An Empirical Analysis Of Price Convergence Within The EU Electricity Sector Master Thesis

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An Empirical Analysis Of Price Convergence Within The EU

Electricity Sector

Master Thesis

M.Sc. International Economics and Business (FEB) - University of Groningen M.A. International Economy and Business - Corvinus University of Budapest

Double Degree Program

07.07.2014 Groningen

Author: Bessenyei Karina Student number: S2567385

E-Mail: k.bessenyei@student.rug.nl

Alternative E-Mail: karinka.bessenyei@gmail.com

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1 Abstract

This study tests to what extent the electricity spot market prices are integrated in the Netherlands, Germany and Denmark in the period from 2007 until 2013. The main aim is to identify whether the interconnectors work between markets in a desired way so they spread the volatility of electricity prices to fellow EU countries. During the analysis attention will be paid to the inequalities of prices and identifying outliers which would help explain the non-integration of the market and to what extent the EC Directives and market regulations contributed to the creation of the European electricity market and to price convergence among the sample countries. The core hypothesis is built on the Law of One Price, according to which the identical commodity within the harmonized and integrated single market possesses a common price. In this case arbitrage is the mechanism which contributes to price convergence towards equilibrium. This research paper uses the Engle & Granger Cointegration method to analyze the electricity market integration. Following this, the Vector Error Correction (VEC) model is utilized to test the short-term market dynamics. Application of the given methodology proves that in the long run perspective the electricity price series converge between The Netherlands, Germany and Denmark, while this trend was different on the short scale analysis in the period between 2012-2013 for The Netherlands and Germany.

In my analysis I follow the methodology of Rudolph Harmsen, who wrote his master thesis on the integration of the natural gas market in the European Union (Groningen, 2010), applying it for a different commodity, time period and a diverse data-set.

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In the recent years the European Electricity markets have been subject to serious changes and reforms. The main reason for this are accepted economic policies, directives and the need to harmonize a single electricity market throughout the European Union. The core idea of the conducted reforms is the creation of a strong market-based system. Within it, the price levels for a single commodity (electricity) are converged and assure clear benefits to the final consumer via stimulating competition, inter-country cooperation and effective utilization of production-/consumption patterns.

The given paper is a valuable contribution to the academic society as it analyzes the efficiency of the energy transmission system and thus the effectiveness of the accepted reforms (EC Directives of 1996, 2003, 2009, 2011) by highlighting the extent to which the electricity prices among sample markets have converged. Hereby the various measures and structural reforms accepted by the European Commission should serve as a foundation to the logic followed by countries in the price-setting. Similar price patterns across neighboring countries (in this paper The Netherlands, Germany and Denmark) would serve as clear indicator of the successfulness of the policy implications.It is important to keep in mind that the integration and liberalization of markets are two processes which are highly correlated and required in order to achieve an integrated, well-functioning single. Obstacles to cross-border trade had to be reduced while guaranteeing non-discriminatory third-party access to markets. The above-mentioned changes were implemented on a different scale in national legislations among different countries respectively. A fully integrated market would guarantee the short- and long-term trade in energy, efficient usage of renewables and balancing services with security of supply without pushing political boundaries. However, following the analysis the market is still not ideally harmonized, contradicting the Law of One Price, according to which the price of the identical commodity within single market system is supposed to be indifferent or the same. It exists due to arbitrage opportunities: As long as the price of a commodity varies within two different markets an arbitrageur will purchase the asset in the cheaper market and sell it to the market in which the

prices are higher (Investopedia, 2014). “Market coupling“1 plays an increasingly important role

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4 in the process of integrating the spot electricity markets among neighboring countries within the EU. According to EC (2011) in the first quarter of 2011 the electricity markets were functioning exceptionally in terms of adverse power flows between the countries of interest. This explains the integration of the markets based on how little the average daily spot prices would have differed. The table below illustrates the annual average day-ahead base load power prices for The Netherlands, Germany and Denmark in the period of 2007-2013 (in EUR/ MWh) (EC, 2011). Table 1 “Annual Average Day-Ahead base load Power Prices (in EUR/MWh)”

2007 2008 2009 2010 2011 2012 2013 Netherlands (APX) 41.3 67.6 39.2 45.4 52.0 48.7 52.1 Germany (EPEX) 38.1 65.7 38.9 44.5 51.1 42.6 38.1 Denmark (NoordPool) 32.7 56.5 37.7 52.4 49.4 36.9 39.1

Source: EC(2011), Available at: http://ec.europa.eu/energy/gas_electricity/doc/20121217_energy_market_2011_lr_en.pdf; page 41. For the years of 2007, 2008, 2012, 2013: on-line spot-exchanges – APX, EPEX, NoordPool

According to the information from Table 1 price levels in The Netherlands were slightly higher than in Germany, though both show the same trend. Meanwhile Denmark shows a significant increase from 2009 to 2010 and since then a continuous decrease. There is a number of reasons why price levels among member states could differ in such a way, among which are levies and taxation policies applied in a given country, subsidizing of certain type of energy (like renewable energy), demand-supply movements through the periods, price levels of other energy means, etc. In order to understand the long run cointegration relation among the price-series the following research question is covered:

Do the average daily electricity spot prices for The Netherlands, Germany and Denmark prove to converge in the period between 2007 and 2013?

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5 the German energy grid (Morris, C., 2013)2. According to numerous researchers the rational reason for such financial insolvency would be that the Dutch company was forced to pay much more for energy than its German competitors (according to the source 25% less). Following this issue, the analysis of similarity/dissimilarity of power prices plays an important role, as it could be a viable explanation to survival of certain industries; especially those which are highly dependent on energy usage. This research question furthermore gives a basis to subsequent questions to arise, such as: “Is market integration is present? In what trend are the prices developing in respect to one another?” If the prices are dissimilar, then: “What are the explanatory factors which have an influence on the dissimilarity of prices?”, is another such example. For the proceeding analysis unique time-series data-sets were retrieved from the Power Spot Exchange on-line servers of the Dutch, German and Danish spot-markets (APX, EPEX and

NoordPool respectively).

Numerous scholars and academics studied the phenomenon of energy market integration. Among all, George Zachmann (2005, 2007) proved that EC directives had a positive effect on price convergence but the market cannot be seen as perfectly integrated yet, while Janssen et al., 2007, with examining a larger sample of countries and for a longer time span also came to similar conclusions. More recent publication of Booz & Company (20133) suggests that much larger gains can be obtained if the market is fully integrated, which would entail the implementation of more profound methods of market integration. Therefore, I base my own motivation to research the before mentioned issue on these academic papers.

Apart from the Introduction (Section1) the study is structured as follows: Section 2 provides a brief description and main characteristics of the European Electricity Market with short historical insight of the formulation and structure. Within the given section Danish, German and Dutch electricity markets are highlighted with respect to typical characteristics of each. In the Literature Review (Section 3) secondary research methods are listed and existing works on the given topic are highlighted. The Methodology and the available data sources are presented in the Section 4. Together with the research question, testable hypotheses and

2 - Morris, C. (2013) - Dutch aluminum firm hungry for German electricity – Available at: http://www.renewablesinternational.net/dutch-aluminum-firm-hungry-for-german-electricity/150/537/75397/

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6 descriptive statistics Section 5 is followed by achieved empirical results (Section 6). All the econometric analyses are conducted with help of the STATA software. Section 7 summarizes the research paper and provides personal opinions and reasoning. Limitations of the dissertation are included in Section 8, while all the citations and bibliography used in writing the thesis are collected in Section 9. Additional tables, figures and other exhibits may be found in the Appendix (Section 10).

2. Electricity market structure in the European Union and in the particular countries

An integrated market is not just a question of price convergence among member states, but also a matter of legislation and the way the needed policies are conducted by the individual bodies. Since early 1990’s, Europe is engaged in reforming and changing electricity market structures in order to achieve an integrated and competitive single energy market. Among the main motivations for reforming it were achieving a more transparent business dealings, a liberalized market, increased efficiency in the supply chain and utilization of resources, adjustments towards a competitive environment and consumer protection issues. After numerous EC meetings and forums member states started to reform their internal energy markets according to accepted directives and regulations in order to achieve a more harmonized, single market for electricity and gas.

2.1. Overview of the Electricity Reforms – EC Directives

The Treaty of Rome (1957)4 and Single European Act (1986)5 established the foundation for the cooperation and creation of the common market within the European member states. Following these the Green Paper of 20056 established a strong base and initiation for the creation of a single energy market. In the given period, among the most important directives for reformation of the energy market, were 96/92/EC7 and 98/30/EC8 which were addressing the creation of common internal markets specifically for electricity and gas. In order to enforce the needed changes monitoring and control over the accepted directives was required. The period of

4 - Treaty of Rome – Available at: http://ec.europa.eu/archives/emu_history/documents/treaties/rometreaty2.pdf 5 - Single European Act – Available at: http://www.eurotreaties.com/singleuropeanact.pdf

6 - Green Paper – Available at: http://europa.eu/documents/comm/green_papers/pdf/com95_688_en.pdf

7 - Directive: 96/92/EC of the EC - Available at: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:31996L0092

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7 the 90’s can be described as the time when European member states were actively working towards market liberalization and opening up in order to reach similar treatment for all the players in the electricity sector. In the 2000th Lisbon Agenda9 process the extended plan of actions in terms of reforming and harmonizing the electricity market, as well as telecommunications market through Europe, was set. In this meeting the delegates agreed that the improvement of the competitive climate and regulation over customer protection are crucial in order to succeed.

The second set of milestones to overcome was gathered in the Directives 2003/54/EC10 and Regulation 1228/200311. These mainly addressed the issues and conditions of access to the network for cross-border exchanges in electricity for member states and common rules for the internal market in electricity. During discussions on the given regulatory packages decomposition and waste management activities were also mentioned.

Further market opening and the establishment of a national regulatory and cooperative body (European Regulators Group for Electricity and Gas - ERGEG) took place in the years of 2004 – 2007. According to Bollino et al., 201312, the inquiries conducted by EC (2006) have established that excessive horizontal concentration in energy generation as well as excessive vertical integration between the generation and transmission systems took place. The authors also point out that the directives were not that successful in establishing fully efficient interconnectors among the national grids.

The Third Package of Directives concerning the electricity market was introduced in 2007, in order to further specify the means for the competitive and integrated energy market which would allow European consumers to choose between different suppliers irrespective of their size and suppliers to access the market without significant market barriers. Among the main principles of the 3rd package were: The unbundling principle (implementation of more structural separation between transmission and production activities); Strengthening national regulators‘ power and independence; Transmission System Operator (TSO) has to ensure the ability of the

9 - Lisbon Agenda- Available at: http://www.nfer.ac.uk/nfer/index.cfm?9B2730F6-C29E-AD4D-03AB-1011FA252DC9 10 - Directive 2003/54/EC - Available at: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:32003L0054 11 - Regulation 1228/2003/EC – Available at: http://eur-lex.europa.eu/legal-content/en/ALL/?uri=CELEX:32003R1228

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8 system to meet the demand and supply equilibrium with regard to environmental issues; Improving cooperation between TSO’s; Creation of European Network Transmission System Operator for Electricity (ENTSO-E), as well as ACER (Agency for the Cooperation of Energy Regulators) and harmonization of technical standards and network codes. (EC, 200713)

Further developments and improvements are taking place. This is an ongoing process of creating well-functioning electricity grids and establishing inter-country interconnectors in order to address volatility and meet the demand/supply equilibrium. Among recent adjustments the “EU 20-20-20 Plan”14

has to be highlighted. In the center of the framework are goals to cut the EU’s Greenhouse Gas (GHGs) emissions by 20% (30% as a part of the international plan), achieving a 20% energy share from renewable sources and increase in energy efficiency by 20%. These targets are to be achieved by 2020. The main motivation for these objectives are raising environmental awareness within the EU and limiting the average global temperature rise to 2°C degree.

2.2.Electricity Market Design and its Characteristics

Throughout the liberalization process of the European energy market and in particular after launching the deregulation package in order to move towards market unification, seven regions were developed throughout the EU. As mentioned in the previous section following an establishment of ERGEG an Electricity Regional Initiative (ERI) was established. The aim of this initiative was to speed up the integration of Europe's national electricity markets.

According to the EC report (2010) prepared by Everis and Mercados EMI15, the following table shows the regions which can be distinguished within the ERI.

13 - Third Package – Available at: http://ec.europa.eu/energy/gas_electricity/legislation/third_legislative_package_en.htm 14 - EU 20-20-20- Available at: http://ec.europa.eu/clima/policies/package/index_en.htm

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9 Table 2 “Electricity Regional Initiatives of the EU”

Regions Countries

Central-West Belgium France, Germany, Luxembourg, The Netherlands

Central-East Austria, Czech Republic, Germany, Hungary, Poland, Slovakia and Slovenia Central-South Italy, Austria, France, Germany, Greece, and Slovenia

Northern Denmark, Finland, Germany, Norway, Poland and Sweden South-West Spain, France and Portugal

Baltic Latvia, Estonia and Lithuania

France-UK-Ireland France, Ireland and the United Kingdom

Source: European Commission (2010): From regional markets to a single European Market (Ervis and Mercados EMI); Available at: http://ec.europa.eu/energy/gas_electricity/studies/doc/2010_gas_electricity_markets.pdf

This type of the structure gave way for an effective bottom-up regional approach, where the territorial specificities are considered in the process of market integration. It is mentioned by the EC (2010) that, in contrast to the originally composed strategy, the regions seem to be overlapping and that there is still a considerable difference in development between, for example, the Northern and South-Western regions. This observation suggests that the countries included in more than one region are not equally committed to the regions they are part of. (Ex: Germany in Central-Western European Market and Central-Eastern).

Exhibit 1 “Electricity Regional Initiatives of the EU”

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10 Taking into account the development of the spot-markets and exchanges, at the current point the most significant ones are: Nord Pool Spot AS16 (Denmark, Finland, Sweden, Norway, Estonia and Lithuania), EPEX GmbH17 (Germany, Austria, France), APX Group18 (The Netherlands, United Kingdom and Belgium), Endex19 (The Netherlands, Germany, Belgium), Italian Power Exchange (IPEX)20(Italy), Powernext21 (Austria, France, Germany and Switzerland), OMIE22 (Spain and Portugal), Belpex23 ( Belgium , but also coupled with APX and Nord Pool), EXAA24(Austria) and Towarowa Gielda Energii25 (Poland). In my analysis I will focus on data presented by the Nord Pool, EPEX and APX exchanges.

2.3.Electricity Market – The Netherlands

The Netherlands’ geographic location is highly favorable for serving as a hub for energy trade and transit. The Dutch power market can be seen as moderately centralized and highly concentrated (according to Herfindahl-Hirschman Index – 1811 out of 5000). As much as 59% of the power generation is conducted through three largest firms. According to the EC report (2011) the Dutch energy consumption in 2010 was mainly based on the fossil fuels (45.7%), crude oil and petroleum (40.8 %) and solid fuels added up in smaller shares to the overall consumption energy mix (8.8%). Compared to other countries, renewable energy still occupies a relatively insignificant share on the demand side (EC, 2011). What concerns energy generation, as of 2010 The Netherlands produced around 118.14 TWhs26 of which natural gas makes up the largest share (around 65.5 %) while the solid fuels constitute 19.1%, nuclear power 3.4 % and renewables around 9.5 %. The share of electricity produced in the combined heat and power plants (cogeneration) rounded up to almost 33.2 %. (EC, 2011)

16 - Nord Pool – Available at: http://www.nordpoolspot.com/ 17 - EPEX – Available at: http://www.epexspot.com/en/ 18 - APX – Available at: http://www.apxgroup.com/ 19 - ENDEX – Available at: http://www.iceendex.com/

20 - IPEX – Available at: http://www.mercatoelettrico.org/En/Default.aspx 21 - Powernext – Available at: http://www.powernext.com/index.php 22 - OMIE – Available at: http://www.omel.es/en/inicio

23 - Belpex – Available at: http://www.belpex.be/ 24 - EXAA – Available at: http://www.exaa.at/de

25 - Towarowa Gielda Energii – Available at: http://www.tge.pl/en

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11 Exhibit 2 “Total Inland Energy Consumption (as % of total Mtoe) and Electricity Production (as % of TWh) – The Netherlands (2010)”

Source: EC(2011) – Country Profile – The Netherlands – Available at: http://ec.europa.eu/energy/gas_electricity/doc/nl_energy_market_2011_en.pdf

Following the EC Directives in order to liberalize and unbundle its electricity grids market integration was also conducted with the neighboring countries, such as Belgium, UK, Norway and etc. Within this framework the Trilateral Market Coupling program (Belgium, France and The Netherlands) was conducted and later extended to the creation of the Central Western European (CWE) power region (added Luxembourg and Germany). Within the Interim Tight Volume Coupling (ITVC) project27 the Dutch prices were coupled with the Nordic area. Since 2011 BritNed started to operate, allowing price convergence and market integration for the UK and Netherlands, it built cable of 700 megawatts28 capacity and connected it to Norway.

Considering the retail markets, according to Eurostat29, the prices for households remained approximately at the same level throughout the period of 2009 to 2013, while they decreased slightly for the industrial users. The charts below visualize the corresponding change.

27 - Interim Tight Volume Coupling (ITVC) project – Available at: http://www.tennet.eu/nl/grid-projects/international-projects/market-coupling/cwe-nordic-itvc.html

28 - NorNed Cable – Available at: http://www.tennet.eu/nl/index.php?id=430

29 - Eurostat Database – Available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/energy/data/database

46%

41% 9%

3% _1%

Natural gas

Crude oil & petroleum Solid fuels Renewables Nuclear Total Mtoe : 86.92 Consumption 65% 1% 19% 10% 3% 2% Natural gas

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12 Chart 1 and 2 “Retail Prices in the Netherlands for the Households and Industries” (in EUR/ KWh)

High scale investment projects are initiated throughout the territory of the country in order to expand the electricity transmission grid and help increase the efficiency of operation. In order to reach its EC 2020 target usage and generation of renewable energy resources have to be further developed. Overall, due to their advanced generation capacity the Netherlands are considered to be a net exporter of electricity, which has a significant impact on its economic status and development.

2.4.Electricity Market – Denmark

Denmark, one of the leading OECD members for showcasing the EU principles for generation and consumption of renewable energy, commits towards energy efficiency and regulations in favor of limiting carbon emission. The country’s electricity market is of a dual nature as it is divided into two smaller markets: Eastern and western (DK1 and DK2 respectively, as they are represented on the Nord Pool exchange). This difference is often showcased through variations in prices between two sub regions due to the energy mix in each. For example more wind energy is generated in the western region which leads to decreased prices. 0 0,05 0,1 0,15 0,2 0,25 2009 2010 2011 2012 2013 Households Net Price

Other taxes and VAT Final price Source : Eurostat 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 2009 2010 2011 2012 2013 Industries Net Price

Other taxes and VAT Final price

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13 Exhibit 3 “Total Inland Energy Consumption (as % of total Mtoe) and Electricity Production (as % of TWh) – Denmark – 2010”

Source: EC(2011) – Country Profile – Denmark – Available at: http://ec.europa.eu/energy/gas_electricity/doc/dk_energy_market_2011_en.pdf

As displayed in the Exhibit 3 crude oil, petroleum and natural gas products occupy the highest share in Denmark’s energy mix on the demand side – around 60%, while the renewables constitute for approximately 20.5%, which is almost six times higher than in the Netherlands. Concerning the production side, in 2010 a total of 38.79 TWh electricity was generated out of primarily solid fuels (55%) and renewables (40.4%). The remaining 4.4 % were crude oil, petroleum and other resources. The market (generating side) can be considered highly concentrated and centralized, insofar as it was dominated by two big players as of 2010: Dong Energy and Vattenfall. Denmark is actively engaged in market integration with the Nordic region; Almost 75% of the energy is traded through the Nord Pool Spot (NPS)30. It also has extended its network towards the Central-Western European region (Germany and others)31.

According to Eurostat the retail market’s price setting is characterized by high taxes (almost 50% of the final price). In regard to the following charts, the prices for the industries experienced a slight increase throughout the period from 2009 to 2013. The same trend can be observed for households until 2012 with a slight decrease in 2013.

30 - EC(2011) – Available at: http://ec.europa.eu/energy/gas_electricity/doc/dk_energy_market_2011_en.pdf

31 - More details upon the electricity flows and capacities – Available at: https://www.energinet.dk/Flash/Forside/index.html 23% 36% 20% 21% Natural gas

Crude oil & petroleum Solid fuels Renewables Total Mtoe : 19.32 Consumption 2% 55% 41% 2%

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14 Chart 3 and 4 “Retail Prices in Denmark for the Households and Industries” (in EUR/KWh)

Danish TSO’s are mainly state-owned32 and include other smaller regional operators, which are somewhat integrated with other companies engaged in production, genaration and trade processes. The total market value of the Danish electricity market rounds up to EUR 7.034 billion (approximately half of the Dutch market value – EUR 13.661 billion).

In regard to the future milestones and projections, Danish supply systems have to be further expanded due to the EU renewable energy objectives and the price difference between two regions of the country, despite the existence of the Great Belt interconnector (which connects Eastern and Western Denmark).

2.5.Electricity Market – Germany

Germany has a comparatively well-developed market since it is interconnected among four regions (Central-East, Central-West, Central-South and Nordic) and highly concentrated. The value of Denmark’s electricity market reaches EUR 88.054 billion (six times larger than the Dutch and almost 13 times than the Danish). (EC, 201133)

32 - Danish TSOs - Available at: https://www.energinet.dk/Flash/Forside/index.html

33 - EC (2011) – Available at: http://ec.europa.eu/energy/gas_electricity/doc/de_energy_market_2011_en.pdf 0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 2009 2010 2011 2012 2013 Households Net Price

Other taxes and VAT Final Price Source: Eurostat 0 0,05 0,1 0,15 0,2 0,25 0,3 2009 2010 2011 2012 2013 Industries Net Price

Other taxes and VAT Final Price

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15 Due to the country’s demand for energy, the majority is covered by crude oil, petroleum, natural gas and other solid fuels. The renewables occupy only 9.7% as opposed to the 2020 target, which equals to 18%. Exhibit 4 suggests, that the vast majority of electricity is produced by a combination of solid fuels and nuclear resources (more than 65%), 17.8 % by renewables (In the NL 9.5 % and in DK 40.4%).

Exhibit 4 “Total Inland Energy Consumption (as % of total Mtoe) and Production (as % of TWh) – Germany – 2010”

Source: EC(2011) – Country Profile – Germany – Available at: http://ec.europa.eu/energy/gas_electricity/doc/de_energy_market_2011_en.pdf

Electricity generation is dominated by four large private companies: EON, RWE, EnBW and Vattenfall, which provide almost 82% of total electricity produced. Its derivatives and spot markets are characterized with high liquidity, thus in 2011 trade volumes in day-ahead deals reached 238 TWh on EPEX Spot (47.1% of German power consumption). (EC, 2011)

In the retail market households as well as industrial users experienced a slight but steady price increase throughout the period of 2009-2013. German legislation requires its users to install smart meters which provide the monitoring agencies with hourly consumption data. This potentially enhances the accuracy of the data provided.

Considering numerous reports and analysis papers, it can be seen that there are certain bottlenecks in the German infrastructure or transmission system which have to be addressed in the near future. Also the sharp increase in production of renewables results occasionally in negative electricity prices as the effect of the demand and supply disequilibrium. Accordingly it

22% 34% 23% 9% 11% 1% Natural gas

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16 has a significant influence on the interconnected neighboring markets (Ex: Poland and Czech which despite the idea of getting the electricity for free in some cases may suffer of voltage inconsistency).

Chart 5 and 6 “Retail Prices in Germany for the Households and Industries” (in EUR/KWh)

3. Literature Review

A significant collection of academic work is devoted to the issue of energy market integration and effectiveness of accepted EU directives, the majority of which confirms that the EU reforms were not fully successful in the establishment of single electricity market. Also, a vast number of papers I have found are conducted in an earlier period from 2002 until 2009. Thus I believe my work would be a valuable contribution to the missing time frame.

For instance George Zachmann (2005) in his paper tests whether the restructuring process in

the EU has led to price convergence and towards arbitrage freeness among certain European countries. His analysis is based on the electricity wholesale day-ahead prices and hourly cross border capacity traded in the period between 2002 and 2004 for Germany, Denmark and the Netherlands by applying time-varying coefficient model. The author came to the conclusion that overall the accepted reforms and regulations had a positive effect on the gradual convergence process; Exceptionally so on the Dutch market during rare peak-periods in which considerable uncertainties were present. Thus the market can’t be considered integrated perfectly, as long as

0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 2009 2010 2011 2012 2013 Households Net Price

Other taxes and VAT Final Price Source: Eurostat 0 0,05 0,1 0,15 0,2 2009 2010 2011 2012 2013 Industries Net Price

Other taxes and VAT Final Price

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17 there are still certain bottlenecks in the transmission system. Boisseleau34 (2004) states in his

work: “The role of power exchanges for the creation of a single European electricity market. Market design and market regulation” analyzes the prices from APX (The Netherlands), LPX (Germany), Powernext (France), UKPX (UK), NordPool West Denmark, Norway, Sweden) and OMEL (Spain) on the weekly base load and peak-periods level for the year 2002.The aim of his study is to test integration among markets by applying the simple correlation and OLS analyses. His study proves that the market integration process started early but the methods used received numerous criticisms regarding their simplicity and multicollinearity of certain price-series (for example, significant but highly imperfect integration among Germany, France, Denmark and Norway; also questionable levels of significance of R-squared).

Zachmann (2007) rejects the assumption of full market integration and instead finds that

with application of the Principal Component Analysis (PCA) and via usage of the Kalman Filter method the efforts towards developing a single European market by 2006 were only partially successful. He ultimately fails to identify the explicit reasons or existing inefficiencies and causes for non-integration. Janssen et al., 2007 considered the price developments of per hour one-day-ahead wholesale market prices in the Netherlands, France and Germany for the period from January 2002 until July 2007 and found no evidence of a fully integrated market between the sample groups. In that analysis special attention was paid to peak-hours in which a flexible trend pattern was constructed in order to observe the clear difference between those. They found that the Dutch prices of the test group were significantly higher while the German and French price-series proved to be only partially integrated. Nevertheless, the forecasts of their analysis suggest that in the near future the structural reasons for non-integration are likely to diminish, as far as further development of interconnectors occurs between regions which would eventually lead towards complete market integration.

Further attention was paid to the research and case analysis of Vladimir Parail (2009), who examines the case of NorNed (high-voltage interconnector between Norway and the Netherlands) and whether the merchant interconnectors can deliver lower and more stable prices. The Auto Regressive Moving Average (ARMA) model was used in the study in order to estimate the existing price effects and the Exponential Generalized Auto Regressive Conditional

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Heteroskedasticity (EGARCH) process helped estimate the volatility effects. The conclusion states that: “… the effectiveness of merchant interconnectors on the scale of NorNed in

reducing the electricity price volatility is likely to be limited…”. Therefore it means that full

integration of the market is still not observable.

Apart from the analyses on electricity market, Rudolph Harmsen (2010) in his master thesis applied the Engle & Granger Cointegration Test and Vector Error Correction Model (VECM) in order to identify the extent of natural gas market integration in the European Union for the period of 2007-2010. In this study a similar technique is applied, because it appears suitable for the time-series data as far as the given technique proves to work effectively for the analysis of price convergence. Even though the given paper covers a different commodity and different sample of countries, I still believe that it is highly relevant considering the EU directives towards market liberalization, market coupling and harmonization of prices were addressing the electricity and natural gas markets in a similar manner. Harmsen also proves that the natural gas market integration is present among UK, Belgium, the Netherlands, Germany and France, but leaves space for further research of LNG effect on it and examination of other regions of the EU.

Ludovic Autran (2012) in his master thesis report on convergence of day ahead and future

prices in the context of European power market conducts a thorough analysis based on the country data of Germany, the Netherlands, France and Belgium. He studies the convergence of prices in the period less than a year after the market coupling (2010) program between the given countries was launched. He approaches the hypothesis via utilizing the Kalman Filter and Mean Reversion Jump Diffusion parameters estimation technique. In his findings, he highlights that a hedging strategy is practicable among the countries despite the strong patterns of convergence and integration towards one another.

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19 political boundaries throughout EU area. This work was essential in order to understand the facilitation of energy trade and to build personal reasoning.

As a result of the secondary research numerous studies were considered, the majority of which is focusing on electricity market integration. Various types of techniques are used the focus lies in price convergence as an indicator of an effectively operating single market. Mainly they find that the market is not fully harmonized yet but evidence of market integration in special regions may be noted. Based on the reviewed bibliography I find it appropriate to use the Engle & Granger Cointegration Method and the VARM because these techniques were used in several cases and prove to be effective in analyzing continuous time-series. Also I anticipate problems might occur with the dataset selected because of their quantity. The patterned trend may erase the peak indicators, which are of outstanding importance since they would be the indication of the presence of periodical non-functionality of the interconnector and thus the presence of arbitrage or dissimilarity of prices between regions. Subsequent sections cover the theoretical framework and methodology considered in order to determine if such disruptions are present and what the reason for their occurrence might be.

4. Research Framework

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20 “ … The central point of a market is the public exchange, mart or auction rooms, where the traders agree to meet and transact business…the more nearly perfect a market is, the stronger is the tendency for the same price to be paid for the same thing at the same time in all parts of the market: but of course if the market is large, allowance must be made for the expense of delivering the goods to different purchasers; each of whom must be supposed to pay in addition to the market price a special charge on account of delivery …” (Marshall, 1920; pp.189)35

Emphasis of such tendency is most valuable at stock exchanges or spot-markets, as is the case since the markets are considered to be perfectly integrated as the price movements are happening almost simultaneously. Any delay in price increases/decreases in the segments of the united market would create room for arbitrage36. Hence the given relation between production and consumption (in standard view demand and supply) would merge the price levels towards the equilibrium level. Following this logic the Law of One Price should be regarded, according to which the identical commodity within the integrated markets should be priced at the same level. The logic of this concept relates to the impact of arbitrage on the price levels within efficiently functioning markets (Persson, 2008)37. Incidentally the concept fails to hold in all the cases and there might be fundamental price differences due to geographical distances which determine transportation costs, imposed taxes and levies by the national legislation and so on. In this case the LOP (Law of One Price) is reformulated to be relative, where the systematic, fundamental price differences are discounted in a needed way. Therefore LOP is a clear characteristic of the integrated market. According to the above formulated theoretical background it is possible to gain further economic insights and create tools in order to build the hypothesis and to measure the extent of market integration among the sample of interest. Following the theory and the main characteristics stated by the Law of One Price the testable hypothesis is presented in the following form:

35 - Marshall, A., (1920) – Principles of Economics 8th e. – Available at: http://eet.pixel-online.org/files/etranslation/original/Marshall,%20Principles%20of%20Economics.pdf

36 - Arbitrage - The simultaneous purchase and sale of an asset in order to profit from a difference in the price. (Available at – Investopedia - http://www.investopedia.com/terms/a/arbitrage.asp

37 - Persson, Karl. “Law of One Price”. EH.Net Encyclopedia, edited by Robert Whaples. February 10, 2008. URL Available at: http://eh.net/encyclopedia/the-law-of-one-price/

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21 Hypothesis: Electricity prices tend converge in different geographical locations (the Netherlands, Germany and Denmark) if their markets are integrated.

Following this logic tests will examine for country-pairs the degree and extent of the market integration with application of the precise econometric model, which is examined in detail in the following section.

5. Methodology

Following the academic literature it is appropriate to conclude that in general scholars examine price convergence or the relationship between the series in order to measure integration within the market. In order to examine this relationship, price series in two different locations (in pairs for examined countries) of the homogenous product (electricity spot prices) have to be cointegrated in spatially diverse regions. Based on this idea the Cointegraion Method (Engle & Granger) is suitable for dealing with the stationary time-series data like the time-series utilized in the given study. Following that, the VECM is applied in order to analyze the short-term dynamics of the examined variables. Prior to the thorough explanation of the used econometric methods, sub-sections with descriptive statistics are highlighted.

5.1. Descriptive statistics 5.1.1. Time Period

The given research examines electricity spot prices for The Netherlands, Germany and Denmark. Time series data-set contains 2550 observations on a daily basis. The examined period starts at 1st of January, 2007 and lasts until 24th of December, 2013. Observations are conducted for each of the following variables:

- Dutch daily average spot price for electricity (Price_nl), accessed from Statistics Nedrlands38 and EnergyMarketPrice39

- German daily average spot price for electricity (Price_ge), accessed from EPEX40

38 - Statistics Nedrlands – Available at: http://statline.cbs.nl/StatWeb/search/?Q=apx&LA=EN 39 - EnergyMarketPrice – Available at: http://www.energymarketprice.com/

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22 - Danish daily average spot price for electricity (Price_dk), accessed from

NordPoolSpot41 5.1.2. Variables

Dutch daily average spot prices for electricity (Price_nl) are examined, in order to determine how the market is integrated among the given countries. The price-series are accessed from

Statistics Netherlands and Energy Market Price online databases, which use the information from

APX Power Spot Exchange. Measured in Euros per megawatt hour (EUR/ MWh). The majority of prices are unevenly distributed around 49.3 EUR/MWh (the mean).

Exhibit 5 “Dutch Daily Spot Prices, 2007-2013”

German daily average spot prices for electricity (Price_ge), are accessed from EPEX

European Power Exchange. They are measured in Euros per megawatt hour (EUR/ MWh). According to descriptive statistics, the majority of prices are concentrated around 45.6 EUR/MWh.

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23 Exhibit 6 “German Daily Spot Prices, 2007-2013”

Danish daily average spot prices for electricity (Price_dk) are accessed from the Nord Pool Spot exchange. The observable prices are the average of DK1 and DK2 (Eastern and Western Danish markets). A decisive number of the prices are concentrated around 43.4 EUR/ MWh. Like in the case with previously mentioned variables, these price series are measured in Euros per megawatt hour.

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24 5.2.Engle & Granger Cointegration Method42

Time-series variables following a common trend are said to be cointegrated. Robert F. Engle and Clive W. J. Granger studied jointly the concept of cointegration and in 2003 got the Nobel Prize in Economics43 for their work on the relationship between the time-varying series. According to the theory there is a general rule following which non-stationary44 time-series variables should not be used in regression models, in order to avoid spurious regression problem. Hence if the variables yt and xt are stationary over time, their mean and variance are said to be constant over time and the series might possess the property of mean reversion. Furthermore, if yt and xt are integrated to in order I(1), then their difference or any linear combination of that, such as et= yt - β1-β2xt45 would be in order I(1) too. In case when the errors are stationary, the

examined series are said to be cointegrated. This phenomenon of cointegration suggests that the series of variables yt and xt have to share the parallel characteristics. As far as the difference, et, is stationary the series should not diverge too far from each other or their equilibrium. Here, it is the econometric relationship which in itself holds for a long-run relationship. Following this logic the natural way to test whether the examined time-series are cointegrated is to test their error terms for stationarity (last step of the Engle & Granger Method).

It might be the case that in the short-run the examined price levels would show difference in their values and move in a different trend, this way resulting in non-integration. In this case arbitrage plays an important role moving the series closer back to its equilibrium. This difference in price levels might be caused by stochastic shocks (such as seasonality, market failures, legislative adjustments, changes in demand/supply, etc.). Considering everything mentioned above, I believe that Engle & Granger method for testing cointegration is appropriate for analyzing market integration among the examined locations and convergence of electricity prices and with it, testing the main hypothesis.

42 - Principles of Econometrics, 4rd edition by Hill, Griffiths and Lim, John Wiley & Sons, Inc., New York etc., 2012 43 - Robert F. Engle receives a Nobel Prize – Available at: http://www.nobelprize.org/mediaplayer/index.php?id=926

44 - Non-stationary –time-series described as ones that possess non-constant means; often described as not having the property of mean reversion. (Hill et al.,2012)

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25 Further I will continue introducing the main phases of the used econometric tool together with descriptions on the covered variables.

5.2.1. Augmented Dickey-Fuller Test: Order of Integration of the Individual Price Series

The starting point of the Engle & Granger cointegration test would be to determine if the individual price-series are stationary (on individual basis) and if they prove to be, then whether they are integrated in the same order. According to Hill et al. (2012) ADF Test is useful in order to account for any autocorrelation of the error terms. In general the model looks like follows:

(1)

The given equation is built according to the methodology applied by Harmsen (2010) or alternatively by Hill et al. (2012) as Equation 12.6 (pp.485). Considering the variables ∆Pit stands for the price difference of price series i at point of time t, whereas in my case I will examine this difference for 3 sets of price-series: ∆Price_nl – Electricity Spot Market Price difference on the day t (accessed from the Dutch APX – Power Spot Exchange), ∆Price_ge – for the German market and ∆Price_dk holds for the Danish market. In the official equation (Hill et al. (2012) equation 12.6 (pp.485)) term α represents the intercept underlining the nature of the price series (non-zero value), however I exclude this term, because in my analysis the given variable is not relevant. γ is the parameter of interest, νt represents the residual and in case of application of the given test, ADF will take into account the autocorrelation of the given term.

∆Pit-s represents the price difference of price series i at point of time t at the certain lag s.

Accordingly Pt-1 stands for price in one day t lagged by 1 period. From the equation above as determines the number of lags –here the significance of it is examined.

Pursuing this logic, if the parameter of interest (γ) takes the value zero, then the null-hypothesis cannot be rejected, whereas if the parameter’s value is strictly smaller than zero, then the other case is applied (the null hypothesis has to be rejected). Based on this the examined hypothesis would take the following form:

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26 In case the null-hypothesis is rejected, the series is said to be stationary, which in turn means that each of the examined price-series are characterized as stationary and integrated to their own order.

5.2.2. Ordinary Least Squares (OLS) to prove the Law of One Price

In this phase every price-series is organized into pairs, which means that if I am examining three individual series I will get six pairs. In my analysis, for the sake of simplicity, I examine just the price series (Price_nl; Price_ge; Price_dk) alternatively represented by the

following pairs:

o Price_nl and Price_ge o Price_dk and Price_nl o Price_ge and Price_dk

o Price_ge and Price_nl o Price_nl and Price_dk o Price_dk and Price_ge

This consideration is important for further estimations and the core Ordinary Least Squares regression (OLS). will represent the difference in the prices in the following equation:

(2)

PA is the price in location A and expressed through PB, which is defined as the price at

location B. Thus clarifying the variables PA,t refers to PNL,t; P DE,t; P DK,t – Price on day t at the electricity spot market of The Netherlands (APX – Power Spot Exchange), Germany (EPEX – European Power Exchange) and Denmark correspondingly (DK1 and DK2 - NoordPool Spot). Also completing the explanation, β0 stands for systematic price difference while β1 for the relationship between prices and ε is the error term.

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27 5.2.3. Augmented Dickey-Fuller Test (ADF) for stationarity of the residuals (ε)

In this phase the stationarity of the residuals (from the previous phase’s equation) is examined, which is based on the estimated residuals, as long as the true residuals are not subject to examination. In order to analyze the estimated residuals the following equation is used for the Augmented Dickey-Fuller Test:

(3)

Here stands for the estimated residuals of . is the lagged residual by one, while

stand for the lagged residual by s number of lags. refers to the parameter of interest and to the lag coefficient. No intercept is added to this equation as far as may have a mean of the zero value. In this analysis I am observing whether the long-run relationship between the price variables is proven and, if they are then I can speak of ‘one market’. Based on this, the Null Hypothesis and the alternative one will take the following form:

H0: γ = 0 => the series are not cointegrated H1: γ < 0 => the series are cointegrated

Accordingly, if the term of interest will fall below zero value with tau-statistic, then I have to accept the alternate hypothesis H1 and reject the null-hypothesis. The given outcome

would serve as evidence to the integration of the market and that arbitrage solves volatility, thus the interconnectors work in a desired way. In case of periodical price-jumps speculation on the market may take place, resulting in great losses and gains for different parties.

5.3.Vector Error Correction Model

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28 get insights on how efficiently the prices respond to a short-term disequilibrium. This will provide me with information if the short term price disequilibrium takes place, how that volatility impacts the market and with what efficiency it responds in order to restore the equilibrium. It will provide me with a crude idea on whether the interconnectors are functioning in a desired way while assuring that corrections take place on the market in a natural way.

Referring to Hill et at., 2012 the given method in the standard case consists of the following phases: In the first step it is important to use Least Squares to estimate the cointegration relationship ( ) and to generate lagged residuals (

) in order to proceed to the second step, where Least Squares Estimation of the variables and is conducted. Therefore applying the above mentioned formula to my variables the equations of the second Least Squares would change the model in the following way:

(4) (5)

Where the estimated error term ( ) is expressed in the following way: (6)

Variable represents one of the price-series examined for the country a

Price on day t at wholesale electricity market APX (NL) or EPEX (DE)

or NoordPool (DK)), while represent the comparable country pair’s price series. Accordingly and stand for the residuals. are error correction coefficients, which in their turn measure the speed of adjustment (and are subject of interest in the analysis).

Thus after testing the cointegration relationship as in section 5.2.2 I continue with an estimation of the residuals (as explained in the above paragraphs) and conduct the final step: The OLS estimation of the equation:

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29 With Hypothesis:

H0: = 0 => the error is not corrected H1: < 0 => the error is corrected And alternatively for another price series:

(8) With Hypothesis:

H0: = 0 => the error is not corrected H1: > 0 => the error is corrected

Based on this αl and αn will show the responses of price series. Thus, if the Null hypothesis

in both cases is rejected it means the market corrects the error and that integration is present. The main assumption is that the faster the error is corrected the more integrated the market is. If the coefficient of correction is low then it shows that the disruptions are corrected slowly and there are certain burdens on the interconnectors’ market harmonization. Onwards, I will apply the methodology introduced and explain the achieved results of the analysis.

6. Empirical Results

In this section the obtained results are introduced after tests and estimations were successfully conducted. The acquired results are presented according to the logical structure of the Section 5 - Methodology.

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30 6.1.Results of the Engle & Granger Cointegration Method

6.1.1. Stationarity of the individual Price-series (ADF and KPSS)

In order to identify the stationarity of the price-series ADF and KPSS tests were conducted. Both tests showed that all three price series are stationary: price_nl (-2.426), price_ge (-2.772), price_dk (-2.706). The given results were obtained by suppressing the constant term and at a 95% confidence interval. The findings imply that the Null-Hypothesis has to be rejected, as far as the variables show a tendency to be stationary and thus possess no unit root. This shows that the mean of the time series is constant or close to constant over time and the covariance occurring between the values is decided upon time between them. This is fully logical as the prices of yesterday have strong impact on determining the prices of today and tomorrow. The results for the corresponding Augmented Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests can be found in the Table 5 in the Appendix.

6.1.2. OLS: Estimation of the corresponding pair price series

Further the pair-estimations were conducted in order to prove the Law of One Price in accordance with the Engle-Granger Cointegration Test. 6 pairs of differenced price series were examined:

- Dutch  German (estimation of the relationship between d.price_nl and d.price_ge) - Dutch  Danish (d.price_nl and d.price_dk)

- German  Danish (d.price_ge and d.price_dk)

The OLS results (Table 6 in the Appendix) of the Equation (2) are explicitly used for further estimation of the residuals in order to proceed towards Step 3 (Section 5.2.3). The coefficients of the potential relationship are not subject to analysis considering the t-distribution of the OLS has to comply with special requirements46.

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31 6.1.3. Augmented Dickey-Fuller Test (ADF) for Stationarity of the Residuals (ε)

Even though the findings of the Section 6.1.2. show that the selection order of the dependent and independent variables in the examined price-pairs have no significant effect on the predicted residuals, all the pairs were used in order to predict residuals. Following the Equation (3) the constant term was suppressed. Using the same technique as in previous sections the optimal number of lags is determined to be (4). Obtained results (Table 7 in the Appendix) illustrate that the absolute value of the t-statistics is significantly higher than the critical values at the confidence intervals of 99%, 95% and 90% for all the estimated residuals:

 Dutch – German (R1) 41.791  German – Dutch (R2) 44.586  Dutch – Danish (R3) 44.475  Danish – Dutch (R4) 39.830  Danish – German (R5) 40.109  German – Danish (R6) 47.289

These results prove that the series of error terms are strongly stationary and are hence cointegrating to the same order. Following this, the Null-Hypothesis of non-integration has to be rejected. Alternatively the H1: the series are cointegrated - has to be accepted. The given conclusion implies that in the long-run there is electricity price convergence between the Dutch, German and Danish electricity spot prices, thus the Law-of-One Price holds. Accordingly the main hypothesis is positively supported.

Nevertheless, it is interesting to see how the price series respond to the short-term shocks, thus the following section provides a more detailed insight.

6.2. Vector Error Correction Model

The price series proved to have a cointegrating relationship in the long-run, thus VECM is an appropriate framework to describe the dynamic interrelationship among stationary variables to detect the speed of adjustment of the variables and from the equations 5 and 6.

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32 Table 3 “Summary of the speed adjustment coefficients as according to the VECM”

Speed of adjustment of the

equation 5 Speed of adjustment of the equation 6

Price_nl <=> Price_ge -0.138*! 0.192*

Price_nl <=> Price_dk -0.142* 0.066**

Price_ge <=> Price_dk -0.131* -0.069**

*- 99 % significance level ; **- 95% significance level ( More details in the Table 10 in the Appendix.)

Consequently all the results prove to be significant on the 95 % level, while in case of The Netherlands and Germany the level reaches a 99% confidence interval. As long as the coefficients are non-zero and their absolute value is smaller than [1], the model proves to be not explosive, thus the results are reliable and the error term leads to correction. Following this the null hypothesis of non-correction can be rejected, since the errors are corrected.

This result reveals that the spot prices correct themselves in the long-run and do not deviate too far from one another. In other words, the speed of adjustment shows that in case of even small deviations from the previous day’s level how much the given price responds to changes next day. This relation is the strongest in the case of Dutch and German spot prices pairs (if to consider the overall basis) and the coefficients are the highest in these two price-series. Nevertheless in the case of German prices this conclusion is valid for the maximum lag only, what means the error correction is conducted on a 3 day period basis. The speed of adjustment is the highest in these two cases, which can be explained by the faster restoring to equilibrium or better arbitrage. Alternatively, another explanation can be the capacity of the interconnector – if it increases the speed of adjustment rises, too.

6.3. Additional Research and Conclusive Findings

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33 Consequently, I decided to test this phenomenon by restructuring the database and examining spot-prices only for the given period (2012-2013) and on monthly basis in order to catch the minor changes in the trend. The graph below illustrates evidence for the phenomenon.

Exhibit 8 “Development of Average Monthly Electricity Spot Prices, 2012-2013”

Following this, I have repeated my empirical tests and the obtained results differed from the ones for the full-time period. According to my findings the Dutch and German spot price series proved to diverge in the period between 2012 and 2013 (on the monthly basis). This conclusion was derived from the outcomes of the Engle-Granger cointegration test while the residuals proved to be non-stationary, meaning the Null-hypothesis of non-integration has to be accepted for the period between 2012 and 2013. (Detailed findings of the tests are seen in the Appendix Tables 9 and 10).

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34 One interesting observation I made was in the development of the EU ETS (allowances for CO2 emission) prices in the recent years: Due to reduced industrial demand (as a result of the

2008 crisis) the demand for EU ETS certificates dropped, too, followed by an increase in the overall overcapacity on the carbon market. The following the data published in the Economist47 displays that prices for EU ETSs dropped from 20 EUR / ton of CO2 in 2011 to 5 EUR / ton in 1st

quarter of 2013 (The Economist , 2013). This kind of the trend explains why for instance heavily polluting coal would seem more attractive for the energy producers than gas-fired electricity generation. The latter option (natural-gas) is much more desirable from such perspectives due to flexibility and ease of operating the power plant, as well as percentage of pollution. Nevertheless the decreasing price for allowances drives the tendency in the opposite direction. If we are to consider the previously mentioned Exhibit 2 and Exhibit 4, which charts visualize “Production”, it is obvious that the share of solid fuels (coal) is much higher in Germany (42%) compared to the Netherlands (19%). This could be one of the explanations to the significant decrease in the German electricity prices in the recent year.

Another noteworthy observation is building upon the impact of renewables in energy generation. As it was mentioned in the section 2.1, over the last decade the EU was subsidizing more environmentally friendly energy producing methods. Thus in line with the accepted reforms the share of renewables in the production process increased sufficiently, especially for developed countries of Western Europe. Nevertheless the Exhibit 2 and 4 show that this share is considerably different for Netherlands (10%) and Germany (18%). When considering the consumption side the Netherlands lack behind because their usage of renewables is almost 3 times less than in the case of Germany. Renewable energy producing technologies are commonly characterized with the fluctuation (seasonality and availability of the resources) and low marginal cost, which may cause certain volatility in the price level. One of the reasons for reoccurring, negative electricity prices is that demand is really low, but the share of renewables is exceptionally high in the system and then it is more rational to use the electricity than to find ways to store it. This observation is in line with the findings of the latest report published by

47 - The Economist - The failure to reform Europe’s carbon market will reverberate round the world – Available at:

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35 TENNET48 in which they highlight that the contribution of renewables to electricity production is indeed the primary reason for the increasing gap in the wholesale price difference between the countries.

Exhibit 9 “Contribution of renewable energy production to total electricity consumption”

It can be observed in Exhibit 9 that indeed in case of the Netherlands solar energy has an insignificant contribution to the overall renewables impact on the consumption patterns in the country, whereas the total contribution of renewables on the electricity consumption rounds up to almost 10 % versus Germany with 22%.

It is important to note that the given problem rejects the theory about a common energy market and the idea of market coupling between neighboring countries – in particular Germany and the Netherlands. This kind of issue bears great significance and therefore has to be considered by the EU, considering the market players are the ones who become victims of the increasing price difference. Of course it is not easy to address these disruptions because a lot of factors contribute to ensuring of the market coupling and subsequently market integration. Nevertheless, I suggest that more profound and innovative market tools should be designed in order to address the above mentioned characteristics of the renewables. Also, based on the conducted analysis and observations I suggest that the composition of the energy mix is among

48 - TENNET – “Market Review 2013”- pp.13 Available at:

http://www.tennet.eu/nl/fileadmin/downloads/About_Tennet/Publications/Technical_Publications/TenneT_Market_Review_interactief.pdf 0,00% 5,00% 10,00% 15,00% 20,00% 25,00% Netherlands Germany 2012

Solar Biomass Wind

Source: TENNET , 2013 , p.13 0,00% 5,00% 10,00% 15,00% 20,00% 25,00% Netherlands Germany 2013

Solar Biomass Wind

An Empirical Analysis Of Price Convergence Within The EU Electricity Sector Master Thesis