Data Management and Business Opportunities inEmerging Smart Metering Market

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Bachelor of Science Thesis

KTH School of Industrial Engineering and Management Energy Technology EGI-2016

SE-100 44 STOCKHOLM

Data Management and Business Opportunities in Emerging Smart Metering Markets

Filip Christiansen

Matilda Tranell

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Bachelor of Science Thesis EGI-2016

Data Management and Business Opportunities in Emerging Smart Metering Market

Filip Christiansen Matilda Tranell

Approved

^ExaminerPer Lundqvist

Supervisor

Hatef Madani

Commissioner

Contact person

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Abstract

Major changes in the energy systems throughout Europe have resulted in the implementation of new technologies such as smart grids and meters, enabling a two-way flow of information and electricity. This results in large volumes of metering data which needs to be efficiently managed for market and grid operational purposes. In addition to this, a new market for third parties seeking to enhance and convert data into valuable information has emerged. Current data management strategies vary between countries, resulting in a great diversity of data management models. To reach consensus, the European Commission has developed three theoretical reference models in order to cover all possible options. For the success of third parties, it is important to understand the rather complex mechanisms of these reference

models. This can ease the process of recognizing the implemented data management model on a given market, as well as the interaction with related obstacles or barriers, in order to

determine business opportunities.

This report aims to present market conditions for third party actors in two European countries that have implemented different data management models. The Netherlands and Great Britain are selected based on certain conditions. With existing theories of the reference models, the actual models will be defined in each country. Key barriers are also identified. This report will then study how appropriate the implemented models are in relation to the barriers.

Therefore, these two countries will also serve as case studies for evaluating the applicability of the reference models.

In the Netherlands, case 1 of the reference models is identified per definition, although a transitioning towards case 2 can be observed. The major barrier consists of privacy concerns although customer engagement is becoming a central focus. In relation to these issues, targeted regulations seems to have more positive impact than the implemented model. The Dutch market is evolving and it is shown that the customers are open to new innovative services, although the intent to purchase such services is low. A central point of access to data facilitates efficient data management, however this only includes data with a 15 minute frequency. Data with a 10 second update interval can currently be accessed only via a physical smart meter port.

In Great Britain, parts from both reference model 2 and 3 are implemented and the main barrier is currently customer engagement. The model has been developed with high emphasis on earlier privacy concerns, but it has potential to also address customer engagement by supporting innovation and new services. However, earlier restrictive regulations have only allowed certain feedback services, i.e. In Home Displays, to be offered to customers. As of 2015, other options are allowed which opens up a promising market for third party actors.

Data can be accessed either centrally, with half-hourly updates, or via so called Consumer Access Devices providing data with updates every 10 second.

A gap between the theoretical models and reality is observed; theoretical benefits are not always evident in practice. It is also observed how all possible data flows are not always properly described or included in data management model mappings. Therefore, it is

important for third parties to look beyond such mappings to understand the access to certain data that fits their purpose. At last, privacy concerns can be eased through increased customer awareness and empowerment, which is also related to the receptivity to innovations among customers.

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Sammanfattning

Uppkomsten av smarta elnät och elmätare möjliggör ett dubbelriktat flöde av information i elnät. Detta ger upphov till stora datamängder och för marknadsaktiviteter och

elnätsrelaterade åtaganden krävs därför en effektiv datahantering. Dessutom uppstår en ny marknad för tredjepartsaktörer som kan använda datan och göra om den till värdefull information. Strategier för hur datahanteringen ska gå till skiljer sig åt mellan länder och mångfalden är stor. Europeiska Kommissionen har tagit fram tre olika teoretiska

referensmodeller för att uppnå konsensus inom detta område. Dessa modeller kan fungera som verktyg för tredjepartsaktörer i syfte att identifiera verkliga modeller för datahantering.

Dessutom kan de ge värdefull information om relationen mellan datahantering och försvårande omständigheter; något som är viktigt att förstå för att bedöma

marknadsmöjligheter.

Målet med denna rapport är att presentera marknadsmöjligheter för tredjepartsaktörer i två europeiska länder som har olika modeller för datahantering. Utifrån särskilda kriterier väljs Nederländerna och England. Med hjälp av existerande teori kring referensmodellerna definieras de reella modellerna i länderna. Därefter utreder rapporten hur lämpliga de reella modellerna är i relation till identifierade barriärer. Därmed fungerar de två länderna även som fallstudier för utvärdering av applicerbarheten hos referensmodellerna.

I Nederländerna identifieras den verkliga modellen för datahantering som en variant av modell 1 av referensmodellerna, och en utveckling mot modell 2 kan observeras. Den avgörande barriären är integritetsrelaterad, men kundengagemang blir ett alltmer centralt fokus. I relation till dessa problem kan det konstateras att specifika regleringar har större positiv genomslagskraft än själva modellen. Den holländska marknaden befinner sig i ett tidigt utvecklingsstadie men det har visat sig att kunder är positivt inställda till innovativa tjänster. Effektiv datahantering främjas av en central åtkomstpunkt, men detta inkluderar endast data med en uppdateringsfrekvens om 15 minuter. Data med uppdateringsfrekvens om 10 sekunder är tillgänglig via en fysisk port på själva elmätaren.

I England identifieras den verkliga modellen för datahantering som delar av både referensmodell 2 och 3, och den största barriären är brist på kundengagemang. Tidigare utbredda integritetsproblem har delvis utformat modellen, men trots detta återfinns positiva funktioner sett till rådande utmaning då modellen främjar högre innovationsnivåer för tjänster.

Regleringar har dock tidigare begränsat utbudet av sådana tjänster till endast s.k In Home Displays. Under 2015 förändrades denna reglering vilket medför lovande

marknadsmöjligheter för tredjepartsaktörer. Datatillgång sker antingen via en central

åtkomstpunkt, med en uppdateringsfrekvensen om 30 minuter, eller via s.k Consumer Access Devices där uppdateringsfrekvensen är 10 sekunder.

Ett gap mellan de teoretiska modellerna och den verkliga implementeringen kan observeras eftersom teoretiskt beskrivna fördelar inte alltid förekommer i praktiken. En annan viktig upptäckt är att visualiseringar av datamodeller inte alltid beskriver eller inkluderar samtliga dataflöden. Därmed bör tredjepartsaktörer inte enbart förlita sig på sådana kartläggningar;

andra metoder kan vara nödvändiga för att bedöma tillgången till nödvändig data. Till sist kan det konstateras att integritetsproblem kan motverkas med metoder som ökar

uppmärksamheten hos kunder. Ett viktigt samband mellan detta och mottagligheten för innovation hos kunderna kan påvisas.

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List of Abbreviations

BSI Germany Protection Profile

CAD Consumer Access Device

CDH Central Data Hub

CEER Council of European Energy Regulators

DAM Data Access-Point Manager

DAPF Data Access and Privacy Framework

DCC Data Communications Company

DECC Department of Energy and Climate Change

DSO Distribution System Operator

EG3 Expert Group 3

EDRP Energy Demand Research Project

EDSN Energie Data Services Nederland

HAN Home Area Network

ICT Information and Communications Technology

IEA International Energy Agency

IHD In Home Display

IT Information Technology

NRA National Regulatory Authority

Ofgem The Office of Gas and Electricity Markets

SEC Smart Energy Code

SGTF Smart Grid Task Force

TSO Transmission System Operator

WAN Wide Area Network

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1. Introduction

The global energy systems are currently going through major changes. This is mostly due to increased focus on environmental goals, forcing the systems to transition toward sustainability (Bertling Tjernberg, 2014). Authorities and governments are consistently working with

frameworks and regulations to spur this change, but also to handle it. One of the most important parts of this movement is the development of smart electricity grids, providing more efficient power distribution and a two-way flow of both electricity and information (European Commission, 2011). These features are very important for the actors on, and hence the structure of, the electricity market since it makes communication and interaction between different parties possible.

One of the most important parts of the smart grids are the smart metering systems. A smart meter can, in difference to a traditional electricity meter, measure real time energy

consumption and provide additional information about the energy usage (Alejandro et al., 2014). This results in vast streams of data, which can be valuable for the consumer but also for other actors on the market who monitor, plan and manage the electricity (Kabalci, 2015).

The new data flow also opens up a new market for third party actors that can enhance and transform data to valuable information in innovative ways.

In order to make use of the metering data in the best way possible, data management has become one of the most important questions for authorities to handle (CEER, 2015). Not only does the data have to be manageable, but it must also be managed in a way that take count of privacy and security. For most of the market actors, data accessibility is a vital factor. In order to reach consensus within this area, the European Commission developed in 2012 three theoretical market reference models which should cover all different options for handling the smart metering data in Europe (Expert Group 3, 2013).

1.1 Problem

As regulations and structures in the electricity sector vary, there is a great diversity of data management models in Europe. The way personal consumption data is being collected, stored and distributed differs. One of the reasons for this is how national smart metering markets, and the development of them, have been hampered by a range of different obstacles. These are referred to as barriers in this paper, and there are still barriers creating friction in further development. In order to create value-adding services for consumers, third party actors need efficient access to consumption data. The challenge is to find ways of using this data to engage customers in energy management services. In order to succeed, recognition of the data management model, its opportunities and challenges in a given country is required.

Furthermore, barriers that could harm business opportunities needs to be identified.

1.2. Goals and Research Questions

Our aim is to present the market conditions for third party actors in two countries with different data management models. One of the main goals is to achieve greater knowledge of mechanisms and interactions involved in different data management models. We also aim to describe and visualize theoretical data management models as well as data flows in

implemented models in a simplified yet correct way, essentially lowering the level of complexity often seen in research.

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For each country, we will identify the currently implemented data management model, map important data flows, and present key barriers. Based on the three theoretical data

management models proposed by the European Commission, the goal is to present

opportunities and challenges of the actual implemented data management model in relation to barriers that are identified. We will also investigate possible benefits from the other

theoretical data management models. At last, market conditions for third parties will be presented.

In order to cover these areas, the following research questions are set for each country:

• How appropriate is the currently implemented data management model with regards to the key barriers identified?

• What benefits could be drawn from other data management models?

• What are the market conditions for third party actors?

2. Method

In the initial steps of this project, a broad literature review was made, consisting of three major parts visualized to the right in figure 2.1. The purpose of this part in the report is to introduce the reader to state of the art of smart meters, their emergence and appearance, definitions, the market structure and the role of metering data on this market. An introduction of the theoretical models proposed by the European Commission, used as central analytical tools in this report, was then made as a continued part of the literature review. These models were explained and visualized, and theories on benefits and drawbacks associated with each model were presented. The literature review then concluded in a presentation of common and prominent market barriers in Europe.

The selection of two countries was then made to make a more detailed mapping of

implemented data management models and prominent barriers. This process, composing the result part of the report, is visualized to the right, in figure 2.2.

The selection of countries was based on the findings in the literature review. To get a good

coverage of different market conditions and the existing data management strategies in Europe, the countries were chosen from examples of the theoretical models. This does not essentially mean that an explicit theoretical model is predominant, but it ensures that parts of it can be observed. The selection was also based on how far the smart metering roll-out had come nationally. Due to the focus on business opportunities for third parties, countries where regulation on the actual roll-out of smart meters had reached consensus were considered.

Otherwise the regulatory decision itself could have posed as a significant barrier. Also,

Figure 2.1 Literature Review

Figure 2.2 Results

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countries with barriers related to data management were considered in order to be in line with the focus. To summarize, the selection of countries was based on the following aspects:

• Connection to at least one of the theoretical data management models

• Reached regulatory consensus on smart metering roll-out

• Have barriers related to data management Once the selected countries were mapped in detail, a more in depth analysis and case study of each country could be done as visualized to the right, in figure 2.3. The identified data models and barriers were put against each other in order to analyze the suitability of the model. The aim was also to briefly investigate if there was another model that would be more appropriate.

Finally, a discussion about the business opportunities for third parties was presented. In the final parts of the report, general conclusions regarding the studied barriers, models and the applicability of these were presented. The entire process of this project is visualized in figure 2.4.

Figure 2.4 Flow Chart of Method

Figure 2.3 Discussion

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2.1. Reference Qualification

The main part of this paper is based on existing literature and research. In order to get correct information with high scientific significance, focus has been on choosing appropriate sources.

Since the main analytical tool used in this project are reference models developed by the European Commission, this authority naturally becomes a core reference in this report. It should be noted that theories regarding these models are mainly provided in existing research from instances of the European Commission, or by external authors on their behalf. To limit the risk of influence from shared perspectives within this subject, several relevant sources have been included and put in relation to each other. Also, the evaluation of the models in this report has been done with a critical approach.

In order to strengthen the level of credibility and to cover gaps in literature regarding the Dutch market, personal contact has been established with one of the currently active Dutch Distribution System Operators (Alliander). Since this report covers a controversial subject, critical perceptions have intentionally been included. When references from the critic side have been used, they are clearly underlined in the text to make a distinction between facts and opinions. Also, several scientific sources with a critical approach have been included in line with the problematizing focus of this report.

2.2. Scope

The smart metering market structure varies globally. Not only do the electricity systems work differently between continents in particular, but frameworks, regulations and definitions of smart meters and smart grids also varies. Therefore, to have a consistent scientific base for our work, this report will focus only on the European market since countries within Europe all are connected to the same authority and share the same fundamental parts in the electricity system. For the case studies performed in this report, the Netherlands and Great Britain are chosen. This selection is motivated in section 6. Country Selection. Great Britain is separated from Northern Ireland due to their superior maturity within smart metering, which is

explained in section 3.3 Smart Meter Rollout.

This report is focusing on smart meters that measures electricity. Smart meters measuring gas consumption are not within our scope. Therefore, any reference to smart meters in this paper refers to electricity smart meters.

3. State of the Art

The global energy systems are currently going through major changes. This is mostly due to the relatively new focus on the environment, forcing the systems to transition toward

sustainability and renewables. This change is spurred by climate goals set by authorities such as the European Union, which was one of the first to set up these kind of goals in 2007 (Bertling Tjernberg, 2014). These are known as the 20/20/20 targets, which are targets of the Europe 2020 strategy for smart and sustainable growth (European Commission, 2016a). This was followed up in 2014 with a new framework with new targets for 2030 by the European Commission, in which the goals were updated and further developed (European Commission, 2014a).

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One of the most important tools to reach these goals is the development of the future energy infrastructure. This has lead to a widespread development and implementation of new technology in the European grid system. These new grid technologies, referred to as Smart grids, is believed to be an important contribution to the strategies for smart, sustainable and inclusive growth in Europe (European Commission, 2011).

3.1. Definition of Smart Grids

Traditionally, an electricity grid is only used for transporting power from a few central generators to a large number of end consumers. The new generation of grids, smart grids, are in contrast able to have a two-way flow of electricity and information (Fang et al. 2012). The smart grids utilize modern information technologies making them able to deliver power in a more efficient way (Fang et al. 2012) and being able to respond and adjust to any changes that may occur in power generation, transmission, distribution or customer demand (Kabalci, 2015; European Commission, 2016b).

These features enable communication in the electricity system which make integration of renewable energy sources into the system easier (European Commission, 2016b). Smart grids can also contribute to a better management of peak energy loads. Providing information and incentives to consumers makes it possible to increase the demand response by motivating them to shift consumption away from periods of peak demand (IEA, 2011). According to a technology roadmap for smart grids made by IEA in 2011, the peak demand will increase globally and with the smart grid deployment it would be possible to reduce this increase with 13 to 24 percent.

By implementing smart metering systems in the smart grid, consumers can be exposed to their consumption patterns. In theory, these could be combined with time-dependent electricity prices, which could act as strong incentives for a more efficient energy usage (European Commission, 2011).

3.2. Definition of Smart Meters

A smart meter can, in difference to a regular meter requiring manual readings on a monthly basis (Ziviü et. al. 2015), measure the real time energy consumption in a household and both transmit and receive information remotely (Alejandro et al., 2014).

The definition of a smart meter varies globally. In order to reach consensus around the

functional specifications and be able to set goals, compare and analyze member countries, the European Commission (2011b) has defined key features. According to the European

Commission (2011b), a smart meter should:

• Provide the consumer with meter readings

• Be able to update readings frequently enough for energy savings and for network planning

• Allow remote readings for meter operators and remote ON/OFF control of supply by grid operators

• Provide a secure two-way communication flow with external networks

• Support advanced and dynamic electricity tariffs

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• Prevent and detect data frauds

• Provide functions allowing distributed generation, e.g. residential solar panels (EPA, 2015)

One of the main purposes with smart meters is to acquire data about the customer as well as the utility grid. This leads to a large collection of data that can provide parties with full information about both energy consumption and behavioral patterns. Because of this, smart electricity meters become an essential part of the smart grid, and the electricity market in whole (Kabalci, 2015).

3.3. Smart Meter Roll Out

The European Union has set several goals regarding the smart meter roll out. The overall aim is to replace at least 80 percent of currently installed regular meters with smart meters by 2020 in European countries where it would be cost-effective according to specific guidelines (European Commission, 2016b; European Commission, 2014b). 16 member states; Austria, Denmark, Estonia, Finland, France, Greece, Ireland, Italy, Luxemburg, Malta, Netherlands, Poland, Romania, Spain, Sweden and the United Kingdom (UK), had positive results in this analysis and will proceed with large-scale roll outs of smart meters by 2020 or earlier (European Commission, 2014c). It should be noted that the cost-benefit analysis for the UK consisted of two separate analyses for Great Britain and Northern Ireland. The market

conditions and maturity differs greatly between these two areas, Great Britain is considered to be far ahead of Northern Ireland (European Commission, 2014e).

National governments have a central role in the introduction and implementation of smart meters since they decide the policies for smart meter deployment. Hence, the status of smart meter deployment varies throughout Europe (Zgajewski, 2015). According to Smart Regions (2013), with support from the European Commission, countries in Europe can be divided into four different categories, depending on the progress in implementation and legal and

regulatory status of smart meter deployment. This can be seen in figure 3.1.

Figure 3.1 Overview of the legal and regulatory situation and the implementation status in EU countries and Norway 2014 (USmartConsumer, 2014)

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Countries with a mature regulatory status and a great progress in implementation, or with clear decisions on strategy and timetables, are considered to be in the frontline of roll outs and are referred to as dynamic movers. The countries who do not have obligations to introduce smart meters to all customers, thus do not have legal requirements for full roll-out but

proceeded with implementation to some extent, are defined as market drivers. In the countries characterized as ambiguous movers some regulatory progress is evident, but crucial parts are still missing. In the countries referred to as waverers, smart meters have been introduced in small projects, and laggards include countries where smart meters are yet to be discussed (Smart Regions, 2013).

3.4. Market Actors and Data Management

There are several actors with different roles on the electricity market. The roles include activities such as production, distribution or consumption of energy or financial aspects.

Regulations exist in order to determine the market rules and define specific duties and

responsibilities. In order to fulfill their tasks, all actors are dependent on accessing meter data in some way. For example, customers need it in order to manage energy consumption and grid operators use it for operational tasks and planning (CEER, 2015).

A data management model is the structure of interconnections between different actors, and it visualizes the roles as well as the distribution of data in a given system. Through regulations, the design of a data management model is set nationally and therefore varies throughout Europe (CEER, 2015). Below, figure 3.2 is used to visualize and simplify the theoretical appearance of a data management model¹, and in the following sections the market actors, their roles and relation to data is explained.

Figure 3.2 Market Structure and Data Management Model

3.4.1. Regulatory Authority

Regulatory authorities within the energy sector are created at national level in European member states as independent legal entities, separated from other governmental bodies.

1 The market model could also include other stakeholders, e.g. balance responsible companies, energy utilities, etc. These are however excluded in the simplified structure in this report.

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According to European Commission each member state should guarantee the independence of the authority, and also ensure that it works impartially and transparently (European

Commission, 2010). The main areas covered by the national regulatory authorities (NRAs) are (European Union, 2010):

• Transmission/distribution tariffs

• Ensure compliance of European law among market actors

• Guarantee access to customer meter data

• Supervise investments made by TSOs

3.4.2. TSO

A transmission system operator (TSO) is a network operator responsible for the stability and security of the electricity supply in larger transmission grids. It has to be a non-commercial organization, in other words neutral and independent (Nord Pool Spot, 2013). According to the European Union (2010), some of the key tasks and responsibilities of TSOs are:

• Ensure that the system has a long-term ability to meet the electricity demand

• Manage the electricity flow in the system and contribute to the security of supply

• Ensure that discrimination does not exist between the system users and provide them with needed information in order for them to access the system

• Provide information related to the operation, development and interoperability of the interconnected system to operators of other systems

Smart metering data is very valuable for TSOs in order to fulfill grid responsibilities, especially long-term planning (EDSO, 2014). The exact tasks and responsibilities of TSOs varies between countries, and the same applies for their activities within data management.

For example, in some countries the TSO is responsible for developing data exchange platforms, e.g. data hubs, which gives them a more active data management role on the electricity market (THEMA, 2015). This is seen in for example Denmark and Norway, and is a possible development for most of the Nordic countries (CEER, 2015; THEMA, 2015).

3.4.3. DSO

The distribution system operators (DSO) are network operators responsible for the

distribution of electricity in the distribution grids; at a more local system level than the TSOs.

The main tasks of the DSOs are summarized as (European Union, 2010):

• Manage vital operational parts of the distribution system such as distribution of electricity and maintenance, but also development

• Cover for transmission losses in the system

• Ensure reserve electricity capacity

The differences in the energy sectors of European countries, where the number of DSOs and their responsibilities varies, results in different data management roles for DSOs. Smart meter data is important for their specific activities such as monitoring grid functions to ensure quality and security of supply, e.g. electricity loss supervision, and for planning purposes (EDSO, 2014). The most common structure in Europe is that the DSOs are the smart meter owners, and therefore responsible for the data handling. This structure is true for most of the

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European countries that are proceeding with large roll-outs by 2020 (European Commission, 2014d).

3.4.4. Energy Suppliers

Energy suppliers are active on the wholesale market where energy is traded between several actors, such as energy retailers and investment banks (European Commission, 2016c), before reaching the end consumer (Energy UK, 2015). An example is the Nord Pool Spot, a power market in the Nordic region. A customer holds a contract with a specific energy supplier acting on this competitive market where prices are set by market mechanisms

(Energimarknadsinspektionen, 2012).

Energy suppliers are dependent on customer data for basic procedures such as billing purposes, but also for designing appealing contracts and agreements. This is important in order to remain competitive in a market where customers are able to switch supplier (CEER, 2015).

3.4.5. Customers

With the changes that currently are occurring within electricity sectors in Europe, the role of the customer is expected to evolve. They are supposed to go from being passive recipients of energy services to active participants on the market (Expert Group 3, 2013). It is of high importance for customers to access data, especially in terms of their own levels of consumption.

In this report the term customers will refer to electricity consumers such as households and small enterprises. It also includes prosumers, i.e. consumers that contributes to the electricity supply by producing electricity that is distributed in the system (EURELECTRIC, 2015).

3.4.6. Third Party Actors

Due to vast streams of data that smart meters generate, a new market for third party actors has opened up. Third party actors can be new potential energy suppliers, aggregators of data or energy service companies (CEER, 2015). For example, in Germany the customers can choose a third party actor as their smart meter operator (Zgajewski, 2015).

Third party actors can offer customers alternative routes to engage with the energy market.

This can help customers become more energy efficient and reduce their expenses. Within this category, energy ICT (Information and Communications Technology) service companies developing applications or software based services for customers are included. By developing new, innovative services these companies become very important for the evolution of the energy market. The access to high quality customer data has a crucial impact on the opportunities for these companies (CEER, 2015).

3.5. The Role of Data

One of the main challenges in the current era of Big data, in which large quantities of different types of data are processed and stored, can be described as:

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“You can have data without information, but you cannot have information without data.”

(Shah & Ibbott, 2014, p. 20) Large quantities of raw data theoretically offer many great possibilities such as historical mapping or predictions about the future. However, it needs to be extracted, filtered and converted to appropriate formats depending on the target audience in order to actually be valuable. This targeted selection and conversion of data transforms it to information (Business Dictionary, 2016). However, raw data is always needed in order to create information. Since different types of information will be needed at different times, data pools have to be massive.

3.5.1. Feedback Services

Research shows that feedback services in combination with smart metering are important tools in order to increase energy efficiency (van Elburg, 2014). Feedback services include services transforming energy consumption data into valuable information for consumers. This information can be presented and visualized in different ways and on different frequencies, referred to as direct feedback and indirect feedback. Direct feedback includes the real time energy consumption data presented by In Home Displays and visible energy meters. Positive effects from this feedback relies on customer interest and how frequent the customer actually monitors the data. Indirect feedback is processed data (information) presented to the user, e.g.

enabling comparisons to historic consumption or between different consumers. This may cause a time delay in availability for the consumer which is directly related to potential positive effects (Zvingilaite & Togeby, 2015).

Research shows that passive statistical presentation of energy consumption alone has low impact on customer engagement and energy efficiency (Pyrko, 2011). A more active approach where such services offer the ability to compare consumption levels between different periods of time or when economical values are also presented seems to be more appealing to

customers (Energy Saving Trust, 2014). Services can also take on a competitive approach, e.g. offering the ability to compare own household energy consumption levels between

households in the neighborhood. Such functions are believed to contribute to increased energy efficiency, as well as services that gives targeted advices on how to actually lower the energy consumption (Weiss et al., 2012). It is a challenge for third party actors to design compelling services that actually engage the customer.

3.5.2. Data Quality and Frequency

In order to use the metering data for purposes such as improvement of energy efficiency, analyzing load curves and billing, it has to be of good quality (Joos et al., 2014). The definition of data quality varies in literature but there is some consensus about parameters such as accuracy, consistency, completeness and timeliness (Ma, 2014; Veregin,1999;

Viklund, 2015).

Accuracy refers to how well the data describes reality, meaning how close the measurement is to the correct value. Consistency refers to the level of difference in how the same data is presented in different systems and databases. Completeness means if the data is sufficient for the purpose it is collected for. Timeliness is defined as the time between when the

measurements are taken and when it becomes available for the users (Ma, 2014;

Veregin,1999; Viklund, 2015).

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Depending on the purpose of data collection, the frequency of readings is of high importance.

Readings could happen twice a year, monthly, hourly or every few seconds, leading to completely different market conditions. At least hourly readings are essential in order to reveal information about consumption behavior, even if monthly allows comparisons of different periods. For grid operations, 10 to 30 minute readings may be needed. Minutely readings are necessary to obtain detailed profiles of consumers (Joos et al., 2014). In order to create direct feedback services, near real time data is needed (DECC, 2015).

Depending mostly on billing customs, European countries may have different frequency of metering and granularity of the data (Marine et al., 2014). Granularity refers to how often the measurements are done, also sometimes referred to as resolution or precision of the data (Veregin, 1999). Frequent meter readings and fine-grained data is a necessity for accurate data management (Feuerriegel, Bodenbenner, & Neumann, 2016).

4. Models for Data Management

In 2009 the European Commission set up the Smart Grid Task Force (SGTF), a group of stakeholder representatives from the energy industry, consumer groups and the European Commission. The purpose of SGTF is to advise issues related to smart grid deployment and help the European Union develop smart grid policies. SGTF is divided in five expert groups with expertise in different areas (EDSO, 2016a).

In 2012 it was decided that the Expert Group for Regulatory Recommendations, also known as Expert Group 3 (EG3), should develop a market reference model for data management.

The intention was to make a reference model that should:

“...exploit the synergies with the ICT sector and recommend regulatory incentives and

obligations that protect and empower consumers and at the same time encourage the roll-out of smart metering.” (Expert Group 3, 2013, p.6)

As it turned out, the situation throughout Europe was too diverse to define one feasible model, resulting in three different cases (Expert Group 3, 2013):

1. DSO as market facilitator

2. Third party market facilitator - independent central data hub 3. Data access-point manager (DAM)

The three cases represent different options for handling smart grid data, and should cover all of the possible scenarios either by themselves or combined with each other. The models are designed to allow transparent communication between market actors and are supposed to be easily definable (Expert Group 3, 2013). In the following section the three cases will be presented. Their structure will be explained and their benefits, opportunities and challenges will be examined.

4.1. Case 1: DSO as Market Facilitator

The first case is based on a standardized and either centralized or decentralized data hub, to where meter data can be submitted and stored. The idea is that the DSO should serve as a

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neutral market facilitator in a non-discriminatory way. The distinct characteristics of this case is that the data hubs are owned and operated by the DSOs, meaning that they will provide their collected data to the market (Expert Group 3, 2013). The decentralized and centralized versions of this case are visualized in figure 4.1 and figure 4.2.

In Europe, several countries are transitioning towards this model. Some have already

implemented it, for example the Netherlands, and some have made the decision to use it, for example Belgium. Some have decided to use it just for some processes; e.g. Portugal will use it for supplier switching (Expert Group 3, 2013).

4.1.1. Benefits and Opportunities

According to EG3 (2013), most of the European countries would not have to make significant changes to regulation or supervisory mechanisms in order to engage in this model. This is due to the fact that many already use a model where the DSOs are responsible for data collecting and distribution. The model would therefore not have any large transition costs (Expert Group 3, 2013), while transitioning towards a different model could carry extensive costs (EDSO, 2014). Also, if additional actors are to be involved in data management activities the

complexity of the data management model could unnecessarily increase (van den Oosterkamp et al., 2014; EDSO, 2014).

The structure of the case 1 model strongly emphasizes customer privacy and security (Expert Group 3, 2013; EDSO, 2014). The roles and responsibilities in this case are well known and clear since they are not far from most of the implemented models seen in Europe today, which is positive for customer reliance (Expert Group 3, 2013). Since the DSO is a regulated market actor undertaking the role of a neutral market facilitator, high confidentiality and neutrality can be achieved (van den Oosterkamp et al., 2014). According to CEER, the role of a neutral market facilitator in accordance to case 1 model is indeed needed to ensure this (CEER, 2015b).

Since DSOs are viewed as experienced in implementing new technologies in the system, they can encourage the development of such technologies in the grid to foster innovation and quality (Expert Group 3, 2013). Multiple DSOs could together use their expertise to jointly set up a centralized national data hub, like seen in the Netherlands. This could result in synergies such as economies of scale, greater economical savings for the DSOs and easier regulatory

Figure 4.1 Decentralized Case 1 Model

Figure 4.2 Centralized Case 1 Model

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processes to ensure neutral data distribution. Having a centralized hub might also lead to more efficient access to data (van den Oosterkamp et al., 2014).

4.1.2. Challenges

In order for the DSOs to meet future demands on the market they must be able to innovate and integrate new needed services (CEER, 2015b). To do this it might be necessary for DSOs to cooperate with ICT-companies who have beneficial expertise within this technological area. It is important for DSOs to find ways to collaborate with companies that have

operational strengths and abilities in innovation that can be exploited (van den Oosterkamp et al., 2014). Outsourcing parts of the DSO’s processes can also lead to greater cost-efficiency, which might become necessary due to economical challenges. For example, regulated revenues alone may not cover all of the costs for data management (EDSO, 2014).

In a centralized case 1 model, with only one regulated actor managing the data hub, the incentives for innovation might be weak since it will not encourage competition on the market. According to literature, this problem is shared by the centralized approach of both model 1 and 2 (van den Oosterkamp et al., 2014). Without competitive incentives, there is a risk that the collected data might not be sufficient for other activities than those of the DSOs, raising the need for strict regulations to prevent data discrimination (Brunekreeft et al., 2015).

4.2. Case 2: Third Party Market Facilitator – Independent Central Data Hub

Case 2 is based on the idea of having one central communication platform, referred to as a central data hub. The major difference from case 1 is that the data hub is independently operated by a regulated third party, with clearly defined responsibilities. This entity can be a new actor specifically created for this task, or it could be an already existing actor such as the TSO (Expert Group 3, 2013).

The key objective of the hub is to communicate and interact with different stakeholders in order to store and distribute the data. Collecting the data from households will still be the responsibility of the meter operators, meaning that these actors have to ensure the data quality and delivery to the hub. Since the data hub will be independent, this model will allow equal access to all market participants (Expert Group 3, 2013). These participants must however be authorized, according to privacy legislations (Zgajewski, 2015; Expert Group 3, 2013). The appearance of the case 2 model is visualized in figure 4.3.

Figure 4.3 Case 2 Model

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Case 2 is already implemented in some European countries including Denmark, Estonia, Poland and the Great Britain. In some countries the model is under development. Italy, for example, is in the first stages of implementation; having a central hub processing data for aggregation and statistics although the DSO still have the responsibility for metering and communication systems (Expert Group 3, 2013).

According to EG3 (2013), free and equal access to any information by any actor is promoted in this model since all stakeholders and actors on the market are committed to share data through the hub. One of the main benefits with an independent central data hub is that the model promotes transparent, non-discriminatory and neutral data handling (van den Oosterkamp et al., 2014). Data security and efficient data management which is also

emphasized in this model, is essential for market processes such as billing, supplier switching, contracting etc. (CEER, 2015c). Efficiency in the data management is achieved through better interoperability and standardized communication infrastructure since all authorized parties have to be able to provide data to, as well as extract data from, the central hub (Expert Group 3, 2013).

The model also lowers the market entry barriers for new suppliers and energy service companies due to easy access to all data through one central contact point (Norstedt et al., 2015). The single point of access to data also promotes a competitive market (CEER, 2015a).

An example is the energy supplier switching process which is made more simple in this model (Zgajewski, 2015) and therefore can stimulate the market through greater customer participation. Customer engagement in the energy sector is key for other activities such as demand response when integrating more renewables in the system (Expert Group 3, 2013).

Setting up an entirely new centralized system gives the opportunity to build a system based on the most recent technologies (EDSO, 2014). Also, substantial economies of scale can be achieved with this model due to the fact that only one actor collects data that all of the other actors can use (van den Oosterkamp et al., 2014).

4.2.2. Challenges

When setting up a new regulated actor for operating the centralized hub, regulatory and administrative costs appear (van den Oosterkamp et al., 2014). Otherwise, this task has to be assigned and implemented in the business of an already existing actor (Expert Group 3, 2013).

The transition time needed in order to build up sufficient experience in the new or existing organization could span over several years. There would also be extensive investment costs and a great effort for NRAs in the required regulatory changes, e.g. concerning data

protection (EDSO, 2014).

Creating an independent actor to operate the hub naturally means that this body will be given a monopolistic nature, potentially resulting in doubtful perception among other actors in the energy sector (Expert Group 3, 2013). For example, literature shows that the case 2 model is rejected by DSOs for being an inefficient model (van den Oosterkamp et al., 2014).

Centralizing all of the important data related processes of the electricity market into a centralized hub carries certain security risks, essentially making the system more vulnerable

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and raising the need of extensive IT-protection (Norstedt et al., 2015). With this model structure, there is also an increased risk of data transmission failure since the total number of communications between actors sharing information increases with an intermediate actor (EDSO, 2014).

4.3. Case 3: Data Access-Point Manager

The third case introduces a new role on the market called Data access-point manager (DAM) acting as a data gatekeeper. The DAMs will be independent and certified actors handling the access to the large volumes of data collected from the smart meters and grids (Expert Group 3, 2013). In contrast to case 1 and 2, this case does not involve data hubs. The DAMs will retrieve the data directly from the meters and distribute it to other actors (Ruester et al., 2014).

Hence, the data is stored in each individual meter (EDSO, 2014). This means that actors such as the DSOs will receive metering data based on regulation (van den Oosterkamp et al., 2014). The purpose with the creation of the DAMs is to complement and boost existing market structures, roles and responsibilities; not necessarily change them (Expert Group 3, 2013). This case is visualized in figure 4.4.

Figue 4.4 Case 3 Model

There is no example of countries where the case is fully implemented. There are however elements of it that is in use or being developed within a few parts of Europe. For example, in Great Britain parts of the Smart Energy Code (SEC) have elements of the case, and in Germany the BSI protection profile includes offering customers data gateways that corresponds to the gatekeeper approach (Expert Group 3, 2013; Ziviü et. al. 2015).

The third case will create great flexibility in the system in regards to data access and data processing. A DAM will pose as a certified service company, and due to the competitive environment created from a market with several DAMs, innovative services and technologies can be implemented more easily (Expert Group 3, 2013). The DAM model is in fact expected to provide a higher level of innovation than the other two models (van den Oosterkamp et al., 2014). Also, the market entrance for new actors will be facilitated. This is especially true for ICT companies who will be irrespective of, and unlimited by, the DSO’s priorities and interests (Expert Group 3, 2013).

The DAM model promotes the integrity of the customer due to high emphasis on privacy in the gatekeeper approach. Since data is not stored centrally, the vulnerability of the system is decreased. Additionally, it is only the consumers themselves that have the full control of their

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data. The model also encourages consumers to participate more, especially in a decentralized approach where they actively have to choose between DAMs and different interfaces (Expert Group 3, 2013).

The model is seen as the most appropriate for the future development of smart grids since it can facilitate expansion and investability of the system. The model is efficient for

management of large amounts of data from a range of devices. It is also predicted to be sufficient in promoting functions such as demand response (Stromback, Quinn, & Pachlatko, 2015).

4.3.2. Challenges

The implementation of this model will require extensive regulatory changes as certification rules for data exchange need to be set (Expert Group 3, 2013). One challenge is the required regulation of companies acting as DAMs to properly include these actors in the electricity sector (Brunekreeft et al., 2015). As distribution of data becomes competitive, there is risk for limited access to the smart meter data for other actors on the market, including grid operators such as DSOs (van den Oosterkamp et al., 2014).

Since the data storage is decentralized, the model will require a high level of standardization in order to ensure a well functioning system (Brunekreeft et al., 2015). This is could be a massive challenge for complex systems such as energy sectors. The usage of this model might also lead to a general perception of a more complex market structure, making it harder for non-experts, such as regular consumers, to interpret it. Additionally, the fact that the DAM model requires more effort and participation from the consumers might not only pose as an opportunity. The model is based on their participation as they have to make more decisions and will be exposed to more interfaces (van den Oosterkamp et al., 2014).

Lastly, economies of scale could be lower within the DAM model in comparison to the other two models. This is because several DAMs may use the same data for competitive purposes such as delivering services to the grid users. Therefore, this case might not be the most efficient or cost effective alternative (van den Oosterkamp et al., 2014).

5. Barriers

There are several obstacles to consider on the smart metering market in order to study the market conditions for third parties. These obstacles could be seen as barriers, affecting the business opportunities and reducing the efficiency in the data management. Depending on already existing technology, legislation and culture, the barriers can look very different throughout Europe. In this section the most common and important barriers within the scope of this paper are presented.

5.1. Customer Integrity

One major concern within the smart metering market is the matter of integrity of personal information that is processed in smart metering systems. The collected data have a high level of personal information details, making it possible to draw precise conclusions about the user.

In opposition to traditional electricity meters, the smart meters are able to track near real time

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data on electricity consumption in a household, e.g. every 15 minutes (Murrill, Liu, &

Thompson II, 2012). This type of data could reveal clear patterns in the everyday energy consumption of a household or a specific person, raising widespread ethical concerns (Wilson, 2015). For instance, energy usage profiles could tell about daily routines and lifestyles, e.g. when someone is home and when they are not (Balmert, Grote, & Petrov, 2012).

5.1.1. Data Privacy

The data acquired from smart meters is referred to as private data by the European

Commission. In general, there are no specific regulations on smart meter data privacy in the European member states. The privacy is instead addressed on European level in the Data Protection Directive (Cervigni & Larouche, 2014). In 2015, a new proposal on a data protection legislation applicable in all member states was agreed upon by the European Parliament, which should be implemented before 2017 (European Commission, 2016d). Until 2015, only two European member states, the Netherlands and UK, have put extra effort in national data protection legislations in order to strengthen data privacy and security when implementing smart meters (Zgajewski, 2015).

5.1.2. Data Security

Data security refers to the protection of data processing, storing and transmission. Sufficient data protection is one way to ensure increased customer integrity and privacy, and therefore becomes an important aspect to consider. Security can be achieved on the technological system level in terms of data protection softwares or encryption preventing breaches and data theft (Trans-Atlantic Consumer Dialogue, 2011). It can also be achieved on a regulatory level where the access to data in the system is addressed. Such regulations can set out frameworks for which parties in the energy system that should be granted access to data, and under what conditions (European Commission, 2016b).

Private data is transmitted and made available to a range of different actors depending on customer consent, including energy suppliers as well as network operators and third party actors, and the consumption data is also transmitted between devices in the Home Area Network (HAN) in order to be visualized to the consumer (DECC, 2015e). As data is processed and transmitted in the system on a frequent basis, significant security risks are evident (Cervigni & Larouche, 2014). The diverse transmission of data to several locations, and the fact that this communication is often through wireless technologies, increases the risk of unauthorized access (Murrill, Liu, & Thompson II, 2012).

5.2. Access to Data

The structure of the data management model, and therefore the infrastructural solutions and ways of accessing metering data, differs between regions in Europe. The opportunities for energy service providers to design innovative services, and the extent of customer value that these services can actually carry, are highly dependent on the data management model and requires equal and free access to data (CEER, 2015a).

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5.2.1. Ownership of Data

The matter of ownership of the energy data that is processed in smart grids, including

electricity consumption data generated in smart meters, is another current ethical subject that is yet to reach clear consensus. According European Commission (2015), this matter will be further investigated during 2016 as part of the “Free flow of data initiative”.

A problematic situation of data ownership occurs since there are many different stakeholders involved. From the perspective of a meter operator installing a smart meter in a household, often a DSO, the device that it owns is generating the data which is useful to analyze the usage of its services and for its grid operational activities. From a customer’s perspective, the generated data is a record of their everyday life, and the ownership should naturally remain at household level (Information Age, 2012).

The ownership of data is defined differently on national level in European member states. For example, in the Netherlands the DSO who installs the smart meter and collects the data also owns it (ETSI, 2015), although customer consent decides on the frequency and level of detail of the information that the DSO can share with other actors (Cuijpers & Koops, 2013). In Sweden and France the control of data lies with the customers (ICER, 2012). In Great Britain, there is a lack in clarifying information regarding the ownership of data (Shah & Ibbott, 2014), however emphasis is put on allowing the consumers to decide upon the access to their consumption data except for data needed in fundamental regulated duties (Ofgem, 2010).

Despite how the data ownership structure is defined in a country, a general rule and a pre- requisite for data sharing with third parties is customer consent (CEER, 2015a). This makes the matter of access rights an important aspect for third party actors on the energy market.

5.2.2. Cost of Access

There are incremental costs associated with collecting and distributing smart metering data.

The costs to collect and make metering data available to other parties are estimated to be 17 percent of total costs associated with smart metering in the UK (Cervigni & Larouche, 2014).

If regulations do not ensure free and equal access to data, this could be a barrier for third party actors since these costs could potentially exceed the level of economical benefit that can be exploited by developing and selling services (CEER, 2015a).

5.2.3. Interoperability

There are technical obstacles such as lack of standardization in fundamental parts of hardware and communication infrastructure in smart metering systems (CEER, 2013). Due to problems such as network communicational issues and insufficient interoperability, the number of smart meters actually operating in smart mode in UK was less than the total number of installed smart meters in the end of quarter two in 2015 (DECC, 2015f). Interoperability refers to the capability of two or more systems to connect without complications and their ability to share information securely and effortlessly. This is essential in order to allow third parties and end users to combine and change different technologies and still trust the

operations to work (U.S. Department of Energy, 2014). For example, in absence of

interoperability a new energy supplier might be unable to operate the existing smart meter in smart mode upon a switch of supplier (DECC, 2015f).

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Since there is a variety of actors involved in smart meter implementation, the structure and functionalities of smart meters differ. One of the major technical challenges in Europe are identified as the need for common standards of interfaces, in order to ensure that certain technical functionalities are always supported and available to users. By recommendations from European Commission, this includes access to actual momentary levels of electricity usage and a specific update frequency of this data (European Commission, 2012). Also, certain standards in communication components are important in order to ensure data

protection, as incompatible components can pose as a security risk (Murrill, Liu, & Thompson II, 2012).

5.3. Customer Engagement

With all the changes that are occurring in the electricity system, the role of the customers is also expected to evolve, assuming they have to change from being passive recipients of energy services to active participants on the market (Expert Group 3, 2013). This requires customer engagement and participation in the energy sector, which can be hampered by a range of factors.

5.3.1. Awareness

As of today, many customers do not take direct control over their own metering data. This is to some extent caused by insufficient information and transparency about what data they actually can control. In addition to this, there is a lack of awareness about how to access their own data, which parties that have access to it and what other stakeholders can do with it (CEER, 2015a).

The responsibility to give customers information about smart meters and highlight benefits and functions lies with the meter operator (EURELECTRIC, 2013b). However, the process of informing customers has shown to be insufficient in some parts of Europe. The European customer awareness is generally low, especially regarding how smart meters can create value for them (Giglioli, Panzacchi, & Senni, 2010).

It is shown that awareness raising campaigns, as well as more targeted information for

households, are important in order to engage customers (DECC, 2015c). For example, without information on potential economic benefits, the demand for smart grid related services will grow more slowly (Giglioli, Panzacchi, & Senni, 2010). When aware of functions and opportunities, the customers will be empowered to participate in the market (CEER, 2015c).

5.3.2. Behavior

The ability to change consumption patterns is dependent on several factors such as consumer engagement, level of flexibility and willingness to adjust, e.g. to the dynamics in electricity prices. These factors in turn depend on reliance to the electricity provider as well as various consumer incentives such as economic or environmental (Vassileva & Campillo, 2016).

On a more general level, consumers are having difficulties recognizing the connection between different energy demanding activities and their corresponding electricity usage.

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Electricity is regarded as something abstract, and the actual consumption of this invisible resource could be hard to grasp (Vassileva & Campillo, 2016).

Feedback on energy consumption is one tool to bridge this gap between energy demanding activities and the use of energy resources. In UK, complementary technology for visualizing consumption levels for customers such as the In Home Display (IHD) are shown to be able to benefit the customer and positively impact consumption patterns. However, in the Early Learning Project where these displays were deployed in a selected amount of households, only six out of ten still had their displays connected and active after a period of 6 months to 2 years (DECC, 2015c). In a case where consumption data was made available to customers in web-based solutions by the electricity provider in Sweden, the level of customer interest was still low (Vassileva & Campillo, 2016). Behavioral trials in Ireland where a feedback monitor was combined with smart metering showed positive perceptions among customers, however the majority of them only made minor changes to their behavior (ERKC, 2014). The issue of low customer engagement is described to exist on a general level in Europe (Koski et al., 2015).

In order to engage all customers, different customer groups need to be treated differently.

Studies implies that different customer segments have various views on smart metering and their response to the same feedback system will therefore be diverse. Hence, one system should be tailored for one target audience (Energy UK, 2013). Services may have to be differentiated not only in terms of interest for energy savings among different target groups, which might be the most natural reason, but also based on the different technological abilities of customers (van Elburg, 2014). For example, different generations will not always be able to appreciate and work with the same technologies. Therefore, creating tailored and appealing services is a challenge.

6. Country Selection

Based on findings in the literature review the Netherlands and Great Britain are chosen for further analysis. These countries fit the criteria for selection well, making them interesting and desirable to evaluate for the purpose of this report. How they are connected to the presented criteria of selection is explained in chapters 6.1. - 6.3.

6.1. Front Line with Smart Metering

In order to ensure the countries being in the front line with smart metering roll out, a selection can be made from the category Dynamic Movers in figure 3.1. The countries in this category have a clear regulatory status and a clear strategy for the implementation of smart meters.

Both The Netherlands and Great Britain are classified as Dynamic Movers.

6.2. Theoretical Connection

Literature indicates that the Netherlands as well as Great Britain have certain connections to the theoretical models. The Netherlands is mentioned in relation to case 1 of the reference models, and Great Britain is mentioned in relation to case 2 and 3. These connections will be more specifically investigated and mapped in the continued parts of this paper.

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6.3. Data Related Barriers

According to literature, the market situation in the Netherlands as well as Great Britain has been hampered by privacy concerns in customer segments, which have a strong connection to data management. Additionally, efforts to engage the customers in energy management activities in Great Britain have shown to be not as successful as predicted. Since this requires enhancing and converting data into appealing and valuable information, it is also related to data management. The prominent barriers in these two countries will be more specifically analyzed further on.

7. The Netherlands

In following chapter the Dutch market will be analyzed. Background information, the most important barriers and a model analysis will be presented. The chapter will end in a discussion about the model suitability and conclusions regarding the market conditions.

7.1. Background

In this section, background information about the Netherlands will be presented, consisting of an introduction to the smart metering market, the data accessibility and feedback services.

7.1.1. Smart Metering Market

The smart metering market had a rough start in the Netherlands. Even though the first introduction of smart meters came as early as in 2004, a regulated pilot could not start until eight years later. The reason for this delay was mostly the unexpected resistance that came from the public, who were worried about privacy. Several actions were taken in order to handle these privacy concerns. Rules to facilitate possible privacy rights infringements were laid down in the proposition regarding the roll out and in the proposed interoperability

standard for the meter. The smart meters received a lot of negative attention which resulted in a bad reputation, slowing down the progress further (Hoenkamp, Huitema, & de Moor-van Vugt, 2011).

The roll out that eventually took place was divided in two stages. A small initial roll out from 2012 until 2014 aimed at increasing the experience within the field. This small scale roll out was evaluated by the Dutch parliament and research about the roll out, customer satisfaction and possible energy savings were conducted. These had a positive outcome, and together with positive results in the official European cost-benefit analysis, a large scale roll out could begin in 2015 (European Commission, 2014c). The aim is to have smart meters in at least 80

percent of the households and small businesses by 2020 (USmartConsumer, 2014). Due to the careful initial steps of the roll out, the market for smart metering services was quite small but emerging in 2014. Service providers were cautious and rather passive, in order to observe the development of the market (van Elburg, 2014).

7.1.2. Data Frequency and Access

From a Dutch smart meter there are two different channels that meter data can be

communicated along. The physical P1-port, referred to as consumer port, has the ability to