te beheren en te verminderen, als ook het beter zichtbaar maken voor de klant van energie reducties, door energie-efficiënte maatregelen, en kunnen de Smart Grid toepassingen naar de woonomgeving brengen. HEMS faciliteren de integratie van lokale opwekking en koppelen dat aan de behoefte van de gebruikers, en ondersteunen de betrouwbaarheid en robuustheid van de het energievoorzieningssysteem. Hun toepassingen worden gemotiveerd door de behoefte aan een efficiëntere werking van het elektriciteitsvoorzieningssysteem, energiezekerheid, vermindering van de milieuaspecten, en klantenbinding voor de energie gerelateerde bedrijven.

Vier hoofdgebieden voor HEMS toepassingen worden beschreven in dit proefschrift. De eerste is het klant georiënteerde HEMS, waar huishoudelijke apparaten worden beheerd om de huishoudelijke klanten te ondersteunen in hun dagelijkse activiteiten, voorkeuren en behoeften, zonder de invloed van externe partijen. De tweede richt zich op de vermindering van de piekbelasting in het net en het voorkomen van netwerkcongestie, door middel van een optimale sturing van flexibele huishoudelijke belastingen, opslag en opweksystemen. De derde is een marktgeoriënteerde

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SAMENVATTING vii

implementatie van HEMS, waarin het huishoudelijke energieverbruik reageert op de fluctuerende energieprijzen. Ten slotte kan HEMS geïnstalleerd worden om klanten te informeren over hun energieverbruik om zo het rebound-effect (het consumeren van meer energie na de implementatie van energie-efficiënte maatregelen) te voorkomen, en binnen de grenzen van de contractuele afspraken te blijven. Verder zijn de belemmeringen voor een grootschalig introductie van HEMS technologieën onderzocht. Conservatisme, kosten en privacy zijn de belangrijkste belemmeringen voor grootschalige implementatie van HEMS. Om deze uitdagingen te adresseren, zijn vijf aspecten benoemd - technologische, economische, sociaal-culturele, structurele en wettelijke - die belangrijk zijn voor duurzame implementaties van HEMS technologieën. Een duurzame HEMS moet robuust, flexibel, en in staat zijn de belangen van verschillende stakeholders te integreren. Dit proefschrift introduceert een multi-agent systeem (MAS) voor het beheer van de huishoudelijke energievraag. Agent-based systemen maken gebruik van gedistribueerde intelligentie om complexe problemen op te lossen, en faciliteren het de implementatie van meervoudige controle algoritmes voor de huishoudens. De voorgestelde MAS architectuur is hiërarchisch en bestaat uit agenten voor de bewaking en besturing van apparaten, en een centrale agent die de activiteiten van alle andere agenten coördineert en de regeldoelstellingen bepaalt. De architectuur is geschikt voor huidige en toekomstige Smart Grid toepassingen, zoals het gebruik van dynamische tariefsystemen, vraagsturing programma’s, of de participatie van huishoudens in zogenoemde ”virtual power plants”. Een co-simulatiemodel van de op MAS-gebaseerde HEMS is getest met behulp van een Java Agent Development Framework (JADE), samen met MATLAB software op basis van een TCP/IP-protocol. Het ontwerp van de agenten en de regelalgoritmen is geïmplementeerd met JADE, terwijl de huishoudelijke apparaten zijn gemodelleerd met MATLAB software. Twee regelstrategieën zijn getest - een groen optimalisatie regelalgoritme dat gebruik maakt van de lokaal opgewekte elektriciteit, en een op prijs gebaseerde regelalgoritme dat de elektriciteitsprijs variaties en de beperkingen in het distributienetwerk integreert.

Tot slot, met laboratorium demonstraties zijn twee onderdelen van home energy management uitgevoerd. Het eerste deel richt zich op de extractie, verwerking en analyse van de slimme meter data voor effectief energiebeheer. Het tweede experiment onderzoekt de monitoring op apparaatniveau en de aansturing met behulp van een vermaasd netwerk van smart plugs, huishoudelijke apparaten en een gateway (die tevens fungeert als coördinator) aangesloten via het ZigBee draadloze communicatieprotocol.

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Contents

Summary i

Samenvatting v

List of Figures xiii

1 Introduction 1

1.1 Background . . . 1

1.2 Towards energy-efficient and smart residential environments . . . 2

1.2.1 Net-zero energy residential environment . . . 3

1.2.2 Smart residential network . . . 4

1.3 Research framework . . . 5

1.4 Research objective and scope . . . 6

1.5 Research approach . . . 7

1.6 Thesis outline . . . 8

2 Residential energy systems 11 2.1 Introduction . . . 11

2.2 Energy consumption in the residential sector . . . 11

2.2.1 Electricity consumption . . . 12

2.2.2 Gas consumption . . . 13

2.2.3 Heat consumption . . . 13

2.3 Demographics and residential energy consumption . . . 13

2.4 Residential loads categorization . . . 16

2.4.1 Inflexible loads . . . 17

2.4.2 Shiftable loads . . . 17

2.4.3 Thermal loads . . . 20

2.4.4 Buffer loads . . . 22

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2.4.5 Energy storage systems . . . 24

2.5 Distributed generation . . . 26

2.5.1 Photovoltaic system . . . 26

2.5.2 Micro combined heat and power . . . 28

2.5.3 Micro wind turbines . . . 29

2.6 Conclusion . . . 30

3 Residential load aggregation 31 3.1 Introduction . . . 31

3.2 Synthetic load profiles . . . 32

3.3 Housing type and residential groups on load profiles . . . 33

3.4 Future residential load profiles for planning . . . 36

3.4.1 Existing situation . . . 37

3.4.2 Scenario-based simulations . . . 37

3.4.3 Penetration of individual technologies . . . 38

3.4.4 Combinations of technologies . . . 40

3.5 Forecasting residential load profiles for operations . . . 44

3.5.1 Load forecasting models . . . 44

3.5.2 An example of load forecasting with artificial neural networks . . 45

3.6 Conclusion . . . 49

4 Smart energy home concept 51 4.1 Introduction . . . 51

4.2 Essence of smart homes . . . 51

4.2.1 Smart homes in smart cities . . . 54

4.2.2 Smart energy home and the Smart Grid . . . 55

4.3 Enablers of smart energy homes . . . 57

4.3.1 Metering devices . . . 57

4.3.2 Smart sensors . . . 58

4.3.3 Smart home communication network . . . 59

4.3.4 The Internet of Things . . . 62

4.3.5 Smart appliances . . . 63

4.3.6 Monitoring and control systems . . . 63

4.4 Essential factors for smart energy home integration . . . 64

4.5 Conclusion . . . 66

5 Energy management for households 69 5.1 Introduction . . . 69

5.2 The evolution of energy management systems . . . 70

5.3 Smart metering system . . . 71

5.3.1 The EU directives . . . 71

5.3.2 The device . . . 72

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CONTENTS xi

5.3.4 The drawbacks . . . 73

5.4 Home energy management systems: drivers and stakeholders . . . 73

5.5 Applications of home energy management systems . . . 75

5.5.1 Customer-based applications . . . 75

5.5.2 Network-based applications . . . 77

5.5.3 Market-based applications . . . 78

5.5.4 Service-based applications . . . 78

5.6 Residential demand response and demand side management . . . 79

5.7 HEMS and energy efficiency . . . 81

5.8 Barriers to home energy management systems penetration . . . 81

5.9 Sustainable HEMS deployment . . . 83

5.9.1 Technological aspects . . . 83 5.9.2 Economic aspects . . . 84 5.9.3 Structural aspects . . . 85 5.9.4 Socio-cultural aspects . . . 86 5.9.5 Legal aspects . . . 87 5.10 Conclusion . . . 87

6 Agent-based framework for home energy management 89 6.1 Introduction . . . 89

6.2 Agent-based systems: definitions and applications . . . 90

6.3 Multi-agent architecture for home energy management . . . 90

6.4 Energy optimization . . . 92

6.5 Multi-agent system model . . . 94

6.5.1 Bid function control scheme . . . 96

6.6 Demonstration of MAS model through co-simulation . . . 97

6.6.1 Green optimization . . . 98

6.6.2 Price-based control . . . 102

6.7 Conclusion . . . 106

7 Laboratory-scale demonstration of home energy management systems 109 7.1 Introduction . . . 109

7.2 Energy management using smart meter . . . 109

7.2.1 Experimental set-up . . . 110

7.2.2 Data extraction and analysis . . . 111

7.3 Device-level energy management system . . . 113

7.3.1 ZigBee network set-up . . . 114

7.3.2 Receiving, parsing and storing data . . . 115

7.3.3 Device control . . . 115

7.3.4 HTTP server interface . . . 119

7.3.5 External networking interface . . . 120

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8 Conclusions, contributions and recommendations 123

8.1 Conclusions . . . 123

8.1.1 Residential energy consumption and demand patterns . . . 123

8.1.2 Smart energy homes and the Smart Grid . . . 124

8.1.3 Smart meters . . . 124

8.1.4 Home energy management systems . . . 125

8.1.5 Multi-agent system architecture for device monitoring and control 125 8.1.6 Testing home energy management systems . . . 126

8.2 Thesis contribution . . . 126

8.3 Recommendations for future research . . . 128

A Appendix for Load aggregation 129 A.1 Examples of special loads and distributed generation in households in the Netherlands . . . 129

A.2 Standard load profile categorisation . . . 130

B Appendix for agent-based home energy management system 131 B.1 Modelling household loads and generation for multi-agent system simulation . . . 131

B.2 Device Bid functions . . . 133

C Appendix for lab demonstration 137 C.1 Demonstration of home energy management . . . 137

C.2 ZigBee network concepts . . . 137

Bibliography 141 Nomenclature 154 List of acronyms . . . 154 List of symbols . . . 156 List of publications 159 Acknowledgements 163 Curriculum Vitae 165

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List of Figures

1.1 Final energy consumption by sector for EU (28 countries), Euro area (18

countries) and the Netherlands (Source: Eurostat). . . 3

1.2 (a)Share of energy consumption by end uses in total households’ consumption in the EU-27 (b)Household energy efficiency index (Source:ODYSSEE). . . 4

1.3 Evolutions in residential loads and comfort levels. . . 5

1.4 IOP EMVT ”Intelligent Power Systems” research framework [1]. . . 6

1.5 Thesis outline . . . 10

2.1 Final energy demand in the residential sector in Europe [2]. . . 12

2.2 Average annual electricity and gas consumptions for a household in the Netherlands [3]. . . 13

2.3 Electricity consumption (a) per sector (b) per domestic activity for an average household in the Netherlands for 2010 [3]. . . 14

2.4 Gas consumption (a) per sector (b) per domestic activity for an average household in the Netherlands for 2010 [3]. . . 14

2.5 Residential energy consumption for building types in the Netherlands (Source: AgentschapNL, 2010). . . 15

2.6 Energy consumption of residential buildings according to year built [4]. . . . 16

2.7 Annual average electricity and gas consumption for residential groups in the Netherlands (Source: CBS, NL). . . 16

2.8 Generalized power and temperature profiles for shiftable loads [5]. . . 18

2.9 Installed heat pumps in the Netherlands (source: CBS, 2014). . . 20

2.10 Measured one week power profile of residential heat pump in a detached house (Source: Laborelec GDF-SUEZ). . . 21

2.11 Measured one day power profile of residential heat pump in a semi-detached house (Source: Laborelec GDF-SUEZ). . . 22

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2.12 Electric vehicles (3 or more wheels) in the Netherlands (Source: RVO.nl-2014). 23 2.13 Average power demand profile of about 15000 electric vehicles charging at

home, office and shopping area for (a)weekday and (b)weekend [6]. . . 24

2.14 Cumulative installed PV capacity for the Netherlands, Europe and the world [7] [8]. . . 27

2.15 Schematic diagram of a single-phase two-stage PV system converter. . . 27

2.16 Energy flows in a residential μCHP system [9]. . . 29

3.1 Synthetic load profiles of residential electricity for the four seasons (Source:EDSN.nl). . . 32

3.2 Synthetic load profile compared with smart meter data. . . 33

3.3 A 3-day load profiles based on smart meter data and Simula software. . . 34

3.4 Load profile for residential groups in a terraced house. . . 34

3.5 Load profiles for a family with children in different types of building. . . 35

3.6 A 2-day electricity consumption profile for 200 houses with weighted mixed residential groups. . . 35

3.7 Winter load profiles for 25, 50, 100 and 200 households from smart meter data . . . 37

3.8 Flow diagram for simulation scenarios and cases . . . 39

3.9 Summer load profiles for penetrations of PV, heat pump, μCHPs and electric vehicle. . . 40

3.10 Winter load profiles for penetrations of PV, heat pump, μCHPs and electric vehicle. . . 41

3.11 Load profiles for one hundred households with PV systems and μCHPs. . . 41

3.12 Load profiles for one hundred households with base load-PV-heat pumps. . . 42

3.13 Winter load profiles for aggregation of houses with equal proportions of PV-heat pump and PV-μCHP. . . 42

3.14 Load profiles for aggregation of houses with PV, electric vehicles and heat pumps in winter. . . 43

3.15 Impacts of loads and distributed generations on future residential load profiles. 43 3.16 Mathematical representation of a feed-forward artificial neural network . . . 45

3.17 Architecture of artificial neural network forecast model. . . 46

3.18 Forecasted data compared with actual data for seven consecutive days. . . 47

3.19 Histogram of error distribution. . . 48

3.20 Boxplot of error distribution. . . 48

4.1 An impression of a smart home [10]. . . 52

4.2 Smart homes as integral part of smart cities [11]. . . 54

4.3 Interaction of residential customers with energy retailers and distribution network operators. . . 56

4.4 Smart energy homes as integral part of the Smart Grid. . . 56

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LIST OFFIGURES xv

4.6 Schematic of an integrated end-to-end Smart Grid communication platform concept [12]. . . 59 4.7 Qualitative comparison of three HAN communication technologies. . . 62 4.8 The Internet of Things enabled by IPv6 protocol. . . 63 4.9 Integrated framework for smart energy home penetration. . . 65 5.1 Timeline of energy management systems evolution. . . 70 5.2 Smart metering system architecture in the Netherlands. . . 71 5.3 Home energy management products available in the market. . . 74 5.4 Comparison of day-ahead market price with residential electricity tariffs. . . . 79 5.5 Example of energy service contract between residential customers and ESCos

[13] [14]. . . 80 5.6 Integrating home energy management systems with smart meters, smart

loads and external parties. . . 84 5.7 Keys aspects for HEMS penetrations. . . 85 5.8 Simplification of residential customer interaction with external parties. . . 86 6.1 Household installations divided into local control areas (cells) monitored

and/or controlled by agents. . . 91 6.2 A multi-agent system architecture for smart home energy management. . . . 92 6.3 Agent platform with message dialogue in JADE. . . 95 6.4 Multi-agent system for device control and agent coordination. . . 95 6.5 Example of device bid curves. . . 96 6.6 Co-simulation platform. . . 97 6.7 Diagram of Coordinator Agent algorithm for green optimization. . . 98 6.8 Determination of Coordinator control signal (λCS) from aggregated bid curves. 99

6.9 Variations of control signal (λCS) for local and aggregated demand-supply

matching (winter day). . . 100 6.10 Variations of control signal (λCS) for local and aggregated demand-supply

matching (summer day). . . 100 6.11 House indoor temperature variations. . . 101 6.12 Freezer compartment temperature variations. . . 101 6.13 Total energy use by the twenty houses. . . 101 6.14 Total power consumption of the twenty houses. . . 102 6.15 Diagram for Coordinator Agent algorithm for price-based control. . . 104 6.16 Residential network used as a case study for agent-based energy

management system. . . 105 6.17 Price variations and power consumptions of a single house. . . 106 6.18 Price variations and power consumption of twenty houses. . . 106 6.19 Power consumption and price variation of the twenty houses for the three

scenarios. . . 107 7.1 Division of a house into zones for energy management. . . 110

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7.2 Experimental set-up for smart meter data extraction and processing . . . 110 7.3 Comparison of energy consumption data from P1 and P3 ports. . . 112 7.4 System overview. . . 113 7.5 Schematic of implemented ZigBee network for the laboratory set-up. . . 114 7.6 Process of receiving, parsing and storing data by the gateway. . . 116 7.7 Connection between the different Python files. . . 116 7.8 Data strings sent by the different plug meters. . . 116 7.9 Priority control mechanism. . . 117 7.10 Power consumption of individual devices. . . 118 7.11 Data gathering with 1 second data transmission interval. . . 118 7.12 Total power consumption with priority control mechanism for the case

without PV system. . . 119 7.13 Total power consumption with priority control mechanism with a PV system. 119 7.14 Dashboard for the Home Area Network Smart Grid Monitor. . . 120 7.15 External communication with ZigBee network. . . 121 C.1 Smart meter installed in the laboratory and in a house. . . 137 C.2 RJ11 to RS232 connection. . . 138 C.3 Domestic appliances, smart plugs and gateway for the device-level energy

management set-up. . . 138 C.4 ZigBee network topologies. . . 139 C.5 Averaging data received from smart plugs implemented in

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C

HAPTER

1

Introduction

1.1 Background

Energy is a life sustaining commodity. As part of the overall energy needs, electrical energy has become a basic necessity for society and a vital entity for socio-economic development. It is one of the enabling technologies that is not always noticed but has become a ubiquitous necessity of human life for the last century. The necessity and dependence on this commodity has never shown signs to recede and it is still expected to increase in the future. In most part of the world, access to electricity is the right of every citizen but has to be paid for according to the market rules. The power industry has been under a constant but slow evolution. Power grids arose because local demand could not be met by local generation. With generators and their natural fuel sources often situated far from consumers, networks were set up to transmit power from generators to consumers. The development of the power system was, and still is, governed by the ultimate goal of providing consumers with quality and reliable power supply at minimum cost. Nowadays, electricity is generated from multiple sources such as hydro, nuclear and fossil fuel power plants, giving it the greatest degree of energy resilience. As our society becomes more sustainable through awareness of future shortages and environmental consequences of fossil fuels, an effective way of ensuring fossil fuel independency is a transition towards alternative energy sources (such as wind and photovoltaic), and a more efficient use of electricity.

In the meantime, energy consumption in Europe keep rising. Table 1.1 shows that final energy consumption increased by 4.7% and 7.4% (for EU-28 countries), 10.8% and 14.7% (for Euro area 18 countries) in 2000 and 2010 respectively compared to the consumptions in 1990. The Netherlands experienced a more significant increase of 22.2% and 30.5% in 2000 and 2010 respectively. The residential sector shares a great deal of the energy consumption in the EU28 countries, accounting for 23.85% -27.35% of the total annual energy consumption from 1990 to 2012 [15] (see Figures

?? and 1.1). Key factors driving the residential energy consumption include growing

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Table 1.1: Comparison of final energy consumptions for EU (28 countries), Euro area (18 countries) and the Netherlands for the years 1990, 2000 and 2010 (Source: Eurostat).

Year Final energy consumption (1000 TOE)

EU-28 Euro area Netherlands

1990 1079865.7 713244.6 41331.9

2000 1130953.1 790372.1 50504.6

(+4.7%) (+10.8%) (+22.2%)

2010 1159826.4 818365.0 53935.0

(+7.4%) (+14.7%) (+30.5%)

incomes, globalization of the economy, technological breakthroughs (such as smart phones and computers), ageing population, as well as habits and cultures [16]. To keep up with the European 20/20/20 objectives (namely, reduction in greenhouse gas emissions by 20% from 1990 levels; increasing the share of renewable energy resources to 20%; and 20% improvement in the EU’s energy efficiency) [17], research and government policies are set on finding ways to minimize energy consumption and consequently reduce greenhouse gas emissions in the residential sector. Scientific advances in sustainable distributed energy generations are promising. The transition from fossil fuels to renewable energy sources (RES) is favored by the majority of the parties involved in the electricity market because they are often considered to be less polluting and more efficient. Furthermore, it is necessary to ensure an efficient use of energy by the end user, reforming the current habits of consumption and shaping the market towards overall energy sustainability.

1.2 Towards energy-efficient and smart residential environments

Energy efficiency is a way of managing and restraining the growth in energy consumption by delivering more services for the same energy input, or the same services for less energy input [18]. Over the last decades, energy efficiency in the residential sector has increased steadily, particularly in areas such as space and water heating (due to better thermal insulation of buildings and high efficiency boilers), and also among large domestic appliances like refrigerators, freezers, washing machines, dishwashers and televisions. Figure 1.2 shows a large increase in the overall energy efficiency and for some electrical appliances (refrigerators, freezers, washing machines, dishwashers and televisions) in the residential sector for the EU-27 countries from 1990 to 2009. Over the same period, energy consumption of households increased by about 13% while that of large electrical appliances increased by 48%. The increased number of devices and demands for higher thermal comforts at homes offset the gains from energy efficiency measures (Figures 1.2 and 1.3).

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1.2. TOWARDS ENERGY-EFFICIENT AND SMART RESIDENTIAL ENVIRONMENTS 3 Year 1990 2000 2010 24,1 28,6 25,1 14,0 8,1 0,2 20,4 29,3 28,3 14,3 7,7 0,0 21,4 26,5 27,8 18,2 6,2 0,0

Share per sector (%) Netherlands 25,3 34,0 26,3 10,1 2,9 1,2 26,0 29,4 30,5 10,3 2,4 1,2 EU-28 26,8 25,1 31,2 13,6 2,1 1,0 ResidentialIndustry Transport Services Agriculture Others 24,9 32,1 28,6 10,9 2,7 0,7 25,0 29,1 32,1 10,2 2,3 0,0 25,3 25,7 31,7 14,0 2,0 1,2 Euro area

Figure 1.1: Final energy consumption by sector for EU (28 countries), Euro area (18 countries) and the Netherlands (Source: Eurostat).

1.2.1 Net-zero energy residential environment

One of the identified key sectors to achieve the vision 20/20/20 is the building sector. Low-energy houses are emerging concepts due to the potential for energy savings within the built environment. The net zero-energy building (NZEB) concept is a trending topic in the field of sustainable buildings and also gaining the attention of municipalities, commercial and residential stakeholders, as a way to enhance energy reliability and efficiency. A NZEB is a grid-connected building with reduced energy demands and high energy performance, such that its thermal and electrical energy requirements can be compensated with local energy generation using the electricity grid as a buffer. It is an approach that involves energy-efficient buildings, installation of distributed generations, and energy-efficient devices. The European Commission Directive 2010/31/EU on the energy performance of buildings (EPBD) sets the principle of nearly zero-energy buildings as one of decisive mechanisms for the development of the building sector [19]. The EPBD directive requires Member States to ”ensure that all new buildings are nearly

zero-energy buildings by 31 December 2020; and after 31 December 2018, new buildings occupied and owned by public authorities are nearly zero-energy buildings ” [20]. However,

it is up to the Member States to develop specific policies and implementation plans for increasing the number of NZEBs.

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Space heating Water heating Cooking Lighting and appliances 80 70 60 50 40 30 20 10 0 1990 2009 Sh are o f ho useh old ener gy co nsu m ptio n (%) (a) 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 70 75 80 85 90 95 100 105 Year R el a ti v e en er g y co n su m p ti o n (% ) Cooking Large electrical appliances Space heating Water heating Overall

Index 100 = 1990

(b)

Figure 1.2: (a)Share of energy consumption by end uses in total households’ consumption in the EU-27 (b)Household energy efficiency index (Source:ODYSSEE).

1.2.2 Smart residential network

Residential loads are changing in power, complexity and quantity (Figure 1.3). Serving the residential electricity demand is the main goal of the power grid with constant monitoring and control to provide a safe, reliable and efficient electricity supply. With increasing share of distributed generation in the home environment, there is a new challenge to operate the power grid in an efficient, safe and reliable manner. The existing electricity distribution system must be transformed to a more robust, reliable, and efficient one with more control functions to enable bidirectional flow of energy and information between households and the power system. The Smart Grid technology is envisioned as an intelligent way to effectively accommodate the changes in the power system.

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1.3. RESEARCH FRAMEWORK 5

(a) early house

(b) modern house

Figure 1.3: Evolutions in residential loads and comfort levels.

At the residential level, the smart meter is recently introduced as one of the means to stimulate energy efficiency culture by creating more energy awareness. However, using several devices at home makes it difficult for consumers to track how much energy is consumed per device and to identify high energy consuming devices. Smart energy homes enabled by emerging technologies and a home area network (HAN), to measure, control and communicate energy consumption are expected to provide customers an enabling tool for managing energy consumption, and to support the power system at the residential level by relieving congestion and enhancing balancing.

1.3 Research framework

This research is carried out within the ”Intelligent Power Systems” research framework of the ”Innovatiegerichte Onderzoeksprogramma Elektromagnetische vermogenstechniek” (IOP EMVT) research program supported financially by Rijksdienst voor Ondernemend Nederland (RVO.nl). RVO.nl is an agency of the Ministry of Economic Affairs in the Netherlands. The Intelligent Power System project has four major parts (see Figure 1.4) involving over 20 PhD students. Consultancy firms and energy companies

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give inputs and advice to steer the various research. This dissertation falls under the self-controlling autonomous networks and it is performed under a joint-project entitled: ”Intelligent energy supply at household and district level”. The project is divided into two parts: ”Intelligent energy management and distribution in homes” and

”Development of energy managements at district level”. Two PhD researchers (one at Delft

University of Technology and the other at Eindhoven University of Technology) worked in collaboration to investigate the feasibility of a comprehensive energy management at household and district levels through design, simulation and testing. The part that focused on the district level was performed at the Electrical Power Systems Group of Delft University of Technology. The main objective was to ” develop a scheduling and

control tool at the district level for small-scale systems with multiple energy carriers and to apply exergy-related concepts for the optimization of these systems” [1]. The project is

completed and the results are presented in the dissertation entitled : ” Optimal Usage

of Multiple Energy Carriers in Residential Systems Unit Scheduling and Power Control”

[1]. This dissertation is devoted to the first part of the project, the intelligent energy management within the home environment. Industrial partners within this research project were Laborelec GDF-SUEZ, Eaton (Nederland) and DWA installatie techniek.

1.4 Research objective and scope

The energy infrastructure of the future must be more efficient, smart and adjustable to reflect the changing needs of users and society. The integration of renewable (intermittent) and other distributed generators in homes, neighborhoods and offices are linked to the need for efficient and cost-effective energy conversion and distribution. The increasing need to keep the grid balanced under high penetrations levels of intermittent resources has sparked interest in designing new paradigms that allow electricity demand to respond to economic signals. For residential neighborhoods there are applications of heat and cold storages with the use of heat pumps and micro-combine heat and power. The deployment of advanced metering infrastructure is one of the necessary steps to exposing customers to the electricity market pressures and analyzing their responses. This has stimulated the research on the design of home energy management systems

Manageable distribution networks Inherently stable transmission system Self-controlling autonomous networks Optimal power quality

Intelligent Power Systems projects

Intelligent energy supply at household

and district level

Energy management at household level (TU Eindhoven) Energy management at district level (TU Delft)

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1.5. RESEARCH APPROACH 7

(HEMS) that handle the consumption and/or generation of customers in response to user-defined goals or dynamically changing price signals. HEMS is crucial for the integration of distributed generators to match the different needs of users, secure the reliability and robustness of the electricity supply infrastructure, and for settlement of costs and benefits among generators, distribution companies and energy consumers. Additionally, socio-economic issues will undoubtedly be crucial in the development and commercialization of new forms of energy at household and neighborhood level. This leads to the following research objective:

To investigate changes in the residential energy supply system and to develop and demonstrate a framework for energy management in homes, where decentralized and renewable energy sources and smart loads are integrated with the public grid and managed in a sustainable way.

To achieve the research objective, the following research questions are addressed: 1. What developments will change future residential energy demand and supply? 2. How will energy conversion technologies affect future residential load profiles? 3. What factors and technological advancements will facilitate sustainable

penetration of smart energy homes?

4. What are the evolutions in energy management systems for households and the role of smart meters?

5. What will be an adequate framework for a sustainable home energy management system with respect to power system requirements and market structures? 6. In what ways can smart energy homes be integrated into the Smart Grid vision?

1.5 Research approach

To achieve the main objectives, the research is approached in the following order:

• Analysis of residential loads, generation and aggregated load profiles:

Analyses into the influences of residential load profiles with focus on changing residential loads - electrification of heating system and mobility, penetration of distributed energy resources, and type of housing and occupants.

• Investigation into smart energy homes and energy management systems: The

essence, fundamental constituents, drivers, and the added value of smart energy homes are investigated. An integrated framework is developed for a sustainable implementation and integration of smart energy homes into the bigger smart grid vision and as part of the infrastructure for the smart energy buildings and cities. Further, analyses are made on energy management systems for households (HEMS).

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• A multi-agent-based architecture for home energy management: Smart

operation of residential grid will require simultaneous optimization of the objectives of various actors present. Agent-based systems which implement distributed intelligence are capable of solving complex and dynamic decision processes. A multi-agent system architecture for home energy management and integration into the smart grid is developed. The architecture is simulated in Java Agent Development Framework and MATLAB simulation platforms. Using bid function algorithms, local demand-supply matching and price-based controls, taking into account customers comfort and priorities, dynamic pricing, network loading or capacity management are demonstrated.

• Laboratory-scale demonstration of home energy management systems: A

practical set-up is built to investigate data extraction and processing from smart meters for energy management. Further, a demonstration test is performed which includes smart plugs, and smart appliances connected via Zigbee network with a central controller to verify device-level home energy management.

1.6 Thesis outline

The outline of thesis is graphically depicted in Figure 1.5. After the introductory chapter (Chapter 1), the rest of the thesis is structured as follows:

• Chapter 2: This chapter provides insight into the energy consumption in

the residential sector. It focuses on electricity and gas consumptions for the Netherlands over the past decade. It describes how housing types and residential groups affect gas and electricity consumption. Furthermore, it presents a categorization of residential loads based on their flexibility and mode of operation. Distributed generation and storage systems applied in the residential environment are also explored.

• Chapter 3: This chapter analyzes the electricity demand profiles for aggregated

households. It explores energy conversion technologies that are expected to substantially change the residential electricity demand. It presents a scenario-based approach for generating representative future residential load profiles for aggregated households. The chapter concludes with an illustration of forecasting model for short-term predictions of residential load demand.

• Chapter 4: In this chapter, the smart energy home concept is elaborated. It

highlights the added benefits of smart homes, and gives examples of smart energy home demonstration projects and the objectives of the projects. It also treats the integration of smart energy homes as part of smart cities and the interaction with the Smart Grid. Furthermore, it discusses the main technologies (matured as well as those underdevelopment) which are driving the penetration of smart energy homes. Finally, it presents overview of interrelated aspects as a framework for sustainable deployment of future smart energy homes.

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1.6. THESIS OUTLINE 9

• Chapter 5: The chapter focuses on energy management systems for households.

The evolution of energy management systems in the energy sector is presented. The smart meter considered as a major innovative technology facilitating energy monitoring and control in homes is addressed. The state-of-the-art, drivers and stakeholders of HEMS technologies are summarily discussed. Different applications of HEM systems are also discussed.

• Chapter 6: A multi-agent system architecture for home energy management is

proposed and demonstrated in this chapter. The MAS-model is developed and co-simulated using Java Agent Development Framework for agent design and control algorithm, and MATLAB for modelling domestic devices. The two platforms are linked via TCP/IP protocol. A demand-supply matching and a dynamic pricing control algorithm are explained and tested with the MAS-model using a residential street for a case study.

• Chapter 7: In this chapter, two types of home energy management are practically

demonstrated. Data extraction and processing from the P1 port of the smart meter is demonstrated in the laboratory and in a residential building. The chapter also presents a set-up and results of a laboratory-scale, device-level energy management using a ZigBee network and Python scripts for device monitoring and control.

• Chapter 8: The conclusions, thesis contributions as well as recommendations for

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Chapter 1 Research Background, Scope Definition

Research Question & Methodology

Chapters 2 & 3 Residential Energy Systems and Load Profiles Chapters 2

Residential Energy Consumption, Load Categorization & Distributed Generation

Chapters 3 Synthetic and Future Residential Load

Profiles, Load Forecasting Chapters 4 & 5

Smart Homes and Energy Management Systems Chapters 4

Essence , Enablers and Essentials of

Smart Homes for Smart Grid Integration Home Energy Management Systems: Chapters 5 Evolutions, Drivers, Applications,

Barriers & Integration Aspects Chapters 6

Multi-agent System for Home Energy Management

Chapters 7 Demonstration of ZigBee-based Home Energy

Management System

Chapters 8 Conclusions, Contributions and

Recommendations Figure 1.5: Thesis outline

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C

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2

Residential energy systems

2.1 Introduction

Many primary energy sources are limited and relatively expensive. The prices of coal, oil and natural gas fluctuate yearly. Solar and wind are unlimited primary sources, yet technologies to fully harvest their energy contents are still under development. In the meantime, there is steady increase in the domestic electricity consumption due to changing needs of residential customers with respect to comfort, convenience and flexibility. This chapter analyses the residential energy consumptions, the changes in residential loads, and the introduction of energy generation technologies at the residential environment. Section 2.2 presents an overview of residential electricity, gas and heat consumptions for the Netherlands. It highlights the trends and possible reasons for decrease or increase in energy use. The impacts of housing types and household compositions on the gas and electricity consumptions are given in Section 2.3. Residential loads are evolving in complexity and power ratings. Section 2.4 presents categorization of residential loads based on their mode of operation and flexibility, while Section 2.5 summarizes developments and operations of distributed generation technologies in the residential sector.

2.2 Energy consumption in the residential sector

The global energy consumptions keep rising yearly, primarily due to increase in global population, and rise in economic activities (particularly in China, Brazil and India) [21]. In Europe, space heating and cooling, and domestic hot water are estimated to account for approximately 80% of the final energy demand in the residential sector for 2010 (see Figure 2.1) [2]. However, the residential energy demand is projected to stabilize after 2015, attributable to policies and regulatory provisions for the residential sector which drives considerable energy efficiency savings [22]. Better insulation of new buildings, retrofitting of existing ones, and the implementation of intelligent technologies are

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2010 2020 2030 2050 0 20 40 60 80 100 120 Year Sha re in %

Heating Cooling Hot water Cooking Lighting Electrical appliances

14 7 1 13 7 1 2 13 7 4 12 7 65 64 60 54 12 14 17 23 2 1 1 1

Figure 2.1: Final energy demand in the residential sector in Europe [2].

measures towards energy savings. In the Netherlands three main energy sources are entering houses and buildings, namely, electricity, natural gas, and heat.

2.2.1 Electricity consumption

Electricity is one of the most efficient and convenient energy carriers. Hence increase in the electricity consumption is not a negative scenario if it contributes to the reduction in the total energy consumption. The EU report on Energy Trends to 2030 describes a growing electrical energy use resulting from the rising demand for increased comfort in households, and a decreased dependency on natural gas for heating and cooking purposes [22]. The expected rates of increase for the future are 1.2% and 0.7% per annum in the periods 2010 - 2020 and 2020 - 2030 respectively, excluding the possible scenarios of increased penetration of special loads, such as electrical vehicles and heat pumps. In the Netherlands, the residential sector takes a sizeable portion of the total electric power consumption. Figure 2.2 shows growth in the average annual electricity consumption per household, mainly due to increasing use of household appliances such as freezers, dishwashers and cloth dryers [3]. Households accounted for 24% of the national electricity consumption in 2010 (see Figure 2.3a) with an average of 3500 kWh per household. Cold appliances, laundry appliances, consumer electronics, and lighting are the top electricity consuming devices in the residential sector on a yearly basis (see Figure 2.3b ). With the emerging of low-energy residential lighting technologies, such as light-emitting-diode (LED) and compact fluorescent (CFL) lamps, electricity consumption due to residential lighting can be reduced by 60% compared to their conventional counterparts [23].

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2.3. DEMOGRAPHICS AND RESIDENTIAL ENERGY CONSUMPTION 13 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 3000 3200 3400 3600 3800 4000 Year E lect ri ci ty co n su m p ti o n ( kW h) 1000 1300 1600 1900 2200 2500 G as con su m p ti on [ m 3] gas electricity

Figure 2.2: Average annual electricity and gas consumptions for a household in the Netherlands [3].

2.2.2 Gas consumption

The Netherlands has a dense gas network with about 96% of all households, businesses and buildings connected to the natural gas network [24]. The trend in domestic gas demand (see Figure 2.2) has shown a steady decline for the past years as building stocks are upgraded with better and more efficient designs, materials and equipment, and the introduction of high efficiency heat boilers. The residential sector was responsible for 20% of the total annual gas consumptions in 2010 (see Figure 2.4a ). About 79% of the gas is used for space heating, 20% for domestic hot water and the remaining for cooking (see Figure 2.4b ). Though there is a shift towards all-electric households, the Netherlands still has one of the highest proportions of gas-heated homes in Europe [3].

2.2.3 Heat consumption

Heat supplies are primarily from conventional generation plants and are consumed by large commercial buildings and some households in specific areas. There are about 4% of residential customers who are connected to the district heating systems as most households have direct connections to the natural gas network.

2.3 Demographics and residential energy consumption

Consumption patterns of neighborhoods are important for supply systems designs. Analyses of residential energy consumption are mostly focused on the physical and technical aspects, such as type of appliances, neglecting the role of the demographics and economic behaviors of residential customers [4]. However,

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Households 24% Industries 28% Commercial 10% Others 38% (a)

Kitchen Heating Living room Lighting Laundry Cold appliances Others 0 5 10 15 20 A n n u a l el ect ri ci ty co n su m p ti o n (% ) (b)

Figure 2.3: Electricity consumption (a) per sector (b) per domestic activity for an average household in the Netherlands for 2010 [3].

Power plants 20% Households 20% Other small consumers 10% Other large consumers 50% (a)

cooking hot water space heating 0 10 20 30 40 50 60 70 80 90 100 An n u al gas con su m p ti on (% ) (b)

Figure 2.4: Gas consumption (a) per sector (b) per domestic activity for an average household in the Netherlands for 2010 [3].

residential neighborhoods evolve in terms of housing types and residential groups and these developments consequently affect gas and electricity consumption patterns. Studies indicate that the building type and the date of construction affect the residential gas consumption whereas the electricity consumption is directly influenced by the households’ composition and level of income [4] [25]. A model framework by Reiss et. al [26] shows a correlation between household composition and energy consumption.

There are primarily five types of residential buildings in the Netherlands, namely, detached, semi-detached, terraced, terraced corner and apartments. The residential groups can also be divided into five and coupled to the housing types for analytical purposes. The classification of the building types and the residential groups are as given in Table 2.1. Detached and semi-detached houses have more energy demands as shown in Figure 2.5. This is attributed to their large size and exposed surfaces. Additionally, occupants of these types of houses have relatively higher income earnings. The variation

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2.3. DEMOGRAPHICS AND RESIDENTIAL ENERGY CONSUMPTION 15

in energy demand according to the building’s date of construction is shown in Figure 2.6. Modern houses have less gas consumptions due to better insulation of the building envelope. However, their electricity consumptions are higher presumably due to the presence of more appliances and the electrification of the cooking and heating systems. Figure 2.7 shows the correlation between the household compositions and the energy demand. Family with children consumes the most amount of electricity attributable to more washing cycles, use of more appliances and for longer duration, electronic devices for children (recreation and education), and more frequent opening of refrigerators. For gas consumption, the elderly group (couple and single) has the highest demand. Their annual gas consumptions exceed the electricity demand since they are mostly indoors, live in relatively old houses, and require higher thermal comfort.

Table 2.1: Household compositions and housing types Residential groups

Family with children Family without children Elderly couples (>65 years) Elderly single (>65 years) Single (30 − 64 years) Housing types Detached Semi-detached Terraced Corner Apartment

Detached Semi−detached Corner Terraced Apartment 0 1000 2000 3000 4000 5000 Housing type A n n u a l el ect ri ci ty co n su m p ti on [ kW h] 0 0 1000 2000 3000 4000 5000 An n u al gas con su m p ti on [ m 3] Electrictiy Gas

Figure 2.5: Residential energy consumption for building types in the Netherlands (Source: AgentschapNL, 2010).

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<1905 1906−1929 1930−1944 1945−1959 1960−1970 1971−1980 1981−1990 1991−2000 >2001 0 1000 2000 3000 4000 5000 Year built An n u a l el ect ri ci ty co n su m p ti o n [kW h] 0 0 1000 2000 3000 4000 5000 A nn ua l g a s co ns um pt io n [m 3] Electrictiy Gas

Figure 2.6: Energy consumption of residential buildings according to year built [4].

FamWithKids FamNoKids OldCouple OldSingle Single Average 0 1000 2000 3000 4000 5000 Residential group An n u a l el ect ri ci ty co n su m p ti o n ( k W h) 0 0 1000 2000 3000 4000 5000 An n u al gas con su m p ti on [ m 3] Electrictiy Gas

Figure 2.7: Annual average electricity and gas consumption for residential groups in the Netherlands (Source: CBS, NL).

2.4 Residential loads categorization

Residential loads may be categorized based on multiple factors. Some studies broadly divide the loads into two categories with respect to energy management possibilities such as deferrable and non-deferrable loads, where the former refers to devices whose operation can be shifted to later times of the day, and the later implies those whose operation cannot be shifted. Other studies use flexible and non-flexible loads to virtually refer to the same category of loads [27]. The loads may also be divided along the ability to control the devices either locally or remotely via automatic actions, hence

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2.4. RESIDENTIAL LOADS CATEGORIZATION 17

the terms controllable and uncontrollable loads. Other categorization are based on appliances’ rated power consumption, dividing into heavy loads (>1000 W), normal loads (100 - 1000 W) and light loads(<100 W). However, the power consumption may vary between two devices of similar use but are from different manufacturers, making this method of categorization unsuitable. Device functionalities or activity groups are other ways of load categorization. This leads to groupings such as: heating, cold, kitchen, lighting, laundry, entertainment, etc. appliances. The different categorizations prove that residential loads are changing in composition, capacity and complexity. In this research, the loads are divided based on their modes of operation and their flexibility and are grouped under: inflexible, shiftable, thermal, and buffer loads. Flexibility is defined as the ability of devices to increase, decrease or postpone their power consumption or generation in time without impacting on the services they provide [28].

2.4.1 Inflexible loads

Inflexible loads refer to domestic appliances whose operation cannot be interrupted or shifted to later periods as this would have significant impact on the service they provide [28]. There are two categories of inflexible loads. There are appliances that are ”Always ON” or on ”Stand-by” throughout (most part of) the day. Examples are internet gateways, modems, telephones, sensors, and answering machines. The second group are appliances that must be in operation at the desired period and cannot (or have very limited potential to) be shifted. Personal computers, television, lighting, printers, and most kitchen appliances fall under this category. They are regarded as inflexible because they are incapable of adapting or changing their operations to meet circumstances without impacting on the service they provide.

2.4.2 Shiftable loads

They are defined as loads with fixed time periods of operational cycles and which are not time dependent [28]. Wet appliances such as washing machine, dishwasher and tumbler dryer are examples of shiftable loads. Their energy consumptions are determined by such factors as: frequency of operation, machine efficiency, selected program, load size and ambient conditions [5]. Due their relatively high power ratings, their aggregated impacts on the electricity network loading are significant. However, the shiftability potentials of these appliances depend on the behavior and needs of the users.

Washing machine

Washing machines are used for cleaning laundry and basically consist of a tub, rotating drum and a heating system. Modern washing machines are in two categories: top loading (vertically-rotating drum) and front loading (horizontally-rotating drum). The complete washing cycles involve immersion of laundry in sufficient amount of water, heating of the water to desired (preset) temperature (usually 30, 40, 60 or 90 degree

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0 15 30 45 60 75 90 105 120 0 500 1000 1500 2000 2500 3000 Time [mins] P ow er [W] 0 10 20 30 40 50 60 T em p er a tu re [ oC ] washing machine Power Temperature

(a) washing machine

0 15 30 45 60 75 90 105 120 0 500 1000 1500 2000 2500 3000 Time [mins] P ow er [W] Power Temperature 0 20 40 60 80 100 120 T em p er a tu re[ oC ] tumble dryer (b) tumble dryer 0 15 30 45 60 75 90 105 120 500 1000 1500 2000 2500 3000 Time [mins] P owe r [W ] dishwasher 15 30 45 60 75 90 T em p er at u re [ oC ] Power Temperature (c) dishwasher

Figure 2.8: Generalized power and temperature profiles for shiftable loads [5].

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2.4. RESIDENTIAL LOADS CATEGORIZATION 19

Celsius), rinse cycles enabled by the rotating drum and the spinning (rotating of the drum at high speed) to extract water from the laundry [5]. Most European washing machines are between 1800 W and 2500 W rated power with annual energy consumptions range from 129 kWh to 300 kWh. The general power demand and temperature profile is represented in Figure 2.8(a). The evolutions in the washing machine technology has resulted in new and energy-efficient devices equipped with start-time delay functions which allow customers to shift the starting time to any period of the day or night when conditions (e.g. prices, local generation) are favorable. The penetration level is about 95% for most Western European countries with the Netherlands having one of the highest ownership rates of approximately 98% [29]. Depending on the size of the family, washing machines are used averagely two to four times per week.

Tumble dryer

Drying laundry by conventional tumble dryers requires two to four times the energy needed to wash the same amount at 60oC [30]. There are two basic types of dryers:

condenser dryers which condense the humid air, collecting it as water, and ventilation (or evacuation) driers, which channel the humid air outdoors. The highest ownership rates are in Belgium, Denmark and Norway with 63%, 62% and 53% respectively [5]. The average penetration level in private homes in the Netherlands is estimated to be 35% [5]. Typical power ratings are from 2000 - 2500 W with an average energy consumption of 1.40 - 2.50 kWh per cycle. The general power demand and temperature curve is represented in Figure 2.8(b). Dryers are not so often used as their washing machine counter-parts on a yearly basis. They are mostly used in winter and spring periods with an average of two to three times per week. Laundry drying process in most cases directly follows the washing process, hence start-delay functions in dryers are hardly used.

Dishwasher

Dishwashers are mechanical devices for cleaning plates, cups, utensils. The standard built-in is the most popular type of dishwasher, mostly installed under kitchen cabinet and connects directly to the household plumbing. The penetration level differs significantly among European countries with a reported average of 42% [5]. The washing has three stages: high temperature washing, rinsing, and drying. The general power demand and temperature curve is represented in Figure 2.8(c). The energy consumptions per cycle are between 0.9 - 2.0 kWh depending on the selected program. Large part of the energy is used to heat up the water to the desired temperature and the dry the dishes. New dishwashers have incorporated time delay functions to start or end the washing process at predefined time. Dishwashers make less noise during operation, hence they have one of the highest potential to be shifted to any time of the day.

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