Degree project in
Impact of German Renewable Energies on the Spot Prices of the French- German Electricity Markets
Bich-Thuy Doan
Stockholm, Sweden 2013
XR-EE-ES 2013:004 Electric Power Systems
Second Level,
IMPACT OF GERMAN RENEWABLE ENERGIES ON THE SPOT PRICES OF THE FRENCH-GERMAN
ELECTRICITY MARKETS
by
Bich-Thuy Doan
A thesis submitted to the Royal Institute of Technology of Stockholm in partial fulfilment of the requirements for the degree of
Master of Science
Supervisors:
Serge LESCOAT (INDAR Energy) & Mohammad R. Hesamzadeh (KTH) Examiner:
Mohammad R. Hesamzadeh
Department of Electric Power Systems, School of Electrical Engineering Kungliga Tekniska Högskolan, Stockholm, Sweden
&
INDAR Energy, Paris, France
December 2012
Abstract
Thanks to growing environmental concerns, renewable energies take a higher and higher share of electricity generating portfolios. In Germany particularly, the installed capacity of wind and solar plants has increased continuously for the past ten years. Given the principle of the merit-order dispatch, a greater use of wind and solar power allows the electricity spot prices to drop significantly. However, wind and sun are both intermittent resources, and this leaves great room for uncertainties on prices. As a consequence, prices become much more dependent on the weather conditions and show greater volatilities, making hedging much more difficult. At the same time, the mechanism of market coupling in the Central West Europe (France, Germany, Benelux) goes toward a harmonization of prices.
As such, the cross-border interconnections play a decisive role in the electricity pricing.
This paper deals with the actual influence of the interconnections between France and Germany on electricity spot prices when renewable energies are added to the energy mix. A model of a French-German market is made in order to see the impact of an increasing penetration of renewable energies on spot prices. The wind and solar generations are modelled using artificial neural networks, ANN. Multiple linear regression is employed to model the French and German loads. The cross-border interconnections are modelled based on the capacity allocations published by RTE (the national French grid operator) and finally the French and German prices are modelled with a GARCH process to study the volatilities.
The study is made for three different scenarios: the reference scenario, with a penetration of renewable energies as seen in 2012, a 2020 scenario, with a penetration of renewable energies as predicted in 2012, and a 2020 scenario with increased interconnection capacities between France and Germany.
Running the models shows that a higher penetration of renewable energies lowers spot prices in average, but introduces price spikes that did not exist beforehand. On short periods of observation, the volatility seems to decrease, but on longer periods, the spikes increase the volatility. Also, increasing the interconnection capacities does make the prices converge, but to a certain extent only.
Finding fitting hedging strategies becomes more delicate when prices vary with such
uncertainty. The study could be more developed (by extending it to the whole European
continent) in order to get a more accurate vision of how energy markets will look like in a few
years. However, it must be understood that the future scenarios depend on many variable
factors, and no mathematical model is able to capture all those factors accurately.
Abstrakt
Till följd av en växande miljöproblematik blir förnybara energikällor en allt högre andel av dagens elproduktion. I Tyskland framförallt, har den installerade kapaciteten av vindkraft och solenergi ökat kontinuerligt under de senaste tio åren. En ökad användning av vindkraft och solenergi resulterar i en avsevärd prissänkning av elspotspriserna, detta med meritorder- principen i åtanke. Dock är vind och sol intermittenta resurser, vilket resulterar i en stor osäkerhet kring priserna. På grund av detta blir priserna mer beroende av väderförhållanden och visar större volatilitet, vilket gör risksäkring mycket svårare. Samtidigt rör sig mekanismen för marknadskoppling i Västcentrala Europa (Frankrike, Tyskland, Benelux) mot en harmonisering av priserna. De gränsöverskridande sammanlänkningarna spelar på så vis en avgörande roll i elprissättningen.
Denna uppsats behandlar det faktiska inflytande sammanlänkningarna mellan Frankrike och Tyskland har på elspotspriserna, när förnybara energikällor blir en del av energiproduktionen. För att se effekterna av en ökning av förnybara energikällor på spotpriser, görs en fransk-tysk marknadsmodell. Vinkraft and solenergi modelleras med artificiella neurala nätverk, ANN. Multipel linjär regression används för att modellera den franska och tyska förbrukningen. De gränsöverskridande sammanlänkningar skapas baserat på de kapacitetsanslag som publicerats av RTE (den nationella franska nätoperatören) och de franska och tyska priserna modelleras slutligen med hjälp av en GARCH process för att studera volatiliteten. Studien är gjord för tre olika scenarion: referensscenariot, med en ökning av förnybara energikällor som vi sett 2012, ett 2020 scenario, med en ökning av förnybar energi som förutspåtts 2012, och ett 2020 scenario med ökad sammankopplingskapacitet mellan Frankrike och Tyskland.
Dessa modeller visar att en ökning av förnybara energikällor sänker spotpriser i genomsnitt, men introducerar pristoppar som inte existerade tidigare. Under korta observationsperioder verkar volatiliteten minska, men under längre perioder så ökar pristopparna volatiliteten. Ökad sammanlänkningskapacitet gör dessutom att priserna konvergerar, men endast i en viss utsträckning.
Att hitta passande risksäkringsstrategier blir känsligare när priserna varierar med sådan
osäkerhet. Studien kunde utvecklats vidare (genom att utvidga den till hela den europeiska
kontinenten) för att få en mer rättvisande bild av hur energimarknaderna kommer att se ut om
några år. Det måste emellertid förstås att framtidsscenarierna beror på många rörliga faktorer,
och ingen matematisk modell har förmågan att exakt fånga alla dessa faktorer.
Acknowledgement
I would like to express my gratitude to several people without whom the writing of this thesis would not have been possible.
First, I would like to give my special thanks to my family, particularly G. NGUYEN and N. DELAUNAY, for supporting me every day throughout the whole thesis writing.
I would also like to thank L. SÖDER, professor at the Electric Power Systems division at KTH, who, through his excellent lectures, gave me the will to start a thesis in this department.
I would not have gone far without my supervisor at INDAR Energy, S. LESCOAT, and his advice and counselling, his suggestions and ideas. His knowledge on energy markets and financial markets came as a great support for my work. My thanks also go to Y.
KOCHANSKA and D. POSE from INDAR Energy, for allowing me to work in their company in the best conditions possible.
Several other people contributed as well to the progress of the thesis, one way or another. Among them, I wish to thank R. KATZ, M. ISSERLIS and S. KHOU from POWERNEXT for giving me access to valuable data which I would not have been able to obtain otherwise. I would like to thank M. THIOLLIERE, vice-president of the CRE (Commission de Régulation de l’Energie), for his time and useful references. My thanks also go to M. DHAUSSY and J-B. BART from EDF R&D for giving me the opportunity to gather more information for my thesis.
And finally, I would like to thank M. GUIHOT from Supélec, for helping me in data
gathering and various other things, including her reading recommendations.
Table of contents
Table of figures ... 8
Table of tables ... 9
Introduction ... 10
1 Renewable energies on the France-Germany market ... 12
1.1 Environmental policies and consequences on wind and solar penetration ... 12
1.1.1 Reduction of CO 2 emissions ... 12
1.1.2 Investments in renewable energies ... 13
1.1.3 Support schemes for renewable energies ... 14
1.1.4 Penetration of wind and solar power ... 16
1.2 French and German generation and consumption ... 18
1.2.1 Generation portfolios ... 18
1.2.2 Consumption profiles ... 20
1.3 Supplying customers ... 21
1.3.1 Intermittency of wind and solar power... 21
1.3.2 Keeping power balance ... 22
1.4 Impact on the France-Germany market ... 23
1.4.1 Pricing system in a perfect market ... 24
1.4.2 Structure of the markets ... 27
1.4.3 Comparison of prices ... 30
1.4.4 French and German interconnections ... 31
1.4.5 Spot prices future evolution ... 32
2 Existing methods of analyses ... 35
2.1 Preliminary observations ... 35
2.2 Marginal cost model and multiple linear regression ... 35
2.3 Power flow solution ... 38
2.4 Statistical analysis ... 41
2.5 Impact on European cross-border transmission ... 43
3 Econometrical analysis ... 47
3.1 Preliminary observations ... 47
3.2 Description of the France-German market model to compute prices ... 52
3.3 Modelling of intermittent energies ... 54
3.3.1 Wind production modelling ... 55
3.3.2 Solar production modelling ... 58
3.4 Modelling of the load ... 59
3.5 Market simulation procedure and results ... 63
3.6 Modelling of the spot prices ... 66
3.6.1 Mean-reverting jump diffusion models ... 66
3.6.2 ARCH/GARCH models ... 68
3.7 Discussions ... 78
4 What can be expected in the future? ... 79
References ... 82
Appendices ... 86
Table of figures
Figure 1.1: Avoidance of greenhouse gases emissions in Germany in 2011 ... 13
Figure 1.2: Investment in renewable facilities in Germany in 2011 ... 13
Figure 1.3: Production from renewable energies in France and Germany ... 16
Figure 1.4: Wind and solar productions in France and Germany ... 17
Figure 1.5: Installed wind capacity in France and Germany ... 17
Figure 1.6: Installed photovoltaic capacity in France and Germany ... 18
Figure 1.7: French installed capacity in 2011 ... 19
Figure 1.8: German installed capacity in 2011 ... 19
Figure 1.9: Monthly load in France and Germany since 2009 ... 20
Figure 1.10: Daily consumption profile in France and Germany... 21
Figure 1.11: Planned and actual wind production in Germany ... 22
Figure 1.12: Pricing system in a perfect market ... 25
Figure 1.13: Day-ahead prices in France and Germany ... 27
Figure 1.14: Calendar 13 prices in France and Germany ... 28
Figure 1.15: Shares of the French power exchange Powernext ... 29
Figure 1.16: Shares of the German power exchange EEX ... 30
Figure 1.17: Difference between French day-ahead prices and German ones ... 31
Figure 1.18: Variations of day-ahead prices in France and of transmissions from Germany to France ... 32
Figure 1.19: Illustration of higher marginal costs to cover for start-up costs ... 33
Figure 2.1: Illustration of marginal cost model ... 36
Figure 2.2: System marginal cost with and without renewable generation ... 36
Figure 2.3: Variation of spot price with wind power penetration ... 41
Figure 2.4: Probability density of spot prices for different wind power penetration ... 42
Figure 2.5: EUPowerDispatch model ... 43
Figure 3.1: German spot prices versus production of renewables ... 47
Figure 3.2: French spot prices versus production of renewables ... 48
Figure 3.3: Studied time series ... 49
Figure 3.4: Diagram of the simplified France-Germany market ... 52
Figure 3.5: MLP Network ... 55
Figure 3.6: Forecast values of wind production using ANN ... 57
Figure 3.7: Linear fit between forecast and expected values of wind production ... 57
Figure 3.8: Forecast values of solar production using ANN ... 58
Figure 3.9: Linear fit between the forecast and expected values of solar production ... 59
Figure 3.10: German temperature, humidity and consumption profile ... 60
Figure 3.11: French temperature, humidity and consumption profile ... 60
Figure 3.12: June 2012 consumption in France and Germany ... 61
Figure 3.13: Modelled and actual French load ... 62
Figure 3.14: Modelled and actual German load ... 62
Figure 3.15: Modelled prices in the 2012 scenario ... 63
Figure 3.16: Modelled prices in the 2020 scenario ... 65
Figure 3.17: Modelled prices in the 2020 scenario with double capacities ... 65
Figure 3.18: Modelled French and German spot prices with mean-reverting jump diffusion . 68
Figure 3.19: Price volatilities in all the scenarios ... 76
Table of tables
Table 1.1: Overall French targets concerning the share of renewable energies ... 14
Table 1.2: Overall German targets concerning the share of renewable energies ... 15
Table 1.3: Market system example ... 24
Table 1.4: Summary of LMP results in a multi-area market ... 26
Table 2.1: Multiple linear regression coefficients for Spotbase ... 37
Table 2.2: Nodal prices for 24 buses ... 40
Table 2.3: Spot price and wind power of each year ... 42
Table 2.4: Results of the dispatching ... 44
Table 3.1: ADF test for German spot prices ... 50
Table 3.2: ADF test for French spot prices ... 51
Table 3.3: ADF test for renewable production ... 51
Table 3.4: Granger causality test results ... 51
Table 3.5: Marginal costs per plant type ... 53
Table 3.6: Regression coefficients for the French load ... 61
Table 3.7: Regression coefficients for German load ... 62
Table 3.8: Mean prices in the three scenarios ... 66
Table 3.9: Coefficients of the mean-reverting jump diffusion ... 67
Table 3.10: Residuals sum of squares from the Box-Cox transformation ... 71
Table 3.11: Autocorrelation and partial correlation coefficients of the raw series ... 71
Table 3.12: ADF test result for trend and constant ... 72
Table 3.13: ADF test result with constant only ... 72
Table 3.14: ADF test result with no trend nor constant ... 72
Table 3.15: ADF test result after differencing ... 73
Table 3.16: AR(1) model results for German spot prices ... 73
Table 3.17: ARCH model results for German spot prices in the 2012 case... 74
Table 3.18: ARCH model results for French spot prices in the 2012 case ... 74
Table 3.19: ARCH model results for German spot prices in the 2020 case... 75
Table 3.20: ARCH model results for France spot prices in the 2020 case... 75
Table 3.21: ARCH model results for German spot prices in the 2020 case with doubled capacities ... 75
Table 3.22: ARCH model results for French spot prices in the 2020 case with doubled
capacities ... 76
Introduction
Nowadays, sustainable development has been fairly accepted and encouraged by most countries over environmental concerns such as CO 2 and other greenhouse gases emissions.
Because of those concerns, renewable energies have been promoted for the past decade now and a focus has been put on wind and solar generations. Those two energies present several aspects which make them all the more attractive: they are inexhaustible, not polluting and require no production cost (there are however installation costs). For those reasons, most countries have seen their wind and solar installed capacities grow continuously. In the European Union, according to EurObserv’ER barometers, Germany and France are leading countries in both the wind and solar areas: in 2010, Germany ranked first with over 27 GW (respectively 17 GWp 1 ) of installed wind (respectively solar) capacity. France ranked third for wind power with over 6 GW and fourth for solar power with 335 MWp.
Environmentally speaking, that continuous growth is very beneficial but one has to wonder about the economic impact of an increasing penetration of renewable energies in the energy mixes. Supporters of wind and solar energies claim that electricity prices on the market will drastically go down since it doesn’t cost anything to produce electricity from wind and the sun. However, that reasoning works only if we consider that the country which produces the green energy doesn’t import or export power with any other country. In reality, this is not the case. In Central West Europe, a market coupling exists between France, Germany and the Benelux. The market coupling has been helping electricity prices to converge through cross-border interconnections, making more expensive areas become cheaper and vice-versa. One of the objectives of this paper is therefore to study the role of the cross-border interconnection between France and Germany in the making of the electricity prices.
Another problem with wind and solar energies is that they are intermittent resources, i.e.
no one can control when the wind will blow or when the sun will shine. This causes problems when it comes to deliver power to the consumers. For example, if there is suddenly no sun nor wind, conventional plants 2 have to be started immediately to produce electricity. Since it has a cost to produce electricity with those plants, the prices can climb up very quickly. And the higher the penetration of renewables is, the more meteorological uncertainties are introduced.
If electricity prices depend on those uncertainties, it can be seen that the prices will be subject to a high volatility. Therefore, the second objective of this paper is to study the impact of a higher penetration of renewables in Germany on the electricity prices volatility in both France and Germany.
In order to carry out those objectives, a model of the France-Germany market is made.
The model will take into account the renewable generation, the loads and the interconnection capacity. In order to model those items, a lot of data must be collected: histories of wind and solar generations, of French and German loads, of temperatures, wind speeds and atmospheric pressures in specific locations, and a history of French and German electricity prices.
Simulations of the market will be run with various levels of renewables in order to get a price curve and a fitting model for the prices will be found in order to study the volatility.
1
Wp: W att-peak: measure of nominal power of photovoltaic cells.
2
We call “conventional plants” all the plants that produce electricity with non-intermittent resources (nuclear,
gas, coal etc.)
The paper is divided into three main parts. The first part gives the current state of electricity generation in France and Germany, with a focus on wind and solar generations. It also explains the problem of supplying the customers with electricity and introduces the notion of intermittency. A general background of the France-Germany market is also given, in order to understand the basic concepts of electricity pricing.
The second part of the paper gives a literature review where some previous work on the area is presented. The literature includes papers that give preliminary results on the impact of wind or solar power on electricity prices, but in very specific conditions.
The last part is the econometrical analysis that is performed to model the France- Germany market. First, the intermittent energies are modelled with artificial neural networks, then the loads are modelled using multiple linear regression, the cross-border interconnection is modelled based on the methods of capacity allocations given by RTE 3 and the prices are modelled with two processes: mean-reverting jump diffusion and ARCH/GARCH.
3
RTE: Réseaux de Transport d’Electricité, the national French grid operator.
1 Renewable energies on the France-Germany market
This first part will describe the environmental policies that have been elaborated in order to promote an increasing integration of renewable energies in electricity production in European countries. Those policies have naturally a consequence on the renewable production farms and we will focus more particularly on wind and solar farms in France and Germany.
We will describe both countries’ consumption profile and generation capacities to introduce the notion of power balance (generation should equal consumption at any time) and see how the introduction of wind and solar energies affects this power balance. Finally, we will have a first overview of the France-Germany market to understand the effects of wind and solar energies on the determination of spot prices.
1.1 Environmental policies and consequences on wind and solar penetration
Several directives have been issued by the EU to promote the development of renewable energies. From the “Renewable Electricity Directive” 4 and the “Biofuels Directive” 5 , it is stated that the EU should reach a share of 21% in electricity generation through renewable energies by 2010 and a share of 5.75% in transport. However, those targets weren’t met by most Member States and brought about the adoption of a new directive in 2009, the
“Renewable Energy Directive” 6 , setting new objectives such that the EU should reach a share of 20% of renewable energies by 2020[1]. In this part an overview of France and Germany’s current progress in term of reducing CO 2 emissions is given, then a quick description of those countries’ investments in renewable energies is made. In a third part, the national support schemes to promote the use of renewable energies are detailed and finally the consequences on wind and solar penetration are described.
1.1.1 Reduction of CO 2 emissions
The main problem, as far as environment is concerned, is the reduction of CO 2 and other greenhouse gases emissions. In that area, both France and Germany have achieved positive results: from 2010 to 2011, the estimated amount of CO 2 that was emitted dropped by 19.8%, going from 34.2 million tons to 27.4 million tons[2]. This can be explained by a higher use of nuclear power, but also of wind and solar power.
On the German side, it has been estimated that the avoidance of CO 2 emissions amounted to 82.1 million tons in 2011[3]. Figure 1.1 shows the part of renewable energies in the avoidance of greenhouse gases emissions in electricity generation. The total amount is 87.3 million tons, with 15.5 million tons from hydropower, 34.2 million tons from wind power, 24.7 million tons from biomass and 12.9 millions tons from photovoltaic cells.
4
Directive 2001/77/EC of 27 September 2001 on the promotion of electricity produced from renewable energy sources.
5
Directive 2003/30/EC of 8 May 2003 on the promotion of the use of biofuels or other renewable fuels.
6
Directive 2009/28/EC of 23 April 2009 on the promotion of the use of energy from renewable sources.
0 5 10 15 20 25 30 35 40
Hydropower Wind Biomass Photovoltaic
M il li on t on s C 0 2 e qu iv a le nt
Figure 1.1: Avoidance of greenhouse gases emissions in Germany in 2011 (source: AGEE-Stat[3])
1.1.2 Investments in renewable energies
In 2011, Germany invested a total of 22.9 billion euros in the construction of renewable energies, from which 17.9 billion euros come from wind and solar power. As can be seen on Figure 1.2, the most significant investment has been in photovoltaic facilities with 15 billion euros. In comparison, France invested 4.1 billion euros in renewable energies, from which 3.6 billion euros come from solar power. The high investments in photovoltaics don’t mean that a huge amount of PV cells have been installed; they come from the fact that the construction cost is much higher than for any other power sources, renewable or not.
70 880 960 1050
2000
2950
15000
0 2000 4000 6000 8000 10000 12000 14000 16000
H yd ro po w er
B io m as s (h ea t)
G eo th er m al
S ol ar th er m al
B io m as s (e le ct ric ity )
W in d
P ho to vo lta ic s
M il li on e uros
Figure 1.2: Investment in renewable facilities in Germany in 2011 (source: AGEE-Stat[3])
1.1.3 Support schemes for renewable energies
Given the costs of implementing wind and solar facilities, it is necessary for governments to give incentives to producers in order to promote a higher integration of those green energies, in order that electricity prices don’t skyrocket. This part will focus on the national action plans of France and Germany for their renewable energies policies.
France:
After the “Grenelle Environment Forum” held in 2007, a working group was formed to establish a reference scenario in order to achieve the target of 23% of renewable energies in electricity production. The adopted strategies combine tariff regulations, incentives and communication campaigns. French targets are displayed in Table 1.1 (the share of energy from renewable resources in 2005 was 9.6% as a base for comparison):
Share of energy from renewable resources in the
gross final energy consumption in 2020 23%
Expected total adjusted energy consumption in 2020
155 268 ktoe 7 Expected quantity of energy from renewable resources
corresponding to the 2020 target 35 711 ktoe
Table 1.1: Overall French targets concerning the share of renewable energies (source: MEEDDM)
France offers subsidies that fit with to the level of maturity of renewable energies sectors. For mature technologies such as hydropower and onshore wind turbines, the subsidy guaranteeing a return on investment by protecting investors from electricity price fluctuations.
However, for less mature technologies such as photovoltaics, the incentives aim at reducing the initial investments.
To support electricity production from renewable energies, electricity distributors such as EDF have the obligation to purchase electricity with renewable origins. The obligation to purchase is usually run on a duration of 15 to 20 years. In the case of onshore wind power, for the wind farm to benefit from the obligation of purchase, it is necessary for the wind farm to be located in a Wind Power Development Area (ZDE). Those areas are specific locations which have been acknowledged as fitting to have wind farms built on. For offshore wind power, the procedure is different as the turbines don't have to be located in a ZDE, and their development is ensured mainly with calls for tenders. In the case of photovoltaic power, the owner of the solar facility must apply to get a certificate that gives the right to benefit from the obligation of purchase. However, since April 2009 and in order to simplify procedures, solar installations with less than 250 kW of power rating are exempt of such certificates.
Another way to prompt a higher penetration of renewable energies is to make national calls for projects. Those projects are entrusted to the CRE (Commission de Régulation de l’Energie). Examples of projects have been: construction of onshore wind farms or construction of photovoltaic centres in each French region.
7
Toe: tonne oil equivalent. 1 toe = 11 630 MWh
In order to guarantee the production of electricity from renewable origin, the EU has implemented the RECS (Renewable Energy Certificate System) certificates. The RECS are managed by each country separately by a single agency (Observ'ER in France). Producers must send a request to the agency to receive the certificates; the latter are allocated after the agency has obtained proof that the producer has indeed produced green power[4].
Germany:
The base for German policy is the Renewable Energy Act (Erneuerbare-Energien- Gesetz, EEG). An interesting fact to notice is that in 2010, an amendment has been made in the photovoltaics in order to adjust compensation rates and to double the target for the yearly volume of solar power. In a similar way to France, Germany's targets are displayed in Table 1.2 below (the share of energy from renewable resources in 2005 was 5.8% as a base for comparison):
Share of energy from renewable resources in the gross final energy consumption in 2020
18,00%
Expected total adjusted energy consumption in 2020 197 178 ktoe Expected quantity of energy from renewable resources
corresponding to the 2020 target
35 492 ktoe
Table 1.2: Overall German targets concerning the share of renewable energies (source: BMU)
Strangely enough, it can be noticed that Germany's target of share of renewable energies is lower than in France. However, Germany assumes that the 18% will be achieved through national measures only, without any help from the other Member States. But in the electricity field only, the target set by the EEG is more ambitious: a minimum of 30% share of electricity should come from renewable energies by 2020.
As Germany is a federal state, each state has its own set of measures to promote renewable energies penetration, especially in the wind power field. In a more general way, Germany applies the principle of feed-in tariffs: the plant operator receives it from the grid operator whose network he supplies. The measure is obligatory, i.e. the EEG compels the grid operators to give compensation for electricity from renewable resources. A special requirement must be met by photovoltaic facilities in order to receive the feed-in tariff: they must be registered at the Federal Grid Agency in a system register. The amount of the tariff depends on the renewable source and on the technologies use. For example, in the case of wind power, offshore turbines receive a higher compensation than onshore turbines as the technology required to install offshore turbines is more complex.
If Germany keeps following its current trend of growth, it is estimated that plants generating from renewable energies will reach an installed capacity of 111 GW by 2020 and will represent 38.6% of the gross consumption of electricity. The most significant investments will be done in solar and wind power, following the trend that will be described in the next part. Conjectures forecast an installed capacity of solar power of 51 753 MW and an installed capacity of 35 750 MW and 10 000 MW of onshore and offshore wind power, respectively[5].
For more complete information on support schemes in France and Germany, the reader
can refer to [4] and [5].
1.1.4 Penetration of wind and solar power
As a direct consequence of all the aforementioned support schemes, and in relevance to the present study, the evolution of the power production from renewable resources in France and Germany from 2001 to 2011 is described in this part. On Figure 1.3, all kind of renewable energies are considered: wind, solar, hydropower, biomass and waste. Figure 1.4 however shows the production from wind and solar energies only.
It can be observed that French production from renewable energies has been following a rather flat trend, remaining in a tunnel of production between 60 000 and 80 000 GWh.
Germany’s production has however been growing steadily to reach 122 TWh in 2011. This represents 20% of the total gross electricity consumption in Germany. As far as wind and solar energies are concerned, Figure 1.4 shows the clear difference between both countries:
while France's production barely reaches 14 TWh in 2011, Germany's production peaks at 65 TWh. Still, both countries present growing penetrations of wind and solar energies, illustrating the efficiency of the environmental support schemes.
From this observation, it can be seen that the impact of renewable energies will come mainly from the German side. However, a focus on the evolution of installed capacity of wind (Figure 1.5) and solar (Figure 1.6) farms shows a continual growth in both countries, reflecting the will to integrate more and more of those energies. Photovoltaic (PV) cells have been installed in France only since 2006 with 4 MW capacity, and 7 MW in 2007. When comparing wind to solar power, one can see that the growth of solar installed capacity is much more significant than that of the wind, especially in Germany.
Renewable production in France and Germany since 2001
0 20000 40000 60000 80000 100000 120000 140000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Year
P rod uc ti on ( G W h)
Germany France
Figure 1.3: Production from renewable energies in France and Germany (source: RTE[2], AGEE-Stat[3])
Wind and solar production in Germany and France since 2001
0 10000 20000 30000 40000 50000 60000 70000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Year
P rod uc ti on ( G W h)
Germany France
Figure 1.4: Wind and solar productions in France and Germany (source: RTE[2], AGEE-Stat[3])
Installed wind capacity in France and Germany since 2001
0 5000 10000 15000 20000 25000 30000 35000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
C a pa c it y ( M W )
Germany France
Figure 1.5: Installed wind capacity in France and Germany (source: RTE[2], AGEE-Stat[3])
Installed photovoltaic capacity in France and Germany
0 5000 10000 15000 20000 25000 30000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
C a pa c it y ( M W p)
Germany France
Figure 1.6: Installed photovoltaic capacity in France and Germany (source: RTE[2], AGEE-Stat[3])
It can be seen that the general trend of evolution for the wind installed capacity is logarithmic, while the solar installed capacity is more exponential. The discrepancy between French and German figures is reflected directly on the generation portfolio of each country, as it will be seen in the next part.
1.2 French and German generation and consumption
In this part, the French and German productions from renewable energies are included in their global portfolio, in order to see the share of renewable in both countries’ energy mixes. Then the load profiles are described to introduce the problem of supplying the load.
We will see later that the integration of wind and solar powers have an impact on global supply, thus on the electricity prices.
1.2.1 Generation portfolios
While French generation rests mainly on nuclear power, German generation finds its biggest share in thermal plants (coal, lignite and gas). Germany’s plan to phase out its nuclear production after the incident of Fukushima in March 2011 has brought about significant changes in the country’s energy mix.
At the end of 2011, France had an installed capacity of 126 GW, including 63 130 MW
of nuclear, 27 790 MW of thermal, 25 400 MW of hydropower, 6 640 MW of wind power, 2
230 MW of solar power and 1 270 MW of other renewable resources.
50%
22%
20%
5%
2%
1%
Nuclear Thermal Hydropower Wind PV
Other renewables
Figure 1.7: French installed capacity in 2011 (source: RTE[2])
On its side, Germany had an installed capacity of 162 GW. The energy mix in Germany is a bit more diversified, with a main share of thermal production. With eight nuclear power plants which have been shut down, only nine remain in function, making the nuclear share fall to 12% of the total installed capacity. In 2010, it represented 22% of the installed capacity.
The share of wind and solar powers represents however 27% of the capacity, against 7% in France.
12%
42%
7%
4%
2%
17%
10%
6%
Nuclear Thermal
Pumped storage Run-of-the-river Seasonal storage Wind
Solar Others
Figure 1.8: German installed capacity in 2011 (source: EEX{1})
1.2.2 Consumption profiles
On one hand is generation, on the other hand is the consumption (or load). In order to know how much generation power should be dispatched, it is necessary to have a fair idea of how the load looks like. Despite the fact that loads follow a stochastic behaviour, a global periodicity can still be observed, especially if a separation is made during a year between winter and summer. As can be seen on Figure 1.9, in winter, the load is much higher since there is a strong need for heating while in summer, there is a significant drop in consumption.
(In some other countries such as the US, it is the opposite due to a high use of air conditioning.)
30000 35000 40000 45000 50000 55000 60000
2 0 0 9 /1 2 0 0 9 /4 2 0 0 9 /7 2 0 0 9 /1 0 2 0 1 0 /1 2 0 1 0 /4 2 0 1 0 /7 2 0 1 0 /1 0 2 0 1 1 /1 2 0 1 1 /4 2 0 1 1 /7 2 0 1 1 /1 0 2 0 1 2 /1 2 0 1 2 /4
C on s um pt ion ( G W h) Germany
France
Average summer Ge Average summer Fr Average winter Ge Average winter Fr
Figure 1.9: Monthly load in France and Germany since 2009 (source: ENTSO-E{1})
When comparing with German yearly load, one can notice that the difference between winter load and summer load is not as pronounced as in France, meaning that the German consumption profile can be considered more ‘constant’ than the French one. The regular pattern that can be observed on a yearly basis can also be observed on a daily basis (Figure 1.10). Again, the profiles of both countries are similar, with a maximum load at lunch and dinner time. In theory, the knowledge of those profiles should be enough to determine the necessary generation that needs to be dispatched. In reality, operators must take into account other factors while forecasting the load. These factors are[6]:
- Overall economic activities and population - Weather conditions
- Price of electricity (so called price-sensitive loads) - Technological improvements of the energy end use
Predicting each of these factors involves uncertainties in the power delivery equation.
40000 45000 50000 55000 60000 65000 70000 75000 80000
01 :0 0: 00 03 :0 0: 00
05 :0 0: 00 07 :0 0: 00
09 :0 0: 00 11 :0 0: 00
13 :0 0: 00 15 :0 0: 00
17 :0 0: 00 19 :0 0: 00
21 :0 0: 00 23 :0 0: 00
C on s um ti on ( M W )
Germany France
Figure 1.10: Daily consumption profile in France and Germany (source: ENTSO-E{1})
Does this mean that if somehow, the load could be predicted accurately, there would be no delivery problem? The answer is no, as it will be seen in the next part, for there is not only uncertainties on the determination of the load, but also on the generation. And the main reason for those uncertainties is the integration of wind and solar powers in the generation mix. This will be seen in the next part.
1.3 Supplying customers
The fundamental characteristic of electricity is that once it is generated, it can’t be stored, i.e. it must be delivered to consumers for immediate use. This implies that at any given time, generation must equal consumption. It is therefore necessary, at any given time, to be able to forecast the value of the load. It will be seen in this part that the integration of wind and solar powers in the grid adds an intermittent factor to the equation; this intermittency has to be taken into account when supplying customers. The intermittency of wind and solar powers is illustrated in this part, in order to understand the problems it poses to calculate the required generation and see how it could impact electricity prices.
1.3.1 Intermittency of wind and solar power
As one cannot control when wind should blow or when the sun should shine, operators
can only rely on models to forecast the production of wind or solar farms. Those models can
be relatively simple or more complex, but in any case they cannot ensure a complete accuracy
in the results and errors are necessarily introduced. In the case of solar power, the errors may
be narrower as there is usually a peak at midday and no sun at night. In the case of wind power, distribution profiles of wind speed have been developed but the errors with the actual wind production can vary from a site to another.
Daily data over a month has been collected from the EEX database in order to illustrate that fact. The data represents the planned production and actual production of wind power in Germany in June 2011.
0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000
01 /0 6/ 20 12
04 /0 6/ 20 12
08 /0 6/ 20 12
12 /0 6/ 20 12
16 /0 6/ 20 12
20 /0 6/ 20 12
24 /0 6/ 20 12
28 /0 6/ 20 12
P ro d u c ti o n ( M W )
Actual production Planned production
Figure 1.11: Planned and actual wind production in Germany (source: EEX{2})
The calculation of the correlation coefficient gives a result of 0.92, which isn’t too bad, but which could be improved. Quite often, it can be noticed that the actual production is lower than the planned production, which means that more conventional units have to be quickly dispatched to serve the load. In the next part we will see how this can affect generation.
1.3.2 Keeping power balance
System operators are in charge of keeping balance between production and generation through scheduling, real-time dispatch and regulation processes. The dispatching is done in such a way that it minimizes electricity price while maximizing reliability towards the customers. As seen above, the traditional strategy is to first dispatch the lowest-cost units, and then supply the remaining load with more expensive units. Normally, when the supply is only conventional (i.e. non intermittent generators), those actions are based on deterministic models of expected load and generation. Following the aforementioned strategy, wind and solar units should be dispatched first as their production costs are very low (no energy costs and very low operating costs). It means that whenever wind and solar resources are available, they should be dispatched immediately by the system operators.
However, the problem of wind and solar powers is that their generations can’t be
controlled as they depend solely on meteorological parameters. As seen before, even if
models can be drawn to forecast expected wind and solar productions, there will always be an error on the estimations that will induce errors on the planned generation. These intermittent generations have an impact on several generation operations[6]:
- Frequency control: normally, when balance between generation and load is kept, the frequency of the grid is constant (it is equal to 50 Hz in France and Germany).
However, if there is a load increase (or generation decrease), the frequency will drop and vice-versa. When the frequency is no longer equal to 50 Hz, units must either be dispatched (if there is a load increase) or stopped (if there is a load decrease). This is called frequency control. It can be easily seen that wind and solar units can’t participate to frequency control as their dispatching can’t be controlled.
- Ramping rate: ramping rate represents how quickly a unit can change its output. With a higher penetration of intermittent units in the grid, the apparent rate of change of the load is likely to increase. Indeed, it is very possible for the load to increase while the intermittent generation decreases, or vice versa. Therefore the ramping rates of conventional units have to increase in order to compensate for the apparent rate of change of the load.
- Unloadable generation: unloadable generation is the amount of generation that can be quickly backed down in case of a decrease in load. Normally, with conventional units, operators don’t bother with unloadable generation: they simply trip a generator to reduce production. Now, in order to use wind and solar powers to their maximum output, conventional units must be able to back down quickly if there is a decrease in load. The backing down cannot be done by tripping a generator anymore because the unit may be needed again shortly after being tripped.
- Operating reserve: the reserve is used to face unexpected changes in load or generation. To determine the amount of operating reserve, the operators need to be able to predict load or generation variation on the short term. The predictions can be quite unreliable when intermittent units are taken into account, and therefore more operating reserves must be available to keep a margin. However, keeping an operating reserve is expensive.
Those impacts have a consequence on the balance regulation costs: they get higher as more conventional units are required to regulate the possible imbalances in the power system.
Therefore it can be seen that uncertainty in wind and solar productions forecasts induces uncertainty in the final price that the consumers have to pay.
1.4 Impact on the France-Germany market
This part will use the information given previously on intermittent resources and
uncertain generation to describe how electricity prices are influenced. The explanation of the
pricing system in a perfect market is first detailed, then the French and German markets are
described in order to understand concretely how spot prices are determined and finally a
comparison of prices between France and Germany is shown.
1.4.1 Pricing system in a perfect market
Single-area market:
A perfect market is a market where all the participants are assumed not to hold any dominating market power, i.e. none of them can influence the price of the commodities that are bought or sold (so called 'price takers'). A perfect market also means perfect information, which means that all the participants have full knowledge of the prices and quality of the products. In such a market, the pricing system to determine electricity spot prices is simple: it is a bidding system. Each supplier offers a bid to the power exchange, stating what capacity he can dispatch and at what price. Normally the price corresponds to its marginal cost. Then the power exchange will sort all the received bids in the merit order, i.e. by ascending prices.
It means that the units will be dispatched in that order. For simplicity's sake, let's assume that the load to be supplied is not price-sensitive, that is to say it is a constant load. The price of electricity is then equal to the marginal cost of the most expensive unit that had to be dispatched.
Let's take an example to illustrate the principle, where all marginal costs are kept constant for simplicity reasons (in reality, that is not the case: every extra MW that is produced costs more than the previous one; that cost is called the incremental cost). Let's assume a system where the suppliers are as listed in the following table:
Generating units Capacity (MW) Marginal cost (€/MWh)
Wind turbine 100 3
Hydro plant 300 5
Coal plant 500 40
Natural gas plant 200 55
Table 1.3: Market system example
The values in the table are arbitrary and are given solely as an example. Now assume a load of 800 MW. The wind turbine and the hydro plant will be dispatched first as they have the lowest marginal cost. When combined, both of them give a generation of 400 MW. The coal plant is then dispatched in order to supply the 400 remaining MW. The electricity price will therefore be 40 €/MWh.
Now assume that the load is 1000 MW. Like in the previous case, the wind turbine and the hydro plant will be dispatched first, then the coal plant, giving a combined generation of 900 MW. To supply the missing 100 MW, the natural gas plant needs to be started up and therefore the electricity price will now be 55 €/MWh.
An easy way to determine the spot price is to plot both the supply and demand curves,
as shown in figure 1.15. The price is the intersection of both curves. This method is called the
Locational Marginal Pricing (LMP) method.
Figure 1.12: Pricing system in a perfect market
The spot price here is the one we will focus on through this thesis, and should not be mistaken with the intraday prices which are prices defined every hour due to readjustments during day.
Multiple-area market:
The main point of the thesis is to study the prices in both France and Germany, knowing that those countries are linked via a cross-border interconnection. In that case, the LMP method is slightly different compared to a single-area market, in order to take into account the interconnection. Two main situations can occur while considering a multiple-area market:
congestion or no congestion. An example is used to illustrate those situations. Consider two areas: area A and area B. The data for both areas are displayed in the following tables:
Generating units in area A
Capacity (MW) Marginal cost (€/MWh)
Incremental cost (€/MWh/MW)
Plant A1 50 0 0
Plant A2 70 5 0.01
Plant A3 20 7 0.01
Plant A4 100 20 0.01
Generating units in area B
Capacity (MW) Marginal cost (€/MWh)
Incremental cost (€/MWh/MW)
Plant B1 80 0 0
Plant B2 30 4 0.01
Plant B3 50 9 0.01
Plant B4 70 18 0.01
Load in area A (MW) Load in area B (MW) Transmission capacity between areas A and B (MW)
200 95 70
An incremental cost has been added to better show the impact of interconnections on prices. The new formula function to calculate the prices becomes: P(G)=MC+G*IC, where P is the price, G is the generation, MC is the marginal cost and IC is the incremental cost.
If there was no interconnection between areas A and B, plants A1, A2 and A3 would produce at their maximum capacities and plant A4 would produce 60 MW in order to meet the demand in area A. Plant B1 would produce at its maximum capacity and plant B2 would produce 15 MW in order to meet the demand in area B. The price in area A would be 20+60*0.01=20.6€/MWh and the price in area B would be 4+15*0.01=4.15€/MWh.
The fact that there is actually an interconnection means that cheap power can flow from area B to area A. In that case, the price in area A will decrease (because it will produce less power) and the price in area B will increase (because it will produce more power). The equilibrium is reached when the prices are equal in both areas. In that case, the multiple-area market can be considered a one-area market. In our example, the equilibrium is reached when plants A1, A2, A3, B1 and B2 produce at their maximum capacity and B3 produces 45 MW (in order to meet the demand of both areas A and B). The price for both areas A and B would be 9+45*0.01=9.45€/MWh. For this to be possible, area B should produce and transfer 60 MW more than if there was no interconnection. The transmission capacity between A and B is 70 MW, which means there is no congestion and it is possible to transfer the 60 MW.
Now assume that the transmission capacity is only 30 MW. It means that area B can only transfer as much, and there is congestion. In that case, plants A1, A2 and A3 produce at their maximum capacities and plant A4 produces 30 MW. Plants B1 and B2 produce at their maximum capacities and plant B3 produces 15 MW. The price in area A is 20+30*0.01=20.3€/MWh and the price in area B is 9+15*0.01=9.15€/MWh.
A summary is given in Table 1.4:
Case Price in area A (€/MWh) Price in area B (€/MWh)
No interconnection 20.6 4.15
No congestion 9.45 9.45
Congestion 20.3 9.15
Table 1.4: Summary of LMP results in a multi-area market
It can be seen that there is a benefit from importing cheap power provided that the capacity of the transmission line is big enough. As said at the beginning of this part, this system works in a perfect market. In reality, there is no such thing as a perfect market, and the way to determine electricity prices isn't so easy, because of market power and imperfect information. In that case, forecasting prices becomes a more difficult exercise.
However, even if the market is assumed to be perfect, it can be seen that the integration
of wind and solar powers makes the determination of prices rather difficult as no supplier can
bid with 100% accuracy a certain amount of MW from their wind or solar farm. Theoretically,
the prices should drop since wind and solar powers have extremely low marginal costs. But
the wind and solar productions can vary at any time, changing the supply curve and therefore
the electricity prices. The consequence is that the prices have to face a greater volatility than
when the production is made only by conventional units. That volatility will have to be taken
into account while modelling the impact of wind and solar powers on the France-Germany market.
1.4.2 Structure of the markets
When talking about the market, a difference must be made between the short-term (or day-ahead) market, and the long-term market. Spot prices correspond to prices on the day- ahead market. It is called day-ahead because the prices are set on the previous day for the next day. Figure 1.13 shows the day-ahead prices in France and Germany since 2008. It can be seen that they present a high volatility, with prices ranging globally from 5€/MWh to 135€/MWh. This is easily explained with the fact that there are several factors influencing the prices on the short term: meteorological conditions which can change abruptly, sudden failure of generating plants, unexpected increase in load etc. For example, the high French peak (612€/MWh) is due to a bad timing in the market coupling with Switzerland while the negative German peak (-139€/MWh) is due to an excess of generation, and the consumers had to be paid to consume electricity.
-150,00 -100,00 -50,00 0,00 50,00 100,00 150,00 200,00 250,00 300,00
0 1 /0 1 /2 0 0 8 0 1 /0 5 /2 0 0 8 0 1 /0 9 /2 0 0 8 0 1 /0 1 /2 0 0 9 0 1 /0 5 /2 0 0 9 0 1 /0 9 /2 0 0 9 0 1 /0 1 /2 0 1 0 0 1 /0 5 /2 0 1 0 0 1 /0 9 /2 0 1 0 0 1 /0 1 /2 0 1 1 0 1 /0 5 /2 0 1 1 0 1 /0 9 /2 0 1 1 0 1 /0 1 /2 0 1 2 0 1 /0 5 /2 0 1 2
€/ M W h
Germany France
Figure 1.13: Day-ahead prices in France and Germany (source: Powernext)
As seen before, adding the intermittency of wind and solar powers is going to increase this volatility even more.
On term market, there are several types of contracts, (derivatives): month-ahead, quarter
and calendar. Quarters represent three months and are numbered: Quarter 1 represents
January, February and March; Quarter 2 represents April, May and June and so on. A
calendar represents a year. Actors buying those types of contract buy or sell electricity for a
delivery on the next month (month-ahead), the next trimester or the next year. Those contracts
aim at reducing the risks taken by the market actors, and especially to limit the volatility of the prices. Figure 1.14 shows the Calendar 13 prices in France and Germany. Calendar 13 means that electricity traded on that contract will be delivered in 2013.
47 48 49 50 51 52 53 54 55
0 2 /0 1 /2 0 1 2 0 2 /0 2 /2 0 1 2 0 2 /0 3 /2 0 1 2 0 2 /0 4 /2 0 1 2 0 2 /0 5 /2 0 1 2 0 2 /0 6 /2 0 1 2 0 2 /0 7 /2 0 1 2 0 2 /0 8 /2 0 1 2 0 2 /0 9 /2 0 1 2 0 2 /1 0 /2 0 1 2
€ /M W h
France Germany
Figure 1.14: Calendar 13 prices in France and Germany (source: Reuters)
It can be seen that the volatility is much lower, with prices ranging globally from 48€/MWh to 54€/MWh. The longer span of time is the main factor in reducing the prices volatility, as they are calculated over an average of spot prices[7].
French market:
Since 2000, the organization of the French electricity network is split in two: the production and retailing part is supervised by EDF (Electricité de France) while the transmission part is managed by RTE (Réseaux de transport d’électricité). Previously, both production and transmission was the sole responsibility of EDF. The deregulation of electricity market has forced France to open its market to competition, which happened in several steps from 1999 where 30% of the market was open, to 2007 where 100% of the market was open.
In 2001, Powernext, the French power exchange, was founded. Several branches of
Powernext are launched almost every year such as Powernext Day-ahead, Powernext Carbon
(spot market for CO 2 allowances) or Powernext Intraday (for electricity to be delivered on the
French hub). At the end of 2008, Powernext Day-ahead and Powernext Intraday have been
transferred to a common platform, EPEX Spot SE, dealing with the markets in France,
Germany, Austria and Switzerland.
German market:
On the German side, deregulation started in 1998. At that time, in Germany, there were eight generation companies. Following the deregulation, some companies merged and others were acquired by foreign companies, reducing their number to four: RWE and VEW merged into RWE then Preussen Elektra and Bayernwerk merged to E.ON. HEW, VEAG and BEWAG merged to the Swedish Vattenfall Europe and the French EDF bought a major part of EnBW[8].
In 2000, Germany’s first power exchange, Leipzig Power Exchange (LPX), was created. During the same year, a second power exchange was started: the European Energy Exchange (EEX). Both power exchanges then merged in 2002 into EEX. As said earlier, EEX entered in close cooperation with the French Powernext through the joint venture EPEX Spot SE.
The shares of the French and German power exchanges can be summarized in Figure 1.15 and Figure 1.16. ECC (European Commodity Clearing AG) is the central European clearing house for exchange and OTC transactions in power, natural gas, emission rights and coal. EMCC GmbH (European Market Coupling Company) is a company which deals with the congestion management at the German-Danish border. Store-x GmbH (Storage Capacity Exchange) is a platform for secondary trading in storage capacities for natural gas, and Trac-x GmbH (Transport Capacity Exchange GmbH) is a platform for natural gas transport capacities.
To summarize the current situation of the France-Germany market, the trades are realized mainly on two common platforms: EPEX Spot SE (common power spot exchange based in Paris) and EEX Power Derivatives GmbH (common power derivatives exchange based in Leipzig).
Figure 1.15: Shares of the French power exchange Powernext (source: Powernext)
Figure 1.16: Shares of the German power exchange EEX (source: EEX)
1.4.3 Comparison of prices
As seen before, the “renewable effect” comes mainly from the German side. The question then is how will this affect the prices in France? First, data on day-ahead prices in France and Germany is collected. Figure 1.17 shows the difference between French day-ahead prices and German ones. It can be seen that the gap is usually positive in winter and negative in summer (the darker curve represents the real fluctuations in the price difference, while the lighter curve represents a mobile average, given for more clarity). This can be explained by the fact that in winter, the demand in electricity is very high in France (as shown on Figure 1.9) and the nuclear production cannot cover it all. As a result, more expensive units must be dispatched. In summer however, the demand is much lower than in Germany, and the nuclear production can cover a much higher part of the load. There are noticeable spikes during winter 2009-2010 and 2010-2011 due to successive waves of cold in France. February 2012 has been particularly cold too, which can be clearly seen on the graph.
If a closer look is given to the graph, it can be noticed that the spread in prices
difference is quite large until 2010 while it is much narrower afterwards. It happened that on
November 9 th 2010, the market coupling of the Central West Europe (CWE, which includes
France and Germany) was launched. As a result, spot prices started converging, hence the
smaller spread in prices difference. As explained in 1.4.1, the fact that prices converge means
that the power produced in each country is distributed all over the CWE in order to level the
prices.
-40 -20 0 20 40 60 80 100
0 1 /0 1 /2 0 0 8 0 1 /0 4 /2 0 0 8 0 1 /0 7 /2 0 0 8 0 1 /1 0 /2 0 0 8 0 1 /0 1 /2 0 0 9 0 1 /0 4 /2 0 0 9 0 1 /0 7 /2 0 0 9 0 1 /1 0 /2 0 0 9 0 1 /0 1 /2 0 1 0 0 1 /0 4 /2 0 1 0 0 1 /0 7 /2 0 1 0 0 1 /1 0 /2 0 1 0 0 1 /0 1 /2 0 1 1 0 1 /0 4 /2 0 1 1 0 1 /0 7 /2 0 1 1 0 1 /1 0 /2 0 1 1 0 1 /0 1 /2 0 1 2 0 1 /0 4 /2 0 1 2 0 1 /0 7 /2 0 1 2
D if fe re n c e ( € /M W h )
Figure 1.17: Difference between French day-ahead prices and German ones (source: Powernext)
This means that the interconnections between France and Germany play a major role in determining electricity prices, and that the German production has an impact in France. The point afterwards will be to see how a higher penetration of wind and solar powers in the grid can influence the prices in both France and Germany through their interconnections.
1.4.4 French and German interconnections
“Market coupling mechanisms allow the optimization of the allocation process of cross- border capacities thanks to a coordinated price formation mechanism, taking into account orders placed by the members of different exchanges.” (Source: EPEX Spot SE). The launch of market coupling in the CWE in November 2010 has allowed prices to converge between France and Germany (among other countries) by using available cross-border transmission capacities for power exchange. Allocation of transmission capacity is made through implicit auctioning, meaning that the transmission capacity is used to integrate the spot markets of the various countries.
Data about the utilization of transmission capacities on the France-Germany
interconnection was collected from RTE in order to pinpoint a relation between transmission
and price. The variation of day-ahead prices in France was calculated every day as well as the
variation of the volumes transmitted from Germany to France. The result is plotted on Figure
1.18.
-40 000 -30 000 -20 000 -10 000 0 10 000 20 000 30 000 40 000
0 2 /0 1 /2 0 1 2 0 9 /0 1 /2 0 1 2 1 6 /0 1 /2 0 1 2 2 3 /0 1 /2 0 1 2 3 0 /0 1 /2 0 1 2 0 6 /0 2 /2 0 1 2 1 3 /0 2 /2 0 1 2 2 0 /0 2 /2 0 1 2 2 7 /0 2 /2 0 1 2 0 5 /0 3 /2 0 1 2 1 2 /0 3 /2 0 1 2 1 9 /0 3 /2 0 1 2 2 6 /0 3 /2 0 1 2 0 2 /0 4 /2 0 1 2 0 9 /0 4 /2 0 1 2 1 6 /0 4 /2 0 1 2 2 3 /0 4 /2 0 1 2 3 0 /0 4 /2 0 1 2 0 7 /0 5 /2 0 1 2 1 4 /0 5 /2 0 1 2 2 1 /0 5 /2 0 1 2 2 8 /0 5 /2 0 1 2
MW
-150 -100 -50 0 50 100 150
€ /M W h
Transmission from Germany to France Price
