Carbon transfer factor in the Nordic power market€¦ · The carbon transfer factor is more uncertain in the future as it is subject to the long-term development of the European

CARBON TRANSFER FACTOR IN THENORDIC POWER MARKET

A report to Norsk Industri

August 2018

CARB

ON T

RANS

FER

FACT

OR IN

THE

NOR

DIC

POW

ERMA

RKET

CARBON TRANSFER FACTOR IN THE NORDIC POWER MARKET

August 2018NorskIndustri_NordicCarbonTransferFactor_FinalReport_v200.docx

PÖYRY MANAGEMENT CONSULTING

Contact details

Name Email Telephone

Geir Brønmo [email protected] +47 40 39 25 05

Clémence Carnerero [email protected] +47 45 17 46 32

Pöyry is an international consulting and engineering company. We serve clients globallyacross the energy and industrial sectors and provide local services in our core markets.We deliver management consulting and engineering services, underpinned by strongproject implementation capability and expertise. Our focus sectors are power generation,transmission & distribution, forest industry, chemicals & biorefining, mining & metals,transportation and water. Pöyry employs over 5,000 experts across extensive local officenetworks. Pöyry’s net sales in 2017 were EUR 522 million and the company’s shares arequoted on Nasdaq Helsinki (Pöyry PLC: POY1V).

Pöyry Management Consulting provides leading-edge consulting and advisory servicescovering the whole value chain in energy, forest and bio-based industries. Our energypractice is the leading provider of strategic, commercial, regulatory and policy advice toenergy markets in Europe, the Middle East and the Americas. Our energy team of 200specialists offers unparalleled expertise in the rapidly changing energy sector.

Copyright © 2018 Pöyry Norway AS

All rights reservedNo part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or byany means electronic, mechanical, photocopying, recording or otherwise without the prior written permissionof Pöyry Norway AS (“Pöyry”).

This report is provided to the legal entity identified on the front cover for its internal use only. This report maynot be provided, in whole or in part, to any other party without the prior written permission of an authorisedrepresentative of Pöyry. In such circumstances additional fees may be applicable and the other party may berequired to enter into either a Release and Non-Reliance Agreement or a Reliance Agreement with Pöyry.

Important

This document contains confidential and commercially sensitive information. Should any requestsfor disclosure of information contained in this document be received, we request that we be notifiedin writing of the details of such request and that we be consulted and our comments taken intoaccount before any action is taken.

Disclaimer

While Pöyry considers that the information and opinions given in this work are sound, all parties must relyupon their own skill and judgement when making use of it. Pöyry does not make any representation orwarranty, expressed or implied, as to the accuracy or completeness of the information contained in thisreport and assumes no responsibility for the accuracy or completeness of such information. Pöyry will notassume any liability to anyone for any loss or damage arising out of the provision of this report.




TABLE OF CONTENTS

EXECUTIVE SUMMARY 1

1. INTRODUCTION 31.1 Report reference date 31.2 Conventions 3

2. THE NORDIC POWER MARKET 42.1 Physical characteristics 42.2 Principle of price setting in the Nordic region 52.3 Drivers of Nordic power prices 7

3. WHAT IS THE CARBON TRANSFER FACTOR? 11

4. CARBON TRANSFER FACTOR IN THE NORDIC POWER MARKET 134.1 Methodology 134.2 Historical model analysis 164.3 Forward model analysis 204.4 Summary of results 26

ANNEX A – METHODOLOGY OF POWER MARKET MODELLING 28

ANNEX B – VALIDATION OF POWER MARKET MODEL 36

ANNEX C – POWER MARKET ASSUMPTIONS 40

QUALITY AND DOCUMENT CONTROL 47



1


EXECUTIVE SUMMARYThis study was commissioned by Norsk Industri (The Federation of Norwegian Industries)and aims at evaluating the carbon transfer factor in the Nordic power market. The Nordicelectricity market consists of four integrated power markets: Norway, Sweden, Finland,and Denmark which comprise the Nordic region in this report.

Why is the Nordic power market influenced by the carbon price?Generation in the Nordic power market is dominated by hydro power, with significantreservoir capacity, followed by nuclear power. Only a small fraction of the generationstems from conventional thermal sources and the Nordic power supply is therefore mostlyfree from CO2 emissions. With 55% hydro power, annual generation levels are determinedto a large extent by the weather, and capacity has been developed to a level thatconsiderably exceeds peak demand.

The Nordic power price is indirectly influenced by the carbon price via the region’sexchanges of power with the Continent. Power prices in the Nordic power market indeeddepend on the value of the imported and exported electricity from the Continent. In broadterms, Continental thermal power plants tend to set the average price level in the Nordicand drive the long-term evolution of prices. And, Continental prices are themselves, to alarge extent, driven by the cost of producing electricity, i.e. the cost of fuel and ofpurchasing CO2 allowances.

What is the carbon transfer factor?The carbon transfer factor, also referred to as carbon pass-through, represents the effectof the carbon price on power prices. This can also be seen as the marginal cost of CO2emissions in the power generation. It is measured as the change in power price due to thechange in CO2 price, i.e. in €/MWh of electricity per €/tCO2 or tCO2/MWh-el1. If the carbontransfer factor is 0.5tCO2/MWh, it means that for a €1/tCO2 increase in the carbon price,the power price increases with €0.5/MWh.

The carbon transfer factor is different across years and geographies as generation typessetting the power price will vary. It is typically higher in thermal markets dominated bycoal/lignite generation and lower in thermal markets dominated by gas generation. This isdue to a higher cost of carbon emissions for coal and lignite power plants compared togas-fired plants. In the Nordic market, the effect of carbon on power prices comes fromthe interconnection to thermal Continental markets. The Nordic carbon transfer factor istherefore influenced by the pass-through found in surrounding countries.

What would have the price been in the Nordic power market with a higher carbonprice between 2013 and 2017?This study evaluates the carbon pass-through in the Nordic market through BID3, Pöyry’spower model for all markets in Europe. BID3 models the power markets in a very accurateand realistic way and is able to provide both price levels and price variations very close tohistorical levels. The model contains a plant by plant database for all European countriesand assumptions for demand, fuel and CO2 prices and transmission capacity for historicaland future years. The model in turn generates power prices, dispatch, trade andsocio-economic benefit by simulating how the power market adjusts to key drivers in input.

1 MWh-el stands for MWh of electricity. For simplification, the unit used for the carbon transferfactor is tCO2/MWh in the rest of this report.



2


BID3 is used here to obtain wholesale electricity price projections and correspondinglyderive carbon transfer factors for a wide range of years and geographies.

On average over 2013-2017, the actual power price averaged across the Nordic priceareas was €30/MWh for an actual carbon price of close to €6/tCO2. Using a carbon priceof €30/tCO2 instead, we find through BID3 that the power price would have beensignificantly higher and at €46.7/MWh. A €24/tCO2 higher carbon price would havetherefore resulted in a close to €17/MWh increase in the Nordic power price.

What was the carbon transfer factor in the Nordic market in the years 2013-2017?On average over historical years modelled, i.e. 2013 to 2017, the carbon transfer factor isfound to be 0.71tCO2/MWh. This means that every time the carbon price increases by€1/tCO2, the power price increases by €0.71/MWh. With an average carbon price in theperiod around €6/tCO2, this quantifies the impact of carbon on the Nordic power price toroughly €4/MWh, or close to 15% of the 2013-2017 average Nordic power price.

The Nordic carbon transfer factor has been influenced over the past few years bydecreasing coal installed capacities and progressively increasing capacity of intermittentrenewables such as wind and solar power. It is also affected by yearly variations in Nordicconditions including installed plant capacity, hydrology, demand, etc.

What will be the carbon transfer factor in the Nordic power market in the future?The Nordic carbon transfer factor has been calculated for a number of future years (2018-2020-2025-2030-2035-2040), still via Pöyry’s BID3 power market model. Simulations arebased on Pöyry’s long-term scenarios for supply/demand, where renewable capacityassumptions are consistent with a 27% EU target for renewables.

The Nordic carbon transfer factor in 2018 is found to be close to the 2017 value obtainedin Pöyry’s historical simulations. It is evaluated at 0.49tCO2/MWh on average over theyears 2020 to 2030. In 2030, the Nordic carbon transfer factor remains significant and isexpected at 0.43tCO2/MWh. The pass-through is projected to decrease gradually ascoal/lignite-fired plants are decommissioned on the Continent and as the capacity ofintermittent renewables, like wind and solar power, increases.

What are the uncertainties?The performance of Pöyry’s market model in estimating actual prices has been validatedthoroughly. This ensures that historical carbon transfer factor estimates obtained in thisstudy are robust. The carbon transfer factor is more uncertain in the future as it is subjectto the long-term development of the European generation mix, i.e. the share of coalversus gas capacity setting the price and the share of intermittent renewable generation.

A way to cope with these uncertainties is to re-evaluate the future carbon transfer factorregularly and to analyse the response of the pass-through to different market drivers. Inthis study we have investigated how sensitive the Nordic carbon transfer factor is tochanges in a few of the main modelling assumptions, i.e. fuel prices and renewableinstalled capacity.

The results show that the Nordic carbon transfer factor is expected to increase with highergas prices or lower coal prices, since coal generation is favoured over gas. Though, this isobserved in the medium-term only as coal/lignite-fired plants are planned to bedecommissioned in the future. A potential downside in the pass-through arises in thelong-term with a greater increase in renewable capacity. The carbon price howevercontinues to affect the power price as thermal plants are still needed to meetdemand and the Nordic carbon transfer factor remains relatively high at0.40tCO2/MWh in 2030.



3


1. INTRODUCTIONThis study was commissioned by Norsk Industri (The Federation of Norwegian Industries)and aims at evaluating the carbon transfer factor in the Nordic power market. Thebackground for the report is the re-evaluation of the maximum regional CO2 emissionsfactors in Europe2 that serve the basis for the carbon price compensation scheme.

The Nordic power market covers four countries: Norway, Sweden, Denmark and Finland.These markets are tightly integrated through physical interconnection, participation in pan-Nordic exchanges for power and have complementary physical characteristics. Sharingclose ties in terms of cross-border trade, market operations and regulation, the Nordiccountries broadly pursue the same energy policy principles. This report provides in-depthinsight into the evolution of the Nordic carbon transfer factor, past and future.

This report is structured as follows:

§ Chapter 2 provides an overall introduction of the Nordic power market and its physicalcharacteristics. It also introduces the principle of price setting in the Nordic region andthe different market drivers that influence the Nordic power prices, including thecarbon price.

§ Chapter 3 defines what the carbon transfer factor is and highlights the suitablemethod to assess it properly.

§ Chapter 4 describes the methodology used to calculate the Nordic carbon transfer inthis study. The chapter then focuses on presenting results and estimates of thecarbon transfer factor for the different analyses carried, historical and forward. Keyfindings of the study are summarised in the last section of this chapter.

§ Annex A provides an overview of Pöyry’s BID3 power model and its modellingmethodology. BID3 is used in this study to obtain simulated wholesale electricity priceprojections and derive the carbon transfer factor.

§ Annex B provides detailed evidence that BID3 simulates the power markets in anaccurate way.

§ Annex C describes the modelling setup of this study as well as main inputassumptions for electricity demand and supply.

1.1 Report reference dateThis study was prepared between June and August 2018. The report therefore does not ingeneral take account of regulatory and policy developments which occurred after earlyJune 2018.

1.2 ConventionsWhere tables, figures and charts are not specifically sourced they should be attributed toPöyry Management Consulting.

We have occasionally in tables, figures and charts abbreviated Denmark to DK, Finland toFI, Norway to NO, Sweden to SE, Estonia to EE, Latvia to LV, Lithuania to LT, Germanyto DE, Netherlands to NL and Poland to PL.

2 Official Journal of the European Union. Annex IV Maximum regional CO2 emission factors indifferent geographic areas (tCO2/MWh). 5.6.2012.



4


2. THE NORDIC POWER MARKETThe Nordic electricity market consists of four integrated power markets: Norway, Sweden,Finland, and Denmark which comprise the Nordic region in this report. The Nordiccountries share several common elements in terms of market design, market operationand energy policy as well as complimentary generation sources. They are therefore seenas one market for analytical purposes.

2.1 Physical characteristicsGeneration in the Nordic power market is dominated by hydro power, with significantreservoir capacity, followed by nuclear power. Only a small fraction of the generationstems from conventional thermal sources and the Nordic power supply is therefore mostlyfree from CO2 emissions.

Unlike Continental systems, where the capacity of generation assets is the maindeterminant of its output, the Nordic countries are heavily dependent on energy inflows inthe form of precipitation. With 55% hydro power, annual generation levels are determinedto a large extent by the weather. The system is therefore prone to energy scarcity andhigh electricity prices in dry or cold years. There is on the other hand a surplus of energyin wet or mild years and electricity prices tend to be relatively low. To offset scarcity risk,capacity has been developed to a level that considerably exceeds peak demand.

Exchanges first take place between the Nordic countries through around 10.5GW ofinterconnection. The Nordic electricity market is also interconnected to other markets: tothe Baltic countries and Russia to the east and Continental Europe to the south throughinterconnection to Germany, the Netherlands and Poland. Current interconnection to therest of Europe totals to 8GW and is planned to be strengthened in the near-future withclose to 4GW of links under construction to Great Britain and Continental Europe.

Figure 1 summarises some of the main characteristics of the Nordic power market.

Figure 1 – The Nordic power market, hydro dominated and interconnected

Generation Nordic interconnection Links to Europe

Source: ENTSO-E, regjeringen.no and Pöyry Management Consulting analysis.

55%21%

10%6%

8%HydroNuclearThermalBiomassWind



5


Extensive cross-border trade provides security of supply for the hydro power system thatis subject to yearly and seasonal generation variations. Figure 2 illustrates yearly andseasonal hydrological variations in the Nordic region by presenting weekly reservoircontent and inflow in Norway and Sweden, average and range for the years 1995 to 2017.

Figure 2 – Reservoir content and inflow in Norway and Sweden, average andrange for years 1995-2017 (TWh)

Source: Nord Pool.

Precipitation in the winter time often comes in the form of snow in the Nordic region, astemperatures are below zero. This leads to a low hydro inflow in the winter time and agradual decrease in the reservoir content as storages are used for electricity generationbut receive at the same time little water. The inflow then quickly rises in the spring thaw assnow melts. This increases the reservoir content which tends towards its maximumcapacity in the summer months, roughly 120TWh. The inflow progressively declinesafterwards as the snow melt ends and precipitation is relatively low, though some spikesin inflow can be observed in autumns with significant rainfall. Noticeable variations in thereservoir content and inflow can also be observed between years, as underlined by therange in Figure 2.

Weather conditions have a significant influence on Nordic generation and demand as theydetermine levels of hydro power production and power consumption. In situations wherethe reservoir storages are empty or full, prices will deviate significantly from normal levels,where high or low prices can last over weeks or months.

2.2 Principle of price setting in the Nordic regionIn thermal markets, e.g. Germany, Great Britain or the Netherlands, power prices are toa large extent driven by the cost of producing electricity, i.e. the cost of fuel and the cost ofpurchasing CO2 allowances. Prices are set at any one point in time by the crossing ofsupply and demand curves for that hour, independently of the situation in the followingdays or months. Hence, fuel and CO2 prices, capacity mix and demand are direct andimmediate determinants of power prices.

In the Nordic hydro market, supply is dominated by zero- (or near zero-) marginal costsources. Looking into the Nordic energy mix, around 85% of the generation comes fromnuclear, hydro and wind power. Such a high share could in principle cause a majority of

Reservoir content - Range Inflow - RangeReservoir content - Average Inflow - Average

0

10

20

30

40

50

0

20

40

60

80

100

120

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

Inflo

w (T

Wh)

Res

ervo

ir co

nten

t (TW

h)

Week



6


zero prices in the market, if price setting was similar to thermal markets. But this is not thecase in the Nordic power market. The large reservoir hydro storage capacity gives theability to shift supply over several months at a near-zero cost. Nordic power prices arehence determined by alternative sources of meeting demand including fuel and CO2prices on the Continent.

With more than 120TWh of reservoir storage capacity, the Nordic countries can use hydroreservoirs to balance periods of high demand with low inflow (typically in the winter) andperiods of low demand with high inflow (typically around the end of spring and summertimes). Increasing production in one period means that less water is available for otherperiods in time and that hydro generation then needs to be replaced by other sources.

If reservoir hydro power was bid at zero like non-controllable renewable sources, wewould have a period of very high generation with very low prices before reservoir storagesrun out of water. Having in mind that reservoirs refill mostly in the spring when the snowmelts, as seen in Figure 2, producers would for a (possibly long) period of time be unableto produce. This would incur a significant loss in revenues for producers as well as veryhigh prices for end-consumers.

Hydro power producers therefore dispatch their generation by bidding their reservoir at anon-zero price: a price which maximises their revenues and balances the market giventhe expected future value of the water storage. Hydro power producers optimise theirbidding in order neither to spill water from their reservoir (which would result in a loss ofincome) nor empty it (which would remove the possibility to produce during high priceperiod at a later stage). The level at which their production is bid depends on the need forelectricity, on expectations of future supply/demand and on the cost of alternative sources.The bidding price for hydro power, so-called ‘water value’, is typically higher in thefollowing conditions:

§ when reservoir levels are low or when reservoir levels are expected to become lowerthan normal in the coming months; and

§ when alternative sources of energy (e.g. coal, gas or biomass generation) areexpensive.

For example, a situation of low reservoir level just before the snow-melting period withhigh expected near-future inflow is much less critical than a situation of low reservoir levelin the beginning of the winter with very low expected inflows. In other words, hydroproducers balance the system by bidding at a price which ensures an adequate supply ofthe market. When their bidding price is low, other sources of power like Nordic thermalplants and incoming interconnector flows will be at their minimum. When the waterresource is tight, a high bidding price will ensure that Nordic thermal plants andinterconnectors supply the Nordic market at their maximum ability.

To summarise, Nordic power prices are set by the opportunity cost of generation – inmany cases thermal generation on the Continent due to the extensive interconnection.The influence of the carbon price is therefore an indirect Nordic price driver – through therole that carbon (among other fundamental conditions) plays in setting the price level atwhich reservoir hydro power production is bid which sets the Nordic power price3.

3 The carbon price influences forward prices as much as spot prices in the Nordic market dueto the role it plays in setting the power price on the Continent, both for forward and spotprices. The CO2 price influences the power price due to the extra cost that carbon incurs tothe total cost of thermal production, for spot prices through the expected CO2 price of thenext day, for forward prices through the expectations of future variations linked todevelopments in the ETS market.



7


2.3 Drivers of Nordic power pricesFigure 3 presents the drivers of power prices in the Nordic region and elements taken intoaccount in the bidding level of the Nordic hydro power generation.

The average or annual Nordic price level is set by commodity prices in thermal marketsand is therefore linked to European and global factors, such as coal or carbon prices. Dueto this, the annual price level is mostly following Continental power price variations. Thelong-term price level in the Nordic region also relates to the hydrological balance of theNordic region. Broadly speaking, in wet years when the hydrological balance is positive(above normal), there is a surplus of energy and the Nordic prices are relatively lowcompared with Continental prices. When the situation is opposite and the hydrologicalbalance is negative (below normal), water resources are more strained, energy is scarceand the Nordic prices are high.

Internal Nordic conditions such as variations in hydrology, temperatures, wind generationor nuclear availabilities are driving fluctuations at the monthly, daily and hourly levels.

Figure 3 – Nordic power price drivers

Source: Pöyry Management Consulting analysis.



8


Influence of hydrology and commodity prices on the Nordic power price

Figure 4 illustrates the influence of hydrology and commodity prices on the Nordic powerprice4. The hydrology is represented by a simplified hydrological balance, i.e. the deviationof the reservoir content from normal added to the inflow5. Commodity prices arerepresented by the cost of production of coal generation (including the cost of carbon) asit has, generally speaking, been the generation type setting the price on the Continent inrecent years.

Figure 4 – Nordic power price and cost of production of coal generation (€/MWh,nominal) versus Nordic hydrological balance (TWh), 2008-2017

Source: Thomson Reuters and Nord Pool.

Coal power plants at the margin on the Continent (and mostly in Germany due to theextensive Nordic-German interconnection) tend to set the average price level in the Nordicregion and drive the long-term evolution of prices. The Nordic hydrological balance drivesvariations around the long-term level set by coal. In dry years when the hydrologicalbalance is largely negative, the Nordic power price tends to average above the cost ofproduction of coal while in a wet year, the situation is opposite.

Influence of the carbon price on the Nordic power price

Due to the carbon price effect on Nordic power being indirect, and one of many drivers ofthe bidding behaviour of hydro producers, it is not entirely straightforward to observe theinfluence of the carbon price on Nordic power prices from the previous figure.

A way to visualise the role the CO2 price plays in setting the Nordic power price is toanalyse the correlation of Nordic power prices with Continental prices and the correlation

4 The Nordic price displayed here is the actual Nordic system price. It is the reference price forthe Nordic countries and is calculated as a (hypothetical) price in which physical bottlenecksbetween the different Nordic bidding areas (NO1, NO2, NO3, NO4, NO5, SE1, SE2, SE3,SE4, DK1, DK2, and FI) are not taken into account. The system price is very often quoted incontracts and has the highest liquidity in trading.

5 And therefore does not account for snow and soil content which also play a role indetermining Nordic hydrological conditions.

-40

-20

0

20

40

60

80

100

-40

-20

0

20

40

60

80

100

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Pric

e (€

/MW

h, n

omin

al)

Hyd

rolo

gica

l bal

ance

(TW

h)

Positive hydrological balance (oversupply)Negative hydrological balance (undersupply)Nordic power priceCost of production of coal generation



9


of Continental prices with the carbon price. Figure 5 illustrates these correlations atmonthly average level between 2008 and July 2018. Germany is chosen as a proxy forContinental prices.

Figure 5 – Relationship between the Nordic power price and the carbon pricethrough the German power price

Correlationbetween theNordic and

German powerprices

Correlationbetween the

German powerand the carbon

price

Source: Nord Pool, EPEX Spot, Thomson Reuters and Pöyry Management Consulting analysis.

It can be observed that the Nordic power price is largely following the German power priceaside from some periods of time in which strong hydrological deviations or particularevents can be observed:

§ 2010 and 2011 were dry winters and this was combined in 2011 to Swedish nuclearoutages limiting the generation capacity of the Nordic region.

§ The summer 2012 was wet due to high precipitation and prices crashed as hydropower plants struggled to produce sufficient energy to avoid having to spill their water.

§ 2015 was, like 2012, also particularly wet and the Nordic power price was below€15/MWh for nearly four months.

When it comes to the German power price, it follows variations in the carbon price due tothe direct impact of purchasing CO2 allowances on the cost of producing electricity.

0

10

20

30

40

50

60

70

80

90

100

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Pow

er p

rice

(€/M

Wh,

nom

inal

)

Nordic powerGerman powerDry winter

Very high precipitationsWet winterand spring

Nuclear outagesin Sweden

0

5

10

15

20

25

30

0

10

20

30

40

50

60

70

80

90

100

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Car

bon

pric

e (€

/tCO

2, no

min

al)

Pow

er p

rice

(€/M

Wh,

nom

inal

)

German powerCarbon

Significant structural surplusin the carbon marketcollapses prices

Nuclear outages in France, coldwinter and fuel prices increase



10


Figure 6 presents the same data as Figure 5 under the form of correlation charts, orscatter plot, to provide another visual representation of the relationship between theNordic and German power prices, and the German power and carbon prices.

Figure 6 – Correlation charts illustrating influence of the CO2 price on the Nordicpower price

Correlationbetween theNordic and

German powerprices

Correlationbetween the

German powerand the carbon

price

Source: Nord Pool, EPEX Spot, Thomson Reuters and Pöyry Management Consulting analysis.

As can be seen from the top chart, Nordic power prices increase as German power pricesincrease. The correlation is 0.62 over 2008-July 2018. When removing years with stronghydrological deviations or exceptional events, i.e. 2008-2010-2011-2012-2015, thecorrelation increases to 0.70. The bottom chart shows that the correlation between theGerman power price and the carbon price is strong, 0.77 over the period analysed.

It is however important to keep in mind that CO2 is only one of the drivers of the German(and in turn Nordic) power prices. Fuel prices also have an effect and decreasing coalprices have typically also contributed to price decreases observed in Figure 5 around2009 and over 2012-2016. But, theoretical price setting principles and historical datavisualisation together confirm the link between the carbon price and the Nordic powerprice.

0

20

40

60

80

100

0 20 40 60 80 100

Ger

man

pow

er p

rice

(€/M

Wh,

nom

inal

)

Nordic system price (€/MWh, nominal)

0.62 correlation

0

20

40

60

80

100

0 10 20 30

Ger

man

pow

er p

rice

(€/M

Wh,

nom

inal

)

Carbon price (€/tCO2, nominal)

0.77 correlation



11


3. WHAT IS THE CARBON TRANSFER FACTOR?The carbon transfer factor, also referred to as carbon pass-through, represents the effectof the carbon price on power prices. It is measured as the change in power price due tothe change in CO2 price, i.e. in €/MWh of electricity per €/tCO2 or tCO2/MWh-el.

Carbon transfer factor and marginal cost of CO2 emission in power generationThe carbon transfer factor can also be seen as the marginal cost of CO2 emissions in thepower generation and can as such be defined for every hour of a period by the followingformula:

In a conventional thermal market, a range of different fuels can intervene in priceformation depending on the generation mix: gas, coal, oil, lignite, oil shale, etc. In Europe,gas and coal generation generally dominate but lignite and oil shale are also used in somemarkets.

Figure 7 presents the marginal cost of CO2 emissions of different types of thermal powergeneration in Europe with corresponding assumptions for the fuel emission factor andpower plant efficiency. Gas generation usually takes place in high efficient plantsequipped with Combined Cycle Gas Turbines (CCGT) while coal, lignite and oil shalegeneration takes place in lower efficient plants equipped with steam turbines.

Figure 7 – Marginal cost of CO2 emissions in European thermal power generation(tCO2/MWh)

Generation type Gas Coal Lignite Oil shale

Plant efficiency (MWh-el/MWh-th) 0.49 0.37 0.32 0.35

Fuel emission factor (tCO2/MWh-th) 0.182 0.322 0.354 0.359Fuel emission factor for electricitygeneration (tCO2/MWh-el) 0.4 0.9 1.1 1.0


0.4

0.9

1.11.0

0

0.2

0.4

0.6

0.8

1

1.2

Gas Coal Lignite Oil shale

Car

bon

trans

fer f

acto

r (tC

O2/M

Wh)

Marginal cost of CO2 emissions [tCO2

MWh-el]=

Fuel emission factor [ tCO2MWh-th ]

Plant efficiency [ MWh-elMWh-th ]



12


The combination of high plant efficiency and low fuel carbon intensity gives a carbontransfer factor for gas generation of around 0.4tCO2/MWh. This means that every time thecarbon price increases by €1/tCO2, the cost of gas generation is increased by €0.4/MWh.

The highest carbon transfer factor is found for lignite generation with 1.1tCO2/MWh. Thismeans that every time the carbon price increases by €1/tCO2, the cost of lignitegeneration increases by €1.1/MWh. The carbon price has a higher impact on lignitegeneration than on coal or gas generation for example. This is due to lignite having arelatively high fuel emission factor in comparison and lignite being burnt in plants with lowefficiencies, typically 32% in Europe.

In a non-dispatchable renewable market, where intermittent technologies like run-of-river hydro, solar photovoltaic or wind power dominate the price setting, the carbontransfer factor is in theory zero. The fuel emission content of renewable energy sources iszero and power prices are set independent of the carbon price. A completely zero carbontransfer factor is however subject to a 100% isolated renewable system with no thermalgeneration intervening in the formation of power prices.

In a dispatchable renewable market, the carbon transfer factor is based on the price ofthe dispatchable generation. This is typically the case in the Nordic hydro market wheredispatchable reservoir hydro power dominates generation. The price of the dispatchablehydro power resource is non-zero and is set by external factors to the carbon content inthe hydro power production – expected fuel and CO2 prices, demand, reservoir filling,inflow, power plant availabilities, system tightness, etc.

How to assess the carbon transfer factor with certaintyThe value of the carbon transfer factor in a market can however not simply be defined bythe cost of CO2 emissions in the power generation:

§ Due to variation in load or availabilities, the type of generation setting the price willvary from hour to hour throughout years. For example, in a country where both coaland gas generation alternatively set the price, the carbon transfer factor can be foundanywhere between 0.4-0.9tCO2/MWh as it is the average of the marginal cost of CO2emissions in each hours during the year. In a country where lignite or oil shalegeneration also intervene, the carbon transfer factor is likely to be found closer to1.0tCO2/MWh but will depend on the number of hours these fuels have been directlyimpacting power prices.

§ The carbon transfer factor in a market dominated by dispatchable renewables is likelyto be found close to the carbon transfer factor in interconnected countries due to therole that exchanges of power play in setting the price. The price of the dispatchableresource is however constantly optimised accounting for a set of internal and externaldrivers and cannot be determined by looking at the CO2 content of generation.

Complex calculations and quantitative analysis are therefore required to evaluate thepass-through with certainty. This is achieved in this study through the use of a powermarket model which is the appropriate method to:

§ Find the optimal power price for each hour of a modelled year and give an accuraterepresentation of the interaction of supply and demand while accounting for seasonal,daily and hourly variations in market drivers.

§ Represent the diversity of interconnected power markets (like all power markets inEurope) – thermal, renewable but also hydro markets.

§ Simulate the optimal decision of hydro dispatch behaviour under uncertainty (like inthe Nordic market) and the way reservoir hydro power is priced, using sophisticatedhydro modelling.



13


4. CARBON TRANSFER FACTOR IN THE NORDIC POWERMARKET

To estimate the carbon transfer factor in the Nordic power market, we have used Pöyry’sBID3 in-house model for the European power markets and simulated prices underdifferent assumptions for carbon prices, capacity mix, demand or fuel prices. Thesesimulations have been carried out historically for the years 2013 to 2017 and in the futurefor selected years between 2018 and 2040. For future years, a set of sensitivities has alsobeen explored to test the response of the carbon pass-through to individual changes inmodel assumptions.

This chapter first introduces the methodology used to obtain the carbon transfer factor.Results are then presented for:

§ historical years in section 4.2; and

§ future years in section 4.3.

Section 4.4 summarises both the historical and forward analyses highlighting key findingsof the study.

4.1 Methodology

4.1.1 Electricity market model

Methodology of power market modelling

We have analysed the effect of the carbon price on the Nordic power price by using ourin-house power market model BID3 in order to obtain precise wholesale electricity priceprojections. The model is introduced in this section and described in detail in Annex A.

BID3 is a comprehensive power market simulator for all power markets in Europe. Themodel contains a plant by plant database for all European countries and assumptions fordemand, fuel and CO2 prices, transmission capacity and plant availabilities both forhistorical periods and future years. The model in turn generates power prices, dispatch(exactly how much and what type of electricity is generated each hour in each country),trade and socio-economic benefit by simulating how the power market adjusts to keydrivers in input.

BID3 is not only equipped to accurately represent thermal markets (including start-upcosts, no-load and scarcity rent) and the variability of intermittent renewable generationbut also includes a sophisticated treatment of hydro power dispatch. Pöyry hasimplemented a Stochastic Dynamic Optimisation (SDP) methodology to optimise reservoirhydro dispatch under uncertainty of future inflows. In the hydro-dominated areas like theNordic region it is critical to use such a technique, as the uncertainty of future inflowsgreatly affects the pricing of electricity. This optimisation methodology is used by mostmarket players in the Nordic countries.

BID3 has an hourly time resolution, which means that it finds the optimal price for all hoursof the year modelled. For any given future year, a total of 175,200 prices, 8,760 hoursmultiplied with 20 historical weather patterns, are created per price area, giving a goodrepresentation of possible interactions between weather and demand. Under thatsequence of 20 weather patterns (1995-2014), the starting position of hydro reservoir for agiven weather pattern will be equal to the ending position of the previous weather pattern:realistic multi-year effects are therefore captured by Pöyry’s methodology. Pricesproduced by the model are then the result of the interaction of supply and demand in anygiven hour, representative of a normal weather year.



14


Model validation

BID3 simulates power markets in a very accurate and realistic way. Several back testing6

exercises have confirmed this point, BID3 is able to provide both price levels and pricevariations very close to historical levels. In particular, the treatment of uncertainty forhydro power captures properly how hydro producers make their dispatch decisions. Thissection briefly introduces Pöyry’s back test results while additional information is providedin Annex A.

Figure 8 presents annual and monthly power price results from the back test obtainedfrom BID3 (in blue) and compares these results against market prices (in orange) for theGerman and Nordic7 power prices.

Figure 8 – Annual and monthly average power prices, Nordic and Germany, backtest versus market, 2013-2017 (€/MWh, nominal)

Ann

ual

Mon

thly

Source: Pöyry Management Consulting.

6 Back testing is the process of evaluating the effectiveness of a model by running it againsthistorical data to see how it would have performed. Obtaining good results demonstrate theability of the model to accurately predict prices given a set of input.

7 The Nordic price displayed here corresponds to the average of the Nordic price areas.



15


Annual power prices typically deviate by less than €1/MWh and monthly modelled pricesfollow observed developments, aside from some specific time periods for the Nordiccountries. These charts underline that BID3 is able to capture the average and variationsof historical prices as well as market dynamics.

Deviations can however be observed in some instances. Most of these come from the factthat BID3 has access to less information than the market. The market gets access toconstant new information during a year – updated weather forecasts, actualmeasurements of inflow, reservoir levels or snow accumulation – while the back test isdone with weather expectations as of 1 January of the year. The market and hydro powerproducers in addition tend to react aggressively in their bidding behaviour if they see therisk of exceptionally high or low prices. These types of somewhat more subjectivereactions are not always perfectly captured by quantitative modelling.

In the Nordic region, the largest deviations can be observed in 2013 and in 2015, twoparticular periods. In 2013, this is due to high winter prices as hydro producers reducedgeneration due to a large snow deficit in the mid-winter and the fear of even higher pricesat a later stage. In 2015, this is due to particularly low prices in the summer months ashydro power producers produced heavily in order not to spill at a later stage, sinceexpectations of short-term inflows were high. It is then challenging to reproduce the veryaggressive behaviour of hydro power dispatch at that time as they feared that reservoirlevels would reach their maximum within a short time.

4.1.2 Carbon transfer factor

Simulations

The carbon transfer factor is analysed in this study in two ways:

§ Historically for the years 2013 to 2017;and

§ Forward for future years 2018-2020-2025-2030-2035-2040.

Historical model analysis aims at estimating the carbon transfer factor in the past fewyears and forward model analysis at evaluating possible developments in the carbontransfer factor and how it responds to a combination of market drivers. Several set ofassumptions are used to analyse the carbon transfer factor in the future:

§ A Base case representing Pöyry’s best estimate of capacity, demand,interconnection developments in the market, where renewable capacity assumptionsfor 2030 are consistent with a 27% target for renewables in the European Union.

§ A set of sensitivities giving variations in fuel prices and production mix along withtransmission capacities from the Base case:

- A collection of four fuel sensitivities – called Low gas, High coal, High gas, Lowcoal – where fuel prices are increased (or decreased) one at a time by 50% fromthe Base case. Changes in fuel prices impact the generation type that will be atthe margin in conventional thermal markets by changing costs of productions andthere is therefore an expected effect of fuel prices on the carbon pass-through.

- A simulation with a greater development of the intermittent renewable capacityand interconnection – called High RES8 – with all other inputs being equal to theBase case. Assumptions for renewable capacity used in this case should bebroadly in line with the recently agreed upon EU target for renewables of 32% in2030. Intermittent renewable generation has a zero marginal cost which is

8 Renewable Energy Sources.



16


expected to lead to a closer to zero carbon transfer factor. The carbon pass-through is in theory expected to be strictly zero only in an isolated 100%renewable system.

The carbon transfer factors obtained in forward model analysis are representative ofnormal weather and availabilities while factors obtained for historical years (2013 to 2017)account for observed fundamental conditions.

Please refer to Annex C for a more extensive description of input data, assumptions andmodelling setups. Annex C.2.2 specifically presents more information on how the EUrenewable targets could translate into renewable capacity and how Pöyry’s long-termassumptions stand in comparison.

Calculations

A series of 4 carbon prices representing different price levels, €10-20-30-40/tCO2, havebeen used in each analysis presented in the previous section. For each carbon price, thecarbon transfer factor is calculated as per the following formula:

The only parameter that varies between the carbon and reference scenarios is the CO2price in order to isolate the effect of the carbon price on power prices. For historicalmodelling, the reference scenario corresponds to observed CO2 prices while in the case ofthe forward modelling, the reference scenario corresponds to a zero CO2 price.

For all simulations carried out, the carbon transfer factor is calculated for each Nordicprice areas. Results are presented in this report for Norway, Sweden, Finland andDenmark as the unweighted average of the price areas of each country. The Nordiccarbon transfer factor is the unweighted average of the pass-through found for the fourNordic countries. The carbon transfer factor is also calculated in other European marketssuch as Germany, Netherlands, Poland or the Baltic countries and presented forcomparison when relevant.

4.2 Historical model analysis

A €24/tCO2 higher carbon price in the past few years would have increased the Nordicpower price by close to €17/MWh

Running our BID3 model on the years 2013-2017 allows us to simulate what the powerprice would have been under a certain carbon price in the past few years. All other inputsbeing equal, this quantifies with a high degree of certainty the increase in power priceassociated with a higher CO2 cost, given a robust performance of the model in predictinghistorical prices. Annex B can be referred to for more information on Pöyry’s modelperformance.

Figure 9 presents the actual price in the Nordic region since 2013 (orange) and the resultof Pöyry’s modelling on historical fundamentals (blue, referred to as back test previously).We can observe that BID3 accurately estimates historical power prices in the Nordicregion. While the historical price was €30.0/MWh on average over 2013-2017 for anactual carbon price of close to €6/tCO2, BID3 simulates the price at €30.4/MWh. Thesimulations give an annual average absolute error over the period of €1.2/MWh only, or4% of the actual price.

Carbon transfer factorcarbon scenario=Power pricecarbon scenario - Power pricereference scenario

Carbon pricecarbon scenario - Carbon pricereference scenario



17


Figure 9 – Effect of carbon price increase on Nordic power prices, 2013-2017(€/MWh, nominal)


The figure also presents the result of a simulation with a CO2 price of €30/tCO2 and whatthe Nordic power price would have been for such a carbon price (dashed blue line). In thiscase, we find through BID3 quantitative modelling that the power price would have beensignificantly higher and at €46.7/MWh, or close to €17/MWh higher than observedhistorically.

The carbon transfer factor in the Nordic region is estimated at 0.71tCO2/MWh on averageover the years 2013-2017

The same exercise as illustrated in Figure 9 has been carried out for three additionalcarbon prices – €10-20-40/tCO2 – to constitute results for the four carbon scenariosmentioned in section 4.1.2.

Figure 10 displays the Nordic carbon transfer factors obtained between 2013 and 2017.The green interval bars represent the range of results for the different carbon scenariossimulated (€10-20-30-40/tCO2) and the green diamonds correspond to the average resultof these four carbon prices.

Costs of CO2 for gas and coal/lignite generation are indicated for comparison as perFigure 7 (page 11). The maximum regional CO2 emission factor currently in place in theNordic market9 and the carbon transfer found in the 2011 study - Carbon Price Transfer inNorway10 have also been included for reference.

9 Official Journal of the European Union. Annex IV Maximum regional CO2 emission factors indifferent geographic areas (tCO2/MWh). 5.6.2012.

10 Pöyry/Thema Consulting Group. Carbon Price Transfer in Norway. March 2011. Available at:http://ec.europa.eu/competition/consultations/2011_questionnaire_emissions_trading/wacker_chemicals_norway_annex_en.pdf

0

10

20

30

40

50

60

7020

13

2014

2015

2016

2017

Pric

e (€

/MW

h, n

omin

al)

Effect of carbon price increase on power priceModelled power price on CO2 price at €30/tCO2Modelled power price on historical CO2 price (back test)Historical power price



18


Figure 10 – Historical Nordic carbon transfer factor, 2013-2017 (tCO2/MWh)

Carbon pass-through (tCO2/MWh) 2013 2014 2015 2016 2017 2013-2017

Average of carbon scenarios 0.83 0.82 0.69 0.64 0.60 0.71Source: Pöyry Management Consulting analysis.

On average over the historical years modelled, the carbon transfer factor is found to be0.71tCO2/MWh. This means that every time the carbon price increases by €1/tCO2, thepower price increases by €0.71/MWh. With an average carbon price in the period around€6/tCO2, this quantifies the impact of carbon on the Nordic power price to roughly€4/MWh, or close to 15% of the 2013-2017 average Nordic power price.

The Nordic carbon transfer factor was the highest in 2013 with 0.83tCO2/MWh and hasgradually decreased reaching 0.60tCO2/MWh in 2017. The Nordic carbon transfer factorhas been influenced over the past few years by decreasing coal installed capacities andprogressively increasing capacity of intermittent renewables such as wind and solarpower. It is also affected by yearly variations in Nordic conditions including installed plantcapacity, hydrology, demand, etc.

It is also found that within the range of carbon scenarios, the lowest CO2 price generallyleads to the highest carbon transfer factor and vice versa. At low CO2 prices, coalgeneration on the Continent typically sets the bidding price of the Nordic hydro powergeneration and in turn the Nordic power price level. At higher CO2 prices, gas generationis gradually incentivised relative to coal which impacts the carbon transfer factor on thedownside, due to a lower cost of CO2 emissions in gas generation compared to coal(higher plant efficiency and lower carbon fuel content).

The Nordic carbon transfer factor is similar within the Nordic countries and in par with theGerman pass-through, larger differences can be observed across surrounding countries

Figure 11 presents the carbon transfer factor obtained for the €30/tCO2 carbon scenariosimulated. The average and range of results over the years modelled, 2013 to 2017, ispresented for the Nordic region as well as each Nordic and interconnected countries. Asstated in section 4.1.2, the carbon transfer factor for the Nordic region is calculated as theaverage of the pass-through found across the four Nordic countries.

0

0.2

0.4

0.6

0.8

1

1.2

2013 2014 2015 2016 2017

Car

bon

trans

fer f

acto

r (tC

O2/M

Wh)

Historical modelling - Range of carbon scenariosHistorical modelling - Average of carbon scenariosGas only marketCoal/Lignite only market

Nordic CO2 emission factor inplace/2011 study - Carbon pricetransfer in Norway: 0.67



19


Figure 11 – Historical carbon transfer factor in selected countries, €30/tCO2carbon scenario (tCO2/MWh)

Carbon pass-through(tCO2/MWh) Nordic NO SE FI DK DE NL PL EE LV LT

Average of years 2013-2017 0.70 0.67 0.70 0.75 0.67 0.70 0.52 0.93 0.88 0.67 0.67Source: Pöyry Management Consulting analysis.

It can be observed that beyond yearly variations represented by the orange range, thecarbon transfer factor varies across countries, though always between the ‘Gas onlymarket’ and ‘Coal/Lignite only market’ bands. We can further notice that:

§ The Nordic carbon transfer factor is found to be close to the German one. This is dueto the extensive capacity between the regions and confirms the theory that, in adispatchable renewable market, the pass-through is influenced by interconnectedcountries.

§ In Germany, either coal or gas generation dominate price setting depending acrossyears on the supply/demand balance and relative commodity prices. This leads to acarbon transfer factor broadly at the average of the ‘Gas only market’ and‘Coal/Lignite only market’ bands.

§ The carbon transfer factors obtained for Norway, Denmark and Sweden are in linewith each other and the Nordic factor. The pass-through found for Finland is slightlyhigher on average compared to the other Nordic countries. This is partly due a directinterconnection to Estonia where oil shale generation, with a higher cost of CO2emission compared to coal/gas generation, dominates.

§ The lowest factor is found for the Netherlands where gas generation dominates.Even though the Netherlands is interconnected with Germany, transmission capacityremains limited and gas generation sets the price more often in the Netherlands thanin Germany – where coal plays a significant role in price setting in the past years.

§ The highest carbon pass-through is observed in Poland where lignite sets the price ina considerable amount of time. Lignite generation indeed has the highest cost of CO2emissions compared to other generation types (see Figure 7, page 11).

0

0.2

0.4

0.6

0.8

1

1.2

Nor

dic

Nor

way

Swed

en

Finl

and

Den

mar

k

Ger

man

y

Net

herla

nds

Pola

nd

Est

onia

Latv

ia

Lith

uani

aCar

bon

trans

fer f

acto

r (tC

O2/M

Wh)

Historical modelling - Range of years 2013-2017Historical modelling - Average of years 2013-2017Gas only marketCoal/Lignite only market

Nordic CO2 emission factor inplace/2011 study - Carbonprice transfer in Norway: 0.67



20


§ When it comes to the Baltic countries, the carbon transfer factor is found slightlybelow 1.0tCO2/MWh in Estonia. This is in line with the cost of CO2 emission in oilshale generation (see Figure 7, page 11). Latvia and Lithuania have lower factors dueto a predominance of gas generation. Even though these markets are interconnectedwith each other, congestion exists and leads to different technologies setting thepower price from hour to hour.

4.3 Forward model analysis

4.3.1 Base case

The Nordic carbon transfer factor is projected to decrease in the future

Figure 12 presents the projected Nordic carbon transfer in modelled years up to 2040 forthe average (blue diamonds) and range (blue interval bars) of the simulated carbonscenarios (€10-20-30-40/tCO2). For the first future year modelled, i.e. 2018, the carbontransfer factor is found to be 0.58tCO2/MWh in the Nordic market, a value in line with the2017 figure of 0.60tCO2/MWh found through historical modelling (see Figure 10, page 18).

It can also be seen from Figure 12 that the decrease observed in the historical analysis isexpected to continue in the future, assuming normal weather and availabilities. In 2020,the Nordic carbon transfer factor is expected at 0.56tCO2/MWh and at 0.43tCO2/MWh in2030. In 2040, the projected pass-through reaches 0.36tCO2/MWh, a level below the costof CO2 emission of gas-fired power generation. Assuming a linear evolution between themodelled years, the Nordic carbon transfer factor is expected on average at:

§ 0.49tCO2/MWh over 2020-2030; and

§ 0.40tCO2/MWh over 2031-2040.

Figure 12 – Projected Nordic carbon transfer factor, 2018-2040 (tCO2/MWh)

Carbon pass-through (tCO2/MWh) 2018 2020 2025 2030 2035 2040

Average of carbon scenarios 0.58 0.56 0.48 0.43 0.42 0.36Source: Pöyry Management Consulting analysis.

0

0.2

0.4

0.6

0.8

1

1.2

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

Car

bon

trans

fer f

acto

r (tC

O2/M

Wh)

Forward modelling - Range of carbon scenariosForward modelling - Average of carbon scenariosGas only marketCoal/Lignite only market



21


Two long-term changes, illustrated in Figure 13, are driving the estimated decrease in theNordic carbon transfer factor:

§ Coal/lignite-fired plants are decommissioned in Europe, both in the Nordic region andon the Continent. This means that gas-fired plants will be called more often to meetdemand and the generation changes from coal setting the price to gas setting theprice. This leads to a lower carbon content in the Continental generation and affectsthe carbon transfer factor downward. The Nordic region is in turn affected as theContinental thermal generation influences the price of the reservoir hydro powerresource that dominates price setting.

§ Intermittent renewable capacity (mostly wind and solar) is increasing significantly overthe projected years. These generation sources contribute to increasingly pushingthermal plants out of merit leading to an increased number of low price periods duringwhich the power price is independent of the carbon price.

Figure 13 – Long-term evolution of markets drivers affecting the carbon transferfactor

Shift from coal to gas and increase inintermittent renewable

Increasing number of low price hoursduring a year11


The range of transfer factors obtained for the different carbon scenarios is also noticeablyreducing throughout the period (see blue interval bars in Figure 12). In the early years, thecarbon transfer factor falls with rising CO2 prices. Gas generation is incentivised at highCO2 prices relatively to coal, same as seen historically. This impact however decreaseswith time as coal and lignite plants are decommissioned on the Continent and gasgeneration becomes the main price setting technology, independent of the carbon price.

11 We define the number of low price hours as periods in which the power price is below thebidding price of nuclear power, i.e. €7/MWh in Pöyry’s modelling. This is representative ofhours where the carbon price does not intervene in price formation. Results displayed herecorrespond to wholesale electricity price projections obtained in the Base case for a carbonprice of €30/tCO2. The ‘Denmark’ data corresponds to the DK1 price area.

0%

2%

4%

6%

8%20

18

2020

2025

2030

2035

2040

Shar

e of

low

pric

e ho

urs

(%)

DenmarkGermany



22


The downward trend in the carbon transfer factor is projected across European markets

Figure 14 displays the projected carbon transfer factor in the Nordic countries, Germanyand Estonia. For simplicity, only the average of the carbon scenarios is presented and therange of carbon scenarios obtained for each modelled year is omitted.

The carbon transfer factor in the Nordic countries is found, like in the historical modelling,to be quite similar from country to country and this justifies once more aggregating themas a market. The Finnish factor trends closer to the rest of the Nordic countries from the2020s, as domestic coal is gradually decommissioned and oil shale plants converted inEstonia. The Danish pass-through remains the lowest factor across all Nordic countriesthroughout the period. This is explained by the transit role that Denmark plays betweenthe Nordic region and the Continent. The Danish price is indeed influenced by Germany,Norway and Sweden at the same time and Denmark ends up having a higher number oflow price hours than the rest of the Nordic region. With the anticipated increase in theintermittent renewable capacity, this gives a downside in the Danish carbon pass-through.

Figure 14 – Projected carbon transfer factor in the Nordic countries and selectedsurrounding countries, 2018-2040 (tCO2/MWh)


A similar downward trend as projected for the Nordic pass-through is also visible in otherEuropean markets (see Germany and Estonia in Figure 14). There is in addition a trendtowards more convergence between European countries in the very long-term. This canbe attributed to a certain similarity in capacity mix between the Continental countries in thelong-term tending towards a closure of installed capacities with high carbon emissions,and an increase in the intermittent renewable and cross-border interconnection capacities.

4.3.2 SensitivitiesWe expect the European power systems to continue changing in the long-term. Thecarbon transfer factor is therefore somewhat uncertain far out in time as it is subject to thelong-term evolution of the generation mix, i.e. the share of coal versus gas generation andthe evolution of the share of intermittent renewable generation. A way to cope with these

0

0.2

0.4

0.6

0.8

1

1.2

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

Car

bon

trans

fer f

acto

r (tC

O2/M

Wh)

Norway SwedenFinland DenmarkGermany EstoniaGas only market Coal/Lignite only market



23


uncertainties is to re-evaluate the future carbon transfer factor regularly and to analyse theresponse of the pass-through to different market drivers.

In this study we have investigated how sensitive the future Nordic carbon transfer factor isto changes in a few main modelling assumptions. Figure 15 presents the Nordic carbontransfer factor obtained for five sensitivities along with results for the Base case. From theBase case to the sensitivities, only one parameter is changed at a time to isolate the effectof changes in fuel prices (±50% for coal and gas) or of a higher renewable generation.

Figure 15 – Projected Nordic transfer factor, Base case and sensitivities, 2018-2030-2040 (tCO2/MWh)

2018

2030

2040


Forward modelling - Range of carbon scenariosForward modelling - Average of carbon scenariosGas only marketCoal/Lignite only market

0

0.2

0.4

0.6

0.8

1

1.2

Basecase

Lowgas

Highcoal

Highgas

Lowcoal

HighRES

Car

bon

trans

fer f

acto

r(tC

O2/M

Wh)

0

0.2

0.4

0.6

0.8

1

1.2

Basecase

Lowgas

Highcoal

Highgas

Lowcoal

HighRES

Car

bon

trans

fer f

acto

r(tC

O2/M

Wh)

0

0.2

0.4

0.6

0.8

1

1.2

Basecase

Lowgas

Highcoal

Highgas

Lowcoal

HighRES

Car

bon

trans

fer f

acto

r(tC

O2/M

Wh)



24


Fuel prices have an influence on the carbon transfer factor in the medium-term

Focusing on the fuel price sensitivities, we can notice the following in 2018 for both theNordic and the German carbon transfer factors:

§ The Low gas and High coal sensitivities lead to a lower carbon transfer than in theBase case. Lower gas prices incur a shift of some coal generation to gas compared tothe Base case. Gas generation therefore sets the power price more often whichdecreases the carbon pass-through towards the 0.4tCO2/MWh value for gas onlymarkets. The same happens in case of a high coal price.

§ The High gas and Low coal sensitivities have an opposite effect. Higher gas pricesmake coal generation comparatively more attractive and lead to a shift of some gasgeneration to coal. More coal generation setting the price increases the carbontransfer factor towards the 0.9tCO2/MWh value for coal only markets. The samehappens in case of a low coal price.

§ The lowest Nordic carbon transfer factor is found in case of a Low gas or High coalprice and is 0.49tCO2/MWh. The highest Nordic pass-through is found in case of aHigh gas price and is 0.67tCO2/MWh, compared to 0.58tCO2/MWh in the Base case.Similar trends can in general be observed in other European countries.

For 2040, the sensitivities yield different outcomes compared to 2018. Close to novariations in the carbon transfer factor can be observed compared to the Base case andfuel prices do not influence the carbon pass-through anymore. Coal and lignite plantshave been partly decommissioned and have by this time a generally limited influence onprice setting on the Continent. This in turns affects the Nordic region and the upsidepotential in the carbon transfer factor seen in 2018 has disappeared.

2030 is at a transition between the two stages that 2018 and 2040 represent. The impactof fuel prices is still noticeable on the Nordic carbon pass-through, mostly in case of Lowgas or High coal prices.

It is however a possibility that the decommissioning of the European coal capacity will beslower than currently accounted in Pöyry’s long-term assumptions. In such case, theimpact of fuel prices may be observed on the carbon transfer factor for a longer period oftime than shown in Figure 15. Higher gas prices or lower coal prices will typically put coalgeneration at the margin more often than gas giving potential for higher carbon transferfactors. The opposite will be true for lower gas prices or high coal prices

A faster increase in renewables influences the carbon transfer factor in the long-term

Figure 16 focuses on the last sensitivity investigated in this report, of a higher renewableand interconnection capacity deployment. The chart presents the evolution of the Nordiccarbon transfer factor in the Base case and High RES sensitivity for the average of thesimulated carbon scenarios.

The number of low price hours in the €30/tCO2 carbon scenario is also displayed toillustrate the correlation between the carbon transfer factor and the number of hourswhere the power price is set independent of the carbon price12.

12 The number of low price hours is presented for Denmark (DK1). Like in Figure 13 (page 21),we define the number of low price hours as periods in which the power price is below thebidding price of nuclear power as per Pöyry’s modelling.



25


Figure 16 – Projected Nordic carbon transfer factor, Base case and High RESsensitivity, 2018-2040 (tCO2/MWh)

Carbon pass-through (tCO2/MWh) 2018 2020 2025 2030 2035 2040

Base case 0.58 0.56 0.48 0.43 0.42 0.36

High RES 0.57 0.53 0.45 0.40 0.26 0.19Source: Pöyry Management Consulting analysis.

While the impact of fuel prices decreases over the projection period, the effect ofrenewables on the carbon transfer factor is observed throughout the years. A fasterincrease in the renewable share in the High RES sensitivity pushes the carbon transferfactor lower as the number of low price hours increases faster than in the Base case.

The impact of more intermittent renewables is however different between years:

§ Up to 2030, even though renewables increase faster in the High RES sensitivity theystill do not represent the major part of the generation and therefore cannot stronglyinfluence price setting. The impact on the carbon transfer factor is therefore limitedand what is driving the downward trend in the pass-through is also the shift fromcoal/lignite to gas in setting the price. The carbon pass-through is still substantial in2030 and projected at 0.40tCO2/MWh versus 0.43tCO2/MWh in the Base case.

§ From the 2030s, the effect of higher renewable generation becomes more significantwhen the share of low price hours exceeds 12%. Thermal plants are however stillneeded to meet demand in a certain amount of time throughout the year and thepass-through remains relatively high. It is expected to be 0.19tCO2/MWh in 2040compared to 0.36tCO2/MWh in 2040 in the Base case.

0%

5%

10%

15%

20%

25%

30%

35%

0

0.2

0.4

0.6

0.8

1

1.2

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

Shar

e of

low

pric

e ho

urs

(%)

Car

bon

trans

fer f

acto

r (tC

O2/M

Wh)

Carbon transfer factor - Base caseCarbon transfer factor - High RESNumber of low price hours - Base caseNumber of low price hours - High RES



26


4.4 Summary of resultsThe quantitative analysis method of power market modelling is a robust way of assessingthe carbon pass-through in a wide range of countries both historically and in the future. Anextensive number of results have been obtained in this study and this report has focusedon demonstrating the influence of the carbon price on the Nordic power market.

Figure 17 summarises the main results obtained in this study for the Nordic carbontransfer factor. The green and blue diamonds respectively represent historical (2013 to2017) and future (2018 to 2040 Base case) analyses carried out, for the average ofcarbon scenarios. The range of results obtained in the sensitivities explored for futureyears is presented to illustrate potential upside and downside explored.

Figure 17 – Nordic carbon transfer factor, 2013-2040 (tCO2/MWh)

Carbon pass-through(tCO2/MWh) 2013 2014 2015 2016 2017 2018 2020 2025 2030 2035 2040

Historical/Forwardmodelling - Base case 0.83 0.82 0.69 0.64 0.60 0.58 0.56 0.48 0.43 0.42 0.36


Key findings of this study include:

§ The Nordic carbon transfer factor is found to be 0.71tCO2/MWh on average over thehistorical years 2013 to 2017. A carbon transfer factor of 0.71tCO2/MWh means thatevery time the carbon price increases by €1/tCO2, the power price will increase by€0.71/MWh.

- This is broadly in line with the regional CO2 emission factor in place in the Nordicmarket and the 2011 study - Carbon Price Transfer in Norway that both give acarbon transfer factor of 0.67tCO2/MWh.

- The Nordic carbon transfer factor has progressively decreased from 2013 to2017. This can be attributed to a general decline in the coal installed capacity on

0

0.2

0.4

0.6

0.8

1

1.2

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

Car

bon

trans

fer f

acto

r (tC

O2/

MW

h)

Forward modelling - Range of sensitivitiesForward modelling - Base case Historical modellingGas only market Coal/Lignite only market

Historical modelling - Average of years 2013-2017: 0.71Nordic CO2 emission factor in place/2011 study - Carbon price transfer in Norway: 0.67



27


the Continent and in the Nordic region along with changes in internal Nordicconditions, including hydrology and demand.

§ The future Nordic carbon transfer factor is evaluated at 0.49tCO2/MWh on averageover the years 2020 to 2030. The Nordic pass-through is expected to decreasegradually in the long-term, declining to 0.43tCO2/MWh by 2030 and 0.36tCO2/MWh by2040.

- This is in part due to expectations of a shift from coal/lignite setting the price onthe Continent to gas, as coal/lignite-fired plants are set to be decommissioned.The emission factor for electricity produced from gas is lower compared to theone of coal which contributes to decrease the pass-through.

- The reduction in the transfer factors is also attributed to an increase in theintermittent renewable generation. Renewables are indeed progressively pushingthermal power plants out of merit more often leading to an increasing number ofhours during which the power price is independent of the carbon price.

The performance of Pöyry’s market model in estimating actual prices has been validatedthoroughly. This ensures that historical carbon transfer factor estimates are robust. Thecarbon transfer factor is more uncertain in the future as it is subject to the long-termdevelopment of the European generation mix, i.e. the share of coal versus gas capacitysetting the price and the share of intermittent renewable generation. A way to cope withthese uncertainties is to re-evaluate the future carbon transfer factor regularly and toanalyse the response of the pass-through to different market drivers. In this study we haveinvestigated how sensitive the Nordic carbon transfer factor is to changes in a few of themain modelling assumptions, i.e. fuel prices and renewable installed capacity:

§ Fuel sensitivity analyses show variations in the carbon transfer factor mostly in themedium-term.

- In the medium-term, the range is significant and the lowest value is obtained forlow gas or high coal prices. Low gas or high coal prices favour gas generation –with a low cost of CO2 emissions – relatively to coal which gives a downside inthe Nordic carbon transfer factor. Upside to the Nordic pass-through is on thecontrary found for a high gas price scenario.

- These variations reduce in the long-term and the carbon transfer factor becomesindependent of fuel prices as coal/lignite-fired plant capacity is decreasing. Achange in the fuel price does not influence the price setting generation anymore.The upside potential in the carbon transfer factor is effectively removed followingPöyry’s long-term view of European thermal capacities.

§ Even in a higher renewable capacity future, the carbon price continues to affectthe power price as thermal plants are still needed to meet demand and theNordic carbon transfer factor remains relatively high at 0.40tCO2/MWh in 2030.

- The effect of more renewable on the Nordic carbon transfer factor becomes moresignificant after 2030 only. In the period up to 2030, the lowest carbon factor isactually found for the fuel sensitivities and the case of low gas or high prices, andnot for a greater renewable capacity.

- In the high renewable capacity sensitivity, the carbon transfer factor is estimatedat 0.40tCO2/MWh in 2030 and 0.19tCO2/MWh in 2040, against respectively0.43tCO2/MWh and 0.36tCO2/MWh with Base case assumptions.



28


ANNEX A – METHODOLOGY OF POWER MARKETMODELLING

A.1 SummaryScenario assumptions and data are entered into BID3 (which stands for Better InvestmentDecisions).The model in turn generates power prices and other featured results bysimulating how the power market adjusts to the key drivers as defined by scenarios. BID3is a comprehensive power market simulator for all power markets in Europe. The modelcontains a plant by plant database for all European countries and assumptions fordemand, fuel (and CO2) prices and transmission capacity both for the current period andfor future years.

BID3 is a fundamental optimisation model. This means that the model finds the cheapestway to balance the demand in all European power markets given13:

§ How much it costs to run the various power plants. This depends on fuel prices andhow much it costs for said plants to adjust generation up and down. For instance, ifthe market can be cleared by either gas plants or coal plants, BID3 chooses the plantwith the lowest overall costs.

§ What price an owner of a reservoir hydro plant should receive given the opportunity tostore or produce at any hour, taking into account uncertain inflow levels in the future.

§ Transmission constraints limiting the possibility for generation in one region tobalance market in some other region. This is more commonly referred to asbottlenecks.

In BID3, all types of power producers bid in their electricity to the market to meet(pre-specified) demand in both their home market and other power markets. Thewholesale price of power is the marginal production cost of the most expensive plantneeded to meet demand. In the real world, this corresponds to the intersection of supplyand demand curves in an auction like the day-ahead market.

BID3 also takes into account grid constraints, or bottlenecks, yielding price differencesbetween different countries (and also within some countries). Effectively, this means thatthe most expensive producers in surplus regions have to retract their bids as they cannotexport to a nearby deficit region, while more expensive producers in the deficit region canbid into the market and still sell their power. The outcome is a higher power price in thedeficit region than in the surplus region. In BID3, bottlenecks in a power market dominatedby reservoir-hydro capacity simply imply that more water is stored in some hours andreleased in others, meaning that the market can still export its entire surplus. Ifbottlenecks occur at times where inflexible generation (say wind power) exceeds demandand export capacity, this generation will get ‘locked in’ and have no alternative cost, andthe model will therefore yield a wholesale price close to zero.

BID3 applies stochastic dynamic programming to handle uncertainty concerning futureinflow. This procedure means that in the model hydro-producers base their generation andpricing decisions at a specific time on probability distributions for future inflow, and thatthis procedure is moved forward for every time interval. If, for instance, at time t a hydroproducer expects a dry period over the next 10 weeks, the hydro producer will berestrictive in releasing water already in period t and t+1 and so on.

13 In fact the model optimises production and flows in order to minimise supply costs. This isequivalent to finding the lowest price possible required to balance supply and demand



29


Market regulatory issues, such as renewable development, nuclear capacity development,and runtime restrictions on CHP plants are featured in the model as exogenous inputs.Regulatory aspects concerning the grid (such as grid tariffs, grid investment rules, etc) arenot covered in the model, nor are taxes such as electricity consumption tax and VAT(Value Added Tax).

The BID3 model simulates the power markets in a very accurate and realistic way.14 Inparticular, the treatment of uncertainty for hydro producers captures exactly how hydroproducers make their dispatch decisions. Moreover, BID3 has an hourly time resolution,which means that it finds the optimal price for all hours of the year modelled. Other mainoutputs from the BID3 model include hourly dispatch (exactly how much and what type ofelectricity is generated each hour in each country), trade and socio-economic benefit.

Figure 18 summarises the main inputs and outputs of BID3.

Figure 18 – Illustration of BID3 functionality

Typically results for the Nordic countries are computed for 20 weather patterns. Everyfuture year (e.g. 2025) is modelled under a sequence of weather patterns between 1995and 2014. Under that sequence of weather, the starting position of hydro reservoir for agiven weather pattern will be equal to the ending position of the previous weather pattern:realistic multi-year effects are therefore captured by Pöyry’s methodology.

A.2 Modelling methodologyBID3 is an economic dispatch model based around optimisation. The model balancesdemand and supply on an hourly basis by minimising the variable cost of electricitygeneration. The result of this optimisation is an hourly dispatch schedule for all powerplant and interconnectors on the system. At the high level, this is equivalent to modellingthe market by the intersection between a supply curve and a demand curve for each hour.

14 Several back testing exercises, in which BID3 has provided both price levels and pricevariations very close to historical levels, confirm this point.



30


A.2.1 Producing the system schedule§ Dispatch of thermal plant. All plants are assumed to bid cost reflectively and plants

are dispatched on a merit order bases – i.e. plants with lower short-run variable costs(costs of production) are dispatched ahead of plant with higher short-run variablecosts. This reflects a fully competitive market and leads to a least-cost solution. Costsassociated with starts and part-loading are included in the optimisation. The modelalso takes account of all the major plant dynamics, including minimum stablegeneration, minimum on-times and minimum off-times. Figure 19 below shows andexample merit order curve for thermal plant.

§ Dispatch of hydro plant. Reservoir hydro plants can be dispatched in two ways:- A simple perfect foresight methodology, where each reservoir has a one year of

foresight of its natural inflow and the seasonal power price level, and is able to fixthe seasonality of its operation in an optimal way.

- The water value method, where the option value of stored water is calculatedusing Stochastic Dynamic Programming. This results in a water value curvewhere the option value of a stored MWh is a function of the filling level of thereservoir, the filling level of competing reservoirs, and the time of year. Figure 19below shows an example water value curve, and section A.5 presents thismethodology in more detail.

§ Interconnector flows. Interconnectors are optimally utilised – this is equivalent to amarket coupling arrangement.

Figure 19 – Thermal plant merit-order and water value curve

A.2.2 Power priceThe model produces a power price for each hour and for each zone (which may besmaller than one country, for example the different price zones within Norway, Swedenand Denmark). The hourly power price is composed of two components, the systemmarginal price and the scarcity rent.

The standard Linear Programming version of BID3 calculates the System Marginal Price(SMP) as the incremental cost of additional power demand during each modelled period(hour) – this is a short-run marginal price or minimum price at which all operating plant arerecovering their variable costs. The model takes into account all variable costs: fuel,carbon, start-up, no-load and other strictly variable costs – these all affect the SMP. TheBID3 model projects SMP as part of a complete economic dispatch of European powersystem which includes hourly plant output and cross-border flows.



31


The scarcity rent is an additional component above the short-run marginal cost in ourhourly wholesale power prices. This mark-up above the short-run cost varies hour by hourdepending on the market conditions. Based on historical analysis we assume thatgenerators are able to bid above their short-run costs during hours when the system istight, i.e. when demand is near to the available generation capacity. The tighter thesystem the higher plant can bid above their short-run cost. Two extreme situations canoccur:

§ In a competitive market with a significant over-capacity the market is rarely tight andgenerators are generally not able to bid above their short-run costs. In such cases,scarcity rent may fall to zero, or close to zero.

§ At the other extreme, where capacity margins are tight, wholesale prices may riseabove the short-run marginal cost frequently. If the power system is allowed to getsufficiently tight, the level of biding above short-run cost and the incidence of highprice spikes should rise to a level such that new capacity is incentivised to enter themarket, i.e. new capacity can recover its capital costs from the energy market.

In between these two extremes, in a stable market with neither significant over-capacitynor need for new capacity, we would expect the level of scarcity rent to be just sufficient toretain existing plant on the system (i.e. to cover the fixed costs of keeping the plant openand available to operate, if needed).

Our scarcity rent methodology is based on the economic principle that for a stableequilibrium in the medium to long-term, the revenues earned by generators should reflectthe full long-run cost of production. If scarcity rent remains at very low levels for longperiods generators will not cover their annual fixed costs (let alone capital costs) andultimately plant will be closed or mothballed. Conversely, higher levels of scarcity rent willincentivise new capacity.

The presence of capacity mechanisms has a strong influence on the level of scarcity rentin our scenarios. A capacity mechanism often requires that an organisation (typically thegovernment, regulator or system operator) specifies the system security standard, andthen the mechanism works to ensure that the required level of capacity is available. Giventhe direct accountability of the organisation setting the system security standard, but theabsence of direct costs, we anticipate that the security standards will be higher (i.e. lesslikelihood of loss of load) than in an ‘energy-only’ market. This leads to a greater amountof capacity than would otherwise be the case. With a capacity mechanism providing alarge capacity margin, the level of scarcity rent will not rise to levels seen in an‘energy-only’ scenario.

Surrounding interconnected markets are also affected. Higher capacity surpluses in themarkets with a capacity mechanism also serve to loosen capacity margins and limit levelsof scarcity rent in surrounding markets.

Scarcity rent is modelled explicitly in thermal markets, but not in Norway, Sweden andFinland where the ‘scarcity’ is more focused on energy rather than capacity.

A.3 Input data

Pöyry’s power market modelling is based on Pöyry’s plant-by-plant database of theEuropean power market. The database is updated each quarter by Pöyry’s countryexperts as part of our Energy Market Quarterly Analysis. As part of the same process wereview our interconnection data, fuel prices, and demand projections.

§ Demand. Annual demand projections are based on TSO forecasts and our ownanalysis. For the within year profile of demand we use historical demand profiles – for



32


each future year that is modelled we use demand profiles from a range of historicalyears.

§ Intermittent generation. We use historical wind speed data and solar radiation dataas raw inputs. We use consistent historical weather and demand profiles (i.e. bothfrom the same historical year) which means we capture any correlations betweenweather and demand, and can also example a variety of conditions – for example aparticularly windy year, or a cold, high demand, low wind period.- For wind data, we use hourly wind speeds at grid points on a 15km grid across

Europe, at hub height. Hourly wind speed is converted to hourly wind generationbased on wind capacity locations and using appropriate aggregated powercurves.

- Detailed hourly solar data, sampled at a 5km resolution is converted to solargeneration profiles based on capacity distributions across each country.

§ Fuel prices. Pöyry has a full suite of energy market models covering coal, gas, oil,carbon, and biomass. These are used in conjunction with BID3 to produce input fuelprices consistent with the scenarios developed.

A.4 Model results

BID3 provides a comprehensive range of results, from detailed hourly system dispatchand pricing information, to high level metrics such as total system cost and economicsurplus. A selection of model results is shown below in Figure 20 to Figure 22.

Figure 20 – BID3 dashboards output examples (1/2)



33


Figure 21 – BID3 dashboards output examples (2/2)

Figure 22 – Geographical representation of results and mapping functionality

A.5 Description of the hydro dispatch optimisationPöyry has implemented a Stochastic Dynamic Optimisation (SDP) methodology tooptimise reservoir hydro dispatch under uncertainty of future inflows. In thehydro-dominated areas like the Nordic region it is critical to use such a technique, as theuncertainty of future inflows greatly affects the pricing of electricity on the spot market. Ifall players knew their future inflows, they would price their water much more aggressivelyand would not hesitate to go down to very low reservoir levels. In reality, market playersare conservative in their use of water, to ensure that they can always meet the demandfrom their customers even in very dry years. This optimisation methodology is used bymost market players in the Nordic countries as one of the steps to determine their biddingprice into the market.



34


The principle of the methodology implemented in BID3 is described in Figure 23 .

Figure 23 – Optimisation sequence

BID3 creates a ‘bidding strategy’ for reservoir hydro assetscalled ‘Water Value Curve’, whereby the dispatch decisions ofreservoir hydro at any point in time are robust to a variety offuture inflow situations.

This takes the form of a bidding price for an asset, called‘water value’, determined according to several parameters: thehydro reservoir level for the asset considered, the reservoirlevel in other assets, and the time of the year.

This bidding price is then used in the Market Simulation like a‘fuel price’ for the use of water.

The water value represents the cost increase in electricity supply that the region wouldface if it had one less MWh of water in the reservoir. This opportunity cost is the value atwhich a hydro market player offers production into the market.

Figure 24 shows a simplified water value curve, where all assets in the scope areassumed to have the same reservoir level. Each week, the model determines a newbidding price for reservoir hydro depending on the reservoirs’ level at the end of theprevious week.

Figure 24 – Example of a simplified water value curve

The two circled areas show interestingperiods:

§ When reservoirs are nearly emptybefore winter period water isexpensive, the hydro players are onlywilling to produce when the powerprices is very high; and

§ when reservoirs are nearly full nearthe snow melting period water ischeap, hydro players want to undercutother generation to avoid spilling incase of high inflow.

Figure 25 shows example of applications of this water value curve. The left-hand sidepicture shows the impact of hydrology on annual prices – the more inflow, the lower theprice. The right-hand side picture shows monthly price results across twenty consecutivehydro inflow patterns, all other inputs being equal. Note that this picture does notrepresent the full range of weather-related price variations: dry years are often cold in theNordic countries, which could create periods of price peaks in winter.



35


Figure 25 – Influence of hydrology on power prices

Influence of hydrology on annual prices Monthly prices across 20 historical hydro inflowpatterns

A.6 Purchase of BID3BID3 is the modelling platform used for Pöyry’s Electricity Market Quarterly Analysis(EMQA) reports, providing European power price projections to major banks, utilities,governments and developers. The model is also used for bespoke projects for a widerange of clients including analysing economics of new interconnectors.

BID3 is finally also available for purchase and has an extensive client base, as shown inFigure 26 below. It has been used by many organisations in the Nordic region includingEnerginet.dk, Fingrid, Statnett, Svenska Kraftnät and Norsk Hydro.

Figure 26 – BID3 clients and data

If you are interested in obtaining BID3 or power plant datasets for your organisationplease visit www.poyry.com/BID3 or email [email protected].

http://www.poyry.com/BID3

mailto:[email protected]



36


ANNEX B – VALIDATION OF POWER MARKET MODELIn order to ensure that results obtained in this study are reasonable and robust, we haveassessed the performance of our BID3 power market model through a back test process.A back test consists in assessing whether the model is able to simulate observed prices,dispatch and flows in a realist manner. BID3 uses in this case historical fundamental andprofiles in order to model prices which are in turn compared to historical prices.

Figures presented in this annex show that BID3 reproduces well the main dynamics of themarket over several historical years (2013 to 2017). The model not only producesaccurate price levels and shapes (presented in Annex B.1) but is also able to delivercorrect hydro generation and reservoir levels (presented in Annex B.2).

B.1 Price levelsFigure 27 presents annual and monthly price results from the back test obtained fromBID3 (in blue, also referred to as back test) and compares these results against marketprices (in orange) for the German and Nordic15 power prices. Figure 28 presentscorresponding hourly average prices and price duration curves. Annual power pricestypically deviate by less than €1/MWh and monthly modelled prices follow observedvariations, aside from some specific period of times for the Nordic countries.

Deviations can be observed in some instances. Most deviations come from the fact thatBID3 has access to less information than the market. The market gets access to constantnew information during a year – updated weather forecasts, actual measurements ofinflow, reservoir levels or snow accumulation – while the back test is done with weatherexpectations as of 1 January of the year. The market and hydro power producers inaddition tend to react aggressively in their bidding behaviour if they see the risk ofexceptionally high or low prices. These types of somewhat more subjective reactions arenot always captured perfectly by quantitative modelling. Aside from some specific timeperiods, BID3 is able to capture the average and variations of historical prices as well asmarket dynamics in a robust way.

15 The Nordic price displayed here corresponds to the average across Nordic price areas.



37


Figure 27 – Annual and monthly average power prices, Nordic and Germany,back test versus market, 2013-2017 (€/MWh, nominal)

Ann

ual

Mon

thly




38


Figure 28 – Hourly average prices and price duration curves, Nordic andGermany, back test versus market (€/MWh, nominal)

Hou

rly p

rice

shap

eP

rice

dura

tion

curv

e


B.2 Hydro power generation and reservoir levelsFigure 29 shows the weekly hydro power reservoir storage for Norway and Swedencalculated by BID3 (blue) and actual as reported on Nord Pool (orange). Figure 30presents the monthly Nordic hydro power generation obtained from BID3 (blue) versusactual generation numbers (orange).

The hydro reservoir levels follow actual data reasonably well, aside from some deviationsin 2013 during which the snow pack was very low in mid-winter. This low snowaccumulation led to a conservative behaviour from hydro producers resulting in relativelyhigh prices and low production levels in the winter months. This was done in order toeliminate the risk for empty reservoirs later during the year. The snow pack informationhas not been modelled in the BID3 back test. This therefore explains the lower price andhigher generation obtained in the back test for the winter period. As a consequence of thedifferent bidding strategies, reservoir levels were higher in the market than in the BID3



39


back test for the rest of the year. The Nordic hydro power generation is rather in line withactual generation levels, again aside from 2013 explained previously.

Figure 29 – Norway-Sweden hydro reservoir levels, back test versus actual (%)

Source: Nord Pool and Pöyry Management Consulting.

Figure 30 – Nordic hydro power generation, back test versus actual (TWh)

Source: ENTSO-E, Eurostat and Pöyry Management Consulting.



40


ANNEX C – POWER MARKET ASSUMPTIONSThe study aims at assessing the carbon transfer factor in the Nordic power market.Modelling of the Nordic and surrounding countries has therefore been the focus of thestudy and hydro power dispatch optimisation (described in Annex A.5) has been part ofeach simulations carried out.

This annex provides a description of model setup and core assumptions used to obtainwholesale electricity price projections for:

§ Historical modelling in Annex C.1; and

§ Forward modelling in Annex C.2.

C.1 Historical modellingFor historical modelling, we have modelled the years 2013-2014-2015-2016-2017 usinghistorical fundamentals and weather profiles for each year. Inputs such as plant installedcapacities, plant parameters, demand data, fuel data, interconnector capacities, reservoirand inflow levels are regularly reviewed by our country experts and gathered from publiclyavailable sources. These include Eurostat, ENTSO-E, IEA, transparency data from powerexchanges, etc. Renewable generation, thermal generation or demand profiles are alsoused as inputs to the model and derived from observed weather, generation or demandpatterns16.

We have for historical modelling ran BID3 on all Europe allowing for prices to be correctlyoptimised and give very accurate results as presented in Annex A.

C.2 Forward modellingFor forward modelling, we have modelled the years 2018-2020-2025-2030-2035-2040and used 20 historical years (1995-2014) that cover a range of different weatheroutcomes (wind, temperature, hydro inflow) to capture a wide range of future probablemarket conditions. This means that for each future year that is modelled, 20 simulationsrepresenting the weather and demand for the historical years 1995-2014 are carried out.In addition, these 20 weather simulations are modelled sequentially – the end hydroreservoir level for one weather simulation is used as the start reservoir level for thefollowing simulation. This means that our methodology captures the historical sequencesof dry and wet years, giving realistic results for the multi-year storage characteristics ofNordic hydro market. Wholesale price projections obtained thus correspond for eachmodelled year to the average of the 20 historical weather years carried out and aretherefore representative of normal fundamental conditions.

16 Wind profiles are for example created from wind speed data from 3TIER. The wind speeddata is then converted to power output by wind turbines using power curves. Theserepresent the power output of a wind turbine for a given wind speed. At low wind speeds,below the cut-in threshold, the power output is zero. Above around 4m/s, the output risesquickly, and flattens as the wind turbine reaches its maximum generation. Above a certainwind speed (around 25m/s for onshore wind, 30m/s for offshore wind), the wind turbine cutsout completely and stops generating to prevent damage to components. The wind speed andpower curves are combined with our assessment of the likely geographical distribution ofonshore and offshore wind development for each market, to produce final wind profiles for allyears from 1995 to today.



41


Due to the computational time required to carry such optimisation, modelled price areashave been restrained to: Austria, Belgium, Czech Republic, Estonia, France, Great Britain,Germany, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Switzerland and theNordic price areas. The border of the optimisation is sufficiently far from the Nordiccountries to deliver precise results for the Nordic power market.

Countries further away from the Nordic region, e.g. Hungary, Ireland, Sicily, Slovenia,Spain, Slovakia, have been modelled through a two stage process. First, BID3 has beenrun on a full European scale with Base case assumptions in a reference run. Then flowsthat are crossing the optimisation border used in this study have been fixed at thereference run levels. The same level of fixed flows is used in all forward analyses carriedin this study (Base case and sensitivities). The supply/demand in these areas is thereforenot impacted by the change in CO2 price. This is a reasonable assumption as flowpatterns far away from the Nordic region are not likely to have an effect on the Nordiccarbon transfer factor.

C.2.1 Base caseThe Base case used in the forward modelling of this study is defined as:

§ Pöyry’s Central scenario for electricity demand, installed capacity and transmissioncapacities assumptions17. The Central scenario is Pöyry’s best estimate of the futureand based on:

- 1.5% GDP growth in the Eurozone over 2018-2040;

- 41% decarbonisation by 2030 and 80% by 205018; and

- a balanced supply/demand in commodity markets with steady OPEC marketshare.

§ Since this study aims at assessing the effect of the carbon price increase on thepower price, we have chosen not to use Pöyry’s coal and gas projections but to ratherset those at flat levels. $80/t for coal and €18/MWh for gas were chosen, i.e. roughlywhat was observed in the spring 2018. This is motivated by their importance in settingthe price in European markets and in order to limit impacts on the generation mix andisolate the effect of the carbon price on power prices.

Electricity demand

The projected electricity demand is based on Pöyry’s three-stage process demand modelillustrated in Figure 31:

§ development of an econometric model linking country-specific electricity demand bysectors (residential, commercial, industrial and agricultural) to GDP growth andpopulation evolution;

§ impact of country energy efficiency measures on electricity demand by sectors; and

§ assumptions on new demand segments, like electrification of the economy throughelectric vehicles, heat, or new consumer groups like data centres.

17 This scenario forms the basis of Pöyry’s Electricity Market Quarterly Analysis (EMQA)reports, providing European power price projections to major banks, utilities, governmentsand developers. Assumptions used in this study were finalised in June 2018.

18 The values given are percentage reductions against the 2005 EU ETS emission levels.



42


Figure 31 – Pöyry demand modelling process


Installed capacity

Figure 32 presents Pöyry’s projected capacity development from 2018 to 2040 for Europe.The main noticeable changes are:

§ The installed capacity of coal and lignite is expected to decrease rapidly towards themid-2020s and to be lower than the nuclear capacity from the 2030s.

§ The installed capacity of onshore wind and solar power is anticipated to more thandouble by 2040. A significant growth is also expected for offshore wind but startingfrom lower levels today, its total installed capacity remains below 100GW in 2040.

Figure 32 – Capacity development in Europe, Base case (GW)


Capacity development in the Nordic region is driven by:

§ Ambitious renewable targets are pursued and onshore wind is expected to bedeveloped on a pure merchant basis in the coming years as wholesale prices rise andcapital and operating costs decrease.

§ Nuclear decommissioning is starting early in Sweden due to poor profitability in therecent years. New developments are delayed or uncertain and furtherdecommissioning is expected in the 2030s as reactors reach end of lifetime.



43


§ Investments in conventional thermal fossil-fuelled capacity are not expected and coalis anticipated to be fully phased-out by 2030 following political commitments.

§ There is some technical potential for new hydro power generation in Sweden andNorway in non-protected water resources, but it is limited in regards to the alreadyexisting hydro power installed capacity.

Capacity development on the Continent is driven by:

§ An acceleration of the development of intermittent renewables and adecommissioning of coal/lignite fired plants incentivised by climate targets andsupport schemes.

§ A need for new capacity to enter the market to meet the increase in demand orreplace plants scheduled to close. In case of new thermal investments, gas-firedplants are often favoured (unless access to specific renewable fuel sources) due to alower carbon emission content compared to other fossil fuels.

§ Different energy policies when it comes to nuclear development, e.g. Germany willphase-out nuclear by 2022 while Great Britain and France are building new nuclear.

Transmission capacities

Finland, Norway, Sweden and Denmark are well-connected power markets, i.e. the Nordicpower systems have a high degree of compatibility and the physical transmission capacityis extensive. This is motivated by the need to:

§ offload surplus generation between neighbouring countries in periods where theability to export to the Continent is limited;

§ provide supply adequacy in periods of scarce supply, such as dry years; and

§ deal with an increasing amount of generation from intermittent sources.

We include in our projections projects under construction, planning or considerationextracted from network development plans published by the Nordic TSOs. Beyond theseprojects, interconnection out of the Nordic region is evaluated on an economic basis. Twofeatures offset interconnector profitability. First, more interconnectors to the Continentimply that price differences are gradually curbed and returns dwindle. The secondparameter is regulatory, where Nordic TSOs may seek to ban merchant cabledevelopments (something currently being discussed in Norway) or intervene in projects ifsocio-economic benefits are poor.

This results in a development of the Nordic interconnection in large parts towards GreatBritain and Germany – with several projects under construction from Norway (NordLink,NSN) and some under planning from Denmark and Sweden (Cobra Cable, Viking Link,Hansa Power Bridge). In the long-term, existing interconnections to Poland, Estonia andthe Netherlands are strengthened.

C.2.2 SensitivitiesThe Nordic carbon transfer factor is in theory influenced by the price setting technology insurrounding markets, and therefore the cost of generation alternatives both thermal andrenewables. In order to analyse the response of the carbon pass-through to other set ofinputs representing another view of the long-term development in the Europeangeneration mix, five sensitivities have been carried out.



44


Sensitivity to fuel prices

High and low coal and gas price together constitute the four fuel price sensitivitiesanalysed in this study. Table 1 presents fuel prices used in each sensitivity where coal orgas prices are varied individually by ±50% from the Base case, all other input being equal.

Table 1 – Fuel price sensitivities

High gas Low gas Base case Low coal High coalCoal price $80/t $80/t $80/t $20/t $120/t

Gas price €27/MWh €9/MWh €18/MWh €18/MWh €18/MWh


Figure 33 presents corresponding marginal cost for the different carbon scenariosanalysed, i.e. €10-20-30-40/tCO2. It is a total of 20 simulations that are carried out over toobtain all results for the sensitivity to fuel prices, i.e. five carbon and reference scenariosfor each of the four fuel sensitivities.

Figure 33 – Marginal cost, Base case and fuel price sensitivities (€/MWh)

Ref

eren

ce s

cena

rioC

arbo

n sc

enar

ios


The figure illustrates the response of the marginal cost to the different inputs. Forexample, it can be anticipated that in the Low coal sensitivity and for carbon costs below

01020304050607080

Gas Coal Gas Coal Gas Coal Gas Coal Gas Coal

High gas Low gas Base case Low coal High coal

Mar

gina

l cos

t (€/

MW

h) CO2 cost at €0/tCO2Fuel cost

01020304050607080



Mar

gina

l cos

t (€/

MW


01020304050607080



Mar

gina

l cos

t (€/

MW


01020304050607080



Mar

gina

l cos

t (€/

MW


01020304050607080



Mar

gina

l cos

t (€/

MW




45


€30/tCO2, we will observe more coal generation than gas due to lower costs of production.This will in turn most likely give a higher carbon transfer factor (closer to 0.9tCO2/MWhand the cost of CO2 emissions in coal generation) than in the Base case.

Sensitivity to capacity mix and transmission capacity

This sensitivity uses Pöyry’s High19 scenario for plant capacity and interconnectors, allother inputs being taken from the Base case presented in Annex C.2.1. Pöyry’s Highscenario compared to Pöyry’s Central scenario:

§ has a higher GDP growth, 2.5% in Eurozone from 2018 to 2040 and a balancedsupply/demand situation in global commodities;

§ is more ambitious on decarbonisation, 42% fall by 2030 and 90% by 205020; and

§ is more monopolistic, the OPEC is stronger and restricts outputs, there is a greater oilindexation of gas prices and higher scarcity rent in energy-only markets.

This Pöyry scenario leads to higher power prices, a more rapid decommissioning ofthermal plants when they reach end of lifetime and a faster deployment of renewablegeneration and interconnection capacity. The High scenario was chosen to carry asensitivity analysis in this study in order to represent a case of High renewable capacity. Itis therefore referred as High RES sensitivity in this report.

Figure 34 presents plant capacity in the Base case and the High RES sensitivity inEurope. The renewable capacity increases at a faster pace in the High RES sensitivitycompared to the Base case. Wind and solar power combined reach 700GW in 2030 and970GW in 2040 compared to 570GW and 780GW in the Base case.

Figure 34 – Capacity development in Europe, Base case and High RES sensitivity(GW)


19 This scenario is also presented in Pöyry’s EMQA reports and was finalised in June 2018.20 This is 10% beyond the EU target for 2050 and 2% beyond the EU target for 2030.



46


Figure 35 displays the renewable capacity in the High RES sensitivity and Base case forcountries in the European Union21. The renewable capacity is projected to 650GW in 2030in the Base case and 770GW in the High RES sensitivity in 2030. This corresponds to asignificant increase from the 2018 renewable capacity, roughly 200GW in the Base caseand 300GW in the High RES.

Figure 35 – Renewable capacity in the EU, Base case and High RES sensitivity(GW)


In a 2017 report from the EC Joint Research Centre, it was assessed that22:

§ to reach a 27% share from renewables in the final energy consumption (EUCO27 andEUCO30 scenarios), the renewable electricity capacity needs to reach 652GW to656GW in 2030, an increase of 61-62% from 2016 levels.

§ to reach a 30% share from renewables in the final energy consumption (EUCO3030scenario), the renewable electricity capacity needs to reach 712GW in 2030, anincrease of 75% from 2016 levels.

An ambitious political agreement was reached on 14 June 2018 for a binding renewableenergy target for the EU for 2030 of 32%23. Extrapolating the JCR study on the newlyagreed renewable target share implies, very approximately speaking, a 760GW renewablecapacity in 2030.

Pöyry’s Base case is therefore consistent with the EU 27% target for renewable while theHigh RES sensitivity should slightly exceed the EU 32% target for renewable, whenlooking at projected capacity numbers.

21 Hydro pumped storage capacity is here excluded from the figure.22 Figure 48, page 56 of JCR Science for Policy report. Renewable technologies in the EU

electricity sector: trends and projections – Analysis in the framework of the EU 2030 climateand energy strategy. 2017.

23 http://europa.eu/rapid/press-release_STATEMENT-18-4155_en.htm



47


QUALITY AND DOCUMENT CONTROL

Quality control

Role Name Date

Author(s): Geir Brønmo

Clémence Carnerero

August 2018

Approved by: Geir Brønmo August 2018

QC review by: Geir Brønmo/Michel Martin August 2018

Document control

Version no. Principal changes Date

v100 Initial client release 31/08/2018

v200 Minor corrections 05/10/2018

Pöyry is an international consulting and engineering company.We deliver smart solutions across power generation, transmission &distribution, forest industry, chemicals & biorefining, mining & metals,transportation and water.

Pöyry PLC has ca. 5,500 experts operating in 40 countries and netsales in 2017 of EUR 522 million. The company's shares are quotedon Nasdaq Helsinki (POY1V).

Pöyry Management Consulting provides leading-edge consulting andadvisory services covering the whole value chain in energy, forestand bio-based industries. Our energy practice is the leading providerof strategic, commercial, regulatory and policy advice to energymarkets in Europe, the Middle East and the Americas. Our energyteam of 200 specialists, located across 12 offices in 11 countries,offers unparalleled expertise in the rapidly changing energy sector.

Pöyry Norway ASGrensen 16 Tel: +47 45 40 50 00N-0159 Oslo E-mail: [email protected] www.poyry.no

ww

w.p

oyry

.com

Documents

Carbon transfer factor in the Nordic power market€¦ · The carbon transfer factor is more uncertain in the future as it is subject to the long-term development of the European