agent-based models of energy investment decisions

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Sustainability and Innovation

Coordinating EditorJens HorbachUniversity of Applied Sciences Anhalt, Bernburg, Germany

Series Editors

Eberhard FeessRWTH Aachen, Germany

Jens HemmelskampUniversity of Heidelberg, Germany

Joseph HuberUniversity of Halle-Wittenberg, Germany

Ren KempUniversity of Maastricht, The Netherlands

Marco Lehmann-WaffenschmidtDresden University of Technology, Germany

Arthur P. J. MolWageningen Agricultural University, The Netherlands

Fred StewardBrunel University, London, United Kingdom


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Sustainability and Innovation

Published Volumes:

Jens Horbach (Ed.)Indicator Systems for Sustainable Innovation2005. ISBN 978-3-7908-1553-5

Bernd Wagner, Stefan Enzler (Eds.)Material Flow Management2006. ISBN 978-3-7908-1591-7

A. Ahrens, A. Braun, A.v. Gleich, K. Heitmann, L. LinerHazardous Chemicals in Products and Processes2006. ISBN 978-3-7908-1642-6

Ulrike Grote, Arnab K. Basu, Nancy H. Chau (Eds.)New Frontiers in Environmental and Social Labeling2007. ISBN 978-3-7908-1755-3

Marco Lehmann-Waffenschmidt (Ed.)Innovations Towards Sustainability2007. ISBN 978-3-7908-1649-5


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Tobias Wittmann

Agent-BasedModels of EnergyInvestment Decisions

Physica-VerlagA Springer Company


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Dedication

This book was written as a PhD thesis at the Technical University of Ber-lin. Four years of research are compiled in this work. Having discussedvarious topics and thoughts throughout these years with people around theworld, some of them have especially contributed to my work.

First, I would like to thank my supervisors Thomas Bruckner andGeorge Tsatsaronis, who have invited me to work with them at TU Berlin.Both have guided me through my research and I owe them much. Second,I would like to thank Christoph Engel from the Max Planck Institute forResearch on Collective Goods in Bonn. The discussions with him alwaysmoved my work forward and helped me to understand the coherences. Fi-nally, Frank Behrendt chaired my viva-voce.

Further, I have spent an inspiring time with my colleagues from the re-search group Energy Engineering and Protection of the Environment. I

would especially like to name Robbie Morrison and thank him for thehelpful discussions and the time spend jointly, both in Berlin and Welling-ton. In addition, my work benefited from the input of my students. I wouldlike to thank Zaida Milena Contreras, Donato Imbrici and Julius Richter.

Most of this work was supported by a scholarship from the Foundationof German Businesses. Providing the money necessary and opening a crea-tive and inspiring environment are just some of the several benefits I havereceived.

Last but not least, I would like to thank Kate and my family for support-

ing me throughout this work and giving me confidence.

Tobias Wittmann Berlin, September 2007


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Abstract

At the start of the 21stcentury societies face the challenge of securing an

efficient and environmentally sound supply of energy for present and fu-ture generations. Sector deregulation, the emergence of novel distributedtechnologies, firms focusing on these new options and competing in se-

lected markets, and the requirements to reduce energy related greenhousegas emissions might change the structure of energy systems significantly.Densely populated urban areas, which allow for the operation of sophisti-cated energy infrastructures are the most suitable to see essential changesin their energy infrastructure.

This book develops a new model to study the development of urban en-ergy systems. It combines a technical, highly resolved energy systemmodel with an agent-based approach. The technical, highly resolved en-ergy model is used to simulate the operation of technologies. Different

agents are developed to capture the investment decisions of actors. Twoclasses of actors are distinguished: private and commercial actors. The de-cisions of private actors are modeled using a bounded rational decisionmodel which can be parameterized by socio-demographic surveys. The de-cisions of commercial actors are approached with a rational choice model,but taking into account different perspectives of firms with regard to futuremarket developments.

A proof of concept implementation demonstrates the potential of the de-veloped approach. Diffusion curves for conversion technologies and effi-

ciency upgrades in the residential sector were obtained and the overall en-ergy savings were calculated. Further, the impact of firms competition ondiffusion curves could be estimated and different business models weretested.


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Contents

1 Drivers of Change and Energy Models.................................................11.1 Introduction ......................................................................................11.2 Drivers of Change.............................................................................2

1.2.1 Introductory Remarks................................................................21.2.2 Market Deregulation..................................................................21.2.3 Technological Change ............................................................... 31.2.4 Energy Firm Conduct ................................................................51.2.5 Climate Policy ...........................................................................8

1.3 Energy Models a Review of the State of the Art ...........................91.4 Motivation and Research Questions ............................................... 11

2 Model Design.........................................................................................132.1 Introduction ....................................................................................132.2 Users and Intentions .......................................................................132.3 Geographical and Socio-economic Scope ...................................... 14

2.3.1 Introductory Remarks..............................................................142.3.2 Geographical Scope.................................................................152.3.3 Socio-economic Scope ............................................................15

2.4 The Layer Concept .........................................................................162.4.1 Basic Concept..........................................................................162.4.2 Modeling Timeframes .............................................................172.4.3 Technical Layer .......................................................................192.4.4 Agent Layer .............................................................................232.4.5 Energy Markets .......................................................................262.4.6 Financial Incentives and Regulations ...................................... 28

2.5 Discussion.......................................................................................28

3 Private Actor Model .............................................................................313.1 Introduction ....................................................................................313.2 Private Energy Investment Decisions.............................................32

3.2.1 Introductory Remarks..............................................................323.2.2 Neoclassical Perspective..........................................................333.2.3 Behavioral Perspective ............................................................34


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X Contents

3.3 Bounded Rational Decision Models ............................................... 363.3.1 Introductory Remarks.............................................................. 363.3.2 Goals........................................................................................ 363.3.3 Search Rules ............................................................................373.3.4 Analysis Tools.........................................................................383.3.5 Decision Strategies ..................................................................40

3.4 Modeling Private Energy Investment Decisions ............................ 413.4.1 Introductory Remarks.............................................................. 413.4.2 Aggregation of Technology and Infrastructure Information ... 423.4.3 Aggregation of Socio-economic Information.......................... 44

3.5 Results ............................................................................................513.5.1 Introductory Remarks.............................................................. 513.5.2 General Decision Matrix .........................................................513.5.3 Single Decision Outcomes.......................................................553.5.4 Aggregated Decision Outcomes .............................................. 56

3.6 Discussion.......................................................................................64

4 Commercial Actor Model.....................................................................674.1 Introduction ....................................................................................674.2 Commercial Energy Investment Decisions ....................................68

4.2.1 Introductory Remarks.............................................................. 684.2.2 Theoretical Background ..........................................................684.2.3 Empirical Evidence .................................................................71

4.3 Aggregation of the Firm .................................................................734.3.1 Introductory Remarks.............................................................. 734.3.2 Aggregation of Options ........................................................... 734.3.3 Aggregation of Business Units ................................................ 754.3.4 Definition of Strategies and Perspectives................................77

4.4 Decisions Model.............................................................................794.4.1 Basic Concepts ........................................................................794.4.2 Operational Decisions.............................................................. 79

4.4.3 Low-stake Structural Decisions...............................................804.4.4 High-stake Structural Decisions .............................................. 82

4.5 Application and Results..................................................................824.5.1 Impact on the Decisions of Private Agents.............................. 844.5.2 Impact of Competition.............................................................88

4.6 Discussion.......................................................................................92

5 Conclusions............................................................................................ 95

5.1 Introduction ....................................................................................955.2 Discussion of the Model Design.....................................................955.3 Outlook...........................................................................................97


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Contents XI

Appendix...................................................................................................99 A.1 Private Actor Model ......................................................................99

A.1.1 Supply Superstructure and Networks .....................................99A.1.2 Agent Specific Diffusion Curves..........................................102A.1.3 Results from Weighted Adding Strategy..............................103

A.2 Commercial Actor Model ............................................................105

References...............................................................................................107


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List of Abbreviations, Symbols and Indices

Abbreviations

CO2 carbon dioxide

CFO chief financial officerdeeco energy model: dynamic energy, emissions, and cost optimization

EFOM Energy Flow Optimization ModelEU European UnionFERC United States Federal Energy Regulatory CommissionGEMS energy model: German Electricity Market SimulationLEX decision strategy: lexicographic strategyMARKAL energy model: Market AllocationOECD Organisation for Economic Cooperation and DevelopmentSAT decision strategy: satisficing strategySO

2 sulfur dioxide

TIMES energy model: The Integrated Markal Efom System

UNFCCC United Nations Framework Convention on Climate ChangeWADD decision strategy: weighted adding

Symbols

a yearA number of accesses to network infrastructureal aspiration level

AL set of aspiration levelsAT set of analysis toolsb business unitBU set of business unitsc contractC cash-flowCP available investment capitalcs share of available investment capitalDS set of decision strategiese energy carrierE set of energy carriersg goalsG set of decision goals


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XIV List of Abbreviations, Symbols and Indices

i interest rateI investment costJND just noticeable differencen number of clients

npv net present valueO set of optionsp pricep change in price

RD set of reference domains

s strategy vector

S set of strategiesSR set of search rulesSD set of search domainst timeT time horizonu utility expected responses towards advertising for a certain contract

expected sensitivity towards price changes for a certain contract

expected sensitivity towards network extensions

pay-back period

weight factor

Indices

b business unitc contractconventional conventional capitale energy carrierel electricalf firm

g goalref referenceR&D research and development capitalt timeth thermal


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1 Drivers of Change and Energy Models

1.1 Introduction

Primary energy demand has been continuously growing over the last cen-tury and is expected to grow further.1A secure supply of energy is a condi-tion for stability and growth of any economy (Ayres et al. 2003). Energy,

as it occurs in nature, is rarely suited to provide energy services; mostforms of energy need to be transformed first. Further, energy is not alwaysfound or cannot be transformed cost-effectively close to demand. As a re-sult, a large international industry that extracts, transports, transforms, andsupplies energy has developed.

At the start of the 21stcentury societies face the challenge of securing an

efficient and environmentally sound supply of energy for present and fu-ture generations. The high dependency on fossil resources and their de-creasing reserves, the prospect of climate change and local pollution, but

also the development of sound technologies and the deregulation of energymarkets have created a demanding environment for researchers, govern-ments, and firms dealing with energy. Classic issues like financial cost,environmental protection, and supply security are nowadays accompaniedby institutional issues like effective regulation and network access, the in-vention and diffusion of new technologies, and the emergence of decen-tralized structures. The development of a consistent public energy policyframework, of successful long-term company strategies, and of researchand development priorities requires that the various complexities involved

are suitably addressed.Results obtained from energy models may provide useful insights to de-

cision makers. Energy models address different questions and have variousscopes, ranging from game theoretical analysis of single market competi-tion (Hffler and Wittmann 2007) to international, intertemporal models ofinterconnected technical systems and markets (Hamacher et al. 2001). So-phisticated energy models have to account for the relevant actors, tech-nologies, markets, and drivers of change in the area they address.

1The International Energy Agency anticipates the demand for primary energy togrow at 1.7% per annum until 2030. The growth rate over the last three decadeshas been 2.0% (IEA 2004).


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2 1 Drivers of Change and Energy Models

A survey of the major drivers of change in the energy industry suggeststhat urban areas are the most likely to see essential changes in their energyinfrastructure. Nonetheless, sophisticated energy models addressing the fu-ture development of urban areas are still insufficient. This work develops

an energy model to be used to investigate how energy systems in industri-alized countries might develop over the next 2050 years. It offers anagent-based, spatially highly resolved model to estimate the dynamicstructure of future energy systems and markets in cities. It can account forthe most relevant effects arising from sector deregulation, the inventionand diffusion of distributed technologies, firms conduct, and policy inter-ventions addressing climate change.

The remainder of this chapter is structured as follows. First, an overviewover the main drivers of change in the energy industry deregulation,technological change, firms conduct, and climate policy is given, fol-lowed by a survey of the state of the art of related energy models. Finally,the motivations and research questions for this work are discussed.

1.2 Drivers of Change

1.2.1 Introductory Remarks

Scientific progress, technological change, economic growth, and globaliza-tion are generally viewed as the main drivers of change. Further, publicpolicy can stimulate or hinder these developments (Freeman and Soete1997). Todays energy industry is undergoing fundamental institutional,commercial, and technological developments. These changes can basicallybe attributed to four main drivers: market deregulation (Pfaffenberger andSioshansi 2006), technological change (Grubler et al. 1999), energy firms(Christensen 2000), and climate policy (IPCC 2001). The importance and

impact of each of these areas are discussed and summarized below.

1.2.2 Market Deregulation

The deregulation of energy markets started about 30 years ago, beginningin North America and spreading towards the European Union. The UnitedStates Federal Energy Regulatory Commission (FERC) issued Order 436in 1985 that ensured open access to all interstate natural gas pipelines for

local distribution companies, gas producers, marketers and large volumecustomers. Access to pipeline capacities was attributed on a first-come-first-served basis. With the deregulation of the gas pipeline transportationservices completed, FERC moved to deregulate the bundled ancillary gas


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1.2 Drivers of Change 3

services such as storage and extraction, in association with interstate deliv-ery. In 1992, Order 636 was introduced to provide for the unbundling ofthose ancillary services as well as to prohibit the interstate pipeline com-panies to own gas for resale. This natural gas deregulation provided a tem-

plate for FERC with regard to the development of competitive wholesaleelectricity markets and open access to transmission capacity. The Electric-ity Title of the Energy Policy Act became law in 1992 and resulted in ma-jor changes in the market for electricity generation and retail access. Thisgranted FERC authority to allow the transmission of power from new in-dependent wholesale generators, and started the move toward a wholesaleelectricity market. The concept of integrated resource planning was pro-moted by state regulators to enhance the move toward retail competitionand to provide an initiative to industry players to restructure their gas andelectric utilities in order to promote wholesale and retail competition andcustomer choice.

In contrast to the United States, the European Union (EU) started to de-regulate electricity, not gas, markets in the 1990s by adopting directive96/92/EC by the European Parliament and the European Council in 1996.This directive specified common rules for the internal market for electric-ity and was revised and replaced by directive2003/55/EC in 2003. Further,regulation 1228/2003 adopted in 2003 sets conditions for network accessregarding cross-border exchanges of electricity. Directive 2005/89/EC,which addresses measures to safeguard the security of electricity supplyand infrastructure investment within the EU, was adopted in 2006. Like-wise, the gas market was deregulated in 1998 by the adoption of directive98/30/EC, which specified common rules for the internal market of naturalgas. This was revised and replaced by directive 2003/55/EC in 2003.Regulation 1775/2005, adopted in 2005, sets out the conditions for accessto the natural gas transmission networks within the EU.

Thesis I Deregulation: Sector deregulation has changed the institutionalsettings of the energy industry considerably by unbundling the verticallyintegrated energy companies, assuring non-discriminatory third-party net-work access, fostering resale and retail competition, enabling consumers tochoose their supplier, and facilitating cross-border energy trading. Thus,the energy industry is not in an economic equilibrium.

1.2.3 Technological Change

Energy services such as movement of goods and people, a comfortable in-door temperature, or task lightning are provided by a range of differenttechnologies. Each technology relies on some sort of energy input since


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energy is necessarily conserved. Therefore, technological change in theenergy industry can affect the whole value chain, from fuel extraction,transportation, transformation, and delivery to usage. This section concen-trates on recent developments in technologies which transform fuels into

heat and electric power and which either have or might gain considerablemarket shares.

In order to understand the importance of technological change, it is use-ful to distinguish between central and distributed energy technologies.Central generation units have capacities well above 10MWel, are connectedto the transmission network and require considerable capital investments.Most central generation units are thermal power plants fueled by lignite,hard coal, uranium, gas or oil. Distributed energy technologies are small tomedium-scale technologies, usually located within distribution networks.

They do not necessarily require well developed transmission networks andcan be realized with smaller investments. Examples of distributed tech-nologies include: micro-gas turbines, reciprocating engine cogeneration,solar cells, solar thermal collectors, micro-hydro generation, stationary fuelcells, and wind generators. While central generation capacities are de-signed to provide bulk energy as efficiently as possible and at lowestprices, distributed technologies offer a different value proposition to con-sumers (Bruckner et al. 2005). They are small, usually flexible to operateand may be installed spatially close to demand. Further, some provide co-

generation, the simultaneous supply of heat and power 2. They take advan-tage of the fact that heat cannot be transported over long distances becauseof losses, and thus district heating grids have a necessarily limited cover-age. Other distributed technologies rely on renewable energy input, and arenot affected by shortages in or price increases for fossil fuel supply andcarbon emissions pricing.

Further, novel and mature technologies can be distinguished. Innovativetechnologies such as CO2sequestration, solar cells, fuel cells, and micro-cogeneration are under development or offered at high prices and thus only

under some circumstances cost-effective. Due to experience effects, aris-ing from labor efficiency, standardization, specialization, technical methodimprovements, a better use of equipment, and product redesign, manufac-turing costs are expected to fall (Junginger et al. 2005, Riahi et al. 2004,Zilker et al. 1997). In contrast, efficiency improvements and cost reduc-tions for mature technologies are neither expected to be as high nor to oc-cur as rapidly.

2Heat can also be extracted with high efficiency from central generation facilities.This option is cost-effective if such stations are located close to demand.


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The recent market launches and ongoing diffusion of distributed tech-nologies such as micro-gas turbines, micro-cogeneration units, Sterlingengines, solar cells, solar thermal collectors, wind generators, pellet boil-ers, and fuels cells can be framed as the emergence of disruptive technolo-

gies. They contrast improvements in sustaining technology settings such asthe central generation of electricity in combination with conventionalbuilding heating systems.

Distributed technologies have in common that they are comparativelysmall, and can be operated close to demand. Further, distributed technolo-gies are easy to operate, they mostly run independently and maintenancecontracts can be signed with specialized firms at appropriate cost.Equipped with such advantages, distributed technologies are attractive to arange of new customers. The required capital is rather small and can be ac-cumulated by small and medium sized firms; even residential buildingowners can constitute suitable purchasers. By investing in distributed tech-nologies, investors mostly replace the demand for a high cost energy car-rier by the demand for technology and a lower cost energy carrier. This isespecially true for technologies which transform renewable resources likewind, water, and solar energy into heat or electricity. Thus, they transformnon-cost energy into a marketable good. Instead of price concerns abouttheir supply chain, investors and operators face the risk of high cost tech-nology investments. Additionally, distributed technologies enable opera-tors to become energy suppliers. Entrepreneurs have profited from theemergence of distributed energy technologies and market deregulation.New start-up businesses have been formed and are successfully competingin the energy market. Likewise, some established firms have taken the op-portunity to enter into these new markets as well.

Among the rapidly growing firms worldwide are companies manufac-turing solar cells. They report growth rates of above 30%, and demand isexpected to increase. The world leading manufacturers are Sharp, Kyocera,and BP Solar, but also a number of new entrants, such as Solarworld andQcells (both German), could gain a foothold. Likewise, the wind powerindustry has seen constant growth rates. In contrast to the photovoltaic in-dustry, Siemens and GE Energy, two large electrical equipment suppliers,recently entered the wind market purchasing established manufacturers 3.But there is still a range of dedicated wind-turbine builders leading themarket. The top manufacturers are Vestas (Denmark), Gamesa (Spain),Enercon (Germany), GE Energy (USA), Siemens (Denmark), and Suzlon(India). The average plant size has increased from 30kW in 1980 to 1

3MW on-shore and 1.55MW off-shore. Consequentially, blade diametershave risen to 125m. Costs have declined by 1218% with each doubling of

3GE Energy bought Enron Wind in 2003 and Siemens bought Bonus in 2004.


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1.2 Drivers of Change 7

the global capacity, thus since 1990 costs have been cut by half (World-watch Institute 2005). Solar cells and wind turbines have been installed bycommercial and private investors in various regions of the earth. Supportedby different market introduction programs, renewable energy technologies

enable operators to make profitable investments and, in some cases, sellenergy to consumers and thereby become energy suppliers4.

Further, micro-cogeneration units were developed for private and com-mercial investors who have to satisfy their heat demand. These systems,which are operated in homes or small commercial buildings, are mostlydriven by heat demand, delivering electricity as a by-product. Heat stor-ages can be integrated to flatten demand peaks or to increase the electricitygeneration. Reciprocating engines with capacities around 5kWel and12kWthwith electrical efficiencies ranging from 2530% and thermal effi-

ciencies around 60% are commercially available. Further, Stirling engineswith a capacity of 1.2kWel and 8kWth are close to market entry and fuelcells are under development 5. Micro-cogeneration also enables private andcommercial consumers to become energy suppliers. Further, there mightbe attractive contracting options for energy firms to enter the generationmarket and to serve consumers via long-term heat supply contracts (Pehntet al. 2006).

Thesis III Energy Firm Conduct: Distributed technologies are disruptive

technologies with the potential to fundamentally change firms, marketsand energy systems. Therefore, firms status quo, their perspectives on thefuture market needs, and their ability to cope with disruptive technologiesare likely to play an important role in the future trajectory of energy sys-tems.

4Worldwatch Institute (2005) estimates the energy cost for solar cells to be 0.160.32/kWh and for wind turbines to be 0.030.05/kWh depending on localconditions.

5For example Senertec (www.senertec.com), Ecopower (www.ecopower.de), andClimate Energy (www.climate-energy.com) offer micro-cogeneration unitsbased on reciprocating engines. WhisperGen (www.whispergen.com) offers aStirling engine micro-cogeneration unit. Vaillant (www.vaillant.com) and Sul-zer Hexis (www.hexis.com) are commercializing fuel cell micro-cogeneration.


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1.2.5 Climate Policy

As part of the Conference on Environment and Development held in Riode Janeiro in 1992, the United Nations Framework Convention on Climate

Change (UNFCCC) was signed with the aim of reducing emissions ofgreenhouse gases in order to avoid dangerous anthropogenic interferencewith the climate system 6. Since this first official recognition of climatechange much has happened. One of the most important developments fromthe original UNFCCC is the Kyoto Protocol negotiated at the third Confer-ence of the Parties in December 1997 in Japan. The signing nations agreedthat a joint effort of the industrialized countries (as specified in Annex I ofthe UNFCCC) will be undertaken to reduce greenhouse gas emissions by5.2% over the period 20082012 compared to 1990 levels. The Kyoto Pro-

tocol let to the development of a range of policy measures aiming to re-duce greenhouse gas emissions worldwide. But even countries which didnot sign or ratify the Kyoto Protocol, like Australia and the United States,have undertaken actions to combat climate change 7.

Emissions trading is similarly regarded to be an efficient means to re-duce greenhouse gas emissions. The introduction of emissions rights,which are to be consecutively reduced in each trading interval, and a trad-ing platform, encourages emissions reduction where cheapest. The Euro-pean Union has successfully established such a scheme amongst member

states8. Emissions arising from road travel, private sector room heating,and agriculture, which are not currently included in emissions trading, maybe addressed through, for instance, energy taxes and energy efficiency pro-grams.

Further, governments are seeking to increase the utilization of renew-able energies and cogeneration by supporting investments or rewarding thefeed-in of electricity. In addition to the emissions reductions realized im-mediately, the support of wind generators, solar cells, solar thermal collec-tors, biomass generation or cogeneration should also stimulate technologi-cal development and might lead to significant cost reductions inmanufacturing and thereby enable long-term cost-effective emissions re-duction.

6 See http://www.unfccc.int for details on the UNFCCC.7Australia, India, Japan, China, South Korea, and the United States engaged in the

Asia-Pacific Partnership on Clean Development and Climate in 2005, formed toestablish a cooperation on the development and transfer of technology whichpotentially results in the reduction of greenhouse gas emissions.

8The European Union Emissions Trading Scheme is based on Directive2003/87/EC and is described athttp://ec.europa.eu/environment/climat/emission.htm.


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1.3 Energy Models a Review of the State of the Art 9

Thesis IV Climate Policy: Efforts to stabilize and reduce greenhouse gasemissions will be continuing. Potential and actual policy instruments couldprice the utilization of non-renewable fuels and efficient and renewabletechnologies would benefit from investment support, feed-in tariffs or sup-portive tax measures.

1.3 Energy Models a Review of the State of the Art

Energy models in most cases attempt to account for the above summarizeddrivers of change in different ways. This section discusses three bottom-upmodeling approaches which are related to this work and which have re-

cently been applied to study energy systems: technical high-resolution en-ergy system models, intertemporal optimization models, and agent-basedsimulations.

Technical high-resolution energy system models are suited to the studyof energy systems operations and to identify synergies and counteractionsbetween technologies. Further, they might be used to estimate the effect ofexogenous price changes, investment, decommissioning, taxes, and sup-port schemes on the operation and performance of energy systems. Tech-nologies are modeled using input-output relations, taking ambient condi-

tions and energy intensities like heat flow attributes into account. Demandprofiles are usually specified by a one hour resolution for a representativeyear. The optimal share of technologies to supply a given demand is eitherdetermined using optimization routines or calculated by applying controlheuristics. Technical high-resolution energy system models attempt tosimulate the real performance of an energy system as close as necessary.Therefore, they may be used to support plant scheduling, to support marketbidding and to control the real time dispatch. This approach is realized inmodels like deeco (Bruckner et al. 2003, Bruckner et al. 1997, Groscurth et

al. 1995), TopEnergy (Augenstein et al. 2005), and BoFit (Scheidt et al.2004, Stock and Mertsch 1997). Technical high-resolution energy systemmodels are especially suited to account for the context sensitive perfor-mance of distributed technologies (Bruckner et al. 2005). If large energysystems are to be investigated or the long-term evolutions of such systemsare to be studied, data and computing time may be considerable.

In contrast, intertemporal energy system optimization models are sup-posed to study the evolution of energy systems over a given time periodwith regard to different price scenarios, policy frameworks, and technical

innovations. In this case a superstructure representing all possible tech-nologies and interconnections of the system under investigation is articu-lated. Technologies are modeled by average-valued input-output relations.


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In most cases, the operation of energy technologies is only simulated forrepresentative days using typical load profiles. The structural developmentof the entire system is then determined using an optimization routine, tak-ing the average operation, investment, and decommissioning of technolo-

gies into account. Intertemporal energy system optimization models aresuited to the study of the long-term development of large energy systemswith respect to different socio-economical scenarios. This approach is real-ized in models like MARKAL (Seebregts et al. 2002, Fishbone andAbilock 1981), EFOM (Van der Voort et al. 1985), MESSAGE (Messnerand Strubegger 1995) and TIMES (Remme et al. 2002). In order to containcomputing time and data needs, the structures of energy systems are sim-plified, single technologies are aggregated to technology types, and repre-sentatively aggregated demand profiles are used. Therefore, intertemporalenergy system optimization models are not well placed to account for dis-tributed technologies. Further, it is assumed that one single rational deci-sion maker with perfect foresight administers the entire system heteroge-neity of actors is not accounted for.

The third approach introduces autonomous agents, which interactthrough defined interfaces. Agent-based models have been used to studythe emergence of social phenomena in general (Epstein and Axtell 1996),to assist urban traffic planning (Casti 1997), to estimate water usage pat-terns and demand profiles (Ernst et al. 2004), and to understand technologydiffusion and resource use in the agricultural sector (Berger 2001). Agent-based models differ from the approaches discussed above in that the evolu-tion of a system is determined by repeated interaction of heterogeneousagents. Moreover, agent decision making procedures do not necessarily in-volve rational choice, but can be based on heuristics instead. Through theintroduction of agents these models account for the heterogeneity of actors,they may include different decision algorithms and market interactions,and they may account for distributed technologies and policy frameworks.This broad scope requires models to focus on specific domains in order tocontain the demand for data. The agent-based simulation approach hasbeen successfully applied to the energy sector as well. Existing modelshave addressed the bidding behavior of actors within energy exchanges(Hu 2004, North et al. 2002) or focused on the consolidation within the en-ergy sector (Bower et al. 2001). Another approach uses agents to investi-gate the long term development of national energy systems (Grozev 2004,Veselka et al. 2002).


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1.4 Motivation and Research Questions 11

1.4 Motivation and Research Questions

Several key subjects have been introduced in this chapter: sector deregula-

tion, the emergence of novel distributed technologies, firms focusing onthese new options and competing in selected markets, and the requirementsto reduce energy related greenhouse gas emissions. These four drivers ofchange might change the structure of energy systems significantly. More-over as argued by some authors, one might expect a shift from central to-ward distributed generation structures. As a result, this may lead to a newparadigm in the energy industry (Silberman 2001). Therefore, models ad-dressing the future evolution of energy systems should address the follow-ing questions:

Which distributed technologies percolate into energy systems, what de-termines the diffusion rate, and how does the demand for and mix of en-ergy change?

How does this diffusion alter the ownership structure of generation tech-nologies and what is the likely impact on central generation?

To which degree do the status quo of infrastructure and ownership, cor-porate strategies, and public policy shape the future structure of an en-ergy system and its related emissions, demands, and prices?

Technical high-resolution models, intertemporal optimization modelsand agent-based models are suited to yield insight into different aspects ofthe questions outlined above. Nonetheless, combined approaches are stillmissing. This work proposes a more integrated modeling framework,which accounts for the drivers of change in a novel way.

This new model will focus on densely populated urban areas, which arethe most suitable for the diffusion of distributed technologies.

The limited spatial scope enables one to use high-resolution modeling

techniques which account adequately for the context sensitive perform-ance of distributed technologies and for the effect of policy measures.

Operation, investment, and decommissioning decisions will be under-taken by heterogeneous agents, who supply and demand energy. Com-petition among firms is modelled as a battle of perspectives.

The subsequent chapters are structured as follows. Chap. 2 will describethe overall model design. A technical layer and an agent layer are intro-duced. Further, two classes of actors private and commercial actors who exhibit distinct energy related behaviors are described. Their agentmodels are combined with a highly resolved technical energy system op-timization model, which simulates the operation of the energy system and


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makes it possible to allocate energy, cash, and emission flows to the dif-ferent agents. Chap. 3 and 4 discuss the decision modeling of private andcommercial actors, respectively. Different modeling approaches for eachactor class and for operation and investment decisions are used relying on

heuristics, rational choice, and bounded rational models. Chap. 5 discussesthe results and gives an outlook.


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14 2 Model Design

Local, regional and national governments seeking to increase the effi-ciency of energy markets, to secure energy supply, to foster technologicalinnovation, to stimulate local and national growth, to increase employ-ment, and to decrease local and global emissions form one group which

can inform their decisions by using results from the model users (Andersen2001, Jank 2000, IPCC 2001). This group implements and modifies regu-lations, incentives or support schemes to reach short and long-term goals.Thus, questions relating to policy design, interdependencies between dif-ferent incentives, regulations, and interventions and their impact on theeconomy are of vital interest.

A second group of users from a commercial background may wish toimprove the competitive standing of a firm or a technology in energy mar-kets. This group often evaluates, alters, and develops business strategies togain sustained competitive advantages over rival firms (Midttun 2001).These analyses typically examine the competitive environment of the firm,evaluate the situation of existing and future suppliers and buyers, searchfor potential new entrants, and assess possible substitutes for products orservices (Porter 2004). Thus, questions regarding strategy design, the mar-ket potential of new technologies, and the current value and long-term per-formance of a company are of particular interest.

2.3 Geographical and Socio-economic Scope


As outlined in Chap. 1, the sector deregulation, the invention and diffusionof distributed technologies, the conduct of firms, and policy measures ad-dressing climate change may especially affect the highly interconnectedurban energy systems found in developed countries. Thus, the model is de-signed to investigate the evolution of municipal energy systems in indus-trialized countries. These energy systems have been established to provideindustry, commerce and households located in urban areas 9with fuels andenergy services. The following subsections specify the relevant geographi-cal and socio-economic boundaries.

9 Dependent on national norms, urban areas are defined as areas in which thepopulation density exceeds 200 inhabitants/km or where houses are not furtherthan 200m apart. The total population should be above 50,000.


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2.3 Geographical and Socio-economic Scope 15

2.3.2 Geographical Scope

A high population density and the resultant large demand for energy ser-vices in urban areas permit the operation of sophisticated energy infra-

structures such as electricity networks, district heating grids and gas gridsto distribute energy among consumers in a cost-effective manner. Like-wise, fuels such as heating oil, coal or wood pellets can easily be deliveredand prices only slightly depend on the transport. As a consequence, highlyinterconnected infrastructures develop within urban areas (Graham andMarvin 2001).

Energy which is distributed and delivered to consumers located withinan urban area is usually extracted and often transformed outside that area.In industrialized countries, a large number of urban areas are connected by

transmission infrastructure and jointly supplied by large wholesale mar-kets. Therefore, a distinction may be drawn between the transmission sys-tem which interconnects urban areas and the distribution system in the ur-ban area itself10. A distribution system is characterized by a highlyinterconnected energy infrastructure, a short distance between stocks anddemands, and a high demand density. In contrast, a transmission systemhas a low demand density and large average distances between stocks anddemand11.

Assumption I Geographical Scope: An urban distribution system can bedistinguished from the upstream transmission system. The distribution sys-tem serves as the original, the transmission system as its environment. Anyactions undertaken in the investigated distribution system do not affect in-tensive properties of the upstream transmission system.

2.3.3 Socio-economic Scope

A variety of actors are involved in the operation and development of dis-tribution systems. Those actors do not only directly and indirectly affect

10The terms transmission system and distribution system, as used in this work,do not only refer to networks such as electricity and gas grids, but are used tocover the whole energy infrastructure including technologies, grids, and trans-port.

11The demand density for electricity for the regional East German energy suppliere.dis Energie Nord AG was 328MWh/km/a in 2001. e.dis supplied 3,257,000customers. In contrast, the Neckarwerke Stuttgart AG faced a demand density of10,938MWh/km/a in 2001. They supplied 2,192,000 customers (ARE 2002).


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16 2 Model Design

such systems; they also influence each other. For instance, the revenues ofan energy trader depend on the contract selections of consumers; localgovernments seeking to increase the sustainability of the energy supply of-fer monetary incentives or educational trainings, which are regularly

evaluated and subsequently either prolonged or suspended; firms representtheir common interests through lobby associations toward governmentsaiming at influencing political decisions; finally regulations are altered as aconsequence of abuse, failures, new insights or elections.

Actors interactions are governed through different institutions such asthe political system, the judicial system, the media, and markets. Amongthose institutions, markets for energy are particularly designed to exchangeenergy and are most relevant for the day-to-day interactions of actors. Ac-tors who do not participate in markets influence the technical energy sys-tem and prices indirectly through e.g. policy, regulations, or advertise-ments.

Assumption II Socio-Economic Scope: The most important interactionsinfluencing the future evolution of distribution systems are governedthrough markets. Therefore, it is only accounted for actors who are directlyparticipating in energy markets and who demand or supply energy in thedistribution system.

2.4 The Layer Concept

2.4.1 Basic Concept

Actors can operate their technologies applying different unit commitmentprotocols and can supply or demand energy. They may change the struc-ture of a distribution system by investing in or decommissioning plants orinfrastructure. They might assess technical and financial performance dataof each technology to better inform their decisions. The interactions be-tween actors and the energy system occur only through a small number ofinterfaces. Generally, one can distinguish between the operation of thetechnical energy system and the actors decision making processes, whichare not highly interconnected.


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2.4 The Layer Concept 17

Fig. 2.1.Technical layer and agent layer

Assumption III Layers: A technical energy system and a socio-economicactor system can be distinguished. The technical system can be modeledusing an energy system model; the decisions of actors can be included us-ing agent-based models. The energy system model is used to inform ac-tors decisions and to calculate actors cash-flows.

The remainder of this section is structured as follows: Sect. 2.4.2 definestimeframes and their alternation, followed by a sketch of the technical(Sect. 2.4.3) and the agent layer (Sect. 2.4.4). Sect. 2.4.5 outlines how thecommercial interactions of actors are captured, and finally Sect. 2.4.6 indi-cates how government policies are integrated into the model.

2.4.2 Modeling Timeframes

To coordinate the interaction between the technical and the agent layer a

definition of timeframes is necessary. The model is built on three differenthierarchic timeframes, the operational timeframe, the structural timeframeand the scenario timeframe. Each of these frames has a different discretetime resolution.

The operational timeframe is supposed to simulate the operation of thedistribution system. Energy firms apply sophisticated software tools tosupport their operational decisions. Demands, prices, congestions, andweather conditions are estimated; the dispatch is scheduled and, if neces-sary, adjusted. Spot markets for electricity enable traders to sell and buy

energy in intervals as short as one hour12

and gas is expected to be traded

12For example spot contracts at the European Energy Exchange and at the NordPool are traded in one hour intervals.


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18 2 Model Design

in one hour intervals in the near future. Further, demand profiles canchange considerably over one day, over weeks, and across seasons due toindividual behaviors, production schedules, and weather conditions13. Fi-nally, the context dependent performance of distributed technologies re-

garding demand profiles, temperatures, and market prices requires a tem-porally highly resolved modeling.

The structural timeframe is intended to simulate the structural changeswhich occur in a given distribution system from a single actors point ofview. Investment in energy technologies is undertaken infrequently. Theaverage lifetime of small boilers, solar panels, solar thermal collectors,wind generators, and cogeneration engines is typically 1020 years; powerstations are designed to operate over 40 years. The construction of energytechnologies ranges from 3 months to over 10 years depending on the

technology. The energy demand growth has been small and without anydiscontinuities over the last 30 years in all OECD countries.14All such in-vestments are undertaken in the structural timeframe.

The scenario timeframe is intended to set the maximum period of timeof a simulation. It further alternates the operational and structural time-frame. The length of the scenario timeframe depends on the intention ofthe user and also on the required accuracy of the simulation results. If thetimeframe is short, imprecision mostly arises from the aggregation of tech-nologies, infrastructures and the inaccuracy regarding the status quo repre-

sentation. If the timeframe is longer, imprecision can mostly be attributedto the assumed exogenous development of energy prices, tax rates, supportschemes, and technological development, and to the simulation model it-self. Further, the scenario timeframe provides time-series for the prices ofany energy which is imported into the distribution system.

Fig. 2.2. Hierarchy of timeframes

13The maximal (minimal) demand for electricity in Germany on the third Wednes-day in January 1998 has been 71GW (52GW), in April 66GW (46GW), in July62GW (36GW), and in October 69GW (45GW) according to Kramer (2002).The related demand curves differ significantly.

14See http://www.iea.org following the link Energy Information Centre, CountrySearch.


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Assumption IV Modeling Timeframes: A one hour resolution coveringeach day of the year is suitable as the operational timeframe to simulate theoperation of a distribution system appropriately. A one year resolutionprovides a sufficient structural timeframe to model the investment deci-sions into energy technologies and infrastructure in a distribution energysystem. The scenario timeframe alternately activates the operational time-frame and the structural timeframe. It should cover at least 10 years andshould not be extended beyond 50 years.

2.4.3 Technical Layer

The technical layer comprises the energy conversion units, storages, andinfrastructure of the distribution system under investigation. A pro-cess/flow graph is used to represent the network of components whichsource, store, transport, and transform fuels and/or supply energy services.All components and storages are connected by the required networks. En-ergy can be imported into and exported from the distribution system. Alltechnical components are aggregated wherever possible in order to reduce

the amount of data needed. The technical network is further subdividedinto control domains, which reflect the ownership and operational charac-teristics of the system. Each control domain can be operated using a unitcommitment protocol, which determines the dispatch of every technology.

Aggregation of Infrastructure and Technologies

To facilitate numerical modeling, the amount of data used to specify thestatus quo and the investment options is contained by a suitable aggrega-

tion of infrastructure, technology, and efficiency options.Urban areas differ significantly with respect to population density,

types of buildings and their utilization, distances between buildings, andthe energy infrastructure in place. As a consequence, demand density, thenumber of infrastructure connections per square kilometer and associatedcosts depend on the location. Roth et al. (1980) developed a typology ofneighborhoods, which defines distinct sets of parameters to characterizeand distinguish different sections of urban areas. It refers to the predomi-nant type of building stock, distances between buildings, the availability of

different infrastructures (e.g. gas grid, district heating grid, electricity grid)and associated connection costs in a specific section.


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20 2 Model Design

Fig. 2.3.Aggregation of infrastructure15

The annual heat energy demand of the buildings in any one of the sec-tions mostly depends on the size and type of the building and the year itwas built. Over different construction periods, different materials werepredominant. Further, the regulation of energy efficiency has evolved overtime. Typologies of buildings with distinct annual heat energy demandsbased on a classification of type (e.g. single family house, semi-detachedbuilding, small residential building, large residential building, multi-storybuilding) and construction period (e.g. before 1900, 19011918, 19191948, etc.) have been derived for different countries (US DOE 2001,Hake et al. 1999). The relevant data of the present state of a building canbe obtained by combining the original building type and construction pe-riod, with the energy efficiency measures undertaken so far, and the heat-ing system in place.

Energy systems evolve when investment or decommission decisions areundertaken. Different technologies or efficiency measures are normallyavailable. Investors can choose among different types of technologies (e.g.conventional boilers, condensing boilers, heat pumps, cogeneration units,gas turbines, power plants) and they can select different manufacturers. Ef-ficiency options can range from simple maintenance to a complete retrofitof a building. Despite the diversity of products distinct clusters of techno-logy options and efficiency measures can be identified based on statisticaldata and market surveys (Wenzel et al. 1997).

15The figure shows how parts of a city can be clustered into neighborhood typesbased on a satellite photograph. The picture was taken from Google Earth(http://earth.google.com/), an internet based software tool offering satellite pho-tographs for almost every city of the world. The clustering should be accompa-nied by field surveys and interviews with the local infrastructure operators.


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Assumption V Aggregation of Infrastructure and Technology: An urbanarea can be divided into a small number of sections which can be assignedto a representative neighborhood prototype. Each building in a section ofan urban area can be assigned to a representative building prototype. Theannual energy demand of a section can be obtained by adding up the an-nual energy demand of each representative building in its present state.Technologies and efficiency measures can be aggregated to investment op-tions which represent the main differences among products in the productrange.

Control Domains

Control domains16cluster network components and connections which arewithin the responsibility of a single actor and are controlled by the sameunit commitment protocol. Thus, each control domain is associated withthe agent who operates it. Connected control domains are interfaced bygateways which pass across energy demand, intensity, and price informa-tion. Each gateway is associated with one or more legal contract, coveringconnection, market participation and supply. The control domains form acontrol domain graph which is operated sequentially. Each upstream con-

trol domain has to supply the downstream control domains with energy.Hence, the control domain graph is directed and acyclic in terms of de-mand transfer.

Fig. 2.4.Example of a simplified control domain graph (cd: control domain)

16I am thankful to Robbie Morrison for helpful discussions and for suggesting thisterm to me.


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22 2 Model Design

Assumption VI: The energy demand and the technology dispatch withindownstream control domains is not affected by the energy demand andtechnology dispatch in upstream control domains in one time interval.Control domains can therefore be operated sequentially.

Unit Commitment Protocols

The technology dispatch in a control domain is determined by the applica-tion of a unit commitment protocol. Each control domain is treated inde-pendently; its operation is constrained by the components capacities, theenergy demand within the control domain, and the supply obligations fordownstream control domains. Different unit commitment protocols are

possible, ranging from simple heuristics over more sophisticated rules likemerit order to the application of optimization tools.

Simple heuristics may be used when the consumption behavior ofhouseholds is to be modeled. Heuristics include maintaining a fixed indoortemperature during days and nights, a day-night dependent demand for hotwater and electricity services, and daylight dependent use of illumination.The operation of energy conversion units and storage can be determinedusing a merit order rule which dispatches conversion technologies with re-spect to their typical marginal costs. More sophistication is obtained when

optimization solvers are applied.The technical layer integrates routines which can be used to simulate the

unit commitment protocols of different agents. In this work, the dynamicenergy, emission and cost optimization model deeco17 (Bruckner et. al.2003) is used to operate control domains. Energy demand profiles arespecified by time-series which can be obtained by applying heuristics,simulation models or real-world historical measurements. With deeco, thecost minimal share of each component of a control domain which meetsenergy demands is determined by a linear optimization solver. The resolu-

tion of deeco is flexible and can range down to 15 minutes. Deeco supportsa large number of technologies such as boilers, heat pumps, cogenerationplants, solar thermal collectors, storages, photovoltaic and wind genera-tors, heating grids, and steam and gas turbines. The impact of environment

17 The software tool deeco was developed as a PhD project at the University ofWrzburg in Germany by Bruckner (1997). It serves as an analysis tool for dif-ferent research projects and has provided decision support to energy companies.Presently, the software is maintained and further developed at the TechnicalUniversity of Berlin, Germany. Other energy system models providing similarfeatures to deeco could be used as well.


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conditions like temperature, wind speed, and insulation on the performanceof technologies may also be included18.

2.4.4 Agent LayerThe agent layer comprises models of all actors who demand or supply en-ergy in the distribution system. Each agent is associated with one controldomain, located in the technical layer. Agents can select the unit commit-ment protocol for their control domain and thereby operate it, they haveaccess to the operational data of its components, and they may change itsstructure by investment or decommissioning decisions. Further, the agentlayer includes models for actors who trade energy across the boundary ofthe distribution system under investigation. Those agents do not necessar-

ily have their own control domain. If a firm possesses more than one con-trol domain which are either not interconnected or operated with differentunit commitment protocols, the associated actors are grouped to form a le-gal entity, to allow common accounting.

Actors in a distribution system are heterogeneous and may include con-sumers, traders, utilities and independent producers. Actors operate theircontrol domain and invest in technology and infrastructure in differentways based on their preferences, knowledge, resources and habits. Thepayments by private households for energy services are only a small partof their annual consumption expenditure.19As a result, they do not regu-larly optimize their portfolio and evaluate energy investments options. Incontrast, local utilities or independent producers sell energy as their corebusiness, develop long-term strategies, regularly evaluate investment op-tions, and try to minimize the costs of supplying their customers.

Assumption VII Decisions and Actors: A distinction is drawn betweenoperational and structural decisions. Operational decisions concern the unit

commitment protocol and are undertaken in the operational timeframe,structural decisions may alter the structure of a control domain and are un-dertaken in the structural timeframe. Two classes of actors private andcommercial are distinguished to build agent-based operational and struc-tural decision models. The decisions of both classes differ considerablywith regard to knowledge, financial and technical resources, access to in-formation and preferences.

18A list of deeco features can be obtained from http://iet.tu-berlin.de/deeco.19Eurostat (http://epp.eurostat.ec.europa.eu/) estimates the 2004 share of the con-

sumption expenditure of private households for housing, water, electricity, gas,and other fuels to be 21.3% in the EU 25. Transport accounts with 13.5% fol-lowed by food and non-alcoholic beverages with 12.7%.


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24 2 Model Design

Private actors

Private actors consume energy services and operate small conversion unitssuch as boilers, solar thermal collectors, solar cells, and micro-

cogeneration units to supply their own demand. Additionally, they buy en-ergy from retail markets and sign contracts for longer time periods20. Anyoperational and structural decision undertaken by a private actor can onlymarginally affect the entire distribution system. Private actors includehouseholds, private building owners, real estate management companies,and small commercial energy demanders.

Private actors make operational decisions quite frequently e.g. switchingon lights, cooking, washing, watching TV, using hot water, and selectingan indoor room temperature. Mostly such choices are made intuitively;

they do not involve careful considerations. Such behaviors developed overa long time and may not be changed easily (Lutzenhiser 1993).

The structural decisions of private actors include investment in energyefficiency measures and small energy conversion technologies as well assupply contract selection. To make such decisions, the performance data ofdifferent technology and efficiency measure options has to be gathered andthe future energy demand and associated costs need to be estimated. Mostof the information required cannot easily be found in the environment.Further, private actors only rarely face such situations, because the average

lifetime of energy conversion units such as boilers is around 15 years, andrenovation cycles of buildings typically range from 2550 years. In addi-tion, such decisions involve high capital investment which may exceed thebudget of private households; therefore loans are often taken out.

Finally, the many private actors in a distribution system are heterogene-ous. Actors have different budgets and preferences, they differ with respectto their knowledge and their ability to gather and process information.Nevertheless empirical sociological research has revealed that a societycan be divided into groups which can be easily distinguished from one an-

other (Bourdieu 1984).

20Only 3.7% of private consumers switched electricity supply contracts between1998 and 2001 in Germany, according to Bauknecht (2003). The highest switch-ing rates are reported from Sweden, Norway, and the UK, where 29%, 24% and13% of consumers switched respectively, between 2000 and 2005, according toPower UK (2005).


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Assumption VIII Private Actors: Private actors use heuristics to makeoperational decisions. Heuristics used for the private actor decision modelcannot be altered in the scenario timeframe. The information which isneeded to make optimal structural decisions is not necessarily provided bythe environment to private actors. Further, private actors differ with re-spect to their abilities to explore the information provided. Therefore,bounded rational decision models are used to simulate private actor struc-tural decision making processes. Distinct clusters of private actors can bederived empirically, so that one operational and one structural agent deci-sion model can be used to simulate the average decision outcome of allprivate actors belonging to the same cluster.

Commercial actors

Commercial actors operate and/or invest into energy systems as one oftheir core businesses. They trade energy across the borders of the distribu-tion system, run different energy conversion units, offer supply contractsto other actors, and have a certain number of clients. Further, they possessan adequate knowledge of the market, develop and adjust their strategiesand have access to a sufficient budget. Commercial actors include utilities,

gas suppliers, heat suppliers, independent energy producers, and largecommercial energy consumers.The operational decisions, such as unit commitment and market bid-set

formulation, of commercial actors are often supported by sophisticatedsoftware tools 21. Such software holds the technical data of all demands,conversion units, storage, and infrastructure in a control domain, and esti-mates future demand profiles and market prices, taking ambient conditionsand consumer behaviors into account.

Some structural decisions such as the investment in small plants like co-

generation units, boilers, and other distributed technologies, small infra-structure extensions, and plant decommissioning are regularly made bycommercial actors. Those decisions require only limited investment and donot fundamentally alter the asset profile of the firm in question. Otherstructural decisions such as contract price offers have a direct and percep-tible influence on other market participants. Therefore, strategic considera-tions play an important role. In contrast, investments in large power sta-tions, and the development of new infrastructure are rarely carried out andhave a major impact on the future evolution of the firm.

21A range of software tools is commercially available. See e.g.http://www.siemens.at/dems/index_en.htm andhttp://www.procom.de/en/products/bofit for detailed information.


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26 2 Model Design

Firms can be conceived as a bundle of resources, which can be classi-fied in three categories: physical resources, human resources, and organ-izational resources. Physical resources are technologies, infrastructures,plants and equipment, human resources are training, judgment, intelli-

gence, relationships, and insights of individual managers and workers, andorganizational resources are the reporting structure, the formal and infor-mal planning, controlling and coordination systems of a firm. Barney(1991) explains the emergence of sustained competitive advantages amongfirms by the heterogeneity and immobility of some crucial resources.Those resources must be valuable, rare among firms, and imperfectly imi-table. Further, resources are only crucial if there are no strategicallyequivalent substitutes that are as valuable and neither rare nor imperfectlyimitable. Moreover, firms seeking to maintain and increase their competi-tive advantage need to develop their resources in a way that future marketscan be exploited.

Assumption IX Commercial Actors: Operational decisions of commer-cial actors can be simulated using optimization methods in order to findminimal cost solutions. Structural decisions of commercial actors are di-vided into two classes: low-stake and high-stake. Low-stake decisions aremodeled using heuristics based on real-world observations and a rational

choice approach. High-stake decisions will be addressed either by treatingeach decision option as a new scenario or by including human subjects inthe run-time decision loop. Key resources can be clustered and attributedto different business units of a firm. A strategy can be modeled by assign-ing different forms of capital structure (e.g. debt, equity, venture capital,rate of return) to the business unit perceived to be essential for developinga sustained competitive advantage. Structural decision options are evalu-ated with regard to the set of strategies and either selected or rejected.

2.4.5 Energy Markets

Energy which is consumed but not extracted within the distribution systemneeds to be purchased on energy wholesale markets and imported into thesystem. Likewise, energy which is extracted or generated from within thedistribution system but not consumed needs to be exported from the sys-tem and sold on energy wholesale markets. Only commercial agents par-ticipate in wholesale markets. Different energy wholesale markets for elec-tricity, oil, coal, gas, and so on exist. Contracts can be signed for a wholenumber of operational timeframes. Commercial agents can decide to relyon the hourly offers of energy spot markets as well. Usually, the aggre-


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gated demand of all commercial agents in one distribution system on anenergy wholesale market is much smaller than the total demand on thatmarket, which itself provides for the supply of the large number of otherdistribution systems. In this scenario, energy wholesale markets provide

the price information time-series for the entire scenario timeframe exoge-nously. Two time-series are fed into the energy wholesale markets: hourlyprice information and the long-term contract price information. If the im-pact of different price scenarios on national and international markets onthe distribution system is to be investigated, the model needs to be exe-cuted several times. Wholesale market time-series can be generated by anational or transnational energy system model 22.

Retail markets are expected to mediate the commercial interactions be-tween agents in the agent layer. Energy which is exchanged between actorsin the distribution system is traded over retail markets. Commercial agentsmostly act as sellers; private agents mostly act as buyers. Different retailmarkets for electricity, oil, coal, gas, heat, wood pellets, etc. exist. Sellerspost their contract offers on markets, buyers can choose among the avail-able offers. Contracts can be signed for various numbers of operationaltimeframes, each agent needs to hold a contract if she is reliant on energyimports into her control domain. If a transaction between agents is agreed,a contract is signed. Each contract is naturally associated with the gatewaywhich interfaces the control domains of the contractual partners in thetechnical layer. It specifies the conditions of energy exchange regardingprices, capacity and quality. Agents operate their control domain takinginto account the actual contract conditions. Contract formation over retailmarkets involves two steps. First, all suppliers post their offers for an op-erational timeframe at the same time. Thereafter, buyers can select amongoffers.

Assumption X Energy Markets: Two markets, the wholesale market and

the retail market, can be distinguished. Energy which is imported into thedistribution system is purchased on wholesale markets; energy which isexchanged between agents is sold on retail markets. The aggregated de-mand of all commercial actors in the distribution system under investiga-tion on a wholesale market does not alter the price. Wholesale markets arethus inelastic, prices are exogenously supplied. Once a supplier has postedan offer on a retail market it is firm for at least one timeframe. The offersof all sellers for a specific operational timeframe are public information.The number of contracts which can be signed is not limited. All suppliers

post their offers simultaneously.

22Models such as MARKAL, TIMES and GEMS may be used.


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28 2 Model Design

2.4.6 Financial Incentives and Regulations

The development of energy systems not only depends on commodity

prices. Local and national governments aiming to reach their policy goalsimplement a range of measures targeting actors decision making pro-cesses. The model accounts for some aspects of two different measuresgovernments can take to influence the future development of distributionenergy systems: financial schemes and market and network regulations.Financial incentives and regulations are always firm during one scenarioand cannot be influenced by actors decisions.

A range of financial schemes can influence actors in energy markets.Taxes increase the price of energy, support schemes provide financial in-

centives to investors to choose innovative technologies, subsidized interestrates make credits easier, feed-in tariffs reward the generation of electivityfrom renewable resources, and emissions trading schemes charge for emis-sions like CO2or SO2.

Regulatory energy acts define rules for network and market accesses.For certain networks, network operators are obliged to connect users totheir networks free of charge. In contrast, suppliers have to fulfill a rangeof criteria in order to be allowed to feed energy into networks. Likewise,the access to markets is restricted. Generators aiming to sell energy have to

obtain a license to be able to make contracts with consumers.Both financial schemes and regulations are provided exogenously foreach scenario timeframe. If the impact of different policy measures is to beinvestigated, the model needs to be executed several times.

Assumption XI Financial Incentives and Regulations: Financial schemesare modeled by fixed or possibly progressively increasing or decreasingsupport schemes for energy, capital or technologies. These changes areexogenously supplied and are unable to be altered endogenously. Regula-

tions are modeled by restricting network or market access to specific typesof agent. Regulations are exogenously supplied and cannot be alteredendogenously.

2.5 Discussion

The assumptions introduced so far enable us to build a computationalmodel exploring the future evolution of urban energy systems in deregu-lated market environments. The model combines approaches from socio-logy, economics, and engineering science within an integrated framework


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2.5 Discussion 29

that enables the actual structure of energy supply systems, their current op-eration, and their future development to be modeled. It places agents thatutilize local profit maximization routines together with agents that exhibitbounded rationality into a complex setting which itself is characterized by

a range of interdependencies arising from the technical system and frommarket interactions. The decisions of agents alter the operation and struc-ture of the networks connecting them and thereby create a complex andadaptive system.

The proposed model design adds a new dimension to energy systemmodeling. In contrast to the planner-orientated structural optimization ap-proaches which usually rely on a single actors rational choice problemwith prescribed energy demand scenarios, the decision making process andthe interactions between energy providers and consumers are modeled ex-plicitly. This allows for the exploration of the impact of the socio-economic structure of an urban area on technology diffusion, market size,competition and environmental performance. Further, the expectedchanges in urban energy systems can be investigated using a high spatialresolution. Technical, infrastructural, economic and socio-economic re-gional differences within cities as well as differences between cities can bemodeled. In addition, the overall model provides detailed decision modelsto the agent-based simulation domain. Most agent-based decision modelsapplied so far use heuristics which are based on trial-and-error rules or ra-tional choice approaches.

Although the model accounts for a range of actors and their interactions,its application has some limitations. Firstly, it is restricted to energy sys-tems within urban areas. The pricing of energy on wholesale and spot mar-kets as well as the emissions in the superordinated systems cannot besimulated and must be supplied exogenously. Further, the set of actors islimited to those who participate in urban energy markets. Interactions be-tween governments, lobby organizations, researchers, and generators andconsumers are not included. In addition, not all actor decisions are mod-eled endogenously; the so called high-stake decisions of commercial actorsneed to be supplied exogenously as scenarios or human input. Finally, themodel requires a considerable amount of structural, technological, andsocio-economical data to be parameterized. Nevertheless, the expectedoutcomes and new insights justify the development efforts undertaken.


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3 Private Actor Model

3.1 Introduction

A good understanding as to how the private demand for energy carriers

(electricity, oil, gas, district heating, wood pellets, etc.) and technologies

will change over time is essential for stakeholders. For instance, residentialheat demand depends on the insulation standard of the building and the

consumption behavior of the occupants. Heat can be supplied by a range of

conventional (e.g. gas and oil boilers) and new technologies (e.g. micro-

cogeneration, pellet boilers, solar thermal installations). Electricity demand

likewise depends on the technologies available and utilization patterns.

The thermal performance of buildings, the conversion technologies avail-

able, and consumption profiles may therefore have a major influence on

market size, competition levels on supply markets, prices, consumer rela-tions, overall CO2emissions, and supply security.Research on technology diffusion especially focuses on the question

when and how fast things happen. Diffusion problems mostly involvemany people making decisions, often in an interdependent manner. Fur-ther, no basic reference points,which could be used as a metric to measurethe passage of time, are available for such processes. Therefore, mosttechnology diffusion models focus on the stylized fact that the time path ofusage usually follows an S-shaped curve: diffusion rates first rise and then

fall over time, leading to a period of slow take-up, followed by a relativelyrapid adoption and finally to a late period of a slow approach to saturation(Geroski 2000).

The two most popular explanations of S-curves are epidemic models ofinformation diffusion, and probit models arguing that differences in adop-tion time reflect differences in goals, needs and abilities of individuals orfirms (Geroski 2000). This chapter develops a probit model of the diffu-sion of energy technologies and energy efficiency measures in urban areas.It is related to two strands of literature. The first one discusses other probit

approaches of technology diffusion such as classic threshold models(Valente 1996), models of firms adaptation (Davies 1979), the TechnologyAcceptance Model (Davis 1989), the Theory of Planned Behavior (Ajzen


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32 3 Private Actor Model

1991), and its application to innovation diffusion (Rogers 1995, Venkateshet al. 2003).

The second strand discusses bounded rational decision models and theirempirical foundation. Simon (1956 and 1957) may serve as a starting point

to bounded rationality decision making. Further, Gigerenzer et al. (1999)identified non optimizing decision heuristics and showed how they canbenefit decision makers. Some further examples from a law context can befound in Gigerenzer and Engel (2006). Finally, Bettman et al. (1998) de-veloped an integrated framework for bounded rational consumer choices.

This chapter introduces a bounded rational decision model of energytechnology and efficiency diffusion, which ideally would be parameterizedusing socio-demographic surveys. The bounded rationality approach rec-ognizes that the information needed to take optimal investment decisions isnot necessarily provided by the environment to building owners. Further,building owners might differ in their abilities to explore the informationprovided. Modeling proceeds by distilling the large number of individualdecision problems into a number of representative decision problems byaggregating the technological and infrastructural data. This technologicalaggregation is complemented by a socio-economic clustering which allowsthe replacement of the large number of individual decision makers bystereotyped decision makers that are representative of the class to whichthey belong. This chapter presents a bounded rational decision model thatenables researches and decision makers to estimate the development of en-ergy demand within the residential building sector with respect to individ-ual investments, indicates how the model parameters might be derivedfrom socio-demographic surveys, and offers some results.

3.2 Private Energy Investment Decisions


There is evidence from numerous classic engineering-economic studiesthat potential investments in energy efficiency, which appear to be cost-effective, remain unexploited (Jochem 1999, Interlaboratory WorkingGroup 2000, Productivity Commission 2005, Jakob 2005). Researchershave sought to understand why the observed investment behaviors ofbuilding owners differ from estimated scenarios applying different frame-

works. The three most important ones are: neoclassical economics, behav-ioral economics, and institutional economics (Sorrell et al. 2000 and 2004,Weber 1997, Jaffe and Starvins 1994). These frameworks offer a starting


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3.2 Private Energy Investment Decisions 33

point to study both the decision maker and the properties and structures oftheir environments. Table 3.1 introduces each perspective.

Table 3.1.Perspectives on energy efficiency investment

Perspective Issues Actors

neoclassical imperfect information, asymmet-ric information, hidden cost, risk,heterogeneity of actors

individuals and organizations con-ceived as rational and utility maxi-mizing

behavioral world does not permit optimiza-tion, problems are computation-ally intractable or poorly defined

individuals conceived as bound-edly rational, who apply identifi-able rules and heuristics to deci-sion making

institutional organizational culture, manage-

ment time and attention

organizations conceived as social

systems influenced by goals, rou-tines, internal culture, power struc-tures, etc.

Adopted from Sorrell et al. 2000

This chapter focuses on investment decisions related to retrofitted en-ergy efficiency measures and energy conversion technologies in the resi-dential sector. These decisions are mostly undertaken by individual house-holds, private building owners, and property management companies.

Despite the institutional character of property management companies, thisperspective is not particularly relevant to the chosen topic and thereforenot applied nor discussed in the remainder of the chapter23.

3.2.2 Neoclassical Perspective

Classic engineering-economic studies rely on the neoclassical perspective.Nonetheless, outcomes deviate from observed behavior patterns so that ar-

tificial constants such as a percentage of compliance to standards or fixedconstruction rates for renewable technologies have to be introduced. Theneed for those constants is motivated by the difficulties to include imper-fect and asymmetric information, the hete

agent-based models of energy investment decisions

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