Dynamics of Energy Systems Methods of analysing technology change

Lena Neij

May 1999

Thesis for the Degree of Doctor of Philosophy in Engineering Department Environmental and Energy Systems Studies Lund University © 1999, Lena Neij and the respective publishers Printed at KFS AB, Lund, Sweden

ISRN LUTFD2/TFEM—99/1019—SE + (1-136) ISBN 91-88360-42-3

LenaNeij Department of Environmental and Energy Systems Studies Lund Institute of Technology Lund University P.O. Box 118 SE-221 00 Lund, Sweden DISCLAIMER

Portions of this document may be illegible in electronic image products. Images are produced from the best available original document. enl SIS 61 41 21 ISSN Supplementary Key Recipient Classification Signature. reference I, Department Distribution Author(s) Title LUND Organization

the undersigned, transformation the technologies. reduction could reduction. illustrates instruments used air-distribution technological change types Technology investments could experience programmes. accelerating analysing Abstract

words

Energy, distribution, and Dynamics Lena Energy Department Gerdagatan and

introduction UNIVERSITY Two

Moreover, to

subtitle

of ’ lead decrease sources key s

Neij

provide

and notes of

methods

a

by Systems energy of methods the

The

system

title Environmental to

need

curves. of bibliographical in change (name PV that methods technology

13,

major The investments, of experience dynamics

permission Energy results bringing in programmes. over

a of insight

being

Environmental generated for SE-223 system,

and/or

model Sweden. effect have Studies new method

are

and

more diffusion will Vintage time,

Systems:

show of

the

generations into

been focused

of the address)

change. 62 has energy have

curves, technology evaluating index

extensive and

copyright

to relative The electricity are energy

of LUND, the potential

costs

publish and information been considered,

The

models, Energy of evaluation

a applied

results prospects and Methods terms

efficiency,

on wind central wind

In

down

systems developed evaluation

of to

SWEDEN evaluation

this analysing

policy owner

for down

PV

Systems change

a power, of (if turbines. to which

reference (through role

thesis, considerable of

the methods modules any) of the

has

disseminate

in renewable

to analysing

of instruments diffusion

analyses in

analysis for

view

PV been the methods and model, Studies, the allow the

achieving The such

the

RD&D,

modules,

scenario, (e.g. level

of abstract

dynamics of the applied

results

evaluation

analysing technology methods of

for energy, cost show of

technology

and

Gerdagatan the than high-efficiency

of

introduction wind effecting

the

market complex a conventional reduction

abstract

Security Number the Sponsoring Date DOCTORAL market CODEN. Document

of that if

those also to sustainable

time

policy sustainable

of turbines the

have assess

assessment and

the

of

of show

change

future

used incentives change dynamics

above-mentioned technology

transformation, 136 May 13,

market

issue Dat LUTFD2/TFEM

of Swedish analysis projecting

Classification of for been

instruments and

S-223 methods the mass-produced and name that today.

Pages

e

Organization base-load 21,19 energy wind-generated

technology

energy,

and

DISSERTATION

developed, above-mentioned

transformation commercialisation photo

major

of of April and 62 electricity

of policy

99

change,

the electricity

the Lund, used system.

vintage investments voltaic annual

are diffusion

9. electricity, evaluation dynamics

Swedish

dissertation, hereby dissertation,

- change;

1999

instruments

to

used. - thin-film applied, Sweden

99/1019

i.e. demand

This electricity, evaluate (PV) models,

streams demand programmes,

market Language Experience ISBN of Price

dissertation. will

of evaluation in calls vintage

modules

PV of

and

cells).

- for niche future

lighting, for

Swedish - depend

91-88360-42-3 English energy-efficient for

modules, of

for which, SE these transformation assessed.

effecting

lighting models

energy markets) +

methods

technology due curves i.e.

(1-136) methods,

purposes

on air- grant

in market to

policy

and

large

turn, Two

cost and and and and are

or of to a

all

Dynamics of Energy Systems Methods of analysing technology change

Lena Neij

AKADEMISK AVHANDLING

som for avlaggande av teknologie doktorsexamen vid tekniska fakulteten vid Lunds Universitet kommer att offentligen forsvaras i Horsal A vid Institutionen for fysik, Solvegatan 14, fredagen den 21maj 1999, klockan 13.15 Preface

This thesis is performed within the interdisciplinary research field of environmental and energy systems analysis. Energy systems involves the interplay between several factors such as energy, social and economic development, technology, the environment, resource management, equity and security. The work presented in this thesis is directed to the analysis of technological systems. The thesis includes five articles which have been published in, or submitted to, scientific journals. The original articles, which are referred to by their Roman numerals I-V, are collected at the end of this thesis. The introductory essay (Chapters 1-8) is intended to provide a summary of the articles and put the research into perspective. The first chapter presents the issues that form the background and the motivation for the work carried out. Such issues include, for example, the need for technology change within energy systems as well as the need for methods to analyse and effect such changes. Chapter 2 describes the theory of technology change, and more specifically technology change within energy systems. The theoretical background presented in this chapter forms a platform for all the methods that are developed, applied and assessed within this thesis, for the analysis of the dynamics of technology change within energy systems. These methods are presented in Chapters 3-5. Chapter 3 characterises the method of vintage models, which is applied to the analysis of the dynamics of energy efficiency. This chapter opens with a general presentation of such models, and is followed by a summary of the work presented in Articles I and II. Chapter 4 characterises the method of experience curves, which is applied to analyse cost reduction as a function of cumulative production. A general description of experience curves is given, followed by a discussion on potential and limitations of such curves. The work presented in Articles III and IV is also summarised. Chapter 5 discusses different policy instruments and the evaluation of energy efficiency programmes, specifically market transformationprogrammes (Article V). The conclusions of the work and a discussion on the aims and directions for future work are presented in Chapter 6. The articles are based on independent work performed by the author of this thesis at the Department of Environmental and Energy Systems Studies at Lund University. However, Article I was drafted mainly by my co-author and advisor at the time, Dr Joel Swisher. In this process, I assisted Dr Swisher in the modelling work, and I also performed the analysis of energy use in industry and street lighting. It should also be mentioned that the work resulting in Articles III and IV, i.e. the use of experience curves, was initiated during my participation in the Young Scientists’ Summer Programme at HAS A in the summer of 1994 under the supervision of Dr Arnulf Griibler. The work presented in the five articles has been further extended and discussed in conference papers, reports and book chapters not included in this thesis. In the introductory essay, however, reference is made to such additional publications.

Acknowledgements

First, I would like to thank my advisor, Professor Thomas B Johansson, for guiding me into the field of energy system analysis. I would also like to thank my assistant advisor, Lars Nilsson, for his support and constructive criticism throughout the course of this work. I would like to express my appreciation to Dr Joel Swisher for supervision and fruitful co-operation during the first year of my post-graduate studies. Likewise, I owe many thanks to Dr Arnulf Griibler, for enthusiastic discussions and supervision during my time as a summer student at HAS A in 1994. I would also like to thank my colleagues, Bengt Johansson and Peter Helby, for constructive criticism. In fact, I would like to thank all my colleagues at the Department of Environmental and Energy Systems Studies for encouraging me in my work. Finally, I would like to thank my husband Hans, for his never-ending encouragement and emotional support. Financial support by the Energy System Studies (AES) Programme of the Swedish National Board for Industrial and Technical Development (NUTEK) is gratefully acknowledged. Abstract

Technology change will have a central role in achieving a sustainable energy system. This calls for methods of analysing the dynamics of energy systems in view of technology change and policy instruments for effecting and accelerating technology change. In this thesis, such methods have been developed, applied, and assessed. Two types of methods have been considered, methods of analysing and projecting the dynamics of future technology change and methods of evaluating policy instruments effecting technology change, i.e. market transformation programmes. Two methods are focused on analysing the dynamics of future technology change; vintage models and experience curves. Vintage models, which allow for complex analysis of annual streams of energy and technological investments, are applied to the analysis of the time dynamics of electricity demand for lighting and air-distribution in Sweden. The results of the analyses show that the Swedish electricity demand for these purposes could decrease over time, relative to a reference scenario, if policy instruments are used. Experience curves are used to provide insight into the prospects of diffusion of wind turbines and photo voltaic (PV) modules due to cost reduction. The results show potential for considerable cost reduction for wind-generated electricity, which, in turn, could lead to major diffusion of wind turbines. The results also show that major diffusion of PV modules, and a reduction of PV generated electricity down to the level of conventional base-load electricity, will depend on large investments in bringing the costs down (through RD&D, market incentives and investments in niche markets) or the introduction of new generations of PV modules (e.g. high-efficiency mass-produced thin-film cells). Moreover, a model has been developed for the evaluation of market transformation programmes, i.e. policy instruments that effect technology change and the introduction and commercialisation of energy-efficient technologies. The method of evaluation has been applied to assess methods used to evaluate Swedish market transformation programmes. The evaluation model, and the assessment of the Swedish evaluation methods, illustrates a need for more extensive evaluation methods than those used today. List of Publications

This doctoral thesis is based on the following articles, which will be referred to by their Romannumerals:

I. Swisher J., Christiansson L. and Hedenstrom C., Dynamics of Energy- Efficient Lighting, Energy Policy, Vol. 22, No. 7, pp. 581-594,1994.

II. Christiansson, L , Time Dynamics of Electricity Demand in Air-Distribution Systems for Commercial Buildings in Sweden, Energy-The International Journal, Vol. 21, No. 10, pp. 879-888, 1996.

III. Neij,* L., Use of Experience Curves to Analyse the Prospects for Diffusion and Adoption of Renewable Energy Technology, Energy Policy, Vol. 23, No. 13, 1997.

IV. Neij,* L., Cost Dynamics of Wind Power, accepted for publication in Energy-The International Journal, Vol. 24, No. 5, pp. 375-389, 1999.

V. Neij,* L., Methods of Evaluating Market Transformation Programmes: Experience in Sweden, submitted, 1999.

nee Christiansson Contents

1. Introduction...... 1 1.1 Energy systems in transition...... 1 1.2 Methods of analysing the dynamics of energy systems...... 4 1.3 Methods of evaluating policy instruments...... 7 1.4 Objectives of this thesis...... 7

2. Theory of Technology Change...... 9 2.1 The dynamics of technology change...... 9 2.2 The dynamics of technology change within energy systems...... 11 2.3 The timing of technology change within energy systems...... 12

3. Methods of Analysing Future Technology Change: Vintage Models...... 15 3.1 Dynamics of energy demand...... 15 3.2 Vintage modelsused to analyse the Swedish electricity demand...... 17 3.3 Analysis of the Swedish electricity demand...... 18 3.4 Concludingremarks ...... 20

4. Methods of Analysing Future Technology Change: Experience Curves.....21 4.1 Definition of experience curves...... 21 4.2 Cost reductions illustrated by experience curves...... 23 4.3 Analysing the cost reduction of renewable energy technologies...... 25 4.4 Cost dynamics of wind turbines and PV modules...... 26 4.5 Concluding remarks ...... 31 5. Methods of EffectingTechnology Change...... 33 5.1 Approaches to promote technology change...... 34 5.2 Approaches to promote energy efficiency...... 36 5.3 The evaluation of policy instruments...... 37 5.4 The evaluation of market transformation programmes...... 38 5.5 Concluding remarks ...... 39

6. Conclusions and Final Remarks...... 41

References...... 43

Articles I-V 1. Introduction

The development of modem society has resulted in a constantly growing demand for energy and the exploitation of energy resources. The increase in energy demand has contributed to wealth in some parts of the world, but has at the same time caused major problems (WCED, 1987; UNCED, 1992; Reddy et ah, 1997). The way in which energy is used and supplied today has a negative impact on the local, regional and global environment and security. This impact will increase considerably if energy demand continues to increase, and if the composition of the energy supply of today remains unchanged. This situation demands a major transition of the energy system and the development and deployment of new and improved energy technologies. This, in turn, calls for methods of analysing the dynamics of energy systems in view of technology change and policy instruments for effecting and accelerating technology change. This thesis describes work carried out to develop, apply, and assess methods that can be used for this purpose.

1.1 Energy systems in transition

Energy systems have changed over time. The demand for energy has increased considerably since the industrial revolution; primary energy use has increased from 0.2 Gtoe in 1850 to 9 Otoe in 1990 (Grubler, 1998). This is the result of an increase in demand for energy services, e.g. transportation, indoor climate regulation, illumination, cooking, and production of consumer goods. The structure of energy supply systems has changed, from being relatively local systems, based on traditional, renewable energy sources, to diverse and complex systems, based on several energy supply sources and energy carriers. Since the industrial revolution, fossil fuels, hydro-power, and nuclear power have been introduced, see Figure 1.1. According to Grubler (1998), two “grand transitions” have governed the development of energy systems: first, the introduction of the coal-fired steam engine which made it possible to convert coal into work, and 2 Dynamics of Energy Systems second, the increase in diversification of energy end-use technologies and energy supply sources. Important innovations for the second transition were the internal combustion engine and electricity as an energy carrier.

Oil (incl. feedstocks)-

Wood

Hydro ~ Nuclear 1850 1870 1890 1910 1930 1950 1970 1990 Year

Figure 1.1 Global primary energy use from 1850 to 1990, in percent (Nakicenovic et al., 1998).

Today, global commercial energy consumption relies, to more than 90%, on fossil fuels (UN, 1997). The use of fossil fuels causes, among other things, urban air pollution, oil spills, acidification, and climate change (Reddy et al., 1997). The risk of climate change has received much attention during the past decade. Despite remaining uncertainties about the rate and magnitude of climate change, there is general consensus that there is an increase in anthropogenic emissions of greenhouse gases. Energy systems alone contribute to more than half of the anthropogenic emissions of greenhouse gases (IPCC, 1996). Apart from environmental problems, energy systems of today cause security concerns. The reliance on fossil fuels is a challenge to geopolitical stability and causes economic vulnerability since oil resources are located in limited regions of the world. Nuclear power is also questioned due to public concerns on the safety of nuclear reactors, uncertainty about the disposal of nuclear waste, and the associated risk of nuclear weapon proliferation. The effects on the environment and security associated with energy systems will increase considerably if energy demand continues to increase and the composition of the energy supply of today remains unchanged. To address these concerns, further development of energy systems will require a major change. Such a change can be described in terms of “sustainable development”, a term that has been used since the 1980s. A general definition of sustainable development is development “that ensures the needs of present generations without compromising the ability of future generations to 1. Introduction 3 meet their own needs” (WCED, 1987). 1 In the 1990s, the United Nations convened several conferences, all with the vision of sustainable development. These conferences have also articulated the need for sustainable energy systems and have called for improved energy efficiency and dissemination of (new) renewable energy. In Agenda 21 (Chapter 9 “Protection of the Atmosphere”), produced at the 1992 UN Conference of Environment and Development in Rio Janeiro, it is stated that (UNCED, 1992):

“Energy is essential to economic and social development and improved quality of life. Much of the world’s energy, however, is currently produced and consumed in ways that could not be sustained if technology were to remain constant and if overall quantities were to increase substantially. The need to control atmospheric emissions of greenhouse gases and other gases and substances will increasingly need to be based on efficiency in energy production, transmission, distribution and consumption, and on growing reliance on environmentally sound energy systems, particularly new and renewable sources of energy.”

Improved energy efficiency and increased use of (new) renewable energy sources will, in turn, depend on the development and deployment of new and improved energy technologies. As with former transitions of energy systems, a shift towards sustainable energy systems will depend on technology change (cf. the “grand transitions” defined by Grubler, 1998). Improving energy efficiency means decreasing the use of energy required to provide energy services, e.g. cooling, heating, lighting and transportation. One indicatorused to evaluate energy efficiency is energy intensity, which describes the energy consumption per unit of value added. Long-term trends in several industrialised countries and in some developing countries show a decrease in energy intensities (IEA, 1987; Nilsson, 1993; Nakicenovic et al., 1998; Schipper, 1997). These trends result from structural changes, i.e. decreasing production in energy-intensive sectors (Williams et al., 1987) and efficiency improvements in extraction, conversion, distribution and end-use technologies. A wide body of literature shows that the potential for further efficiency improvements is considerable, and that this potential could be realised through the introduction of technologies with improved energy efficiency (Johansson et al., 1989; WEC, 1995; Worell et al., 1997). The introduction of energy-efficient technologies is, however, limited by various market barriers, and consequently policy instruments are needed to promote and accelerate the development of sustainable energy systems (see Chapter 5). New energy technologies will also enable the use of (new) renewable energy resources such as biomass, solar energy, and wind power (Johansson et al., 1993). The

1 Several definitions of a sustainable development can be found, see, for example, Daly, 1990; Pearce and Turner, 1990. For a discussion of these definitions, see Kagesson (1997). 4 Dynamics of Energy Systems potential of these resources is many times greater than present energy use (Johanssonet al., 1993). The use of these resources will require development and deployment of technologies for both power and fuel generation (IEA, 1997a). However, the development and introduction of renewable energy technologies also face market barriers and will also depend on policy instruments for their implementation (IEA, 1997a). The central role of technology change in achieving sustainable energy systems calls for integration of the dynamics of technological change and the effects of policy instruments in energy systems analyses.

1.2 Methods of analysing the dynamics of energy systems

Several methods have been developed for the analysis of the development of energy systems; methods that range from simple trend analyses to complicated models. These methods are important tools in understanding the dynamics of energy systems and as an aid in energy planning and strategic decision making. The first approaches to the analysis of energy system development were focused on prediction. This approach, essentially based on methods that extrapolated past energy economic relationships, was unable to capture the uncertainty involved in long-term projections. Moreover, energy planning and R&D priorities were influenced by such predictions. The limitations of the prediction approach, together with environmental and security concerns triggered by the oil crisis in the early 1970s, the perceived risks of resource depletion, and the nuclear risk debate, led to the development of approaches that described alternative future development paths (see, for example, Lovins, 1977; Johansson and Steen, 1977). 2 Such approaches provided a foundation for the development and use of multiple scenarios. Over time, two categories of analysis methods have emerged; the top-down methods, which are based on historical energy-economic relationships, and the bottom-up methods, which explicitly analyse alternative energy use and energy supply options.3 (For structural differentiation between top-down and bottom-up models see Table 1.1.) Both top-down and bottom-up methods have been developed over the years, and today most models use the multiple-scenario approach. The results of the two methods, however, often differ considerably, due to different perspectives of energy systems,

2 The publication of “Energy in a Finite World” (Hafele, 1980; IIASA, 1981) and the criticism of this publication (Keepin, 1984; Wynne, 1984) intensified the debate about alternative energy futures and alternative modellingmethods.

3 The top-down and bottom-up categorisation does not correctly describe the whole spectrum of models. However, this categorisation is often used to group the energy system models. 1. Introduction 5 different model structures and different input assumptions (see, for example, Wilson and Swisher, 1993; IPCC, 1996). Today, there is a growing consensus that both methods have their strengths and weaknesses in analysing the dynamics of technology change and the effect of different policy instruments. The two approaches have also been combined in “hybrid models”.

Table 1.1 Structural differentiation between top-down and bottom-up models (based on IPCC, 1996).

Structural dimension Top-down models Bottom-up models

Predictiveorientation High-decreasing Low-increasing Detail on non-energy sectors High Low-increasing Detail on energy end-uses Low-increasing High Detail on energy supply technologies Low-increasing High

The top-down methods, developed mainly by economists, are based on standard economic indicators (such as GDP growth, elasticities, and energy prices). The methods account for technology change via two parameters, the elasticity of substitution and the autonomous energy efficiency index (AEEI). The AEEI factor, which captures non­ price-induced energy intensity reductions over time (for example, economic structural change, changes in lifestyle, and technology development), is specified exogenously and ranges typically between 0 and 1.5 % per year (Grubb et al., 1993). In top-down models, the energy sector, treated as aggregate or quasi-aggregate demand functions, is linked to other sectors of society. This makes it possible to analyse feedback effects and the impact of energy taxes. Many top-down models have been developed primarily for the purpose of economic policy analysis. The bottom-up methods were originally developed for the analysis of the specific energy needs of a given sector, e.g. transportation, industry, service or households. The methodology is based on customer needs and demand for energy services, not energy per se, and it is recognised that the same energy services can be produced with different levels of energy with different technologies. The models, which are based on disaggregated data, describe the development of energy demand and energy supply per sector and demonstrate potential gains from specific end-use and production technologies. Spreadsheet-based models are the most pronounced type of bottom-up models (see, for example, Goldemberg et al., 1988; Bodlund et al., 1989; Johansson et al., 1993; IPCC, 1996). These models, which aim at a simple and transparent design and analysis, demonstrate the potential gains in adopting different technologies with superior 6 Dynamics of Energy Systems performance.4 At first, these models used only technical parameters to analyse potential gains in adopting efficient technology options. In time, economic parameters were included. The latest models, the vintage models, also take into account the capital stock turnover and can be used to analyse technology-directed policy programmes and their administrative costs (see Chapter 3). Optimisation models are more complex bottom-up models using linear or non-linear programming methods. These models are designed to identify optimal least-cost scenarios by analysing competing energy sources and technologies. The models consider the energy flows and energy technologies from extraction through transformation, transportation and distribution to the different sectoral end-use categories. (In some models, energy demand is treated exogenously.) Together, top-down and bottom-up models embody the dynamics of technology change to some extent. Top-down models, however, only treat technology change as a “black box”, in which technology change will depend on the assumed economic development. Bottom-up models, on the other hand, endogenously use alternative technology options and simulate the introduction of new energy-efficient technologies. The timing of the introduction, however, depends on exogenous assumptions with respect to the time of introduction, investment cost development, and technology lifetime. Until recently, the simulation of technology development (e.g. performance and cost development) had been indistinct in both top-down and bottom-up models. However, during the time frame of this thesis, experience curves have come to be used in energy systems analysis, allowing for the integration of cost development in the analyses (see Chapter 4). In most models, however, technology development is still based on assumptions and expert judgement. When it comes to analysing the effects of policy programmes, top-downand bottom- up models serve different purposes. Top-down models are designed to explore the effects on society of changing fuel prices and taxation. Bottom-up models, however, are unable to capture feedback effects from the energy sector to other sectors and can therefore not be used to analyse macro-economic effects of taxation. Some bottom-up models, e.g., the vintage models, can, however, be used to analyse the effects of technology-directed policy programmes. On the whole, the correct choice of a model will depend on the underlying problem. Top-down models may be used to analyse the impact of taxation, while bottom-up models may be used to analyse the impact of technology-directed policy programmes. A complex issue may also require a combination of different complementary models.

4 The working scheme of a typical bottom-up scenario in a spreadsheet model is described by Christiansson et al. (1995). 1. Introduction 7

1.3 Methods for evaluating policy instruments

The models described above can be used to analyse and project the effects of different policy instruments. The use of policy instruments to effect technology change will require methods for evaluation and verification. Such evaluation methods can be very simple and focus only on the cost of produced fuels or electricity or the cost of saved energy. However, analysing the potential and limitations in effecting technology change will require more extensive evaluation methods. Such evaluation methods must include parameters that describe technology development, market development and changes in actors’ behaviour.

1.4Objectives of this thesis

The primary objective of the work presented in this thesis was to develop, apply and assess methods that can be used to analyse the dynamics of technology change within energy systems - methods that are based on the understanding of technology change within energy systems. The work focuses on methods of analysing the diffusion and adoption of new energy technologies. The aim of Articles I and II was to apply bottom-up vintage models to analyse the time dynamics of energy demand as a function of technology change (i.e. market penetration of specific energy-efficient technology options) and policy programmes. The focus was on improved electricity efficiency for lighting and air-distribution in Sweden. The aim of Articles III and IV was to apply experience curves to the analysis of cost reduction, as well as potential and limitations of the diffusion of modular renewable energy technologies. The focus was on cost reduction of wind turbines and photovoltaic (PV) modules. The aim of Article V was to develop a model for evaluating market transformation programmes, and to use this model to assess methods used today for evaluating market transformation programmes. (Market transformation programmes are policy instruments for effecting technology change towards more energy-efficient technologies.) The evaluations of Swedish market transformation programmes have been assessed. 8 Dynamics of Energy Systems 2. Theory of Technology Change

Interest in technology change has increased within several disciplines, e.g. energy systems analysis, political science, economics, sociology, history and geography. The reason for this is growing awareness of the importance of technology change for the development of society. By improving our understanding of the forces behind technology change, improved methods can be developed for analysing and guiding the development of society, for example, the development of energy systems. The geographer Hagerstrand (1991) says the following about the importance of understanding the forces behind diffusion(including technology change):

“One can think of many reasons why it is important to understand forces behind diffusion. One obvious reason is the use that people in public policy and industry can make of improved methods for prediction. A further reason - I believe the most important in our present historical situation - is the increasing necessity for global and national policy makers to concentrate on such innovations that put the world on the road towards sustainable development. ”

2.1 The dynamics of technology change

Technology change was initially assumed to follow a linear model, i.e. a linear progression from invention through innovation to diffusion5 (Schumpeter, 1934). Over the years, considerable progress has been made in understanding the underlying forces of innovation and diffusion (see, for example, Rodgers, 1962; Nelson and Winter, 1982; Rosenberg, 1982, 1994; Dosi et al., 1988; Nakicenovic and Griibler, 1991; Freeman, 1994). As a result of this research it has been acknowledged that the process of technology change is considerably more complex than first assumed. It has been recognised that an innovation is usually improved and considerably developed during the diffusion process. The improvements may be radical or

5 Diffusion is defined as market penetration over time for an innovation. 10 Dynamic of Energy Systems incremental (Freeman and Perez, 1988). Incremental improvements include minor continuous changes to processes and products often associated with scaling effects and quality improvements. Radical improvements include discrete and discontinuous events that are the result of R&D investment in enterprises, universities or government laboratories. The diffusion of innovations generally generates incremental improvements due to learning effects - learning by doing and learning by using (see, for example, Arrow, 1962; Rosenberg, 1982). The learning process involves experience gained by producers, customers, suppliers etc. The learning process not only leads to improvements in the product and the production process, but also to organisational improvements and competence-building.6 Moreover, it has been recognised thatthe diffusion process and the adoption of new technologies is stimulated by the relative advantages of new products and processes compared with existing products or processes. The relative advantage will be defined by parameters such as performance, profitability, compatibility and complexity (Rodgers, 1962). However, the diffusion process will not only depend on the present relative advantages but also on previous technology choices and expectations of future technical performance and cost (Rosenberg, 1982). Thus, the diffusion process can be described as a trajectory of technical progress, i.e. a path along which technologies develop based on previous choices and on future expectations (Nelson and Winter, 1977; Dosi, 1982).6 The technological progress along any one trajectory is related to the development of infrastructures, complementary technologies, organisations, policy measures, etc. Thus, technology change can be defined by techno-economic paradigms of interrelated technical, organisational, and social innovations (Freeman and Perez, 1988). The trajectories and paradigms will provide the possibility, or the risk, of lock-in effects due to clusters of technology improvements (Arthur, 1988). On the one hand, clustering is advantageous and enables rapid technology change through scale effects, learning effects and standardization. On the other hand, clustering is disadvantageous and limits the introduction of new alternative technology options (due to higher initial costs and/or a need for additional technology investments, new infrastructure, and new educational and training systems). Technology change has been described as dynamic, uncertain, systemic, and cumulative (Griibler, 1998): dynamic since technology keeps changing all the time; uncertain since it will not be clear in advance which technologies have the greatest potential; systemic since the employment of individual technologies depends on the employment of other technologies; and cumulative since the pattern of change depends on previous choices, experience and knowledge.

6 For competence building within the energy sector and wind turbine industry in Denmark, see Kamo (1996). For trajectories within the energy sector and wind turbines, see Kamo and Garud (1997). 2. Theory of Technology Change 11

2.2 The dynamics of technology change within energy systems

The dynamics of technology change within energy systems depends on the flexibility of the system and the diffusion, adoption and development of new energy technologies. The flexibility of energy systems depends on the rate of substitution of technologies in energy systems and the increasing investments in capital stock (defined by the economic growth). Generally, the rate of substitution is closely related to the lifetime of the capital stock of the system. Since many technologies integrated within energy systems have long lifetimes, or are integrated in systems with long lifetimes, the flexibility resulting from substitution will be low. Moreover, many technologies are capital intensive. Especially energy supply technologies have long lifetimes and are often integrated in large electricity or fuel supply systems and infrastructures. Energy end-use technologies, however, constitute parts of heterogeneous systems in which the substitution rate of the technologies differs considerably - some technologies have long lifetimes (furnaces for steal making, etc.) whereas other have short lifetimes (lamps, etc.) - some technologies are integrated in systems with long lifetimes (motor systems, production systems, buildings, etc.) whereas other technologies are not integrated into any system (washing machines, refrigerators, copiers, etc.) - some technologies depend on the existence and characteristics of infrastructure (cars, etc.) while others do not. The diffusion and adoption of new energy technologies will depend on the technological alternatives available and their relative advantages. The alternative technologies will differ in performance (including energy efficiency), profitability, compatibility, complexity, technological expectations, etc. The cost difference (i.e. cost differences between different technologies and the cost of replacing infrastructure) is usually the most important factor with respect to adoption. The profitability of new energy technologies will depend on discount rates.7 The lifecycle cost of energy end-use technologies will further depend on energy efficiency and energy prices. In Chapter 3, the adoption of different alternative energy end-use technologies is analysed, based on energy efficiency, capital cost, energy prices and different policy instruments. When analysing the dynamics of technology change it is important to recognise that the relative advantages, including technology and cost development, will change over time. Technology development will be either incremental (see the development of wind turbines and photovoltaic (PV) modules in Articles III and IV) or radical (see the development of photovoltaic modules in Article III). The development of energy supply technologies will often be directed towards improved energy efficiency. However, the developers and adopters of end-use technologies often prioritise factors other than energy efficiency. Cost development will depend on technology development and on the accumulation of experience in early markets which ensures learning and scale effects

7 The measured discount rates differ significantly between energy users and energy suppliers. Investments in new power plants are often evaluated using real discount rates of 4-6 percent. In contrast, end-users often require discount rates of 30-100 percent (Christiansson et al., 1995). 12 Dynamic of Energy Systems

(see Chapter 4). Many end-use energy technologies and renewable energy technologies are considered suitable targets for cost reduction since they are modular and enable economics of scale, i.e. producing large number of identical units (see Article III). The diffusion and adoption of energy-supply technologies will not depend primarily on the capital cost, but rather on the cost of energy carriers produced (see Chapter 4). The cost of energy, in turn, will depend on investment costs as well as the cost of related installations, fuel costs, resource availability (wind, sun, etc.), operation and maintenance costs, the need for back-up systems, etc.8 Moreover, energy prices may include external costs in terms of taxes. (For a discussion of external costs see, for example, Eyre, 1997). It should be noted that external costs, which are technology dependent, can be includedin the capital cost of technologies, cf. the rising construction costs over the years for coal-fired and nuclear power plants because of efforts to prevent environmental and accident risks (see Chapter 4). The diffusion and adoption of energy technologies will, primarily, depend on the market actors in energy systems and their awareness, values, and behaviour in adopting new energy technologies. The number of market actors may be many, including consumers (individuals and companies that purchase products and services), manufacturers, and trade allies (appliance dealers, contractors, consultants, architects, department stores, regional distributors, etc.). Moreover, the diffusion and adoption will depend on the networks between market actors (channels along which diffusion takes place), marketing management, educational and training systems, policy instruments, competence building, etc. The diffusion and adoption of new technologies will also depend on the evolution of lifestyles (which in turnwill depend on cultures and habits) and competence building.

2.3 The timing of technology change within energy systems

As described in the introduction, the literature indicates huge potential for both improved energy efficiency and increased renewable energy supply through the development and deployment of new and improved energy technologies. The renewable energy resources, which can be characterised as huge annual flows of energy available in the environment, give rise to huge energy potential. This potential will be limited by factors such as social and environmental constraints, possible land use constraints or conflicts, technology performance, capital cost, infrastructure requirements, etc. The size of the potential for efficient end-use will be limited by factors such as technology performance, capital cost and aestethic limitations. For the definition of different potentials of energy efficiency, see Box 2.1.

8 The adoption of renewable energy technologies employing intermittent energy sources will, at high penetration, require backup systems in order to avoid a reduction in system reliability. The need for backup systems depends on the mix of resources and on demand/load. 2. Theory of Technology Change 13

Box 2.1 Potential of energy efficiency

It is important to define the potential of improved energy efficiency carefully, since the definition can be vague and different definitions are used. Normally, a potential is expressed either as a theoretical potential, a technical potential, a techno-economic potential, or as a market potential. A theoretical potential illustrates the minimum energy demand for a given service and is ultimately limited by the thermodynamic laws of physics. The theoretical potential is the maximum potential for improvement (see, for example, Jochem, 1991). The technical potential illustrates what can be achieved by applying the best available technology at a given point in time. The technical potential can also include advanced efficiency technologies, i.e. technologies that have been demonstrated but that are not yet commercially available (see, for example, Block et al., 1996). The techno-economic potential takes into account the requirement that the invest­ ment be cost effective. An efficient technology often has a higher investment cost while the operating cost is lower than for standard technology. However, the difference in total LCC (life cycle cost) between different alternatives is often remarkably small (see, for example, Nilsson, 1995). The market potential is defined as the potential savings that can be expected to be realised in practice. It is important to note that technical, techno-economic, and market potentials change over time as new efficient technologies are introduced and energy prices change. ] Current energy efficiency level Market potential

Techno-economic potential £ Technical potential & Theoretical potential

The difference between an overnight potential and a long-term potential should also be noted. The overnight potential describes the techno-economic potential in existing installations through improved operation and maintenance, or energy efficiency replacement in existing end- uses before the end of the equipment’s lifetime, i.e. retrofit measures. The long-term potential is considerably larger than the overnight potential and involves describes the techno-economic potential when equipment is introduced due to capital turnover (replacement) or due to expansion.

Since the potential is huge for both efficient energy use and renewable energy resources, a system based on efficient energy use and renewable energy resources promises to meet energy needs in the long run. However, the transition towards a sustainable energy system will probably be slow. This is due to the fact that energy systems have, on average, a low rate of technology change.9 A transition towards

To date, technology change towards a sustainable energy system has been slow. According to Martin (1996), technology development and market conditions, such as stiff competition and unstable markets, have favoured existing hydrocarbon technologies. According to Kemp (1994), technology change has been hinderedby small-scale production, lack of learning, and lack of institutional support. 14 Dynamic of Energy Systems sustainable energy systems may also be hindered by the trajectories and paradigms of traditional energy technologies. Directed investments through R&D and policy instruments that provide the opportunity for alternative energy system trajectories will be required to effect and accelerate the timing of a transition towards sustainable energy systems (see Chapter 5).10 Nevertheless, it will be impossible to recognise or foresee the superiority of a new energy technology. Thus, investments in R&D and policy instruments must be diversified and directed towards technological pluralism. Investments leading to a technological pluralism will limit a certain clustering which could lead to an acceleration of the introduction of a specific technology, but hopefully secure the introduction and commercialisation of several superior technologies.

10 For a discussion of technology change and the timing of greenhouse gas emission abatement see, for example, Grubb, 1997; Griibler and Messner, 1998. 3. Methods of Analysing Future Technology Change: Vintage Models

Vintage models are used to analyse the dynamics of energy demand and end-use energy efficiency. The models are based on the bottom-up methodology and describe the dynamics of energy demand as a function of technology change (i.e. the market penetration of new energy technologies). The models require a large amount of disaggregated data to statistically build up the end-use technology framework. Furthermore, vintage models permit the analysis of possible effects of govemmental- and utility-sponsored energy-efficiency programmes. In this work vintage models were applied to the analysis of the dynamics of electricity demand for lighting and air-distribution in Sweden. The analyses include several policy instruments (i.e. programmes) that affect technology change.

3.1 Dynamics of energy demand

Vintage models employ a time-dynamic accounting method to derive the annual energy demand. The driving forces for the dynamics of energy demand are sectoral growth and technology change. Sectoral growth is described by an increase in the level of activity and is based on assumptions about growth in economic activity (illustrated by, for example, growth in heated floor area). Technology change is described by a change in the energy intensity (e.g. energy use per square metre), which is based on the sectoral growth in installed technology and improvements in the efficiency of end-use equipment and systems. Moreover, the customer sector is divided into a manageable number of homogeneous market segments (e.g. building types) in order to allow detailed analysis. For each market segment, the vintage model requires data on energy­ using activities, installed technology, new technology alternatives, technology development, technology trends, market penetration limits, useful lifetime, investment cost, operation and maintenance (O&M) costs, operation data, electricity and fuel 16 Dynamics of Energy Systems prices. The total energy demand is determined by the sectoral use of energy services, the technologies in use, and the activity level, which can be written as:

= (3.1) 5 U where

E‘c is energy demand for energy carrier c and year i, a cm is activity level for end-use technologies u in sector s and year i, for energy carrier c, e'au is energy intensity for end-use technology u in sector s and year i, for energy carrier c

The dynamics of energy demand is based on capital stock investment and turnover, i.e. the dynamics of energy demand will depend on the installation of equipment in new buildings and the replacement of old equipment in existing buildings. The investment in equipment in new buildings is determined by an assumed sectoral growth rate, and the technology replacement is modelled using the age distribution of a building stock together with the assumption that all equipment of a given age is replaced. Every time investments are made in new equipment, energy efficiency can be reduced (although still supplying the desired energy service) by investments in improved energy-efficient technology. Vintage models are used to analyse the acceptance of decision makers (including contractors, consultants, purchasers, etc.) where the purchase of efficient equipment is analysedas a functionof the capital and operating costs of each investment option. The acceptance is modelled using either an optimisation model or a probability model, see Figure 3.1. The optimisation approach assumes that the decision makers select the optimal option according to some criteria of life cycle cost (LCC) or payback time set by the decision maker. The probability approach presents a range of decision alternatives, defined by discount rates, and a distribution of the decision alternatives practised by the decision makers (see Figure 3.1). With vintage models it is possible to analyse the effects of changes in customers' investment response due to energy price changes (i.e. a price elasticity of demand) and government- and utility-sponsoredenergy efficiency programmes. The energy price changes and efficiency programmes affect the economic return of the technologies and thus the willingness of decision makers to purchase efficient equipment. 3. Methods of Analysing Future Technology Change: Vintage models 17

Optimisation model Probability model

Probability of selecting option i Probability of selecting option i

Critical value Cost of option i

Figure 3.1 Alternative decision models, the optimisation model and the probability model. The probability curves in the probability model describe the differences in cost sensitivity.

3.2 Vintage modelsused to analyse the Swedish electricity demand

The work presented in Articles I and II shows how vintage models have been applied to the analysis of the time-dynamics of electricity demand for lighting and air-distribution in Sweden (see also Swisher and Christiansson, 1994; Christiansson and Swisher, 1995; Christiansson, 1996a; Christiansson, 1996b). The models used are based on the commercial computer models Compass and Commend,15 which have been equipped with external spreadsheet interfaces to enable an extensive technology analysis (providing a wide range of equipment options and associated cost data). The Compass and Commend models have also been equipped with external spreadsheet interfaces to analyse the model results. The two modelsapply different approaches to the analysis of the available technology options. (For details concerning technology data, see Articles I andII. 52) The Compass model uses, within each market segment, specific technology options that represent probable options in the modelled time frame. The Commend model, on the other hand, uses a generic technology approach in which the efficiency of new technology is 11 12

11 The Compass model (Comprehensive market planning and analysis system) is a decision-support system developed by The Synergic Resources Corporation, for detailed analyses of utility DSM programmes and strategic marketing options (SRC, 1991; Limaye & Hoog, 1992). The Commend model (Commercial end-use forecasting model) is an end-use forecasting model developed by the Electric Power Research Institute (EPRI, 1992).

12 In general, the background data in Article I and Article II are based on "Uppdrag 2000", a field study of energy-efficiency options by Vattenfall AB (Sweden’s largest electric generator and wholesaler). "Uppdrag 2000" includes the STIL (Statistical study in commercial premises) survey, which is the first detailed statistical picture of energy use in the Swedish commercial sector (Hedenstrom et al., 1992). 18 Dynamics of Energy Systems represented by an average value and a range, see Figure 3.2. (The generic figures are calculated in the external spreadsheet interface and include data regarding energy-using activities, installed technology, new technology alternatives, market penetration limits, useful lifetime, investment cost, operation and maintenance (O&M) costs, operation data, electricity and fuel prices, etc.) Furthermore, the Compass and the Commend models use different methods to model the market acceptance of new technologies. Both methods are, however, based on a probability approach. The Compass model uses a payback acceptance method, in which it is assumed that different decision makers use different payback acceptance for technology investments. The Commend model, on the other hand, uses a distribution of discount rates to model the market acceptance of efficient technologies for different market segments.

A B

Energy intensity (kWh/m 2) Energy intensity (kWh/m 2)

Average cost Specific technology point

Average — - energy intensity

Cost (ECU) Cost (ECU)

Cost range Cost range

Figure 3.2 The approaches used to describe technology options in Compass (A) and Commend (B). The Compass model use specific technology options, and the Commend model use a generic technology approach.

3.3 Analysis of the Swedish electricity demand

In the analyses presented in Articles I and II, scenarios are used to illustrate the dynamics of electricity demand. These scenarios illustrate the difference between an autonomous rate of efficiency improvements in a baseline scenario (i.e. the Reference scenario), a scenario which includes efficiency improvements in response to increased electricity prices, and several scenarios including efficiency improvements in response to policy programmes (information programmes, DSM programmes, energy efficiency standards, and voluntary standards based on technology procurement programmes). The policy programmes are described in detail in Article I and II 3. Methods of Analysing Future Technology Change: Vintage models 19

The baseline scenarios in the Articles show a continued increase in future electricity demand for lighting andair-distribution, see Table 3.1. This is mainly due to an increase in heated area but also due to an increase in illuminance and volume of air flow. The results of both studies indicate that future electricity demand is relatively insensitive to electricity price changes.13

Table 3.1 Summary of scenario results. The figures for Lighting-STIL and Air-distribution-STIL (based on the STIL material) illustrate future electricity demand in the commercial sector. The figures for Lighting-Total (based on the STIL material and additional sources) illustrate the energy demand in the commercial, industrial, and residential sectors, and for street lighting.

Article I Article II

Scenario Lighting-STIL Lighting-Total Scenario Air-distribution-STIL

1989 Consumption 5.0 TWh 13.1 TWh 1990 Consumption 1.9 TWh Reference 2010 6.8 TWh 15.8 TWh Reference 2020 2.4 TWh

Reductions compared withReference 2010: Reductionscompared with Reference 2020:

Price increase (50%) -9% (0.6TWh) -9% (1.5 TWh) Price increase (75%) -1% (0.1 TWh) Mandatory standards -16% (1.1 TWh) -12% (1.9 TWh) Voluntary standards -13% (0.3 TWh) DSM-sharedsavings -22% (1.5 TWh) -20% (3.1 TWh) DSM-direct install. -9% (0.2 TWh) Standards + DSM -28% (1.9 TWh) -25% (3.9 TWh)

Comments: A. STIL (Statistical study in commercial premises) is the first detailed statistical survey of energy use in the Swedish commercial sector performed by Vattenfall AB, Sweden’s largest electric generator and wholesaler (Hedenstrom et al.,1992). B. The technology measures used in the models are based on technologies that are commercially available today (except the most efficient air-handling units in Article II that are about to be commercialised). Although many of these measures represent considerable efficiency gains compared with present practice, they are by no means the best that can be achieved (cf. theoretical and technical potential in Chapter 2; the technical potential of air-distribution shows a decrease in electricity demand of 32% compared with the reference scenario in 2020). Thus, even more efficient measures may be introduced during the time frame of the analysis.

These scenarios illustrate that utility and governmental policy programmes can lead to significant electricity savings. When lighting is modelled, standards show an effective decrease in electricity demand. However, the decrease is limited by the standard itself, and electricity demand starts to increase when the efficiency dictated by mandatory standards has penetrated the entire market (see Article I). Additional savings could be achieved by new standards, or, as shown in the other scenarios, by the use of DSM shared-savings programmes or a combination of standards andDSM programmes. For air-distribution, voluntary standards (based on technology procurement

13 This is consistent with the results of "Uppdrag 2000", where the relation between high electricity prices and high energy efficiency was shown to be weak (Levin-Kruse, 1991). 20 Dynamics of Energy Systems programmes) and direct implementation of DSM programmes indicate the possibility of realising potential savings.

3.4 Concluding remarks

With vintage models it is possible to analyse the dynamics of energy demand due to technology change and the possible effects of governmental- and utility-sponsored energy efficiency programmes. By using the models, it becomes possible to analyse the complexity of annual streams of energy, end-use products, capital investment, building and equipment stocks, and other quantities. The major limitation is the requirement for a large amount of data to statistically build up the end-use technology framework. Despite the use of detailed data, the model provides a simple and transparent framework. The analyses of the electricity demand for lighting and air-distribution (Articles I and II) show that electricity demand could decrease over time, although the level of electricity services increases, if appropriate policy programmes are applied. How fast and to what extent this decrease could occur is determined by the capital stock, the choice of technologies at the point of new investment, the design of the policy programme, etc. The analysis also shows that policy programmes can be combined in order to accelerate the introduction of efficiency measures. Moreover, the timing of policy programmes is found to be important. The later the efficiency programmes are carried out, the more opportunities will be lost and the longer it will take to decrease the energy demand. This is due to the investment cycles of end-use technologies. At the time a building is under construction, many conservation measures can be installed at only a small incremental cost beyond standard technology costs. It is usually much more expensive to do retrofits, i.e. replacement before the end of the lifetime of the equipment. 4. Methods of Analysing Future Technology Change: Experience Curves

Experience curves can be used to analyse the trend of cost reductionof new technologies. The experience curve describes how unit costs decline with cumulative production and illustrates that early investments could reduce initially high costs facilitating the commercialisation of a new product. Experience curves, which have been used to design market strategies for various products (see, for example, Boston Consulting Group, 1972) have come to be used for analysing future energy costs and the potential of the introduction and commercialisation of new energy technologies (see, for example, Williams and Terzian, 1993; Christiansson, 1995; Mattson and Wene, 1997; Messner, 1997; Neij, 1997 (Article III); Neij, 1998 (Article IV)). The work presented in this thesis (Articles III and IV) describes how experience curves can be used to analyse the potential and limitations of future cost reduction of modular renewable energy technologies. Special emphasis has been placed on the analysis of the cost dynamics of wind power (Article IV), and the prospects for the diffusion of wind turbines and photovoltaic (PV) modules (Article III). Furthermore, the work describes the capabilities and limitations in the use of experience curves.

4.1. Definition of experience curves

An experience curve describes how unit costs decline with cumulative production, see Figure 4.1 (Abell and Hammond, 1979). A specific characteristic of an experience curve is that the cost decreases by a constant percentage with each doubling of the total number of units produced. Generally, the curve is expressed as: 22 Dynamics of Energy Systems

C-cum — Co'CUM^ (4 1)

where Ccum is the cost per unit as a function of output, Cq is the cost of the first unit produced, CUM is the cumulative production over time, and b is the experience index. The experience index is used to calculate the relative cost reduction, (1-2^), for each doubling of the cumulative production.14 The value (2^), which is called the progress ratio (PR), is used to express the progress of cost reductions for different technologies. A progress ratio of 80%, for example, means that costs are reduced by 20% each time the cumulative production is doubled.

An experience curve An experience curve plotted on log-log scales

S3 60

200 400 600 800 1000 0 100 1000 Experience (Cumulative production of units) Experience (Cumulative production of units)

Figure 4.1 An experience curve with a progress ratio of 80%.

The concept of experience curves should not be regarded as an established theory or method, but rather as a correlation phenomenon, which has been observed for several technologies. The observed progress ratios for different technologies cover a range from 64% to over 100% (see, for example, Boston Consulting Group, 1972; Krawiec et al., 1980; Bass, 1980). The concept of experience curves is based on learning curves, which have been used since the 1930s to analyse the reduction in man-hours (or cost) per unit of a standardised product produced by an individual firm. (For the first publication of technological learning, see Wright (1936), and for a recent survey, see Argote and Epple (1990)). Experience curves have, however, come to be used in a more general way than the

14 For each doubling of the cumulative production (CUM2 = 2CTJM]) the relative cost reduction will be Ccumi-Q:um?_1 t-Q'(2CUM\)b

Ccumi Cq-CUMf 4. Methods of Analysing Future Technology Change: Experience Curves 23 learning curve, and refer to cost reductions for non-standardised products produced globally, nationally, or by an individual firm. The cost reduction refers to the total costs (labour, capital, administrative costs, research and marketing costs, etc.), and the sources of cost reduction include cost reductions due to changes in production (process incremental innovations, learning effects, and scaling effects), product changes (product incremental innovations, product redesign, and product standardisation), and changes in input prices.15 The experience curve represents the combined effect of a large number of parameters, which may fluctuate on a short time scale. Thus, only after many doublings of production can the underlying pattern or trend be distinguished.16 Experience curves are often based on price data and not on cost data. The use of price data will, however,not be accurate unless price/cost margins remains constant over time or are considered in the analysis.

4.2 Costreductions illustrated by experience curves

The independent variable of the experience curve is cumulative production (not time). Thus, the cost reduction for a technology will be a function of the cumulative production with that technology, which, in turn, will depend on market demand. Market demand will depend on the cost and performance (e.g. quality, function, user-friendliness, efficiency, and durability) of the new technology relative to existing technologies. In the long term, cost reductions will be limited by physical limits in technology development, cost limits, and market potential. In the short and medium terms, cost reduction, and the rate of cost reduction, will be limited by existing market barriers (e.g. high initial cost, limited product performance, limited information, limited product availability, and limited access to capital) and the rate at which manufacturers are able to reduce costs through additional production. Cost reductions may be affected by policy instruments that stimulate technology development and market demand (see Chapter 5). Moreover, experience will be gained (and cost reduced) in niche markets where there is a willingness to pay a higher price. However, it has been shown that cost may not always decrease with cumulative production but may also increase (progress ratio >100%). Therefore, it is important to15 16

15 It is open to debate whether scaling effectsshould be included in the experience effector not. The overlap between experience and scaling effects is, however, so great that it is difficult to separate them. In this thesis scaling effects are considered part of experience. 16 The experience curve is analysedin an aggregated fashion; however, some experts argue that the sources of cost reduction cannot be aggregated, but must be identified and analysed separately (Krawiec et al., 1980; Krawiec, 1983; Hall andHowell, 1985). 24 Dynamics of Energy Systems

point out that experience per se does not cause cost reductions, but rather provides opportunities for cost reductions. An increasing progress ratio may arise when, for example, the total cost cannot be reduced (by product standardisation, process specialisation, scale effects, labour rationalisation, etc.) as fast as costs are incurred through design changes and product performance improvements. Moreover, an increasing progress ratio may illustrate a limitation in using experience curves to analyse cost trends of non-standardised products. Furthermore, the cost reductions illustrated by the experience curve do not always follow a straight line; it has been observed that some experience curves show discontinuities, or a distinct break, see Figure 4.2. Such discontinuities may be the result of a pricing strategy (e.g. price reduction levels off at a different rate than the cost reduction, see, for example, Boston Consulting Group, 1972; Ayres and Martinas, 1992) - or a poorly planned market incentive (e.g. a massive government purchase programme that resulted in limited supply andincreasing prices). Discontinuities may also be the result of technological development. If the development in technology could be described by marginal and incremental improvements (see Chapter 2), such improvements will be reflected in the experience curve. If, however, technology development results in major changes (radical improvements), a discontinuity or a break will appear in the experience curve. It could be discussed, however, whether such a break calls for the use of two separate experience curves. One problem is that the distinction between marginal and major changes is subtle and often somewhat arbitrary.

Cumulative production of units Cumulative production of units

Figure 4.2 Discontinuities in the experience curve (log-log scales) due to (A) a non-idealised price-cost relationship and (B)radical improvements in the technology (innovation). 4. Methods of Analysing Future Technology Change: Experience Curves 25

The historical trend in cost reductions expressed by experience curves has been extrapolated and used to analyse future cost reductions. Such an analysis must, however, take into account possible large variations in the progress ratio for a non-standardised product. The progress ratio should therefore be expressed as a range, rather than as a specific value. Moreover, the uncertainty and diversification embedded in the experience curve limits any prediction of future cost. Experience curves should rather be used to provide insight into the capabilities and limitations of the further diffusion and adoption of new technologies. An analysis based on extrapolation of the experience curve must also be complemented by an analysis of the underlying technology development and market forces causing and limiting cost reductions.

4.3 Analysing the cost reduction of renewable energy technologies

In Article III, a comparison of experience curves, and progress ratios, for different technologies is presented. The technologies are divided into three categories: plants, module technologies, and continuous processes, see Table 4.1. The categorisation of the technologies is based on the parameter economies of scale, which differs for the three categories. The first category (plants) represents economies of scale due to scaling up of units, e.g. larger production plants. The second category (module technologies) represents economies of scale due to mass production of identical units, such as electronics. The third category (continuous processes) combines the scaling effects of "plants" with the scaling effects of "modules" (representing continuous processes for the production of standardised commodities on a large scale), e.g. production of chemicals and plastics. This categorisation illustrates that the possibility of cost reduction appears to be greater for modular technologies and continuous processes than for plants. Most conventional technologies for power production can be characterised as large-scale plants that require extensive construction in the field, e.g. coal-fired, oil-fired, nuclear and hydroelectric power plants. In contrast, most new renewable energy technologies (e.g. wind turbines and PV) can be characterised as module technologies that provide the opportunity for factory-based automatic production, without the need for large-scale site construction. This, in turn, indicates that the possibility of cost reductions for modular renewable energy technologies will be greater than for conventional power plants. 26 Dynamics of Energy Systems

Table 4.1 Progress ratios for three categories of technologies: plants, module technologies and continuous processes. (The figures are based on a literature review including approx. 50 measured experience curves).

Average in the literature Range in the literature

Plants 0.90 0.82 - >1.0 - large scale? >1.0 - small scale b 0.&7

Module technologies0 0.80 0.70 - 0.95

Continuous processes4 0.78 0.64 - 0.90 a Based on the price of coal-burning and nuclear electricity-generating units (Komanoff, 1981; Joskow and Rose, 1981; Cantor and Hewlett, 1988). The development of coal-fired and nuclear power plants has shown rising construction costs over the years, primarily because of efforts to prevent environmental and accident risks. b Based on the price of gas turbines (MacGregor et al., 1991), steam turbines (Clair, 1983), and IGCC (MacGregor et al., 1991). c Based on the price of electronics (Krawiec et al., 1980; Clair, 1983; Bonneville Power Administration, 1980), and consumer durables (Krawiec et al., 1980; Bass, 1980) 4 Based on the price of oil and plastic products (Fisher, 1974; Krawiec et al., 1980; Clair, 1983), metal products (Krawiec et al., 1980) and ethanol (Goldemberg, 1996).

4.4 Cost dynamics of wind turbines and PV modules

The diffusion of new renewable energy technologies will depend on cost reductions in the energy produced. New renewable energy technologies will be considered cost effective and economically competitive only when the cost of the produced energy is as low as, or lower than, the cost of conventionally produced energy. The cost of the energy produced will not only depend on the cost of installed capacity, but also on costs related to installation, O&M costs, fuel cost, or the availability of wind, solar energy, etc., see Figure 4.3. In Articles III and IV analyses are presented for the cost reduction of wind- and PV- generated electricity. The cost analyses of capacity and related installations are based on extrapolated experience curves. (A small interval is used to illustrate the uncertainty of a measured progress ratio.) For PV modules, experience curves found in the literature were used, while for wind turbines experience curves were developed based on historical price data. The analyses were supplemented with technology characteristics and development reviews. 4. Methods of Analysing Future Technology Change: Experience Curves 27

Cost of energy

Cost of technology Cost of related Cost of O&M costs installations energy sources

Wind turbines Building Coal Maintenance PV modules Foundation Oil Insurance Coal- fired power plants Installation Biomass Gas-firedpowerplants Land acquisition (Wind, Solar energy) etc. Site preparation, etc. etc.

Figure 4.3 Factors included in the cost of energy. In Articles I and II, experience curves are used to calculate cost reduction of technology (capacity) and costs of related installations.17

The progress ratio of the experience curve for PV modules, based on cost per peak Watt, has been calculated to be approximately 80% (Tsuchiya, 1992; Cody and Tiedje, 1997; Williams and Terzian, 1993).18 Such a progress ratio is within the span of module technology in Table 4.1. The experience curves for wind turbines, based on cost per Watt, indicate a slower cost reduction and a progress ratio of approximately 95%. Calculations that include efficiency improvements, however, show more progressive cost reductions, see Table 4.2. The analysis of wind turbines, presented in Article IV, shows that the progress ratio of the experience curves for wind turbines depends on the model of turbine chosen (size and manufacturer included in the analysis) and the time period chosen. Moreover, if the generic

17 Experience curves have not only been used to estimate and extrapolate future costs of energy technologies, but also to estimate and extrapolate future costs of produced energy. The use of experience curves for this type of analysis can, however, be questioned. The experience curve is then used to extrapolate not only the cumulative experience in production and use of a certain technology (product), but also to extrapolate parameters such as fuel prices, available wind force and solar energy, etc. The wind force and solar energy available will, to some extent, depend on experience in siting, however, such experience will be national rather than global. Even national experience in siting can be questioned since at a certain point wind turbines and PV modules have to be sited in less windy and less sunny areas since the best sites are already occupied. This will increase, and not reduce, the cost of the generated electricity.

18 The definition of peak Watt (Wp) is the power output at full sunlight, i.e. the energy reaching the earth through a clear sky at sea level. 28 Dynamics of Energy Systems experience curve for wind turbines is divided into curves for different turbine sizes, the curves for the individual sizes indicate no cost reduction. The results of the analysis show that the cost reduction for wind turbines has mainly been due to the upscaling of the turbines. Cost reduction of wind turbines may have been limited due to the rapid introduction of new models (sizes), limiting mass production and incremental improvements. Moreover, cost reduction of wind turbines may have been limited due to performance improvements. (For cost reduction of wind turbines including efficiency improvements, see Table 4.2.) Cost reduction of wind turbines and the upscaling of wind turbines over time is shown in Figure 4.4.

Table 4.2 Progress ratios for the cost of windturbines (US$/kW) andthe cost of wind turbines including efficiency improvements (US$/kWh).

Progress ratio Progress ratio Progress ratio Wind turbines Time period Wind turbine Wind turbine Wind turbine RC1 RC2 (US/kW) (US/kWh) (US/kWh) >55kW 1982-1997 96% 93% 93% >55kW 1990-1997 95% 90% 90% 150kW 1988-1997 101% 95% 95% 200-250 kW 1989-1997 100% 96% 96% 500-550 kW 1993-1997 99% 98% 98%

Comment: The progress ratio for the cost of wind turbines including efficiency improvements (US$/kWh) is based on the cost of theoretically produced electricity for Roughness class (RC) 1 and 2, and the cost reductions are due to decreased manufacturing costs, upscaled wind turbines, increased hub heights, and improved performance. The calculations are based on data published in “Vindmplle oversigten (1982-1997)” sponsored by the Danish Energy Agency. The values of theoretically produced electricity, obtained using two different systems in Vindmplle oversigten (the Beldridge system used in the European Wind Atlas and the system used in the Danish Wind Atlas), have been recalculated (to the Beldridge system) according to a formula prepared by Risp National Laboratory (Hansen and Andersen, 1999).

Article m presents results from the calculations of the future cost trend of PV-generated electricity. Assuming a cumulative installed capacity of 100 GW, and extrapolation of the experience curve with a progress ratio of 80% (78-82%), the cost of generated electricity will reach approximately 60/kWh (5.1-7.3 c/kWh). Such a price will not be competitive with the base-load cost of electricity generated by new conventional power plants, of 2.4-6.S 0/kWh (IPCC, 1996). Thus, the introduction of PV modules with a progress ratio of 80% will require further investments in base load (>US$ 100, 000 million) in order for them to be considered economically competitive (see Article III). However, PV modules are already 4. Methods of Analysing Future Technology Change: Experience Curves 29

economically competitive in some niche markets where the cost of PV-generated electricity is lower than the cost of a conventional electrical supply, by grid extension or diesel generators. Moreover, other niche markets will be within reach long before base-load competitiveness is achieved. Nevertheless,a technology breakthrough and the introduction of a new generation of PV modules (assuming, for example, high-efficiency, mass-produced, thin-film cells), reflected by a more progressive experience curve and a discontinuity in the experience curve, indicates considerable cost reductions and rapid diffusion and adoption of PV modules. Assuming a cumulative installed capacity of 100 GW and a progress ratio of 70%, extrapolation of the experience curve predicts a cost of generated electricity of less than 3 jzS/kWh (through investments of approximately US$ 20, 000 million).

-- 1400

Cost reduction for wind turbines 300 - - 1200

•£ 250 - - 1000

o 200 - 150kW, | 150 - 200-250kW - 600 55kW / 600kW - 400 400-550kW \ ' - 200

Figure 4.4 Windturbines, arranged according to size, installed in Denmark from 1980-1997, and the cost of Danish-produced wind turbines from 1982-1997 (in US$/kW).

The cost of wind-generated electricity has been estimated in both Articles III and IV. The costs estimated by the two analyses differ significantly. The major differences between the 30 Dynamics of Energy Systems results are due to the exchange rate used in the studies (due to different base years)19, differences in cumulative production of wind turbines, and the differences in assumptions regarding the capacity factor (CF), lifetime and O&M costs. The differences are presented in Table 4.3.

Table 43 Parameters used to calculate the cost of wind-generated electricity (Articles III and IV). (The discount rate used in both Articles is 6%.) For references see the separate Articles.

Status Parameter Value Article III Cumulative sales Increase from 5.0 MW to 100 MW Base year 1995 Average investment cost US$ 1000 (1US$=5.5 DKK) PR 96% (94-89%) Lifetime 20 years O&M costs Decrease from 1.3 p/kWh to 1.0 0/kWh CF Increase from 0.25 to 0.27 Cost of generated electricity Decrease from 6.6 0/kWh to 4.4-6.0 0/kWh

Article IV Cumulative sales Increase from 7.6 MW to 188 MW Base year 1997 Average investment cost US$ 855 (1US$=6.6 DKK) PR 95% (97%) Lifetime Increase from 20 to 25 years O&M costs Decrease from 1.1 0/kWh to 0.6 0/kWh Wind capture/(CF) Increase from 2000 kWh/year (CF=0.23) to 2500 kWh/year (CF=0.28) Cost of generated electricity Decrease from 6.1 0/kWh to 3.4-S.9 0/kWh

Comment: CF is the capacity factor; defined as the ratio of the annual average power output to the rated power output for wind turbines

The results of the analyses show that although the experience curve for wind turbines indicates relatively moderate cost reductions there is potential for cost reductions in wind­ generated electricity; limited cost reductions considering a cumulative installed capacity of 100 GW and the assumptions made in Article III, and significant cost reductions considering a cumulative installed capacity of 188 GW and the assumptions made in Article IV. The cost reductions are due to the fact that the cost of wind-generated electricity will be affected by improved performance and reducedoperating and maintenance costs, in addition to the

19 The exchange rate has varied from S.6-6.6 DKK=US1$ during the period 1992-1997. 4. Methods of Analysing Future Technology Change: Experience Curves 31 reduction in the cost of wind turbines. Moreover, it must be stressed that the analyses presented in the Articles describe an average cost reduction. Wind turbines are already economically competitive under favourable conditions, and wind-power subsidies have resulted in a rapid growth in the number of wind turbines installed.

4.5 Concluding remarks

The experience curve approach is used to analyse possible cost reductions linked to technology development and cumulative production. However, the cost reduction described by experience curves represents the combined effect of a large number of parameters. To describe a possible uncertainty in using experience curves to estimate future costs, a progress ratio range could be used rather than a specific progress ratio. In this work, the analyses of the cost reduction of generated electricity were based on a small interval around the estimated progress ratio. The results show that relatively small changes in the progress ratio result in significant changes in future electricity costs. It is therefore suggested that the experience curves be used with due consideration and that they should be regarded as a moderately accurate method of estimating future costs. However, experience curves should be considered a valuable method for providing insight into the potential and limitations of the further diffusion and adoption of new technologies, such as renewable energy technologies. The use of experience curves (Articles III and IV) shows that the cost reductions in modular renewable energy technologies will, in general, be greater than for conventional energy technologies. Moreover, the experience curves illustrate significant cost reduction for PV technologies (expressed as US$/kW p) but only relatively moderate cost reduction for wind turbines (expressed as US$/kW). When calculating the cost reduction for PV modules, it was found that large investments would be required to bring the cost of PV-generated base-load electricity down to the level of electricity generated by conventional power plants. However, the introduction of a new generation of PV modules (e.g. high-efficiency, mass-produced thin-film cells), would lead to considerable cost reductions and rapid diffusion and adoption of PV modules. The analysis of wind-generated electricity shows potential for considerable cost reductions in wind-generated electricity, although the experience curve for wind turbines indicates relatively moderate potential cost reductions. As mentioned, the cost of wind-generated electricity will be affected by improved performance and reduced operating and maintenance costs, in addition to the reduction in the cost of wind turbines (expressed as $/kW). 32 Dynamics of Energy Systems

Furthermore, experience curves show that early investments (in R&D, subsidies, niche market applications, etc.) are necessary to bring down the cost of generated base-load electricity. The combination of investments necessary will probably differ between technologies and between different market maturity levels for one and the same technology. It should be noted that the increase in wind-generated electricity has been due to market- driven incentives (e.g. capital subsidies and premium tariffs on generation) rather than technology-driven incentives (i.e. R&D) (Gipe, 1995). 5. Methods of Effecting Technology Change

As described in the introduction, there is considerable potential for both efficient energy use and renewable energy supply through the development and deployment of new and improved energy technologies (see, for example, Johansson et al., 1989; Johansson et ah, 1993; WEC, 1995; Worell et al., 1997). However, this potential does not guarantee the introduction and commercialisation of energy-efficient technologies and renewable energy technologies. Technology change within energy systems will be slow due to the long lifetimes of many energy technologies and changes required in integrated technologies, in the infrastructure, and in organisation. Moreover, the introduction of new energy technologies may be hindered by the trajectories and paradigms of traditional energy technologies. To effect and accelerate technology change towards sustainable energy systems will require policy instruments, e.g. measures that stimulate the introduction, commercialisation and market penetration of technologies with good performance. Over the years, several policy instruments have been developed to effect technology change (see Section 5.1). Moreover, methods with which to analyse and project the effects of the different policy instruments have been developed (see Section 1.2 and Chapter 3). The use of policy instruments requires methods of evaluation. Such methods should be relevantand systematic in order to verify the effect of the instruments. Up until now, no systematic methods for the evaluation of technology change effected by policy instruments have been available. In this thesis, a model is proposed for the evaluation of market transformation programmes, i.e. energy efficiency programmes that are based on technology change and the introduction and diffusion of new energy technologies. Furthermore, the proposed evaluation model has been used to assess evaluations of Swedish market transformation programmes. 34 Dynamics of Energy Systems

5.1 Approaches to promote technology change

Different policy instruments can be used to effect technology change towards a sustainable energy system. These instruments, which should be based on an understanding of the forces behind diffusion, could be used to stimulate technology and market development. Policy instruments that stimulate technology development will, in the long term, stimulate inventions and innovations, and in the short and medium term, stimulate innovations required for the introduction, commercialisation and market penetration of new technology. Generally, research and development (R&D) is used to stimulate technology development. However, public R&D investments in energy efficiency and renewable energy have been limited. In 1997, the OECD countries used only small parts of their national energy R&D budgets for the development of renewable energy technologies and energy efficiency; approximately 12% for renewable energy technologies and less than 8% for energy efficiency (IEA, 1998). The trend in government energy R&D budgets in the OECD countries is shown in Figure 5.1.

16,000 ---- Total 14,000 -* Nuclear Fossil fuels •§ 12,000 —Renewable g 10,000 —- Conservation

Figure 5.1 Government energy R&D budgets in the OECD countries, 1980-1997. Nuclear includes nuclear fission (approx. 80%) and nuclear fusion (approx. 20%) (LEA, 1998).

Policy instruments that stimulate market development focus on market introduction, commercialisation and market penetration. Different instruments have different effects and will therefore be appropriate at different market maturity levels, see Figure 5.2. The 5. Methods of Effecting Technology Change 35 introduction of new energy technologies can be stimulated by research, development and demonstration (RD&D) and technology procurement programmes. Technology procurement can be used to effect a pre-introduction of energy technologies, i.e. an earlier introduction of state-of-the-art-technology than would have been the case without the technology procurement programme. Policy instruments focused on commercialisation are aimed at establishing an initial market for new energy technologies, big enough to benefit from economics of scale and learning effects. Policy instruments focused on market enlargement aim at available energy technologies that have only reached a limited market share. Policy instruments that aim at commercialisation and market enlargement stimulate market actors to invest in new energy technologies and eliminate market barriers.21

Market maturity level Measures

Research and development Market introduction Technology procurement Demonstration

Technology procurement Demonstration Information (campaigns, seminars, technical assistance, labelling, etc) Commercialisation Bulk purchases Economic incentives Education andtraining Voluntary commitments Certification

Information Economic incentives Market enlargement Education andtraining Voluntary commitments Certification Codes and standards

Figure 5.2 Technology-based market instruments to effect market introduction, commercialisation and market enlargement. (Policy instruments such as taxes and pollution-trading systems will also effect technology commercialisation and market enlargement. However, these policy instruments will not be directed towards anyspecific technology and are therefore not included in this figure).

21 Market barriers identified as preventing investments in energy-efficient technologies and renewable energy technologies are: for example, lack of awareness (information), limited product availability, limited product quality (design, comfort, etc.), high (initial) cost, limited access to capital, perceived risks, etc. (see, for example, Fisher and Rothkopf, 1989; Koomey and Sanstad,1994; Levin et al., 1994; Golove andEto, 1996; IEA, 1997a). Dynamics of Energy Systems

The policy instruments used to effect market development may be voluntary or legislative, financial or non-financial. Financial incentives for renewable energy technologies include both subsidies for energy produced (e.g. taxes, buy-back rates) and subsidies for capital investments (IEA, 1997a). In order to effect, or to accelerate, technology change it may be necessary to use integrated or complementary approaches. To ensure a certain quality in the technology the policy instruments could be combined with certification programmes, i.e. requirements on technical quality guaranteed by testing and research centres. Such programmes have been used, for example, to ensure the quality of wind turbines in Denmark, Germany and the Netherlands.

5.2 Approaches to promote energy efficiency

Energy-efficient technologies are not being implemented although technology options can be implemented at a negative cost and at a lower cost than marginal energy supply options (IPCC, 1996; Worell et al., 1997). This is due to several market barriers preventing the use of and investment in energy-efficient technologies.22 To overcome the market barriers, several policy instruments directed towards energy efficiency have been developed (so-called energy efficiency programmes, see, for example, IEA, 1997b). Some energy efficiency programmes have been directed and developed by the government (e.g. subsidies, standards, and R&D) whereas others have been directed and developed by utilities (so-called DSM programmes, see, for example, Nadel, 1992). In recent years, new energy efficiency programmes directed towards technology change and the introduction and diffusion of new energy technologies have been developed and practised. Examples of new energy efficiency programmes are voluntary agreements, technology procurement programmes, and market transformation programmes. Voluntary agreements are generally contracts, between the government and a company or association of companies, aimed at reaching a negotiated goal - for example, a certain energy efficiency goal, a certain emission reduction, or the production or implementation of a certain technology. Upon fulfilling the negotiated goal the company will receive a tax credit (e.g. voluntary agreements in Denmark), technical support (e.g. EPAs Green Light Programme, Energy Star Computers, and Energy Star Buildings in the US), energy surveys (e.g. Eco-energy in Sweden), etc. Technology procurement programmes involve the purchase of energy technologies, processes or systems that are essentially unavailable on the market. The programmes combine government incentives with guaranteed orders from organised purchasing

22 The concept of market barriers for energy efficiency has been criticized by, for example, Sutherland (1991) who argue that low levels of investments in energy efficient measures can be explained by factors consistent with normal working markets. For a discussion see IPCC (1996). 5. Methods of Effecting Technology Change 37

groups in a competitive solicitation. Manufacturers are invited to enter prototypes of energy efficient technologies with certain features and the entries are judged according certain requirements. The winner receives incentive payments and a guaranteed initial order sufficient to begin production of the new model. An important purpose of these programmes is to reduce the technical and commercial risks faced by the buyers and the manufacturers (see, for example, Westling, 1991; Edquist, 1995; and Neij, 1997 for Swedish procurement programmes). A market transformation programme is a strategically planned approach, based on one or a combination of policy instruments, designed to effect a permanent shift in the market towards more energy efficient products and services (see Article V). Market transformation programmes are based on an understanding of the market and the characteristics of the technology, as well as the interactions between technology, different market actors, market conditions and public policy. In contrast to traditional DSM programmes which focus on end-users of energy only, market transformation programmes, and the underlying market research, focus on several market (or target) actors (manufacturers, wholesalers, retailers, contractors, consultants, end-users etc.).

5.3 The evaluation of policy instruments

The evaluation of policy instruments serves several purposes. The most important of these is to determine whether a specific instrument is successful or not. Another aim of the evaluation is to provide valuable information and guidance for policy implemented that can be used to refine a policy instrument in progress or to improve the planning, design and implementation of other policy instruments. Traditional evaluation methods do not allow a systematic evaluation of technology change effected by policy instruments. These evaluation methods have rather been focused on cost and benefits (e.g. pollution reduction and estimated savings (kW, kWh). Evaluations have occasionally also included a qualitative process evaluation which identifies barriers to successful performance of the programme and suggests modifications to the programme. In recent years, there has been a trend towards integrated process evaluation and impact evaluation. Further development of the evaluation of policy instruments aimed at effecting technology change requires a comprehensive approach. Such evaluation methods should include parameters that describe technology development, market development and changes in actors’ behaviour (see Article V and the evaluation of market transformation programmes). J8 Dynamics of Energy Systems

5.4 The evaluation of market transformation programmes

The introduction and use of market transformation programmes have raised issues concerning programme evaluation. In the literature, several researchers stress the importance of the development of new evaluation methods suited to market transformation programmes. The evaluation of market transformation has been discussed in, for example, Prahl and Schlegel (1993), Feldman (1994-1996), Rosenberg (1995), IEA (1996) and SRC (1996). In contrast to the methods usedto evaluate traditional energy efficiency programmes, the evaluation of market transformation programmes must be comprehensive in order to capture the effect of technology change. The evaluation will require parameters besides estimated savings, which describe changes in the market and their impact (e.g. elimination of market barriers and reduction of transaction costs). In Article V, a model for the evaluation of market transformation programmes has been proposed, see Figure 5.3. The evaluation model is based on a process that deals with the evaluation of the results of the programme and the outline of the programme. The model shows that the evaluation process must be carefully planned already from the beginning and should be integrated into the planning and implementation of the programme. The evaluation of the outline of the programme should be initiated, before the programme is started, with an evaluation of the choice of target technologies, the combination of policy instruments, and the plausibility of calculated goals and costs of the programme. The outline of the programme should be re-evaluated throughout the course of the programme. The evaluation of the results of the programme should be based on monitoring defined market transformation indicators. Based on the pre-programme levels, these indicators describe market transformation effects. A permanent market transformation will be observable only after several years. The market transformation effects can, in turn, be used to analyse the elimination of barriers, the reduction of transaction costs, the energy savings achieved with the programme, and the cost effectiveness and cost benefit of the programme. The results must, however, be adjusted to external parameters and be presented with a discussion of the uncertainty. Evaluating market transformation programmes according to the proposedevaluation model, will require further development of methods to estimate programme goals, methods to develop a baseline, and guidelines for how to adjust for external parameters. The evaluation model has been applied to the retrospective analysis of evaluations of Swedish market transformation programmes undertaken by the Department of Energy 5 . Methods of Effecting Technology Change 39

Efficiency at STEM.23 The results showed that not all the evaluations were focused on market transformation, and that those evaluations that indeed were focused on market transformation, used methods which were only partly consistent with the proposed model.

Evaluation of the programme outline Re-evaluation of programme outline The appropriate choice of target Evaluate the appropriate combination of measures technologies/services Evaluate the reasonableness of calculated goals The appropriate combination of measures and costs Reasonableness of calculated goals and costs Evaluation of programme results Evaluate changes in market transformation indicators anduse them to analyse Programme design including - market transformation effects - market research - elimination of market barriers - identification of target technologies/services - reduction of transaction costs - identification of market barriers - energy savings achieved - identification of appropriate programme activities - implementation plan - evaluation plan - exit plan, etc.

The evaluation plan includes v v 4 v/ v/ \y ^ - objectives of the evaluation - methods to be used - specification of market transformation Programme start Programme end indicators to be used - estimation of pre-programme levels of the defined market transformation indicators - estimation of the baseline - estimation of possible changes in market transformation indicators due to the programme - calculation of the potential energy savings - definition of the goal of the programme - calculation of cost of the programme

Figure 53 A model for the evaluation of market transformation programmes.

5.5 Concluding remarks

Various policy instruments can be used to effect technology change, i.e. technology and market development. The use of policy instruments requires evaluation methods.

23 STEM, the Swedish National Energy Administration was founded in January 1998. Before this the market transformation activities were undertaken by NUTEK; the Swedish National Board for Industrial and Technical Development. 40 Dynamics of Energy Systems

Traditional evaluation methods have, however, not been focused on technology change. Therefore, evaluation methods must be developed for policy instruments focused on technology change, such as market transformation programmes. The model proposed in this thesis for the evaluation of market transformation programmes represents an extensive evaluation process. The use of such an evaluation model will require further development of methods for the evaluation of the programme outline, methods with which to estimate programme goals, methods to develop a baseline, and directions on how to make adjustments for external parameters. The analysis of Swedishmarket transformation programme evaluations shows that Swedish evaluation methods must be more comprehensive than those used today. 6. Conclusions and Final Remarks

In this thesis, methods that contribute to the understanding of the dynamics of technology change within energy systems have been developed, applied, and assessed. The methods considered are vintage models, experience curves, and methods for the evaluation of market transformation programmes. Vintage models, which are important complements to other energy models such as top-down models and bottom-up optimisation models, are useful in analysing the time dynamics of technology change and policy instruments effecting technology change. The application of vintage models in this thesis shows that the electricity demand for lighting and air-distribution in Sweden may decrease over time, relative to a reference scenario, if policy instruments are used. Moreover, the results show that additional savings beyond those achieved by a single policy programme could be achieved with a combination of several policy programmes. Vintage models can be further developed and improved by, for example, integr­ ating experience curves to include technology development and cost reductions. However, the use of such a model will require sensitivity analysis and the use of a range of progress ratios rather than a specific value. Furthermore, the use of vintage models could be improved if they (i.e. the decision model used in the model) were based on more extensive analyses than today, describing different market actors and their behaviour in adopting new technologies. Experience curves provide an interesting tool for the analysis of possible cost reductions linked to technology development and cumulative production. The work presented in this thesis, however, shows that experience curves are only a moderately accurate method with which to estimate future costs. Experience curves, which are given an imprecise definition, must therefore be used with caution. However, the use of experience curves can provide valuable insight into the prospects of diffusion, due to cost reduction, of new technologies such as modular renewable energy technologies. In this thesis, experience curves have been used to analyse potential diffusion of wind turbines and PV modules. The analyses are based on a progress ratio range rather than a specific progress ratio. The results of the analyses, using experience curves to estimate the cost reduction for wind turbines, show potential for 42 Dynamics of Energy Systems

considerable future cost reduction for wind-generated electricity. This, in turn, could lead to major diffusionof windturbines. The results also show that major diffusion of PV modules, and a reduction in cost of PV generated electricity down to the level of conventional base-load electricity, will require large investments or an introduction of a new generation of PV modules (e.g. high-efficiency mass-produced thin-filmcells). In all, the analyses based on experience curves for wind turbines and PV modules show that early investments in technology and market development (i.e. RD&D, market incentives and investments in niche markets) are important in order to reduce costs. Experience curves should be analysed in more detail. Several questions need to be answered, for example, is the current broad definition of experience curves used today useful, or should the definition be more strict (number of producers, standardisation of products, and sources of cost reduction); do the cost reductions dependon cumulative production only or do they, to some extent, also depend on time; and do different policy instruments merely affect the ride down the experience curve or do they also affect the experience curve itself. Experience curves may also be applied to the analysis of cost reduction and technology development for several new energy technologies, including energy-efficient end-use technologies. The third part in this thesis deals with the evaluation of market transformation programmes. Up until now, no systemic methods for the evaluation of technology change effected by policy instruments have been available. The proposed model, however, provides a tool for the analysis of technology change, i.e. technology development, market development and changes in actors’ behaviour. (The model proposed could also be used to evaluate policy instruments, other than market transformation programmes, effecting technology change.) The model proposed in this thesis for the evaluation of market transformation programmes constitutes an extensive evaluation process. The application of such an evaluation model will require further development including methods of estimating programme goals, methods for the development of a baseline, directions on how to adjust for external parameters, and methods for the evaluation of the programme structure. The assessment of the Swedish market transformation programmes also shows that the methods used at present must be more extended. Not only the evaluationof policy programmes will require systematic methods, but also the design and analysis of policy instruments effecting technology change. We need to know more about how to effect and accelerate technology change towards sustainable energy systems. Important areas for future research include analyses of how different policy instruments affect technology and market development and the behaviour of different market actors, analyses of when and where to employ different policy programmes, and analyses of the cost and time factor in using different policy programmes. When designing policy instruments to effect and accelerate technology change, it will be important to take into account the inertia of the energy system and to have a long-term perspective. References

Abell, D.F. and J.S. Hammond, 1979, Strategic Planning: Problems and Analytical Approaches, Prentice-Hall Inc., Englewood Cliffs, New Jersey. Argote, L. and D. Epple, 1990, Learning curves in manufacturing, Science, 247(4945), pp. 920- 924. Arrow, K., 1962, The economic implications of learning by doing, Review of economic studies, 29, pp. 155-173. Arthur, W.B., 1988, Competing technologies: an overview, in G. Dosi, C. Freeman, R. Nelson, G. Silverberg, and L. Soete (eds.), Technical change and economic theory, Printer, London, UK. Ayres, R.U. and K. Martinas, 1992, Experience and the life cycle: some analytic implications, Technovation, 12(7), pp. 465-486. Bass, F. M., 1980, The relationship between diffusion rates experience curves and demand elasticity for consumer durable technological innovations, Journal of Business, 53(2), pp. 51-67. Blok, K., W.C. Turkenburg, W. Eichhammer, U. Farinelli, and T.B. Johansson, 1996, Overview of energy RD&D options for a sustainable future, European Commission, DG XII, Luxemburg. Bodlund B., E. Mills, T. Karlsson, and T.B. Johansson, 1989, The challenge of choices: Technology options for the Swedish electricity sector, in T.B. Johansson, B. Bodlund, R.H. Williams (eds.), Electricity: Efficient end-use and new generation technologies and their planning impacts, Lund University Press, Lund, Sweden. Bonneville Power Administration, 1980, Comparative electric-generation study, Final Report, Golden Co, USA. Boston Consulting Group, 1972, Perspectives on Experience, Boston, Massachusetts. Cantor, R. and J. Hewlett, 1988, The economics of nuclear power, Resources and Energy, 10, pp. 315-335. Christiansson, L., 1995, Diffusion and learning curves of renewable-energy technologies, HAS A Working Paper WP-95-126, International Institute for Applied Systems Analyses (IIASA), Wien. Christiansson, L., 1996a, Dynamics of Electricity Efficiency in Commercial Air-Distribution Systems in Sweden, IMES/EESS Report No. 21, Department of Environmental and Energy Systems Studies, Lund University, Sweden. Christiansson, L., 1996b, Electricity Demand Options -in a Time-Dynamic Framework, Tekn. Lie. thesis, Department of Environmental and Energy Systems Studies, Lund University, Sweden. 44 Dynamics of Energy Systems

Christiansson, L., and J. Swisher, 1995, Dynamics of Energy-Efficient Technology for Commercial Buildings in Sweden: How much can really be achieved and how fast?, Proceedings of the 1995 ECEEE Summer Study, Mandelieu, France. Christiansson, L., A. Martens son, L.J. Nilsson, J. Swisher, 1995, End-use energy technologies and analysis, in L. Lundgren, L.J., Nilsson, and P. Schlyter (eds.), Expanding environmental perspectives: Lessons of the past, prospects for the future, Lund University Press, Sweden. Clair, D.R., 1983, The perils of hanging on, Paper presented to the European Petrochemical Association 17th Annual Meeting, Monte Carlo, 25-28 September. Cody, G.D. and T. Tiedje, 1997, A learning curve approach to projecting cost and performance for photovoltaic technologies, Proceedings of the First Conference on Future Generation Photovoltaic Technologies, Denver, CO, USA. Daly, H., 1990, Towards some operational principles of sustainable development, Ecological Economics, 2, pp. 1-6. Dosi, G., 1982, Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change, Research Policy, 11(3), pp. 147-162. Dosi, G., C. Freeman, R. Nelson, G. Silverberg, and L. Soete (eds.), 1988, Technical Change and Economic Theory, Printer, London, UK. Edquist, C., 1995, Government technology procurement a as instrument of technology policy, Tema T Working Paper, Department of Technology and Social Change, Linkoping University, Sweden. EPRI, 1992, User's Guide for COMMEND, Electric Power Research Institute, Palo Alto California. Eyre, N., 1997, External costs: what do they mean for energy policy?, Energy Policy, 25(1), pp. 85-95. Feldman, S., 1994, Market transformation: Hot topic or hot air?, Proceedings oft the 1994 ACEEE Summer Study on Energy Efficiency in Buildings, Berkely, California &Washington D.C. Feldman, S., 1995, How do we measure the invisible hand, Proceedings of the 1995 International Energy Program Evaluation Conference. Feldman, S., 1996, On estimating the value added through market transformation, prepared by Oak Ridge National Laboratoiy for the US Department under contract DE-AC05- 960R22464, June 1996. Fisher, A. and Rothkopf, M. (1989) Market failure and energy policy: a rationale for selective conservation, Energy Policy 17(4) 397-406. Fisher, J.C., 1974, Energy Crises in Perspective, John Wiley & Sons. Freeman, C., 1994, Critical survey - The economics of technical change, Cambridge Journal of Economic, 18, pp. 463-514. Freeman, C. and C. Perez, 1988, Structural crises of adjustment: Business cycles and investment behaviours, in G. Dosi, C. Freeman, R. Nelson, G. Silverberg, and L. Soete (eds.), Technical change and economic theory, Printer, London, UK. Gipe, P., 1995, Wind energy comes of age, Wiley, New York. Goldemberg J., 1996, The development of ethanol costs in Brazil, Energy Policy, 24(12). pp. 1127-1128. Goldemberg, J., T.B. Johansson, A.K.N. Reddy, and R.H. Williams, 1988, Energy for a sustainable world, Wiely Eastern, New Delhi. Golove W.H. and J.H. Eto, Market barriers to energy efficiency: a critical reappraisal of the rationale for public policies to promote energy efficiency, Lawrence Berkeley National Laboratory Report (LBL-38059), University of California, US. References 45

Grubb, M., 1997, Technologies, energy systems and the timing of C02 emissions abatement, Energy Policy, 25(2), pp.159-172. Grubb M., J. Edmonds, P. Brink, and M. Morrsion, 1993, The costs of limiting fossil-fuel C02 emissions: a survey and analysis, Annual Review of Energy and Environment, 18, pp. 397-478. Griibler, A., 1998, Technology and global change, Cambridge University Press, UK. Grubler, A. and S. Messner, 1998, Technological change and the timing of mitigation measures, Energy Economics, 20, pp. 495-512. Hansen, L.H., and P.D. Andersen, 1999, Wind turbines-facts from 20 years of technological progress, Proceedings of the 1999 European Wind Energy Conference and Exhibition, March 1-5 1999, Nice France. Hall, G. and S. Howell, 1985, The experience curve from the economist's perspective, Strategic Management Journal, 6, pp. 197-212. Hedenstrom C., A. Goransson, U. Lindahl and G. Forsman, 1992, Lokalema och energihushallningen, STIL - studien (The Commercial Sector and the Energy Conservation Possibilities), Vattenfall ReportU:1991/70, Vallingby, Sweden. Hafele, W., 1980, A global long-range picture of energy developments, Science, 209, July 4. Hagerstrand, T., 1991, in N. Nakicenovic and A. Grubler (eds./, Diffusion of Technologies and Social behavior, IIASA, Springer-Verlag. IE A (International Energy Agency), 1987, Energy conservation in 1EA Studies, Paris. IEA (International Energy Agency), 1996, Evaluation, verification, and performance measure ­ ment of energy efficiency programmes, Contractor Report prepared by D.M. Violette (unpublished). IEA (Interantional Energy Agency), 1997a, Renewable energy policy in IEA countries: Volume 1 Overview, IEA/OECD, Paris, France. IEA (Interantional Energy Agency), 1997b, Energy efficiency initiative, IEA/OECD, Paris, France. IEA (Interantional Energy Agency), 1998, Energy Policies of IEA Countries: 1998 Review, IEA/OECD, Paris, France. IIASA (International Institute for Applied Systems Analysis), 1981, Energy in a finite world, Ballinger Cambridge Massachusetts. IPCC (Intergovernmental Panel on Climate Change), 1996, Climate change 1995 (3 Volumes), Cambridge University Press, Cambridge and New York. Jochem, E., 1991, Long term potential of rational energy use - the unknown possibilities for reducing greenhouse gas emissions, Energy & Environment, 2(1), pp. 31-44. Johansson, T.B. and P. Steen, 1977, Solar Sweden, Secretarial of Future Studies, Stockholm, Sweden. Johansson, T.B., B. Bodlund, and R.H. Williams (eds.), 1989, Electricity: Efficient End-Use and New Generation Technologies, and Their Planning Implications, Lund University Press, Lund, Sweden. Johansson, T.B., H. Kelly, A.K.N. Reddy, and R.H. Williams (eds.), 1993, Renewable energy - Sources of Fuels and Electricity, Island Press, Washington D.C., USA. Joskow, P.L. and N.L. Rose, 1985, The effects of technological change, experience, and environmental regulation on the construction cost of coal-burning generating units, Rand Journal of Economics, 1(1), pp. 1-27. Kamo, P., 1996, The social process of competence building, International Journal of Technology Management (Special Issue on Unlearning and Learning for Technological Innovation), 11(7/8), pp. 770-789. Kamo, P. and R. Garud, 1997, Path creation and dependence in the Danish wind turbine field, in J. Porac and M. Ventresca (eds.), Social Construction of Industries and Markets, Pergemon Press. 46 Dynamics of Energy Systems

Keepin, B., 1984, A technical appraisal of the IIASA energy scenarios, Policy Science, 17(3), pp. 199-276. Kemp, R., 1994, Technology and the transition to environmental sustainability- The problem of technological regime shifts, Futures, 26(10), pp. 1023-1046. Komanoff, C., 1981, Power Plant Cost Escalation - Nuclear and Coal Capital Costs, Regulation, and Economic, Komanoff Energy Associates, New York. Koomey, J.G. and Sanstad, A.H. (1994) Technical evidence for assessing the performance of markets affecting energy efficiency, Energy Policy 22(10) 826-832. Krawiec, F., 1983, Concepts of learning and experience in developing solar thermal technologies, Technology Forecasting and Social Change, 24, pp. 207-246. Krawiec, F., J. Thomion, and M. Edesess, 1980, An investigation of learning and experience Curves, Report SERJ/TR-353-459, Solar Energy Research Institute, Golden Co, USA. Kagesson, P., 1997, Growth versus the environment - is there a trade-off?, Doctoral Thises, Department of Energy and Environmental System Studies, Lund University, Sweden. Levin, M.D., Hirst, E., Koomey, G., Me Mahon, IE. and Sanstad A.H. (1994) Energy efficiency, market failure, and government policy, Lawrence Berkeley National Laboratory Report (LBL-35376), University of California, USA. Levin-Kruse, K., 1991, Fran matare till manniska (From Metering to Marketing), Vattenfall Report U:1991/51, Vallingby, Sweden. Limaye, D.R. and D.T. Hoog, 1992, COMPASS: An Integrated Analysis of Demand-Side Management Programs, Energy Engineering, 89(3). Lovins, A.B., 1977, Soft energy paths: towards a durable peace, Penguin Book, New York. MacGregor, P.R., C.E. Maslak, and H.G. Stoll, 1991, The market outlook for integrated gasification combined cycle technology, Report, General Electric Company, Schenectady, N.Y., USA. Martin, J-M., 1996, Energy technologies: Systemic aspects, technological trajectories, and institutional framework, Technological Forecasting and Social Change, 53(1), pp. 81-95. Mattson, N., and C-O. Wene, 1997, Assessing new energy technologies using an energy system model with endogenized experience curves, International Journal of Energy Research, 21, pp. 385-393. Messner, S., 1997, Endogenised Technological Learning in an energy systems model, Journal of Evolutionary Economics, 7(3), pp. 291-313. Nadel, S., 1992, Utility demand-side management experience and potential - a critical review, Annual review of energy and environment, 17, pp. 507-535. Nakicenocic, N. and A. Gnibler (eds.), 1991, Diffusion of Technologies and Social behavior, IIASA, Springer-Verlag. Nakicenovic, N., A. Grubler, and A. McDonald (eds.), 1998, Global energy perspectives, Cambridge University Press. Neij, L., 1997, Market transformation by technology procurement - The Swedish Experience, ENER Bulletin 20. Nelson, R. and S. Winter, 1977, In search of a useful theory of innovation, Research Policy, 6(1), pp. 36-76. Nelson, R. and S. Winter, 1982, An evolutionary Theory of Economic Change, Belknap Press of Havard University Press, Cambridge, MA, USA. Nilsson, L.J., 1993, Energy intensity trends in 31 industrial and developing countries 1950- 1980, Energy the International Journal, 18(4), pp. 309-322. Nilsson, L.J., 1995, Air-handling energy efficiency and design practice, Energy and Buildings, 22(1). Pearce, D., and R. Turner, 1990, Economics of natural resources and the environment, Harvester Wheatsheaf, New York. References 47

Prahl, R., and J. Schlegel, 1993, Evaluating Market Transformation, Proceedings of the Energy Program Evaluation: Uses, methods, and results - 1993 International Energy Program Evaluation Conference, Chicago, USA. Reddy, A.K.N., R.H. Williams, and T.B. Johansson, 1997, Energy after Rio - prospects and challenges, United Nations Development Programme, New York. Rodgers, E.M., 1962, Diffusion of Innovations, The Free Press New York NY, USA. Rosenberg, M., 1995, Strategies to quantify market transformation and spillover effects of DSM programs, Energy Service Journal, 1(2), pp. 143-157. Rosenberg, N., 1982, Inside the black box: Technology and Economics. Cambridge University Press, Cambridge, UK. Rosenberg, N., 1994, Exploring the blackbox: Technology, economics, and history, Cambridge University Press, Cambridge, UK. Schipper, L., 1997, Indicators of energy use and efficiency, IEA/OECD, Paris. Schumpeter, J.A., 1934, The Theory of Economic Development, Havard University Press, Cambridge, MA, USA. SRC (Synergic Resources Corporation), 1991, Compass User's Manual, Synergic Resources Corp., Bala Cynwyd, PA, USA. SRC (Synergic Resources Corporation), 1996, Market transformation in a changing utility environment, Report for the National Association of Regulatory Utility Commissioners. Sutherland, 1991, Market Barriers to Energy-Efficiency Investments, The Energy Journal, 12(3), pp. 15-34. Swisher, J., and L. Christiansson, 1994, Dynamics of energy efficiency in lighting and other commercial uses in Sweden, IMES/EESS Report No. 15, Department of Environmental and Energy Systems Studies, Lund University, Sweden. Tsuchiya, H., 1992, Photovoltaic cost analysis based on the learning curve, Report, Research Institute for Systems Technology, Tokyo. UN (United Nations), 1997,1995 Energy Statistics Yearbook, United Nations, New York. UNCED (United Nations Conference on Environment and Development), 1992, Rio declaration on environment and development - Agenda 21: Programme of Action for Sustainable Development, United Nations Publication, New York. WCED (World Commission on Environment and Development), 1987, Our Common Future, Oxford University Press, Oxford, UK. WEC (World Energy Council), 1995, Energy efficiency improvements utilizing high technology, London. Westling, H., 1991. Technology Procurement for Innovation in Swedish Construction, Building Research Council, Stockholm. Williams, R.H., E.D. Larson, and M.H. Ross, 1987, Materials, affluence, and industrial energy use, Annual Review of Energy, 12, pp. 99-144. Williams, R.H., G. Terzian, 1993, A benefit/cost analysis of accelerated development of photovoltaic technology, Report No. 281, Center for Energy and Environmental Studies, Princeton University, USA Wilson D., and J. Swisher, 1993, Exploring the gap: top-down versus bottom-up analyses of the cost of mitigating global warming, Energy Policy, 18, pp. 550-562. Worell, E., M. Levine, L. Price, N. Martin, R. van der Broek, and K. Blok, 1997, Potentials and policy implications of energy and material efficiency improvements, United Nations, New York. Wright, T.P., 1936, Factors affecting the cost of airplanes, Journal of the Aeronautical Science, 3, pp. 122-128. Wynne, B., 1984, The institutional context of science, models and policy: the HAS A energy study, Policy Sciences, 17(3), pp. 277-320.