Exploring energy neutral development for Brainport Eindhoven
Citation for published version (APA):Han, Q., Schaefer, W. F., & Blokhuis, E. G. J. (Eds.) (2010). Exploring energy neutral development for Brainport Eindhoven: part 1, TU/e 2010. Technische Universiteit Eindhoven.
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EXPLORING ENERGY NEUTRAL DEVELOPMENT
FOR BRAINPORT EINDHOVEN
part 1
TU/e
2010
Edited by
Dr. Qi Han
Prof. dr. ir. Wim Schaefer
Dr. Erik Blokhuis
Contact Detail: Qi Han Postbus 513 VRT 8.12 5600 MB Eindhoven Tel: +31 (0) 40 247 5403 Fax: +31 (0) 40 243 8488 E-mail: q.han@tue.nl Copyright © 2010 CME@TU/e
Eindhoven University of Technology, the Netherlands
Group of Construction Management and Engineering @TU/e
Printed by
Eindhoven University of Technology Press Facilities
1
Contents
INTRODUCTION
Prof. dr. ir. Wim Schaefer
3
THE REBOUND EFFECT: A Research For Indications Of The Rebound Effect On The Dutch Housing Market
Richard Adrians
7
SMART GRIDS IN EINDHOVEN: An Explanation
Bart Brouwers
17
ADVICE NEW LOW-ENERGY DWELLINGS
Ronald Hennekeij
27
INSTRUMENTS FOR SUCCESSFUL ENERGY NEUTRAL HOUSING DEVELOPMENTS: Lessons For Eindhoven From The Danish And UK Municipalities
Sahul reddy Kadarpeta
37
INCREASING NEW VENTURE PROTENTIAL: Design And Validation Of A Format Business Plan To Increase Start-Ups Success Rates
Chanakya Kalyanapu and Giel-Jan Bogaert
47
GREENING OF GRAY BUILDINGS: Research Into The Chances Of Making Vacant Offices Sustainable In Eindhoven
Martin Kraaij
57
A SUSTAINABLE HOUSING STOCK BETWEEN DREAM AND REALITY
Sophie Nauwelaerts de Agé
67
URGING RESIDENTS IN EINDHOVEN TO SAVE ENERGY
Ingrid Nieuwenhuijsen
77
DESIGN OF AN AFFORDABLE AND SUSTAINABLE HOUSE CONCEPT FOR THE NETHERLANDS
Filique Nijenmanting and Mehmet Senel
87
COMPREHENSIVE MODELLING OF ENERGY USE IN HOUSEHOLDS: An Agent Based Case Study On Potential Behavioral And Technical Measures Towards An Energy Neutral Urban Environment
Cyrille Pennavaire
97
THE INDUSTRIAL CONTRIBUTION TOWARDS AN ENERGY NEUTRAL ENVIRONMENT: An Investigation On How To Initiate A Transition
Ruud Schoenmakers
107
APPENDIX: KENWIB – Report Study trip Denmark, March 2010
2 Preface
3 INTRODUCTION
Organization and Partners
The ‘Kenniscluster Energie Neutraal Wonen in Brainport’ is a project which is based upon a cooperation between governmental organizations, university and entrepreneurial companies. The partners for this project are: the Municipality of Eindhoven, the Province of Noord Brabant, the Promotie Installatie Techniek and the Eindhoven University of Technology. The cooperation is established and financially supported for a period of two years. The project started in September 2009 and the final evaluation of the project is planned in February 2012. The aim of this project is to stimulate the realization of energy neutral urban districts by joint interdisciplinary development and dissemination of knowledge. Therefore, during a period of two years, several groups of master graduation students will be directed to execute dedicated investigations and research on combined technical, organizational and socio-technical subjects. Also students will be stimulated to start up new entrepreneurial activities which can contribute to implement technology for energy neutral urban districts developments. Each of the graduation projects is also guided by a dedicated company or institution giving input from practical and societal points of view. This publication contains the summaries of the first set of graduation studies, which were realized in the period from February until September 2010.
The KENWIB project is organized in two sets of activities: the ‘Brainport Energy Research-Lab’ for knowledge development, containing the graduation projects, and the ‘Brainport Energy Academy’ for Knowledge dissemination. Furthermore, as one of the ‘research lab’ activities, a short excursion was organized to Denmark in March, in order to understand the details of realized low energy district heating concepts. The summary of the lessons learned from this joined short journey is presented in the appendix of this publication.
Vision and mission
The climate is changing, that is no surprise. The climate on Earth is not constant. Cosmic influences, such as the tilt of the axis around which the Earth rotates, the periodically changes of the orbit around the sun and the wandering of our solar system in the galaxy provide varying climatic conditions, in which human beings are at the most a modest spectator. Actually very little is known about how things like orbit features and cosmic characteristics influence the climate on Earth. Geologists and climatologists explain how in the recent millions years climates have changed, from extreme cold to tropical heat. Moreover, the sea level has come up and again went down for more than 100 meters in height. Based on climate reconstructions in the past ten thousand years and more, scientists worked out and developed the prediction for the next Ice Age. After a relatively short geological period of hundreds or thousands of years in the future, the temperature will drop significantly on Earth and it will run into the next Ice Age.
Climatic changes appearing in our modern time can also be the result of human activities. This phenomenon of human behavior interacting with the climate on Earth is still in debate. There is no precise knowledge about it. However, overall, we can perceive a complex climate change: an increasing dryness of countries around the equator, a rise in the number of hurricanes in Central America, and the expected heightening of the sea level, which have severe consequences for humans.
4
When looking closer to the ancient landscape maps of our country of the past hundred years, we might recognize that in the coming decades and hundreds of years, the Netherlands will transform since it is a country close to sea. The Netherlands should take steps to protect urban areas and infrastructure in order to adapt to these changes. Every city, or if you prefer, each urban area, should take specific measures depending on its features: a polder, a riverside or an urban areas on the high sandy soils.
Parallel to the ongoing climate discussions, the need for the establishment of a sustainable economy becomes emphatically recognizable. A world wide economic model based upon growing consumption is questioned. To that end, debates are constantly conducted in different sectors of society, business circles and public institutions such as schools and universities. The topics include issues such as recycling of materials, use of sustainable energy and sustainable water use. The importance of this development is significant, and perhaps also links to us personally. We know that the major international conflicts, evoking terrible acts of violence, which we can observe every day, are related with the availability and distribution of raw materials and energy stocks. The establishment of a regional, national sustainable economy, which is in substance no more or no less dependent on consumptive use of raw materials and fossil fuels, will directly contribute to achieving global peace and security situations.
Results
During the period of February until September 2010, thirteen Master of Science graduation students worked on research assignments, relevant for development of energy neutral urban districts. The different research projects cover a broad variety of subjects. It is interesting to observe, that the mission of developing an energy neutral city triggers both technical engineering research as well as socio-technical and technical-organizational subjects.
Most of the students worked individually and some of them worked in teams. Each of the projects was guided by a team of science oriented and practice oriented specialists. Together they have produced eleven different graduation reports. The summaries of their studies are brought together in this book. The general meaning of the presented studies is that they introduce and analyze ideas and concepts that are relevant for developing energy neutral districts.
The results of these studies will help to structure discussions and policy making activities. The scientific value of this book is limited to each of the contexts of the individual studies. Graduates have not only developed knowledge and understanding of the theme, but they may also have the personal meaning of 'ambassadors'. This new group of young engineers has, now with internationally recognized academic qualifications, a personal mandate to realize this mission of sustainable development.
Appendix: KENWIB – Report Study trip Denmark, March 2010
7 THE REBOUND EFFECT
A research for indications of the rebound effect on the Dutch housing market Author: ing. R.F.E. Adrians
Graduation program:
Real Estate Management & Development 2007-2010 Graduation committee: Dr. W.J.M. Heijs Dr. J.J.A.M. Smeets Date of graduation: 14-10-2010 ABSTRACT
This project provides insight into indications of rebound effects on the Dutch housing market. A categorization has been made of behavioural actions and purchases that can indicate direct respectively indirect rebound effects. The results show that it is likely that rebound effects are present among occupants that moved to low energy dwellings. The presence of indirect rebound effects seems to be less evident than direct rebound effects.
Keywords: Dutch housing market, direct and indirect rebound effect, categorization, behavioural actions and purchases.
INTRODUCTION
It is clear that it is necessary to decrease the emission of carbon dioxide but one of the problems is that the future demand for energy will increase (i.e. through the increase in wealth in China). To create a better environment and to meet the future energy demand the so-called ‘Trias Energetica’ model has been designed (Agentschap NL, 2010). This model describes three important steps (see figure 1).
8 Figure 1: Model of ‘Trias Energetica’.
Step 2 and 3, the use of sustainable sources instead of fossil fuels and using the fossil energy as efficiently as possible are necessary, but reducing the energy demand (step 1) is probably the most important because the planet won’t be able to cope with the energy demand if this will continue to increase so rapidly.
One of the ways to reduce the energy demand is by making more energy efficient products and making the production process more energy efficient. Energy efficiency may lower the use of energy. However, William Stanley Jevons (Jevons, 1866) showed that energy efficiency can also lead to an increase in energy consumption because of the lower production costs. Expected savings are (partially) taken back by higher energy consumption so that the real savings are lower. This is the so-called ‘rebound effect’ or ‘take-back effect’. It is possible to find indications for this effect by examining behavioural actions and purchases of consumers. GOAL AND PROBLEM
Goal
The goal of this research is as follow:
‘To gain insight in possible rebound effects among residents due to a lower energy bill and to suggest possible measures to reduce this effect’
Problem
The corresponding problem of this research is as follow:
‘Which rebound effects occur among residents due to a lower energy bill and which measures can be taken to reduce this effect?’
9 MODEL AND RESEARCH QUESTIONS
Figure 2: Model
The model illustrates the primary en secondary research questions:
• What is the rebound effect and what consequences does it have for society? o What is the rebound effect?
o What is the expected size of rebound effects?
o Which measures can be taken to reduce the rebound effects?
• What is the connection between the disposable income and rebound effects?
• Which control variables have to be taken into account to find indications of possible rebound effects (e.g. the role of household size, motives to save energy, and dwelling situations)?
• What is the relation between lower energy costs and the presence of rebound effects? Control variables Disposable income Rebound effect Difference in costs energy + Difference in household size VI Energy efficiency previous situation Living period previous dwelling Difference in type of dwelling Incentive to move Living situation
10 LITERARY STUDY
Reducing the energy demand in relation to consumer behaviour
There a different ways to reduce energy demand. Technologic developments and innovation are very important (Berkhout, Muskens & Velthuijsen, 2000) but economic and political measures are also needed. In addition, a change in consumer behaviour will be necessary. Technological development and innovation
Renewable energy sources are one of the technological developments that can reduce the energy demand of fossil fuels. Biomass, wind and solar energy will become important in the future, with solar energy having the largest potential. A transition of fossil fuels to renewable sources should be possible before 2050 (Uyterlinde, Ybema, Brink, Rösler & Blom, 2007; Greenpeace, 2007). It is important that the transition of fossil fuels to renewable sources soon takes place because fossil fuels are running out.
Another technological development that can reduce the demand for energy is by making the production process and the products more energy efficient (Madlener & Alcott, 2008). This should lead to energy savings, but only when the demand remains constant. If the demand increases potential energy savings due to the energy efficiency will not be effective.
Economic and political measures
Another way to reduce the demand for energy is the use of economic and political measures. People tend to use or buy less when it becomes more expensive. In the Netherlands prices of energy have increased rapidly the last years and it is expected that they will continue to do so. Besides taxes it is also possible to subsidize certain actions or measures that consumers can initiate to reduce the demand for energy.
Consumer behaviour
Research has shown that there is a large variation in the energy use of households of similar dwellings due to the lifestyle of the occupants (Melasniemi-Uutela, 1992). So the human factor will always play a role in realizing a better environment. Research has also shown that consumer behaviour in the Netherlands has not changed very much during the last years. The intention is there but there is a lack of will power (Fygi, 2007)
Energy efficiency could save money (Herring & Roy, 2007). These savings may then be used to buy products and/or services that are more energy intensive or may lead to behavioural changes which will lead to an increase in the energy use (Takase, Kondo & Washizu, 2005). In the literature this is called the ‘rebound effect’ or ‘take back effect’.
11 Types and indicators of rebound effects
Usually a rebound effect is measured as a percentage of the ratio between the expected and real savings. If the rebound effect is 100% or larger then it is called ‘backfire’ (Madlener & Alcott, 2008).
A distinction can be made between:
• Direct rebound effects: These effects are the increase in the use of a product or service duet to the desire of consumers to use more of a product or service when it becomes cheaper;
• Indirect rebound effects: These effects are an increase of the purchase of goods and services due to an increase in the disposable income;
• Economy wide effects: These effects are the consequence of the direct and indirect rebound effects that can lead to changes in multiple sectors of the economy (these are excluded in this research).
Direct rebound effects are indicated by behavioural actions of consumers such as driving more kilometres and heating more rooms simultaneously. The presence of indirect rebound effects can be demonstrated by looking at the purchases of goods and services that can lead to a possible increase in the energy use (i.e. the purchase of a waterbed or a dryer.
Expected size of rebound effects
The opinions about the size of rebound effects vary widely. Evidence for direct rebound effects can be found through evaluation, econometric and sector specific studies. Evaluation studies are confined to personal transport, heating and cooling because relevant data in other sectors are often unavailable. According to these studies the size of direct rebound effects is estimated to be between 0 – 30%. The estimates in econometric studies on personal transport by car vary from 20 – 35% for the short term to 80 – 85% for the longer term. Sector specific studies estimate a direct rebound effect between 0 – 50% for heating, cooling, lighting and transport but these studies are largely confined to the United States. Because this research meets with a number of methodological difficulties the results are not always reliable.
Evidence for indirect rebound effects is even scarcer because of the amount of research that has been done. Therefore it is not possible to do statements about the size of indirect rebound effects.
12 RESEARCH DESIGN
For accurate statements about the presence and size of rebound effects a longitudinal research would be required, but because of the timeframe this was not possible. However, it is possible to examine possible indications for the presence of rebound effects. To find these indications, situations can be studied that involve a potentially high decrease of energy costs, e.g. after a move from a normal house to an energy efficient dwelling. Differences in consumer behaviour or purchases that may indicate rebound effects should be most obvious in this situation.
Therefore a sample was taken from the population of occupants of low energy dwellings in the Netherlands. These households were asked to take part in a survey which could be filled in on paper or on the internet.
Participants were households of energy low dwellings of ‘In Goede aarde’ in Boxtel and in the district ‘Schoenmakershoek’ in Etten-Leur. The total households approached were 450, of which 90 of the project at Boxtel and 360 of the district at Etten-Leur. The response was 12% (54 households). This fairly low response rate is due to the summer holidays but the planning did not allow a different time schedule.
RESULTS
Usability of variables
Some of the variables proved to be unsuitable for further analysis. Questions about income and energy costs seemed to have been interpreted in different ways and the size of energy bills or the amounts of energy used were unreliable. Some households switched energy providers, most households were no longer connected to the gas network, and in all cases a heat pump that constantly using electricity.
After removing cases that were unfit in view of the control variables (e.g. an increase in household size or a former dwelling situation that was also energy efficient so that the difference with the new situation was too small) 21 out of 54 cases remain for further analyses.
Direct rebound effects
To show the presence of direct rebound effects behavioural actions of occupants of low energy dwellings are used as indicator.
The results show that 3 of the top 5 behavioural actions that lead to an increase in the energy use concern heating (see table 1). These are ‘living room temperature during the night in the winter’, ‘how often the heating is turned down in case of absence*’ and ‘number of rooms that are heated simultaneously’. In some cases this increase can be attributed to the system itself (e.g. some systems and dwellings make it impossible to differentiate the temperatures of separate rooms).
When comparing the number of actions per household that are potentially decreasing energy use to those that are potentially increasing that use, it is shown that the presence of
13
direct rebound effects is likely (21 out of 29 actions indicate a rise).
In a classification of all behavioural actions per household most households indicate 1 – 5 behavioural actions that lead to an increase in the energy use.
Behavioural actions Lower The same Higher N.a.
1. Living room temperature during daytime in the winter 2 14 5 0 2. Living room temperature during the night in the winter 1 5 15 0 3. Temperature at which the laundry is done in the washing
machine 1 20 0 0
4. Temperature of the waterbed 1 0 0 19
Shorter The same Longer N.a.
5. Period of taking a shower 0 17 4 0 6. Period that the lighting is on 1 20 0 0 7. Period that there is lighting on in case of absence 4 17 0 0 8. Period that the TV is on 2 14 5 0 9. Period that electrical appliances are on standby 2 16 3 0 10. Period that there is special lighting on for safety 2 10 5 4 11. Period that the lighting of the garden is on 3 6 6 6 12. Period that the computer is used 2 9 10 0 13. Period that the air conditioning is on 0 0 1 20
Less often The same
More
often N.a.
14. How often the shower is used 0 17 4 0 15. How often there is cooked by the occupants 1 19 1 0 16. How often the coffee machine is used 2 14 5 0 17. How often the washing machine is used 2 15 4 0 18. How often the heating is turned down in case of
absence* 10 7 1 0
19. How often the occupants take a bath 2 10 4 5 20. How often the microwave is used 2 15 4 0 21. How often the dish washer is used 2 13 6 0 22. How often the dryer is used 3 10 3 5 23. How often the DVD-player is used 3 16 2 0
Less The same More N.a.
24. Number of rooms that are heated simultaneously 3 6 12 0 25. Number of used lighting points 2 12 7 0 26. Number of energy bulbs 1 4 13 3 27. Number of holiday flights that are being made 6 9 1 5 28. Number of car trips that are made for recreation 1 17 3 0
Less
further The same Further N.a.
29. Distance car trips to work 4 7 7 3
* To indicate for direct rebound effects the second column ’Less often’ has to be used.
14 Indirect rebound effects
To find indications for the presence of indirect rebound effects purchases that can lead to an increase in the energy use were used as indicators. The top 3 consists of the purchase of a separate freezer/cabinet and lighting for the garden (see table 2). The purchase of a LCD/LED or plasma TV is not very indicative because this can be explained by the fact that these are popular at the moment.
For the indirect rebound effects a classification of households shows that most had two or more purchases. These results indicate that the presence of indirect rebound effects is also likely.
Purchase Not present in previous situation,
present in current situation One or more extra
Has been replaced 1. LCD/LED/plasma TV 9 1 1 2. Surround system 1 0 0 3. Game console 4 0 1 4. Microwave 2 0 13 5. Dryer or wash/dry combination 0 0 8 5. Separate freezer/cabinet 7 0 0 6. Mobile airco 1 0 0
7. Lighting for the garden 5 0 7
8. Car 0 0 5
Table 2: Overview results by purchases
MEASURES TO REDUCE THE REBOUND EFFECT
A first and necessary condition to reduce the rebound effect is that people should become aware of the problem because most people don’t know what the effect is. Governments should take the initiative. People also get too little feedback on their energy use. It has already been established that electronic feedback can lead to energy savings.
It is also possible to tax the gains that are obtained due to improvements in energy efficiency. This money can then be invested in sustainable development and the use of renewable sources. Another way is to tax only when there is more energy used than is been determined in advance. But these tax measures could be problematic because they in fact seem to ‘punish’ good behaviour (savig energy).
Furthermore, people could be stimulated to only buy energy efficient products from money that is obtained as a result of energy savings. However, in reality this is difficult to realize and should also mean that the privacy of consumers will be harmed. In summary, this problem is very hard to counteract and it will need much study and further discussion.
15 CONCLUSIONS AND RECOMMENDATIONS
The goal of the research was to find indications for the presence of rebound effects and to formulate possible measures to reduce this effect. The available data show that the presence of direct rebound effects among occupants of low energy dwellings is likely. Statements on the size of the effects are not possible because of the design (a longitudinal design would have been necessary but this was impossible to realize, and some of the variables proved to be unreliable).
Most of the behavioural actions that can lead to an increase in the energy use concern heating behaviour. Indirect rebound effects are also indicated.
In case of a follow-up study a longitudinal design and concrete data about the disposable income, the difference in energy costs and the difference in the use of gas and electricity are required. Long-term cooperation of the occupants will be necessary.
In future research a larger sample and different populations may be needed (e.g. also dwellings in which the energy use is not influenced by heat pumps). Another point of attention is that the sample consisted out of home owners or tenants in the private sector that are characterized by middle or high incomes. Research in the social sector and people with lower incomes is also needed.
Finally, if rebound effects are present and their size is relevant, measures need to be taken. Although there are some suggestions for measures that can reduce rebound effects, they all have disadvantages. Most important is that measures have to ensure that money saved on energy does not lead to an increase in the energy use generated by fossil fuels.
REFERENCES
• Agentschap NL (2010). De effectiviteit van elektronische feedback op het energie- en waterverbruik. Publicatienummer 2KPGA1005. Geraadpleegd op 12 september 2010 http://www.senternovem.nl/mmfiles/Factsheet%20De%20effectiviteit%20van%20el ektronische%20feedback%20op%20energie-%20en%20waterverbruik_tcm24-289731.pdf
• Berkhout, P.H.G., Muskens, J.C., Velthuijsen, J.W. (2000). Defining the rebound effect. Energy Policy, 28, 425-432.
• Dimitropoulos, J., Sorrell, S. 2006. The rebound effect: micro-economic definitions, limitations and extensions. UKERC Working Papers, SPRU, University of Sussex. • Frondel, M. (2004). Empirical assessment of energy price policies: the case for cross
price
elasticities. Energy Policy, 32, 989-1000.
• Fygi, K. (2007). ‘Verleid consument om milieubewuster te gaan leven’. Geraadpleegd op 9 mei 2010
http://www.molblog.nl/bericht/Verleid-consument-om-milieubewuster-te-gaan-leven/
• Jevons, W.S. (1866). The Coal Question: An inquiry concerning the progress of the nation, and the probable exhaustion of our coal-mines. London, England: Macmillan and Co. First published 1865.
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• Herring, H., Roy, R. (2007). Technological innovation, energy efficient design and the rebound effect. Technovation, 27, 194-203.
• Greenpeace (2007). Greenpeace en de Europese Raad Voor Hernieuwbare Energie formuleren antwoorden op de klimaatverandering. Geraadpleegd op 15 maart 2010 http://www.greenpeace.org/belgium/nl/press/releases/energy_revolution
• Madlener, R., Alcott, B. (2008). Energy rebound and economic growth: A review of the main issues and research needs. Energy, 34, 370-376.
• Melasniemi-Uutela, H. (1992): Energy use in single-family households in a cold climate. Proceedings of the American Council for an Energy Efficient Economy 1992 Summer Study on Energy Efficiency in Buildings. Washington DC: American Council for an Energy Efficient Economy.
• Sorrel, S. (2007). The Rebound Effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency (ISBN 1-903144-0-35).
• Takase, K., Kondo, Y., Washizu, A. (2005). An analysis of sustainable consumption by the waste input-output model. Journal of Industrial Ecology, 9 (1-2), 201-219. • Uyterlinde, M.A., Ybema, J.R., Brink, R.W. van den, Rösler, H., Blom, F.J. (2007). De
belofte van een duurzame Europese energiehuishouding; Energievisie van ECN en NRG (ECN-E--07-061). Geraadpleegd op 14 maart 2010
http://www.ecn.nl/publicaties/default.aspx?nr=ECN-E-07-061
ing. Richard Adrians
After completing my study of architecture I noticed that I had a bigger affection for occupants and their behaviour on the housing market than for designing. For this reason I decided to follow the master course of Real Estate Management &
Development. In the future there are many great challenges to make the housing stock more sustainable and I hope to make a contribution to the solution of the problems that are involved. During my years at the university I was also involved with SerVicE, the study association of real estate.
Short Curriculum Vitae
2003 – 2007 Bouwkunde, differentiatie architectuur, Hogeschool Zuyd 2007 – 2010 Master Real Estate Management & Development, TU Eindhoven 2009 – Now Salesman at IKEA Eindhoven
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SMART GRIDS IN EINDHOVEN:
AN EXPLORATIONAuthor: B.W.Brouwers Graduation program:
Construction Management and Urban Development 2009-2010 Graduation committee:
prof. dr. ir. W.F. Schaefer (TU/e) dr. ir. E.G.J. Blokhuis (TU/e)
ir. E.W.F.M. van der Putten (Endinet) drs. V. van Hoegaerden (Endinet) Date of graduation:
26-08-2010 ABSTRACT
The energy sector is undergoing some fundamental changes. The implications of smart grid technology for electricity grids are as yet unknown. This paper presents the results of a graduation research project exploring developments in smart grids carried out at Endinet. A general exploration constitutes the first phase of the project. It entails a broad study of all aspects of smart grids. Based on the insights gained during this exploration, possible implications for the expected future development of the Eindhoven electricity demand are researched. This is done through the development of a realistic worst-case scenario for electricity demand for Eindhoven in 2040. Using the results from this scenario an estimate of the investments needed in the network to maintain operational quality at the current level under the 2040 scenario loads is drawn up.
Keywords: Smart grid, scenario, electricity use, electricity peak load, required investment INTRODUCTION
Both Endinet and the KENWIB are in the early stages of exploring smart grids. Since the field is broad and the technologies and market developments involved in smart grids are multiple it is important to explore the scope of developments and identify which might be relevant for Eindhoven. For Endinet it is relevant to create an understanding of the implications these developments might have in terms of investments and network operation.
It is to be expected that the energy sector will undergo some fundamental changes in the near future. These might include electric cars, heat distribution networks, decentralised generators, sustainable intermittent generators and ‘smart’ technology. International agreements on sustainability as well as economic and political considerations will help these changes to come about. Network operators like Endinet are at the core of these changes. What these changes entail, what they could mean for Eindhoven and the implications it could have for Endinet are as yet unknown.
Goals
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Eindhoven. The ability to consider the entire lifespan of components will allow for more efficient and more economical and sustainable choices. The developments in electricity network technology are multiple and there is as yet little knowledge about their potential relevance to the Eindhoven region. The project serves to broaden the planning horizon of Endinet’s asset management by generating insight into future requirements. This will allow for more economical and sustainable investment decisions.
The second goal is to explore the developments in the field of smart grids and generate insight into the potential consequences for stakeholders in Eindhoven, in order to initiate and stimulate an open multilateral discussion about developing the future energy system of Eindhoven. It would be beneficial to the process of achieving ‘Eindhoven energy neutral by 2045’ if there were to be a societal discussion about the future of energy supply and use in Eindhoven. This discussion should focus on achieving an integrated sustainable energy strategy. Endinet wishes to contribute to this discussion by supplying its knowledge and experience in operating energy networks. In this role it is paramount that there is a familiarity with current vision and policy, the associated scenarios for future use of energy systems and developments in all aspects of the field of ‘smart grids’.
Research questions
Research question 1: What aspects and developments of energy networks are described by the term ‘smart grids’, what are the current developments in this field and what future developments might be expected?
Research question 2: What would be a worst case scenario for the electricity consumption and expected load for Eindhoven’s electricity distribution grid by the year 2040?
Research question 3: In a worst case scenario, what will be the investment needed to meet the requirements for Eindhoven’s electricity distribution grid by means of conventional technologies and methods?
Project design
The first stage of this project comprises an exploratory study of smart grids and new developments in this field. The current status of smart grid development is explored. In order to predict the transition path towards smart grids and the current sector, its stakeholders, organization and technical status are described and subsequently analyzed using Multi Level Perspective (MLP) as a framework. The main level of MLP is the level of socio-technical regimes. This level includes the current state of technologies and social ‘rules’ that form a dynamically stable situation. At this level it is not possible to explain radical change to regimes. In order to explain this the ‘niche’ and ‘landscape’ levels were introduced. The Niche level is a micro level in which radical new technologies emerge. The socio technical landscape form the surroundings in which a socio-technical regime and a number of niches might exist. Pressure from certain landscape changes can make decisions on regime level easier or more difficult. Though niche level developments are a prerequisite for transitions of the regime they are not always a sufficient condition to see the transition through. The best chance for a transition to take place is when niche developments coincide with landscape pressures pushing the regime. (Geels, 2002)
The second part of this study will investigate the future challenges faced by networks and their consequences when faced without the aid of smart technology. Based on the results
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and insights gained in the exploratory study a realistic worst-case scenario for Eindhoven is developed. The final part of this project results in an estimation of the investments needed in the electricity grid in Eindhoven to cope with the scenario loads. This estimation is generated using indicator numbers on network redundancy and component prices. There are two factors that are important to future grid development. These are peak loads and decentralised generation capacity. Given the importance of peak capacity to dimensioning the grid this scenario will focus predicting the future load profile.
EXPLORATION OF THE TERM SMART GRIDS
By studying literature and interviews with people active in the electricity sector an overview of smart grid developments and the current status of the electricity sector is created. This overview includes current stakeholders, regulations, technical aspects, infrastructure and infrastructure at the socio- technical regime level, pressures on regime from the landscape level and a description of niche developments. This overview is used to identify the most likely transition pathway and the characteristics of the socio-technical regime in its end state.
Definitions of smart grids
There are a lot of interpretations of the term smart grid. There is no consensus about the exact definition of a smart grid. To be able to understand the different meanings and definitions it is important to consider who is using the term and in which context it is being used. There are three types of definitions that are being used to define a ‘smart grid’. Problem based definitions are based on the textual context in which the term is being used. Standard definitions are objective, unbiased and generally applicable definition. The third and final type of definition are based on a time and location specific context resulting in a demarcating definition that defines the smart grid as it is applicable to that specific context. The definition proposed by the European Regulators’ Group for Electricity and Gas (ERGEG) is the most defined definition that is generally applicable for the Dutch context.
“Smart Grid is an electricity network that can cost efficiently integrate the behaviour and actions of all users connected to it – generators, consumers and those that do both – in order to ensure economically efficient, sustainable power system with low losses and high levels of quality and security of supply and safety.” (ERGEG, 2009) The definition reflects the level of demarcation that applies to the term given the current state of development in the Netherlands. At this time this demarcation level is also suitable for Eindhoven since the future of the Eindhoven energy systems has not been further defined than the national systems have.
Functionalities attributed to smart grids
Even though there is no consensus about what a smart grid should exactly be, there is a number of functionalities that are generally attributed to smart power grids. There is an almost endless list of possibilities and the goals set will eventually determine which functionalities will be realized. Most stakeholders directly involved with networks, including ERGEG and NBNL, consider smart grids solutions to apply measures that allow for increased intelligence for planning, operating and maintaining networks. Even though these three tasks in a network life span are very different the smartness always starts with information. Nearly every functionality described as part of a smart grid starts with having more accurate, often
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real time information about network and component performance. The smartness of the network then lies in utilizing the available information to a certain goal.
Smart metering is a development that is often associated with smart grids. Often confused with smart grids it is important to realize that smart metering in itself does not constitute a smart grid. Only when the information provided by smart meters is used to provide functionalities like demand management/powermatching will the grid become a smart grid.Error! Reference source not found.
Transition pathway towards smart grids
At first glance the electricity sector is in a suitable state for technological transition to take place. There is tension and dynamism in the regime, there are strong and concrete landscape pressures and a number of niche developments have taken place. Public infrastructural regimes are by nature of great stability and possess great inherent resistance to change. This resistance is mainly caused by the high economic value of current infrastructure and conservative regulation. There is however a lot of tension and dynamism in the current regime due to recent unbundling and privatization.
On the landscape level there are forces pushing for change in the energy sector. The social and political willingness to achieve energy transition is there though it hasn’t been translated to actual commitment yet. Both the dependency on a limited number of countries with often politically unstable or inconvenient regimes as the need to improve environmental sustainability are strong factors empowering the drive for energy transition. In the niche level several technological and market developments have spawned and have proven to be ready for penetrating the current socio-technical regime.
Until a structured societal approach to energy is adopted, the development of energy networks remains reactive and a pro-active development of ‘smart’ networks will be near impossible. The government is looking at the free market to take up the challenge to provide energy transition trough innovation and development, keeping those parts of the sector in public ownership that are critical to ensuring quality and safety of energy supply. This approach presents two problems to the development of smart grids.
Figure 1: Current situation energy sector; Alternative suited for achieving sustainability Primarily the unbundling isolates innovations in either generation or consumption making system integration more difficult. This prevents the sector from being totally reformed, limiting developments to adjustments and localized innovation within the existing system. Secondly the regulation, and the interests it protects, restricts the ability of the networks to pro-actively participate in innovations. Current regulation promotes passive network
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operators that reactively facilitate the market. The regulators focus on the existing interests and lack of motivation to incorporate other public interests in regulation makes it very unlikely network operators will instigate change without public consensus.
Based on the current state of all three MLP levels it seems a Hybrid grid is the most likely future for the Dutch electricity network. This hybrid grid will include both central and decentralized generation and will utilize smart technologies to let both types of generation as well as active demand to contribute to balancing supply and demand. This hybrid grid type will not require major rebuilds of the existing electricity networks but can be realized by ‘smart’ replacements during routine maintenance and adding of smart components like smart meters. The transition process associated with hybrid grids will allow the current stakeholders to remain the key players.
REALISTIC WORST CASE SCENARIO FOR EINDHOVEN’S ELECTRICITY GRID
The goal of the scenario is to generate an idea of what maximum investment could lie ahead for Endinet. The scenario developed should be a worst-case scenario seen from the perspective of the electricity grid in Eindhoven.
For Eindhoven, being an urban environment, it is expected that peak loads in the network constitute the main problem. (Laborelec, 2009) The availability of small scale DG in urban areas on a LV level is expected to stay a niche as long as an individualist approach to reshaping the energy system is chosen. It was therefore decided to develop a scenario in which all changes in the socio-technical regime are adversely influencing the worst case aspect ‘maximum peak loads’ on the network. The socio-economic framework for the scenario is based on CPB’s Global Economy scenario since this presents the economic growth and individual orientation provide the highest potential for peak loads.
Figure 2: Annual electricity use Eindhoven 2009 (kWh)- (household/services ; industry)
In projecting the future peak loads two components are separately determined. The basic electricity use is determined based on the current electricity use, EDSN standard load profiles for different connection capacities and an extrapolation factor. The current representative electricity use per connection is determined through analysing the invoicing data from Endinet from between 2004 and 2010. The results from this analysis are shown in figure 2. The extrapolation factor was based on projections from ECN and Telos that were also based on CPB’s Global Economy scenario.
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New technologies have their specific characteristics and subsequent consequences for the total energy use, electricity use, load profiles and reactive power components. When technologies are introduced on a large scale, changing the characteristics of the current socio-technical regime these specific characteristics will have consequences for the capacity and design of the energy distribution networks. From all new developments the developments were chosen that meet two criteria, they should fit the individualistic nature of the Global Economy scenario and they should adversely affect the maximum electricity load on the network. The technologies that were identified to meet these criteria were Electrical transport and Heat pumps. (figure 3)
Figure 3: Technologies relevant to electricity peak load-worst case scenario
Table 1 shows the resulting values for the different elements and total worst case scenario developed.
Domestic
Domestic
double tariff Services
Domestic
double tariff Industry Nr. of connections 91,713 8,664 2,736 906 1,289 Extrapolation factor 1.42 1.42 1.68 1.68 1.62 Extrapolated peak load kW 1.12 1.77 7.09 13.11 121.54 Electrical transport kW 0.25 0.25 0.25 0.25 Heat pumps kW 4.55 4.55 Total scenario loads per connection category:
Average peak load kW 5.92 6.57 7.34 13.36 121.54 Total peak load kW 542,835 56,934 20,069 12,105 156,054 Table 1: Peak loads scenario 2040 per connection type
Total peak load Eindhoven 2010: 199 MW Total peak load worst case scenario Eindhoven 2040: 788 MW PROJECTED TRANSITION INVESTMENT
To estimate the investments for facilitating the projected peak demand through traditional means four aspects are important. These are the current network capacity, network load compared to maximum capacity, the scenario peak loads determined in the previous section and the price of network components at all network levels.
At 7 levels in the electricity network the number of components and their average capacity is determined. At each level the ‘n-1’ redundancy criterion is used in the Eindhoven network.
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This means that at all levels and at all times a critical component may fail without the network capacity dropping beneath the peak requirements. In this calculation this is accounted for by reducing the real number of components by the number of cables, sections or cable routes at each level. The capacity was calculated using the following formula: P(n-1) = N (n-1) * U (V) * I (Amp) * √3 *Cos φ = max. Capacity (MW)
In which: Nn-1 = Number of components (n-1); U = Voltage level (MV= 10 kV; LV= 400V);
I = average component capacity (Amp); Cos φ = factor representing the difference between real (energy used) and apparent (energy transported) power
For the extrapolated basic use and EV it is assumed that consumer behavior stays the same as it is in 2009. This means that the spread in usages at peak time and the concurrency of these loads remains the same. With these conditions remaining the same it is wise to maintain the same redundancy, and thus utilization percentage, to be sure that the network can cope with local variances. Utilization percentages range from 12% (LV level) to 76% (main distribution stations)
The heat pumps are assumed to be 100% concurrent loads, every heat pump operating at full capacity during extreme winter weather. For the average load the distribution of the load over the connections is identical every time because all pumps are at maximum capacity per connection. With predictability being higher for this type of load the network redundancy can be allowed to drop with respect to these loads. Currently the higher network levels reach a utilization percentage of up to 76% so it is assumed the Eindhoven network can cope with this percentage. Using the component of the peak load that requires the current utilization percentage and the heat pump load that is allowed 76% a permitted average load percentage is calculated for every level. From this the required network expansion is calculated. This expansion factor is then multiplied by the value of the current network based on 2009 component prices.
Network level Worst case scenario 2040 (Basic use, EV and heat pumps) Projected max load Permitted load % Required network capacity Required expansion factor Estimated required investment Purchase stations 3.51 € 10,000,000 Main feeder cables 774 MW 76% 1,017 MW 3.51 € 45,000,000 Main distribution stations 774 MW 76% 1,017 MW 4.08 € 45,000,000 Transport cables 756 MW 63% 1,190 MW 3.39 € 40,000,000 District distribution stations 756 MW 69% 1,102 MW 3.69 € 45,000,000 MV ring 756 MW 62% 1,227 MW 3.28 € 105,000,000 Transformer stations 756 MW 55% 1,369 MW 2.99 € 140,000,000 LV network 756 MW 24% 3,162 MW 2.07 € 90,000,000
Total required investment € 500,000,000
Cost per connection € 5000
Table 2: required investment estimate worst case scenario 2040 CONCLUSIONS
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transition resulted in the following bullet point conclusions.
- There is no consensus about the exact specifications of a smart grid
- The public sector needs to define a direction for the energy sector and direct or guide developments
- Landscape pressures and Niche developments are available to trigger a technological transition in the sector
- There is an inherent resistance against transition but also a lot of tension and dynamism that favor transition within the current socio-technical regime
- A Hybrid grid adding ‘smart’ elements to existing grids and combining central and distributed generation is the most likely future for the Dutch electricity network
In developing the realistic worst case scenario it was shown that there can be several types of worst case scenario for electricity grids. Maximum peak loads and distributed generation capacity are both potential worst case aspects. The scenario developed addresses the maximum peak load aspect. In a scenario based on economical growth and an accent on individual responsibility, heat pumps and electrical vehicles are identified to be developing technologies that present the biggest threat to increase peak loads.
Based on scenarios by CPB, ECN, Ecofys and Telos the total annual electricity use in Eindhoven was shown to increase by 80-100% from 1.0 billion in 2010 to approximately 1.9 billion kWh in 2040 according to this scenario. The increase in peak load will be even more profound rising 280% from 199 to 790 MVA. This shows that in this scenario the maximum load flow increases more than the average load on the network. This means the average network utilization over the year will also decrease even further.
The investment needed to facilitate the realistic worst case scenario developed is € 500 million. This number is not corrected for inflation and would have to be invested in the thirty year period between 2010 and 2040. The estimate is based on facilitating the scenario using only the traditional network, without the benefits of new technologies like distributed generation or smart functionalities like active demand. Over 80% of the total required investment has to be invested in the MV network levels, only 20% in the LV network. The total required investment would mean a € 5000 investment per connection.
DISCUSSION
Due to the fact that energy carriers are often interchangeable, It has however been proven to be very hard to accurately predict future developments for individual networks. Only when the choice of energy carrier for specific purposes can be predicted can accurate projections for individual networks be generated.
This project focussed on the future of the electricity network. Considering the difficulty in projecting future requirements for individual energy networks when the balance between the energy networks is not yet decided, it was decided to create a lower and upper limit to the range of possible future requirements. This project aimed to provide the upper limit for the electricity network requirements (research question 2) and the investment that would then be needed (research question 3). The scenario developed only addresses the impact of increased peak loads on the network. It discarded the potential risk distributed generation poses to network operations. The aspects on which a worst case scenario can be modelled
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do affect each other. Even though their relation was not researched it stands to reason that both scenarios will positively affect the impact of the effects of the individual scenarios. It is therefore important to realize that the projected result is also the upper limit, the maximum investment requirement, not the ‘expected’ investment requirement.
The projected required investment is specific to Eindhoven. Whereas the general scenario is based on national projections and therefore valid for most urban areas in the Netherlands the cost estimate is are not. Network philosophy, design, operational procedures and prices on which the projection is based are specific to Endinet’s networks. It would thus be unwise to simply assume the projected investment requirement to be valid for other urban areas. It seems that changes in time, place and form of electricity demand and supply have potential consequences for energy networks and systems. Smart solutions are as yet optional and thus represent opportunities to cope with developments by reducing the impact of consequences these changes have. Obviously choosing to implement smart elements will have consequences of its own but at this point I feel that because ‘smart’ is still optional it represents opportunities rather than said consequences.
Increasing sustainability and security of energy supply is the main driver for energy transition. It seems that based on the existing drivers’ sufficient motivation is generated to keep discussions going. Eventually economic aspects come into play when changes need to be realized. Currently the costs for transition are considered very high resulting in a significant barrier for actual commitment to realizing change. Low energy prices are often blamed for the high perceived cost associated with energy transition. The cost estimate developed in this project shows that significant investments might be needed even when no smart investments are realised. In actuality smart grids might contribute to reducing the total costs of changing the energy system. I hope this long term investment projection will eventually contribute to an integrated approach of evaluating the economic implications of changes to the entire energy system, including (smart) grid development.
RECOMMENDATIONS
The results from this project have answered the research questions posed but both within the scope of these research questions as in the scope of the goals some aspects were not (fully) included. Additionally the process and results present new insights and these give rise to new questions to be researched.
With respect to generating insight into future requirements this project has also generated insight into the current operation of the electricity network. An indicator number based method has been generated providing insight into utilization factors at different network levels. The representative annual usage analysis creates insight into current electricity use distribution. The possibility of matching this usage data to both network sections as well as other postal code based databases can in future provide insight into consumer behaviour and contribute to accurately predicting load profiles throughout the network. Further research into energy use and possible correlations with other databases containing demographical data can establish more reliable or specific indicators for energy use.
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estimate for a realistic worst case scenario. This project discarded the risk distributed generation poses to Electricity networks. I would recommend developing this scenario and estimating the associated network investments needed. The interchange ability of energy networks make that for decisions on the electricity network to be made similar insight into other networks will be needed. Research into the possibilities, implications and required investments of other options is therefore needed. Hydrogen cars, heat distribution, biogas are amongst the alternatives available. Especially the possibilities of heat distribution networks are not yet fully known in the Netherlands. It is recommendable that we learn from experience abroad with unfamiliar network types.
Further research will then be needed to not only show limits but also differentiate the costs associated with all variables that make up realistic scenarios. Only then can a proper decision on the future energy system for Eindhoven be made considering all technological options and based on all interests including security, sustainability and economic aspects.
REFERENCE
Centraal Planbureau (CPB), Milieu- en Natuurplanbureau (MNP) en Ruimtelijk Planbureau (RPB) (2006) Welvaart en Leefomgeving: een scenariostudie voor Nederland in 2040 (hoofddocument), http://www.welvaartenleefomgeving.nl [geraadpleegd april 2010] Commission of the European Communities (2008b), COM(2008) 782: Green Paper “Towards
a secure, sustainable and competitive European energy network” , 13-11-2008, Bruxelles Energy Data Services Nederland (EDSN) (2009) Profielen 2010 Elektriciteit, versie 1.00,
19-06-2008, Baarn
European Regulators’ Group for Electricity and Gas (ERGEG) (2009) E09-EQS-30-04: Position Paper on Smart Grids, 10-12-2009, Bruxelles, Belgium
European Smartgrids Technology Platform (ETP Smartgrids) (2006) Vision and Strategy for Europe’s Electricity Networks of the Future, European Commission: Directorate General for Research, Bruxelles, Belgium
Geels, F.W. (2002) Technological transitions as evolutionary reconfiguration processes: A Multi-level perspective and a case-study, Research Policy 31 (8/9), 2002, pp 1257-1274 Laborelec (2009) Impact DG en ‘nieuwe belastingen’ op het LSnet in bestaande woonwijken,
10-11-2009, Linkebeek, Belgium
BART WILLEM BROUWERS
This graduation project at Endinet was a project combining the technical background of my Bachelor Building, Architecture and Planning with the managerial aspects of the Master Construction Management and Engineering. The enthusiasm and involvement from colleagues at Endinet for achieving a sustainable and secure future energy network for Eindhoven greatly contributed to the final result. I sincerely hope this project will contribute to achieving the KENWIB goals.
2001 – 2007 Bachelor Architecture Building and Planning 2007 – 2010 Master Construction Management and Engineering
2010 – 2010 Graduation project at Endinet, Eindhoven
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ADVICE NEW LOW-ENERGY DWELLINGS
Author: Ronald Hennekeij Graduation program:
Real Estate Management and Development 2009-2010 Architecture Building and Planning
Graduation committee: Ir. Ing. I.I. Janssen Dr. J.J.A.M. Smeets Ir. M. Prins
Date of graduation: 02-09-2010
ABSTRACT
An attempt to improve available information and communication supply of property developers for low energy houses in the Netherlands. If property developers do not change their policy for low energy houses, it will be very hard to compete with existing building stock on the housing price, due to the extra investment costs for regulated energy efficiency performance. This paper deals with the research and development of an instrument, aimed to support property developers in their challenge to inform the customer of the added-value of low energy houses. Consumer behavior, energy efficient techniques and the finance situation of green houses will be investigated in order to develop a decision support system for the potential customers.
Keywords: Low energy performance, house developments, added-value, decision support system
INTRODUCTION
In order to meet the demands of the Kyoto-protocol on carbon emissions, the Dutch government changed the building code. This only partly occurred in consultation with the property developers and resulted in the ‘Lente-Akkoord’ in the spring of 2008. Through this agreement property developers and the government agree to improve the energy performance of new houses. The energy performance is presented as an EPC (energie prestatie coëfficiënt), which is incorporated in the building code. The agreement indicates a reduction of the EPC-value from 0.8 to 0.6 in 2011, to 0.4 in 2015 and to zero-energy buildings in 2020. Property developers are only able to realize that reduction if the implement energy efficient techniques, such as a heat pump. The extra investment costs related to the energy efficient techniques leads to a higher housing price. In a time where the housing market has changed from a supply driven to a demand driven market, there are many alternatives available to potential housing buyers. Hence, it is important for property developers to communicate the added value of energy efficient techniques to the consumers in order to explain the higher housing prices of low energy buildings in comparison to the existing building stock. An example of this is the geothermal heat pump with investment costs of almost € 16,000. These extra costs are calculated in the housing price.
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Recent research of Bouwfonds (2010) pointed out that there is a lack of information among consumers regarding energy efficient techniques. This is the result of the historic position of the customers in the housing market. Because of the current supply driven situation in the housing market, it was easy for the property developers to sell new houses. Nowadays, this situation has changed. This has occurred not only because of the changed market situation, but also due to the required reduction of energy performance in the housing sector. This makes communication with the consumer essential.
Energy efficient techniques can be categorized as innovative products. Although most of the techniques have already been used for several decades, these techniques are generally seen as new within the housing market. Until recently, most low energy housing projects where these techniques have been implemented, were seen as experiments. That is why most consumers are not aware of the specifications of energy efficient techniques. Figure 1 point out the importance of information and communication in the case of innovation.
Figure 1: Five stages in the innovation-decision process (Source: Rogers, 2003)
Communication and information are essential parts to the success of innovations. Without these parts, the added value of the innovation stays unknown and therefore leading to the potential failure of a product.
In the case of low energy houses and energy efficient techniques, consumers need to be informed. In the ‘Lente-Akkoord’, the government and the property developers agreed that the consumer is the important actor which needs to be informed to fulfill the agreement. Currently, this is a big challenge for the property developers based on their history. The Government introduced the energy label for the housing sector as a result of the European Performance Directive for the building sector in 2002, where the nation states committed themselves to the introduction of a standardized measurement tool for the energy performance of new buildings. The energy labels vary from a G-label for the worst performing buildings to an A++ label for the best performing buildings. However, shortly after introduction of the labels in 2008, the home sales associated with an energy label declined from 25 % during the first three months in 2008 to less than 7 % in October 2009 (Brounen, Kok & Quigley, 2009). The introduction of energy labels can therefore not be seen as a success yet.
Figure 2 presents the current situation in relation to low energy buildings in the Netherlands. The government and property developers signed an agreement, but it is still a challenge for
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both actors to involve the consumer. The consumer is an essential actor that should be involved in order to complete the puzzle in figure 2 and to create an attractive environment for energy efficient techniques.
Most research in this area is focused on the theoretically side of this subject. Eck (2009), Griess (2009) and Vernooij (2009) all analyzed this topic and the willingness to pay for low energy houses, but did not translated that analysis to a practical tool for the consumers. For that reason, the main objective of this research is to contribute to the development of the last part of the puzzle in order to complete it, from the point of view of the property developer. This leads to the following practical research objective: “To design a decision support system to inform and support consumers in their choice of energy efficient techniques in new houses, with the aim of achieving better energy performance of new houses.” This research will focus on single family dwelling in the Netherlands, where individuals can decide whether or not they will invest in energy efficient techniques.
METHOD
To be able to develop a model to support consumer, the input for the model needs to be investigated. There are three parts which are investigated, namely consumer behavior & marketing, energy efficient techniques and the financial situation of low energy houses. Consumer behavior and marketing
To be able to support the consumer in his decision, it is important to investigate the behavior of the consumer related to the buying process. This can be used as the main plot line for the model. A widely used theory on this subject is the buying decision process, presented in figure 3.
Figure 3: Stages in the buying decision process (Source: Kerin et al. 2006).
The buying decision process consists of five stages, in which the second, third and fourth stage are most important in the buying process of low energy buildings. Before consumers purchase a product, they will search for information. In the case of energy efficiency techniques which are relatively expensive, consumers will search for detailed information.
