Policies and Market Developments - ECN · Policies and Market Developments Subtask: Review and analysis of market models Report nº D3A Final Version December 2002 Work Package 3

1 -

REMAC 2000Contract No. ERK5-CT2000-80124

Policies and MarketDevelopments

Subtask:Review and analysis of market models

Report nº D3AFinal Version

December 2002

Work Package 3Author: Philippe Menanteau

Organisation: IEPE / CNRS

REMAC 2000 Contract nº: ERK5-CT2000-80124

Work Package 3- review and analysis of market models Policies and market developments- 2 -

Acknowledgement

I am grateful to François Cattier and Maria Argiri from the International Energy Agency fortheir useful comments, but obviously the remaining mistakes and omissions are my ownresponsibility.

Abstract

The objective of this part is part of the Work Programme 3 is to review the existing marketstudies and energy models and to analyse their capacity to reflect the evolving policyframework and the new performances of renewable energy technologies.

Different market studies and energy models reviewed include RE market studies (Atlas orEcofys), sector market studies (European PV Industry Assessment, European Wind EnergyAssessment) and energy models (Safire/Teres, Poles, Primes, Markal).

The interest of market studies is in the possibility to analyse in great details the evolutions ofrenewable energy technologies on specific markets. Their drawback lays in the methodologiesthat are used to forecast these evolutions : extension of past trends or realisation of achievablepotentials (hypothesis of technical, economic and social barriers removal). As such, theypresent a limited interest for assessing the impacts of specific policies such as R&D publicpolicies, fiscal incentives, CO2 taxes or tradable emission permits.

Energy models can help decision making by offering a better insight into complex systemssuch as the energy system. In order to correctly reflect the expected growth of RE sources, thechallenge for energy models is to simulate the different sources of technological change:innovations, improvement of existing technologies due to technological learning, progress ofexisting technologies or development of new ones stimulated by modifications in energyprices.

Important progress has been done in this respect with the integration of learning curves. As aresult, evolutions of technologies' characteristics are more consistent with market deploymentand the impact of incentive policies appears to be greater.

The ability of energy models to simulate the future dissemination of RE technologies shouldnot however be overestimated. A number of limitations have been stressed among which thedifficulties resulting from the specific nature of RE technologies (intermittent, nonpredictable, dispersed generation, etc.). Maybe more significant, energy models hardly reflectthe positive impact of distributed generation on the reliability and quality of electricity supplyor the consumers' growing interest in green electricity which may be very important drivers ofRETs' dissemination in the future.



CONTENTS :

LIST OF FIGURES 4

INTRODUCTION 5

1. RENEWABLE ENERGY STUDIES 51.1 ATLAS project 61.2 ECOFYS study 61.3 Photovoltaics in 2010 (EPIA) 71.4 Wind Energy – the facts (EWEA) 91.5 Conclusion : effectiveness of renewable energy studies in forecasting the future share of

renewable energy technologies 9

2. ENERGY SYSTEM MODELS 102.1 Methodological approaches 112.1.1 Optimization models 112.1.2 Simulation models 122.2 Impacts of policy instruments 132.2.1 Taxes on CO2 emissions (Markal) 132.2.2 Constraints on CO2 emissions (Primes) 142.2.3 CO2 taxes and feed-in tariffs (Poles) 15

3. NEW DEVELOPMENTS IN ENERGY MODELS 163.1 Mechanisms of technical change 173.2 Endogenous technical change 183.3 New renewable energy modelling exercises 20

4. LIMITS IN SIMULATING THE DEVELOPMENT OF RETS 234.1 Limits in modelling approaches 234.1.1 Optimization of behaviour 234.1.2 Lack of heterogeneity 234.1.3 Uncertainty 244.1.4 Snowball and lock-in effects 244.2 Specific difficulties associated to renewable energy technologies 254.2.1 Assessment of technical potentials 254.2.2 Evolution of preferences / Social dimension 264.2.3 Associated benefits 264.2.4 Variations in performance and costs / Data reliability 264.2.5 Experience curves 274.2.6 Spillover effects. 274.2.7 Intermittence of RETs 274.2.8 Incentive policies 28

5. CONCLUSIONS 29



LIST OF TABLES

Table 1: Comparison of the 2010 targets for renewable energy sources in the EuropeanCommission White Paper with Ecofys results and ATLAS project....................................7

Table 2: Impact of emission reduction constraints on electricity production – Primes -.......................15Table 3: Impact of feed-in tariffs on renewable electricity in Western Europe (TWh) .........................16

LIST OF FIGURES

Figure 1 : Simulation of wind power deployment in the Poles model...................................................12Figure 2 : Electricity production by source with increasing CO2 taxes (2040)......................................14Figure 3: Examples of learning curves of energy conversion technologies...........................................18Figure 4: Marginal cost of CO2 reduction, Endogenous (ETL) versus non Endogenous (NETL)

Technological Learning (Markal) .....................................................................................19Figure 5: Marginal supply cost curves (Elgreen)...................................................................................21Figure 6: EU cost / potential curve for RES-E in 2010 (REBUS) .........................................................21Figure 7: Exporting / importing countries in a EU TGC market (REBUS)...........................................22



INTRODUCTION

Either renewable energy studies and energy system models are used to simulate thedeployment of renewable energy technologies according to different energy policy options.

Renewable energy studies are sector-based analyses which estimate the possibledissemination of renewable energy technologies using detailed analysis of ongoing evolutionsfor each technology. Dynamics of technological change, costs-effectiveness on differentmarket niches, existing market or institutional barriers, and incentive policies or instruments,are described and analyzed for estimating future developments.

Energy system models are representations of the complex relationships and processes thatcharacterizes the functioning of the energy system. Energy system models simulate the energysystem and the decisions of agents to generate energy demand functions, energy prices,energy supply mix and related emissions. They are used to estimate current and future trendsin energy markets according to different assumptions regarding economic activity, energyprices, population growth, technological change, energy and environment policies, etc.

In the following section, we are presenting different renewable energy studies and energysystem models that have been used to simulate the dissemination of renewable energytechnologies. The objective is not to undertake a detailed comparison of their respectiveresults but to present the methodological processes that are used in both cases and to point outthe advantages and limits of both approaches.

1. RENEWABLE ENERGY STUDIES

A lot of different studies are focused on the estimation of the future contribution of renewableenergy sources to national, regional or world energy supply. Some of these studies areconsidering a broad range of renewable energy technologies (ATLAS1, 1997 ; ECOFYS2,2001, for example), others are technology specific (EPIA3, 1996 or EWEA4, 1998).

The status of these studies vary from one to another ; some aim at foreseeing future marketdeployments considering that present conditions remain when others describe the marketpenetration under various scenarios among which specific policies in favour of renewableenergy technologies. As a consequence, these estimations of the future share of renewableenergy technologies may represent very different views, from market potential with present

1 ATLAS, Energy Technologies, the Next Step,http ://europa.eu.int/comm/energy_transport/atlas/html/renewables.html2 Ecofys, Economic Evaluation of Emission Reduction of GHG in the Energy Supply Sector in the EU, Contribution to aStudy for DG Environment, European Commission, Economic Evaluation of Sectoral Emission Reduction Objectives forClimate Change, Final report, March 2001.3 European Photovoltaic Industry Association (EPIA), Photovoltaics in 2010, European Commission, Directorate General forEnergy, Brussels, 1996.4 European Wind Energy Association (EWEA), Wind energy : the facts ; European Commission, Directorate General forEnergy, Brussels, 1998.



policies or much more favourable incentive policies, to "realisable" potential provided thateconomic, technological and institutional barriers are removed.

The following studies have been selected to illustrate the approaches that are commonly usedto forecast renewable deployment using detailed sector-based analyses. They do not pretendto cover the whole range of approaches used to estimate the future share of renewable energytechnologies. But given the limited scope of this exercise, they offer a rather representativeillustration of the forecasting studies that have been realised at the European level in the past5 years.

1.1 ATLAS project

The ATLAS project proposes an overview of historical, current and future projections for thedeployment and costs of renewable energy technologies to the year 2010. The information onfuture markets is based on "trends continued" views of the future which means that noneimprovement or removal of existing policies are considered. These projections are based onthe views of the key actors in the market, manufacturers, trade associations, user associations,local utilities, electric utilities, etc.

The future potentials that are estimated by the ATLAS project are presented as "commercialpotentials". They reflect the vision of the stakeholders in the year 1997 with regard to thedeployment of the different renewable energy markets. As an example, the expected installedcapacity for wind power in 2010 (17 500 MW) is estimated on the basis of the yearly addedcapacity in 1995 (1000 MW/yr), extended to 2010 (14 x 1000 + 3500). Obviously, theexpectations would have been different considering the present rate of increase in the windenergy sector (4600 MW have been added to installed capacity in 2001 in the EuropeanUnion).

The future potentials are estimated considering that existing incentive policies in 1997 aremaintained but also existing barriers that hinder the deployment of new technologies evenwhen they are cost-effective (lack of awareness of potential users, difficulty in access tocapital or legislative barriers to distributed production).

Accordingly, ATLAS results are close to a market potential which underestimates thepotential which could be attained if an intensified renewable or climate policy wereimplemented. These figures are logically much lower than Ecofys' results which consider thatthe main barriers are removed as a result of successful sector-based policies.

1.2 ECOFYS study

The objective of the Ecofys report is to evaluate the options to reduce carbon dioxideemissions from the energy supply sector at the European level, notably by increasing the shareof renewable energy technologies. For that, an “implementation potential” for renewableenergy technologies is estimated for the year 2010. This "implementation potential" is definedas the part of the technical potential which could be implemented "if economic, technologicaland institutional barriers and constraints were overcome".



In the following table, this implementation potential is compared to the targets set forrenewable sources in the White Paper for a Community Strategy and Action Plan (EC5, 1997).

Table 1: Comparison of the 2010 targets for renewable energy sources in the EuropeanCommission White Paper with Ecofys results and ATLAS project

2010 targetsWhite Paper (1997)

targetsATLAS (1997)

2. Hydropower 355 TWh 328 TWh2.1 Large (> 10 MW) 300 TWh 282 TWh2.2 Small (< 10 MW) 55 TWh 39 TWh

3. Photovoltaics 3 TWh 2 TWh230 TWh 27 TWh

3,1 EJ(th)5. Geothermal

5.1 Electric

7 TWh 9,4 TWh 5.2 Heat (incl. heat pumps) 42 PJ (th)

6. Solar Thermal Collectors 170 PJ (th) 36 PJ (th)7. Passive solar 1.5 EJ8. OthersTOTAL Elec (TWh) 686,7 403,4

37 TWh

1. Wind energy

80 TWh

4. Biomass

ECOFYS (2001)

82 TWh / 34,5 GW Onshore

50 TWh / 16,5 GW Offshore

353 TWh312 TWh41 TWh

2 TWh / 2 GW68 TWh

3,5 EJ (th)

9 TWh20 PJ (th - exc. heat

237 PJ (th)

569,6

Source : Ecofys, 2001, ATLAS project

These figures are estimated by different experts on the basis of current trends andcomparisons with previous studies. For example, the implementation potential for windenergy is derived from various references but adjusted according to available newinformation. As a consequence, estimated capacity for wind energy in 2010 is superior to thefigure resulting from trends continued assumptions. With regard to offshore wind energy,strong incentive and continuous technological improvement will take place stimulatingproduction. Total estimated potential for wind energy in 2010 is 10 GW higher than thetargets of the EC White Paper (40 GW).

These estimates should be considered as the achievable potential that could be reached byrenewable sources of energy in the medium term (2010) if successful policies in favour ofrenewables dissemination were implemented : research and development, financial incentives,elimination of institutional barriers, etc. With the removal of these barriers the"implementation potential" lies between an economic potential and a market potential (part ofthe economic potential not affected by barriers or constraints).

1.3 Photovoltaics in 2010 (EPIA)

The European Photovoltaics Industry Association (EPIA) has been commissioned by theEuropean Commission in 1996 to study the status of PV in Europe and Worldwide and to

5 European Commission, 1997, Energy for the future : renewable sources of energy. White Paper for a Community Strategyand Action Plan, EC, COM (97)599, Brussels, November 1997.



develop a strategy for the development of the European industry and market. This study hasbeen undertaken with due consideration to the targets for PV set by the Altener Programme of500 MW of PV installed in the EU in 2005. The main objective of the study was to proposean Action Plan coherent with an extrapolated Altener target to the year 2010.

The EPIA report presents an assessment of the world PV market to the year 2010 based ondifferent contrasted scenarios. The "business as usual" scenario assumes that the PV market isexpected to continue to grow at the same pace during the period 1995-2010, as observedduring the decade 1984-94 (15%/yr). In this BAU scenario, there are no changes in incentiveframeworks (subsidies and public financial support are maintained), which means that PVcompetitiveness remains globally unchanged.

The other scenarios are simulating enhanced market growth in the PV sector worldwide bysimply assuming different market growth rates, from 15% in the BAU scenario to 35%/yr inthe extreme growth scenario. Apart from a different growth rate, enhanced market growthscenario is similar to the BAU scenario.

The hypothesis of increasing market growth rates are supposed to be related to more and morefavourable conditions (cf infra) but these conditions are not specified or remain very general :• 15%/yr : business as usual, spontaneous development• 20%/yr : elimination of market barriers• 25%/yr : consistent market simulation• 30%/yr : breakthrough in technology and costs• 35% : extreme scenario

There are detailed analysis of specific market niches and technical potentials (ruralelectrification, grid connected systems, stand alone uses, etc.) but these markets or potentialsare not directly linked to the evolution of prices (PV modules or fossil fuel prices), totechnical change (development of thin film technologies for example) or to public policies(new buy-back rates in European countries, CDM in developing countries, green certificates,etc.)

As a consequence, this simulation is useful in order to estimate resulting installed capacitiesfrom different growth rates in PV shipments and the needed investments in manufacturingcapacities but it does not allow to predict the future PV market with changing conditions. Itis, for example, not possible to infer from this study what would be the consequences on thePV market of publicly funded rooftop programme at the EU level or what would be theimpact on dissemination of the marketing of thin film technologies at 1€/W.

New assumptions regarding the dissemination of PV technology has been published recentlyin the SolarGeneration study6 which is based on a similar methodology. The study compiles a"detailed quantitative knowledge base, coupled with clearly defined and realistic assumptionsfrom which extrapolations could be made". The idea is to estimate the share of the solarpower electricity market up to 2020 and beyond, if the right market conditions and anticipatedcosts decrease are fulfilled.

Main inputs are PV potentials (solar irradiation), targets for manufacturing capacities, nationalsupport programmes and observed PV market development over last few years. The resulting

6 EPIA / Greenpeace, 2002, "2 million jobs", http://archive.greenpeace.org/~climate/climatecountdown/solargeneration/



solar electricity market for OECD Europe would be 740 TWh in 2010 and 9445 in 2020.These figures should not be considered as previsions but prognoses of what could be the shareof solar electricity if very favourable market conditions were in place.

1.4 Wind Energy – the facts (EWEA)

The EWEA estimation of the future world wind energy markets to 2005 is rather similar tothe EPIA study in the sense that it is based on informed national market evolutionexpectations. Similarly, the predictions made are based on an analysis of the wind energymarket over previous years but using a disaggregated approach of national markets. Also,recent and expected public policies designed to promote renewable energy are taken intoaccount.

As such, expectations do not simply reflect past growth rates or hypothetical future growthrates, but rely on informed analysis of leading markets, including expected changes in publicpolicies. This is feasible in the present case given the limited time horizon of the study (2005)and the limited number of leading markets at the global level which allows detailed marketanalyses.

Another joined study between EWEA and Greenpeace has been published more recentlywhich methodology is very similar to the one followed by the EPIA/GreenpeaceSolarGeneration study mentioned above7. Accordingly, it is not a forecast but an exerciseaimed at assessing whether it is feasible for wind power to achieve a very high level ofpenetration within 20 years. The main assumptions are similar to the EPIA study :assessments regarding wind resources, expected costs decrease (based on learning curves) andpotential growth rates (20-25% up to 2012 and 15% afterwards).

However, the limits of the predictions are similar to the ones in the EPIA study. Theseapproaches based on expert analyses may provide accurate predictions for short to mediumterm if they are based on recent and detailed analyses of market drivers and if the trendremains globally the same. But uncertainty increases as the time horizon reaches medium tolong term. Moreover, it is difficult with these approaches to take into account dynamic effectssuch as increasing returns to adoption, for example, or simply to take account of competitionwith existing technologies as the market share of renewable energy sources enlarges. Theseanalyses necessitate energy system models with explicit representation of technologies andsimulation of the behaviour of economic agents.

1.5 Conclusion : effectiveness of renewable energy studies in forecasting the futureshare of renewable energy technologies

The objective of this limited analysis of some renewable energy studies is not to drawdefinitive conclusions but mainly to highlight the main differences with energy models and topoint out possible limitations of such studies in forecasting future market share of renewabletechnologies.

7 EWEA / Greenpeace, "Wind Force 12, The New Global Challenge",



The renewable energy studies that have been selected for this limited review are notnecessarily the most recent and significant ones, but they allow to understand what are thebasic approaches commonly used in such studies.

From this review, the key issues are the following :• renewable energy studies which are based on expert views may offer accurate visions of

future installed capacities for specific renewable energy technologies with assumptions ontrends continued or reinforced incentive policies. The relevance of these forecasts arebased on a detailed knowledge of markets, industrial strategies and regulations.

• these forecasts may be relevant for short to medium term. Beyond, growth based on theextrapolation of trends is very uncertain and the more the market share of a technologyprogress the less it is possible to ignore competition with existing ones.

• some studies also give results for the long to very long term ; these "forecasts" do not relyon expected diffusion trends, but on some kind of acceptable technical potentials. Thesepotentials may be helpful to estimate the ultimate contribution of a technology with givenperformance but again they do not take into account possible competition with existing orother new technologies.

The impossibility for renewable energy studies to accurately reflect competition betweenenergy technologies is their main limitation. As long as renewable energy technologies arelimited to small niche markets, it is possible to estimate future dissemination trends byconsidering technologies independently from one another. With increased dissemination,competition matters. It becomes necessary to simulate the complex relations that characterizethe energy system and this can only be done by energy system models.

2. ENERGY SYSTEM MODELS

Energy system models allow to simulate the evolution of energy supply and demand sectorsunder different assumptions (economic growth, population), parameters (demand/ priceelasticity, technology performances and costs, …) and constraints (with or without CO2

emissions control, for ex.). These models have become central for decision makers, forexample, in providing insight on the relevant strategies and technologies to reduce futuregreenhouse gas emissions.

There are basically two types of models :• engineering models adopt a more disaggregated approach to demand and supply ; they are

traditionally used to study the dynamics of a given sector (energy, agriculture)• economic models view energy systems as a part of the global economic activity ;

description of specific sectors is rather limited in economic models ; their main interest isin allowing to appreciate economic feedback effects of sector-based policies.

Engineering models are considered more appropriate to examine contrasting scenarios withdifferent technological possibilities because they contain detailed assumptions abouttechnology in energy sector. Conversely, economic models are weaker in exploringtechnological options and potentials at sector-based levels. They rely on the aggregatedeconomic behaviour described by past energy-economy interactions but lack technologicaldetails of "bottom-up" (engineering) models.



The models identified in order to illustrate this section (Primes, Poles, Safire, Markal) areengineering models even if some, such as Primes or Poles, may be considered as hybridmodels because they combine the detailed bottom-up technological approach and a feedbackbetween supply and demand through energy prices.

The main characteristics of these models are the following :• Safire has been developed under the Joule II programme. It is used to make forecasts of

market penetration of renewable technologies (16 renewable technologies are considered)at the horizon of 2020. The results of the TERES studies are based the Safire model. Thegeographical area covered is limited to the European countries (including EasternEuropean countries). Different scenarios are considered : present policies, industrialpolicies, ExternE policies and best practice policies.

• Poles is a sector-based model of the world energy system. Dynamics of the model is basedon a recursive simulation process of energy demand and supply with endogenous energyprices. Renewable energy technologies are explicit in the model (12 different technologiesare considered). The final year of the simulation is 2030.

• Primes is a partial equilibrium model for the European energy system which is used by theEuropean Commission for its energy forecasts. Primes represent in an explicit and detailedway a large number of energy demand and supply technologies. Model produces long andmedium term projections (2010 and 2030).

• Markal model belongs to a family of models which are used to represent energy system atthe global level, except for the Markal application for Western Europe. Contrary to othermodels listed above, Markal considers EU as one region and does not present detailedresults per country. Markal Europe application can be considered as a rathertechnologically detailed model. Markal can provide results in the very long term (2050).

2.1 Methodological approaches

2.1.1 Optimization models

Primes and Markal are optimization models : considering an explicit representation oftechnologies, the models look for an optimal least cost operation and configuration of thesystem that produces electricity so as to meet the demand and satisfy constraints such as fuelavailability, generation capacities, emission restrictions, etc. The models project the futureconsumption and investments in energy taking into account future costs and performance. Thechoice of future technologies is generally based on perfect foresight in which the decisionmaker has full and correct information about the future (Markal). One may argue that thisanticipation regime does not correspond to real world decision making, so it is also possible toconsider a myopic anticipation (Primes) in which the decision maker has information onlyabout the past and the present time period.

A model such as Primes integrates 148 plants types per country for the existing thermal plantsand 678 different plants per country for the new thermal plants. MARKAL Europe considersabout 40 technologies for electricity generation, including for instance, 4 different windenergy technologies (existing wind turbines, large onshore with and without storage, and largeoffshore with storage). Each technology may be characterised by a large number of technico-economic parameters such as capital cost, variable cost and annual fixed cost, efficiency,availability, technical lifetime, intermittence, possibility of retrofitting, etc.



In simulation models, such as Poles and Safire, renewable energy technologies are notintegrated in the electricity supply module but dealt with separately given their specificnature.

2.1.2 Simulation models

Simulation models differ from optimisation models in the sense that they do not look for anoptimal situation, ie the least cost option for satisfying a given demand, but try to reflectdecisions of actors in the real-life economy. This allows for example to take account oftechnologies which are not already cost-effective but appear on niche markets because theycan satisfy specific needs.

In Poles, the energy production from renewable energy sources is estimated as follows :- the technical potential is estimated first based on resource or demand constraints ; for

wind power, the technical potential is estimated from wind speed bands, land area foreach band, suitable land for wind power, wind generators per suitable land and presentand future wind turbines efficiency.

- the economic potential is then derived as a percentage of the technical potential : themore cost-effective the technology (shorter payback time), the larger the economicpotential.

- finally the market penetration of the technology is estimated based on an S-shapeddiffusion curve which simulates market penetration profiles of innovations.

Figure 1 : Simulation of wind power deployment in the Poles model

Technical potential

Economic potential 2

Economic potential 1

Time

GWh

Diffusion (low ROI)

Diffusion (high ROI)

Source : IEPE

This approach is illustrated on the Figure 1. A renewable energy technology with a low returnon investment will have a lower economic potential and a slower diffusion profile when amore cost-effective one will have a higher economic potential and a faster diffusion profile.

With such a simulation, even technologies which are not yet cost-effective compared toexisting proven technologies can have a small market share corresponding to an initial marketniche.



Both optimization and simulation models are used to help decision-makers to test differentpolicy options, for example the introduction of quotas on CO2 emissions, the impact ofdifferent levels of carbon taxes on greenhouse gas emissions, and more generally taxes orsubsidies on energy prices or investment costs. This comparison of different policy options isoften realized using scenarios approaches which help to explore possible future options.

2.2 Impacts of policy instruments

Energy system models are very imperfect representations of the reality. They are not intendedto provide predictions but to bring insights on key interactions and key parameters and helpdecision-makers to better understand possible consequences of different policies.

The impacts of different policy instruments are often compared using contrasted coherentscenarios. Scenarios are descriptive images of the future, meant to project what may happenunder several alternative development pathways and to evaluate the consequences of differentcoherent strategies.

The results of different policy instruments on the deployment of renewable energytechnologies are presented thereafter as illustrations of the sensibility of energy models toprice or quantity-based approaches for reducing CO2 emissions.

2.2.1 Taxes on CO2 emissions (Markal)

The Markal model has been used to analyse future development of CO2 emissions fromWestern Europe and the possibilities to limit these emissions with technological measures8.Two scenarios have been considered : Rational Perspective and Market Drive scenarios.

In the Market Drive scenario, the penetration of new, more efficient technologies depends onmarket forces and no specific public policy for environmental protection is implemented. TheRational Perspective scenario is ecologically driven with a strong penetration of new, moreefficient technologies stimulated by incentive instruments. The differences between the twoscenarios relate to their perspectives towards decision criteria on energy investments (higherexpected return on investment in Market Drive), level of energy demand and energy prices.Assumptions for the costs and performance of technologies are the same.

Increasing levels of CO2 taxes (from 20 to 200 €/tCO2) are introduced in each scenarioresulting as one could expect in lower CO2 emissions. The introduction of CO2 taxes has alsoimportant consequences on the mix of energy sources for electricity production (Figure 2),low carbon emission technologies becoming progressively more cost-effective.

8 Scenarios for Western Europe on Long Term abatement of CO² emissions, Dutch National Research Programme on GlobalAir Pollution and Climate Change, ECN, 1997.



Figure 2 : Electricity production by source with increasing CO2 taxes (2040)

Market Drive Rational Perspective

Source : ECN, 1997

The contribution of renewable sources of energy increases slowly with higher CO2 taxes inthe Market Drive scenario : a level of 100 €/tCO2 is necessary to observe a real change. In theRational Perspective, the changes in renewable electricity production are more apparent withmodest CO2 taxes and they are very important at higher levels. With CO2 taxes levels of 100€/tCO2, the contribution of wind and solar amounts to 15% of electricity production in theMarket Drive scenario but it raises to 23% in the Rational Perspective.

This study illustrates the impact of price-driven approaches on CO2 emissions and stimulationof renewable energy sources. It also points out the importance of the key scenarioassumptions on the final result.

2.2.2 Constraints on CO2 emissions (Primes)

The Primes model has been used to observe how the European electricity sector adjusts whenCO2 emission reductions are introduced in order to meet the Kyoto commitment9. Twodifferent scenarios are considered : a baseline and a Kyoto commitment scenarios.

In the baseline scenario, renewable technologies are supposed to progress over time at rathersmooth rates, without major technological breakthrough, current policies promotingrenewable sources of energy continue but no specific constraints favouring renewables areimposed, prices of fossil fuels grow very smoothly. In the Kyoto scenario, these conditionsremain the same except that the power sector has to adjust in order to take account of CO2

emissions constraint.

In the baseline scenario the deployment of renewable energy technologies is rather limited,except for wind energy (from 8 MW in 2000 to 22 in 2010) and to a lesser extent, waste andexisting biomass (there is no development of new biomass). The growth of renewableelectricity production is lower than the total electricity generation growth.

9 P. Capros, N. Kouvaritakis and L. Mantzos, Top down Analysis of GHG Emission Reduction Possibilities in the EU,Contribution to a Study for DG Environment, European Commission, Economic Evaluation of Sectoral Emission ReductionObjectives for Climate Change, Final report, March 2001.



The emissions constraint induces changes in fuel mix, reorientation of investment choices andhigher use of carbon free sources, particularly renewables given the short time horizon –2010. The share of renewable electricity increases to 18% in 2010 against 14% in the baselinedue to additional investments in renewable technologies which are competitive or nearcompetitiveness (significant growth of wind energy -38.5 MW in 2010- and biomass/wastebut no development of new biomass).

Table 2: Impact of emission reduction constraints on electricity production – Primes -

% GW % GWAustria 43,6 1,3 63,8 0,47 46,1 2,7 69,3 0,96Belgium 3,4 2,5 3,4 0,75 7 5,8 7 1,7Denmark 10,3 7,3 23,2 3,37 12,1 9,9 29,5 4,71Finland 25,2 1,5 27,7 0,49 29,4 2,7 33,4 0,86France 78,8 2,1 13,4 1,04 89,8 8,9 14,8 4,24Germany 54,7 22,9 9 8,19 75,3 33,7 13,1 12,36Greece 7,3 1,9 10,2 0,54 9,7 2,8 15,4 0,8Ireland 2,9 1,7 8,7 0,54 5,6 3,2 17,9 1,07Italv 55,8 6,5 16,7 2,39 65,5 12,1 20,3 4,73Netherlands 4,7 1,5 3,6 0,61 8,5 4,2 7 1,66Portuqal 15 0 23,8 0,03 18,1 0,2 30,6 0,12Spain 50,4 6,7 20,2 2,08 68,5 8,9 28,1 2,78Sweden 75,1 0 46,5 0,07 75,8 0 47,6 0,07United Kingdom 13,6 4,4 2,8 1,34 25,3 8,2 5,4 2,47TOTAL EU 440,8 60,3 14,6 21,9 536,7 103,3 18,2 38,5

Kyoto

TWhTWh

BaselineTotal from

renewablesTotal from

renewablesof which

from windWind

capacityRenewable

s in total of which

from windRenewable

s in total Wind

capacity

Source : Capros, et al, 2001

Further emission abatements are also tested which enlarge the contribution of renewableenergy technologies. Renewable sources that are already largely explored cannot furtherexpand (large hydro) but the ones that are nearly competitive (wind or traditionalbiomass/waste) largely increase their market share and even emerging sources make asignificant contribution for the higher levels of constraint. The reason is that higherconstraints on emissions force the model to look for higher cost emission reduction options tothe benefit of innovative technologies. With high emission reduction targets, the economicpotential of renewables would be 27% of total electricity production in the year 2010.

2.2.3 CO2 taxes and feed-in tariffs (Poles)

As an illustration of the possibilities to simulate different policy instruments favouring thedevelopment of renewable energy technologies with system energy models, the following testhas been realized with the Poles model10.

Three scenarios are considered. The business-as-usual (BAU) scenario is considered as thereference case ; no climate change policies are implemented (CO2 taxes or emission quotas)but pre-existing sector-based policies, notably in the renewable energy sector, are maintained.In the second scenario (Sapient11), energy taxes are introduced in successive phases : first,taxes concern only EU and rest of annex B countries in order to reach Kyoto targets(respectively, 55 $/tC for EU countries and 22 $/tC for other Ann B countries from 2003 to2010) and then, taxes apply to all countries (121 $/tC from 2010 to 2030). In the third

10 This test has been conducted with the Poles team at IEPE for a illustrative purpose. It is not published yet.11 Sapient project, contract for EC DG Research



scenario (REN), the taxes are maintained and policies favouring the development ofrenewable energy technologies are reinforced12.

The results of this simulation are presented in the following table.

Table 3: Impact of feed-in tariffs on renewable electricity in Western Europe (TWh)

1990 2000 2010 2020 2030Small hydro 29 43 62 69 74Biomass 15 27 31 43 58Solar + wind 1 16 68 166 255Small hydro 29 43 63 75 81Biomass 15 27 37 81 138Solar + wind 1 16 80 219 311Small hydro 29 48 75 86 88Biomass 15 33 100 185 226Solar + wind 1 16 141 297 369

Sapient

BAU

REN

Source : IEPE

According to the results observed with Primes, the short term horizon of 2010 does not allowa great expansion of renewable energy technologies when limited CO2 taxes are considered. Amore that doubling of the CO2 taxes after 2010 improves the situation of renewables exceptfor small hydro which reaches its maximum potential. However, it is noticeable that thedevelopment of renewable energy technologies is much more important when their costeffectiveness is further stimulated with improved buyback rates.

The aim of this section was not to conduct a detailed comparison of models results because itwould not have been conclusive. It is difficult to compare results from different modelsbecause of the differences in the models inputs including technologies performance and costs,availability of resources, limits on development rates (for some models), etc. Assumptionsconcerning the evolution of future technologies, in particular, largely affect models results.Our objective was to illustrate the impact of different policy instruments on the developmentof new technologies and particularly renewable energy technologies. In the following section,we will present new developments that will improve the representation of technical change inenergy models and their interest for decision makers.

3. NEW DEVELOPMENTS IN ENERGY MODELS

Technical change in energy supply and demand technologies is an important driver ofstructural changes in energy systems, efficiency improvements and limitation ofenvironmental impacts. Despite this important role, the mechanisms behind invention,innovation and diffusion of new technologies remain poorly understood and their integrationin energy models is still in the beginning. 12 For estimating the cost-effectiveness of grid connected renewable technologies, the Poles model calculates a paybackperiod considering that the electricity produced is sold to the grid at a premium price (ie, the bulk power price multiplied byfactor greater than one, the buyback rate). In this scenario, the buyback rates are increased by a factor 1.2.



3.1 Mechanisms of technical change

Broadly speaking, the literature on technical change identifies at least three main sources ofinnovation that may partially overlap.

Progress of scientific and technological knowledge base

Innovations may result from new opportunities associated to the regular development orbreakthroughs in the scientific and technological knowledge base. As far as radicalinnovations are concerned (innovations which imply a totally new product or a new factory),this process incorporates a dimension of « surprise » that will be difficult to reflect in energymodels. Progress in modelling this source of technological change is crucial anyway as publicand private R&D programmes should belong to this category.

Technological learning

Some economists consider that technological innovation is basically a learning process.Learning and knowledge accumulation are particularly important for incremental innovationsthat occur more or less continually in any industry or service activity. These innovations arenot so much the result of deliberate research and development, but the outcome of inventionsand improvements suggested by engineers and others directly engaged in production (learningby doing). A lot of progress has been done is the representation of technological learning inthe past few years with the integration of experience curves in technology detailed models (cfinfra).

Inducement factors

In the two previous approaches the direction of technical change is either purely exogenous orendogenous to the process of innovation. The inducement theory tries to assess the role offactors which are both exogenous to the technological system considered and endogenous tothe economic system as a whole. Direction and rate of innovation can either be stimulated by"demand pull" or "supply push" factors such as modifications in the relative prices of energysources, abundance or scarcity of resources, availability of new technologies, quantitativeconstraints, etc., which forces firms to innovate.

Public policies classically rely on these driving forces for stimulating the development of newtechnologies. These policies may be :

- price-based approaches : taxes on fossil fuels or CO2 emissions or subsidies onemission reductions or renewable technologies modify the relative prices ofenergy sources in favour of less emitting technologies;

- or quantity-based approaches : quotas on CO2 emissions or Renewable EnergyPortfolios are policy instruments based on quantities that force the energy systemto innovate.

These policies are easily simulated in energy system models but with one important limitation: the future evolution of existing and emerging technologies is exogenous to the model.Instead of evolving as a function of dedicated R&D actions or as a result of enlargeddiffusion, the characteristics (cost and performance) of energy technologies are based on



expert views. Of course, energy system models assume that these characteristics can changeover time ; in an ecologically driven scenario, for example, the costs of renewable energytechnologies will be lower than in the reference case due to expected result of increasedpublic R&D budgets. But these trends are often a function of time, ie they are totallyindependent of the evolution of the model.

This limitation has lead to move research on endogenous technical change in energy modelsthe aim of which is to link technology diffusion to R&D policies and learning processes.

3.2 Endogenous technical change

Until recently, technological progress was assumed to be autonomous in most energy models,ie, independent of policy instruments or private incentives, whereas it is evident that marketconditions and policies largely affect technological progress. With the introduction of learningcurves, major improvements have been achieved in representing technological change as anendogenous process within energy system models13.

Experience curves are well-known representations of the evolution of costs for specifictechnologies as a function of the cumulative installed capacity. Experience curves generallypresent a downward trend which corresponds to declining costs as a result of increasingdiffusion into the market and resulting learning processes (Fig 2). Cumulative capacity isused as a proxy of the knowledge accumulation during the manufacturing and diffusion of thetechnology.

Figure 3: Examples of learning curves of energy conversion technologies

Source : IIASA-WEC, 1998

The usual version of the observed relationship is that each doubling of cumulative productionresults in a decline a in unit costs, where a typically lies between 0.1 and 0.4 depending on the

13 Cf the TEEM Project (Energy Technology Dynamics and Advanced Energy System Modelling), a research funded in partby the EC in the framework of the Joule Programme.



technology 14. These classical experience curves have been incorporated in most energymodels just as new formulations intended to reflect the effect of R&D policies (public andprivate) on technological progress as an experiment. The results of this integration oflearning-by-doing concepts energy models is conclusive even if progress are still needed onthe "two factors learning curves" because of uncertain relationships between R&D policy andlearning data parameters.

According to the experiments realised with Markal15 a comparison between the originalmodel with exogenous costs projections (either constant over time or assuming a regularprogress trend) and endogenous technological learning shows significantly different results.

With endogenous technological learning, it becomes beneficial to invest early in emergingtechnologies that are not yet competitive. Early action will enlarge diffusion and improve thefuture competitiveness of the technology allowing to recover investment costs increases in theshort run. As a consequence, the cost of satisfying emission constraints is observed to belower in the models with endogenous learning (Fig. 3).

Figure 4: Marginal cost of CO2 reduction, Endogenous (ETL) versus non Endogenous (NETL)Technological Learning (Markal)

Source : TEEM project, 99

Also important is the fact that the introduction of learning by doing mechanisms reinforcesthe internal consistency of the models compared to exogenous cost projections. Situations of"learning without doing", ie in which a technology would become cheaper and cheaper overtime without being developed, are no more possible. The cost developments are necessaryconsistent with the real dissemination of the technology.

Finally, endogenous technological learning allows to assess adequately the long term impactof instrument policies such as taxes or emission limits, R&D, or strategic niches management,

14 Neij uses the following figure : 0.02-0.06 for wind turbines and 0.18-0.22 for PV modules -Neij, 97).15 Cf Modelling energy technology dynamics in TEEM project Final Technical Report, EC, 1999.



as reinforcing mechanisms can reveal the economic potential of technologies with highlearning potentials.

3.3 New renewable energy modelling exercises

The European Directive on the promotion of renewable energy sources in the internalelectricity market has set very ambitious objectives for the use of renewable energy sources inEU Member States. The objective is to double the share of renewable energy in the grossenergy consumption from 6% in 1997 to 12% in 2010. This corresponds to an increase of theshare of renewable electricity from 14% to 22 % in electricity generation in 2010. Anobjective that will not be met at reasonable costs without proper policy instruments.

One important policy instrument in order to limit the global cost of reaching the objectives setby the EU Directive is to create a market of tradable green certificates at the EU level. Theinterest of a TGCs market is to allow an equitable distribution of costs among the differentcountries : the countries with low cost potentials will be able to produce more than their targetand sell certificates to the ones which should otherwise be obliged to utilize high costpotentials. Several studies have been published recently with the aim to compare theefficiency of different policy instruments in reaching the EU Directive targets and evaluatethe economic interest of creating a market of TGCs.

The "Elgreen16" and "REBUS17" projects are two examples of these new renewable energymodelling exercises. These studies aim at answering to the following questions :

- what are the costs of realizing the Directive's targets ?- what are the costs / benefits of trade ?- which will be the exporting / importing countries- which technologies are likely to penetrate ?

And more generally, what is the cheapest and more efficient way to bring about theenhancement in market penetration of electricity produced from renewable energy sources.

To simulate the impact of a TGC market, these two studies use simplified national marginalsupply cost curves (static cost curves) which establish a correlation between the costs forelectricity per unit and the cumulative amount of electricity generated that can be generatedfrom a given source per annum. The construction of such curves necessitates to estimate thesupply cost of a given potential and the amount of electricity that this potential may generate.For example, it can be estimated from the fig.4 that potential B (~ 100 TWh) is accessible atan average cost of 6 eurocents/kWh.

16 Action Plan for a Green European Electricity Market, General Information, produced by Energy Economics Group (EEG),Institute of Power Systems and Energy Economics, Vienna University of Technology, 2001.17 Renewable Energy Burden Sharing (REBUS), Effects of burden sharing and certificate trade on the renewable electricitymarket in Europe, Executive Summary, ECN, Risoe, Serven, 2001.



Figure 5: Marginal supply cost curves (Elgreen)

The aggregation of the different existing potentials by increasing order of costs per unitgenerates the supply cost curve. Figure 5 represents the aggregated cost curve for EU in 2010(REBUS project). This representation allows to estimate the marginal cost of reaching theaggregated potential proposed in the EU Directive (662 TWh at 9.2 eurocents/kWh) and alsothe total overcosts of meeting the target which is given by the surface between the cost curve,the horizontal axis, and the vertical dotted line (Fig 5).

Figure 6: EU cost / potential curve for RES-E in 2010 (REBUS)

With these national and aggregated (at the EU level) cost curves, it possible to estimate theequilibrium price of the certificate and to infer which countries will export and which willimport certificates, and the resulting economies (Fig. 6).



Figure 7: Exporting / importing countries in a EU TGC market (REBUS)

These studies are based on models whose main objective is to simulate the functioning of atradable green certificate market at the EU level. These models do not seek to forecast futuremarket shares of renewable energy technologies but to estimate the costs of reaching differenttargets and the benefits associated to the introduction of trading.

Because they do not represent the whole energy system, the status of such studies isintermediate between renewable studies and energy models. Allowing for competitionbetween different renewable technologies, they represent a progress compared to energystudies. They allow to estimate the costs of given renewable targets and the most efficientway to reach these objectives (optimal mix between different renewable energy sources). Butthe static cost curves which assumes that the performance of a technology is given and thesplitting of the electricity market between renewables on the one hand and conventionaltechnologies on the other, are their main drawbacks

Present developments with REBUS and ElGreen are focusing on the introduction of dynamicsupply curves in order to reflect technological change (cf infra). ADMIRE-REBUS18, forexample, is a new version of the previous REBUS model which pays explicit attention totrade barriers and distortions between national policies. Moreover, potentials are no moreestimated on the basis of expected performance and costs but allow for technologydevelopment and learning effects through time. The model matches national supply curveswith demand curves in a dynamic market simulation. The simulations are done for severaltarget years up to 2020, taking account of discriminative characteristics of some policies andvarious other factors complicating investment in renewables, such as risks, transaction costsand delays due to planning and permitting processes.

These models are sub-sectorial models with some interactions with the energy system. Theyhave the capacity to react to the implementation of different incentive policies but they do notintegrate complex system behaviour as energy models do. Their basic aim is not to simulate 18 See www.admire-rebus.net



the share of renewable energy within the whole energy system but mainly to estimate thecosts of reaching specific renewable targets and the economic impacts of introducingflexibility among countries with certificate trading.

4. LIMITS IN SIMULATING THE DEVELOPMENT OF RETS

Despite recent progress in modelling technological change, energy system models still facesome difficulties in simulating the deployment of renewable energy technologies and inassessing the possible impact of different policy instruments. Some of these difficulties areinherent to modelling approaches, some others are specific to renewable energy technologies.

4.1 Limits in modelling approaches

Energy systems models are simplified representations of reality. They cannot simulate thediversity of behaviour that exists in the real economic world. The consequence is a difficultyin reflecting some of the behaviour that may sustain the emergence of new innovativetechnologies.

4.1.1 Optimization of behaviour

Energy system models are simulating the evolution of energy supply and demand on the basisof a rational economic behaviour of the market actors (end-users, energy compagnies,utilities, local or regional authorities, etc.). For a specified energy demand, the model assumesa perfect competition between the technologies and identifies the solution with minimal costsunder existing constraints. The choice is fully based on cost effectiveness of technologies.

The interest of this approach is clear ; no technology can expect to reach significant marketshares if it is not cost-effective. But the drawback is the risk that only cost-effectivetechnologies are considered. Consequently, one or a limited number of cost-effectivetechnologies will take a major share of the market and other technologies will be withdrawnof the market or limited to very specific market niches.

This may be avoided by defining sufficiently disaggregated markets so as to preserve aminimum diversity in the technology portfolio but also by taking account of additionalbenefits resulting from the development of renewable energy technologies such asimprovement of the environment, diversity and security of supply, local employment.

4.1.2 Lack of heterogeneity

In the real world, economic agents behave differently from one to another for differentreasons. Their own preferences may lead them to opt for environment friendly technologiesfor example. Their risk aversion could incite them to prefer existing and proven technologiesto emerging new ones. Some others may have difficulties in accessing to capital and as aconsequence have higher discount rates which will incite them to prefer shorter payback time,etc. With similar conditions, different agents will not in the real world opt for similartechnologies because a lot of different parameters will interfere in the decision.



Being a simplified representation of reality, energy models will hardly reflect thisheterogeneity of economic agents or take into account specific situations in which newtechnologies would be preferable to existing ones. For example, the lack of electricity grid orconstraints on reinforcement, or availability of cheap renewable resources (wood wastes) orlost heat, or limited quality of grid electricity, etc, could favour the use of renewabletechnologies or peak shaving or storage solutions. These situations are important for thedevelopment of market niches that will allow new technologies to profit from increasingreturn to adoption and ultimately improve their competitiveness.

4.1.3 Uncertainty

Technological change is fundamentally an uncertain process which is difficult to simulate inenergy models. Some technological change, mainly incremental innovations, may beanticipated or linked to other parameters such as cumulated capacity or public investment inR&D programs. But technological change may also result from unanticipated progress in thescientific knowledge base or from innovations in parent technologies (effects of clusters orspillovers). These positive retroactions are difficult to anticipate; for example, the very fasttechnological change in communication and information technologies19 may stimulate thedeployment of distributed production ; in the longer term, the development of hydrogen gridsand fuel cells, or superconductors, may facilitate the exploitation of low cost but remoterenewable resources.

Uncertainty is increasing from incremental to radical innovation and also with the extensionof the time horizon. Simulations to 2030 are based on already known technologies because thetime lag for diffusion of innovations in the energy sector is often more than 25 years. Butsimulations to very long term (2050) may necessitate to consider totally immaturetechnologies and to anticipate their future performances.

Another important source of uncertainty lies in the link between R&D policies and evolutionof the performance of energy technologies. Some models are experimenting so-called "twofactors learning curves" in order to reflect the positive impacts of R&D policies ontechnological change. Further work is needed to provide more accurate data on this issue butthere is clearly a dimension of uncertainty in the relation between R&D investments andimprovement of performance which will remain difficult to reflect in energy models.

At last, uncertainty is similarly important for competing technologies, fossil based electricityproduction, new nuclear devices or carbon sequestration technologies. It is obviouslyinappropriate to consider that technological progress will profit only to renewable energytechnologies. On the contrary, improvement of the performance of renewable technologiesmay increase competition and stimulate new innovations for existing energy technologies.This sort of "rebound effect" in favour of old technologies may be difficult to forecast inmodels which use simplified representations of technological progress.

4.1.4 Snowball and lock-in effects

19 CIC may facilitate the remote dispatch and control of distributed electricity generation units by the distribution utility orthe system operator and contribute to a closer integration of renewable energy sources in electricity supply.



The introduction of learning effects for new technologies in energy models is a verypromising new feature but it may present some drawbacks. One is the snowball effectresulting from increasing returns to adoption : the more a technology is used, the better theperformance (and the lower the cost) and the larger the dissemination, etc. This effect hasbeen described by economists as the lock-in effect.

The consequence is that new technologies have huge difficulties to emerge and to competewith an old one that has already benefited from dynamic increasing returns. Initially cost-effective technologies may penetrate the market faster because of learning effects andprogressively limit the market share of competing technologies and their capacity to furtherbecome competitive with increased experience.

In some models, this risk has been taken into account with lower limits imposed to investmentcosts in order to avoid ever decreasing costs. Similarly, maximum growth rates are defined forsome technologies for preventing lock-in situations.

4.2 Specific difficulties associated to renewable energy technologies

Moreover, energy models face difficulties in simulating the deployment of renewable energytechnologies because of specific characteristics of these technologies.

4.2.1 Assessment of technical potentials

The modelling of renewable energy technologies needs an estimation of available potentialsfor a given cost. In Poles, for example, the economic potential is estimated as a percentage ofthe technical potential : the more cost-effective a technology, the larger the economicpotential. In other models (REBUS, Elgreen) detailed supply cost curves must be constructedfor each technology and country. The difficulty is to estimate which part of the resource willbe available and at which cost.

The estimation of the resource is largely uncertain for renewable energy technologies. Forwind power, for example, the modeller has to estimate the surface of land in a given windband and the percentage of this area that will be used for siting wind turbines. This issue is ofcourse extremely hazardous and has huge consequences on the amount of resource available(without mentioning uncertainties regarding future performance of the turbines). Similarly,the estimation of biomass availability often relies on an estimation of the surface that will beexploited for energy crops or the percentage of forest yield that will be available. Again, whois able to estimate which percentage of the roof areas could be used for installing integratedPV systems in the long term ?

Moreover, these estimations should consider the possibility of competing uses of land. For thetime being, it is possible to consider that the surfaces that are no more used for agriculture inEuropean countries could be used for raising energy crops. But what if biotechnologiesnecessitate to raise new crops on these lands ? The price of these agricultural surfaces that arelow for the moment would raise in the future as a consequence of competing uses.



4.2.2 Evolution of preferences / Social dimension

Evolution of individual preferences is a major driving force for the dissemination ofrenewable energy technologies which is largely under estimated by energy system models. Asa result of marketing campaigns or more generally of changes in value of households,enterprises and public institutions, the demand for "green electricity" is increasing in somecountries, stimulating the installation of new renewable energy plants. This evolution istypically difficult to simulate in energy models for the reason explained before : all the agentsare behaving as cost minimisers and are not supposed to have different preferences.

Conversely, the absence of social behaviour in energy models may favour renewable energytechnologies. Neither conflicts with the preservation of landscape or coastal areas that mayimpede the deployment of wind farms nor opposition of fishermen to the implementation ofsmall hydro plants are naturally taken into account by energy models.

This social dimension that may be very important to foster or to impede the deployment ofrenewable energy technologies cannot be dealt with in energy models.

4.2.3 Associated benefits

Other driving forces for the development of renewable energy technologies may not becaptured by energy system models. For example, agricultural and forestry policies maystimulate the development of biomass energy but are generally not reflected in energy models.Accordingly local authorities may favor the choice of local energy resources (micro-hydroplants, biomass-fired power plant, combustion of solid urban waste) even if they areapparently not cost effective for reasons that are not considered by the model (availability oflow cost resources, existence of district heating facility, local employment, abatement of localpollution, etc.)

These associated benefits may be the main reason for the deployment of some renewableenergy technologies in specific locations but they are often totally underestimated by energymodels.

4.2.4 Variations in performance and costs / Data reliability

Energy system models require very detailed data bases on energy technologies includingperformance and cost information. Different assumptions regarding present and futureperformance of energy technologies would naturally generate totally different results.

This diversity in technology characteristics is an issue for all technology detailed models butparticularly as far as renewable energy projects are concerned. Real investment costs maypresent important variations around the average because of project specific costs (engineeringstudies, preparation of the ground, reinforcement needs on the electricity grid, etc.) asillustrated in IIASA CO2DB database for example which reveals a wide spectrum of costs forsimilar technologies.

This range will be progressively reduced with enlarged diffusion and increased maturity ofnew technologies, probably also due to the internationalization of the technology which will



limit the price variations among countries. For the time being, this inability to reflect varyingcosts and performances of renewable energy technologies from one site to another may be asource of inaccuracy in models simulations.

4.2.5 Experience curves

Endogenous learning is certainly a key improvement in energy system modelling with somedrawbacks (cf snow ball effects) and several limitations.

The integration of R&D policies in experience curves should be considered as a promising butstill unsatisfying experiment because basic information is lacking regarding the consequencesof R&D investment on innovation and diffusion of technology.

Apart from R&D policies, endogenous learning in energy models is limited to investmentcosts when learning effects may also result in decreasing operation and maintenance costs orimproved efficiency.

Experience curves present a unique profile for one technology whether it is emerging ordeclining and for the world market as a whole. The observation of past experience curvesreveals varying shapes for different phases in the lifecycle of a technology (emergence,diffusion, maturity) which could be reflected in the progress ratios. Similarly, one maywonder if the progress ratios should apply identically to different regions of the world(internalization of technology) or if they should be differentiated as far as technology transferis incomplete.

4.2.6 Spillover effects.

Spillover are complementary effects to learning-by-doing or learning-by-using that reflect thepossible outcomes of technological innovations from one sector to another or one technologyto another. A well known example of spillover effects in the energy sector is the gas turbinewhich has benefited from research programs in the aeronautic sector. Energy models do nottake into account spillover effects for the time being but one may imagine improvements inexperience curves in order to reflect positive consequences of innovations in neighbortechnological sectors (inter-dependent learning).

4.2.7 Intermittence of RETs

The intermittent character of some renewable energy technologies is an obvious difficulty forenergy models that use to rely on almost perfectly predictable contribution of energy sources,particularly in electricity sector.

One part of the problem could be solved by using chronological curves in order toapproximate random availability of intermittent sources. But correct modelling also requires arepresentation of geographical characteristics of production and transmission, and arepresentation of existing electricity grids in order to reflect possible network congestion orcompensation between neighbouring regions.



It should be possible to complement existing models with probabilistic approaches in order toreflect intermittency of the resource. But intermittent resources have consequences forinvestors (risk assessment) and utilities or generators (necessity to provide backup units) thatwill also have to be taken into account in energy models.

4.2.8 Incentive policies

The deployment of renewable energy policies is induced by different categories of instrumentpolicies which may partially be simulated by energy models.

So far, the impact of R&D policies was only reflected in different scenario assumptions butnew developments on "two factors learning curves" open the possibility to simulate theimpact of intensive R&D policies with energy models.

Energy models can simulate the impact of environmental policies on the deployment ofrenewable energy technologies. The introduction of emission taxes or emission quotas, withor without flexibility, modifies the relative prices or the constraints put on technologies andforces the model to identify a new least cost solution more favourable to carbon freesolutions.

Energy models are also able to estimate the impact of specific instruments focused onrenewable technologies such as investment subsidies, or feed-in tariffs for some of them.None has produced so far, a simulation based on the creation of a green certificate market butresults are expected soon with new developments on existing models (cf. Admire-Rebus forexample). Past simulations on green certificate markets have not been realized with realenergy models but based on marginal supply cost curves and exogenous demand curvescorresponding to renewable energy quotas. These models were used to estimate the cost ofreaching quantified objectives (the objectives set in the European Directive for example) butnot to simulate the production of renewable energy.

Demand for green electricity by private or commercial consumers, investments in greenenergy by utilities, large companies or local authorities, may also be stimulated by publicpolicies but are not yet reflected in energy models. Similarly, the evolutions of theinstitutional context with the liberalization of the electricity sector and the new incentives todistributed production in certain countries which can be decisive driving forces for renewabletechnologies are hardly simulated in energy models.

Among the flexibility mechanisms associated to the Kyoto Protocol, the Clean DevelopmentMechanism and Joint Implementation projects are also potential drivers for the deployment ofrenewable energy technologies in economies in transition or developing countries. To ourknowledge, these new drivers just as policies in favour of technology transfers towarddeveloping countries are not taken into account either by energy system models. Theintegration of renewable energy technologies in Southern countries would raise specificproblems because of the huge amount of subsidies and transaction costs associated to specificprojects.



5. CONCLUSIONS

This rapid assessment of energy studies and models has pointed out the following results :

1. Energy studies are not the relevant instruments for comparing different policy options.They are mainly focused on short to medium term prospective based on trends continued.On medium to long term it is much more difficult to assume that past trends will continue.Energy studies may anticipate the impacts of different policies (accelerated R&D, removalof barriers, information and education, …) but the resulting market development are notcalculated by any model but purely based on expert statements. Finally, energy studiescannot reflect the competition between different technologies. In the initial diffusion stageit may be acceptable to estimate autonomous development trends but the dissemination ofnew energy technologies beyond the initial niche markets will necessitate to simulate thecomplex relations that characterize the energy system. This can only be done by energysystem models.

2. Energy system models are more appropriate to reflect the possible deployment of newenergy technologies. They simulate the complex relationships within the energy systemand reflect competition between energy technologies for satisfying a given demand :competition between renewable energy technologies and competition between new andexisting technologies. These models are technology specific, ie, their database contain alarge number of energy technologies with detailed characteristics (costs and performance).Their main interest is not to estimate the effectiveness of policies and measures but tocompare the impacts of different strategies such as environmental constraints or taxes forexample.

3. Either optimization or simulation models may be used for assessing the potential impactof different policies on RETs as they are technology explicit energy models. Both of themare able to simulate the dissemination of renewable energy technologies taking account ofcompetition between technologies and impacts of price changes or new policyinstruments. Taxes or subsidies on energy prices are easily integrated in these models,therefore it is easy to assess the consequences on the technology mix of taxes on CO2

emissions or constraints on GHG emissions.

4. The integration of experience curves in the new generation of energy system models is animportant improvement which allows to simulate endogenous technological change : thefuture characteristics of emerging and existing technologies are no more based on expertsviews but result from internal relations between costs and cumulated installed capacity(experience curves). With endogenous technical change, the evolutions of technologies'characteristics are more consistent with market deployment and the impact of marketopening policies on technological change is acknowledged. Similarly, it should bepossible in a near future to estimate the impact of R&D policies on the dissemination ofRETs, but this issue is still experimental for the time being.

5. Energy models can help decision making by offering a better insight into a complexsystem with numerous interactions. They are able to estimate the increase in renewableenergy production in case of oil or gas price growth, or the new technology mix whenconstraints on CO2 emissions are introduced, or the increased RETs disseminationresulting from an improvement in performance (costs and efficiency).



6. But the limits of energy models should not be neglected.

• They simulate the behaviour of economic agents on the assumption that they are rationaland try to identify least cost solutions for a given set of technologies and constraints.These models can hardly simulate behaviour of agents with different preferences whichwould give a greater value to green energy for example.

• Energy models do not estimate the relative effectiveness of policy instruments such asquota based or price based approaches. In the real world these instruments producedifferent results due to the existence of transaction costs or less predictable character ofcompetitive bidding, which are hardly reproduced with existing models.

• The introduction of learning curves in energy models raises some important issues such asfuture progress ratios of new (or existing) technologies, scale at which learning takesplace (regional or global), existence of lower limits to cost decrease, sharing of learningbetween clusters of technologies, and necessity (for some models) to introduce exogenousbarriers on market shares to avoid snowball effects .

• Even if endogenous technical change has been integrated in new energy models, it is stilldifficult to assess the respective impact of different instruments on technical change as faras complex mechanisms of innovation are concerned (industrial spillovers, tacitknowledge, networks of innovation, …).

• Some policy instruments are hardly simulated by energy models because they maypartially rely on extra economic drivers, for example, green electricity (greening ofimage), CDM and JI (technology transfer, new markets), distributed generation(reliability, quality, etc.).

• The capacity of models to simulate the dissemination trends of RETs is also limited bytheir specific characteristics such as intermittent production, small size, capital intensity,or the economic and technical barriers linked to the grid connection, …, which willinfluence their real deployment. Similarly, it is difficult to simulate the real adoptionbehaviour of firms (private investors, utilities, manufacturers) which have totally differentaversion to risk, technological anticipations, marketing strategies, etc). Finally, modelshave huge difficulties in simulating the social evolutions such as the modification ofindividual preferences for example, or the institutional evolutions (liberalization ofelectricity sector and consequences for distributed generation for example) which couldhave important positive consequence on the deployment of RETs.

Documents

Policies and Market Developments - ECN · Policies and Market Developments Subtask: Review and analysis of market models Report nº D3A Final Version December 2002 Work Package 3