Journal:Smart grids and ethics: A case study

Abstract

This case study explores the principal ethical issues that occur in the use of smart information systems (SIS) in smart grids and offers suggestions as to how they might be addressed. Key issues highlighted in the literature are reviewed. The empirical case study describes one of the largest distribution system operators (DSOs) in the Netherlands. The aim of this case study is to identify which ethical issues arise from the use of SIS in smart grids, the current efforts of the organization to address them, and whether practitioners are facing additional issues not addressed in current literature. The literature review highlights mainly ethical issues around health and safety, privacy and informed consent, cyber-risks and energy security, affordability, equity, and sustainability. The key topics raised by interviewees revolved around privacy and to some extent cybersecurity. This may be due to the prevalence of the issue within the sector and the company in particular or due to the positions held by interviewees in the organization. Issues of sectorial dynamics and public trust, codes of conduct, and regulation were raised in the interviews, which are not discussed in the literature. Rather, this paper hence highlights the ability of case studies to identify ethical issues not covered (or covered to an inadequate degree) in the academic literature which are facing practitioners in the energy sector.

Keywords: smart grids, ethics, big data

Introduction

As a crucial element of our overall energy and climate strategy, we need to ensure that our energy infrastructure is sustainable, goal-oriented and operational. - Miguel Arias Cañete, European Commissioner for Climate Action and Energy

The energy sector represents the critical infrastructure upon which all other economic activities, modern life conveniences, and services—including the wide spectrum of information and communication technologies (ICT)—are based. The expected demands on the energy sector over the coming years will be immense due to the proliferation of ICT technologies and their ubiquitous use in all aspects of social and economic life.

Many factors will increase society’s electricity demands in Europe, such as the advent of internet of things (IoT) sensors, the increased digitization of social life due to robotics and blockchain, the further digitization of industry, and the transition from vehicles using fossil fuel to electric vehicles. In parallel, to tackle climate change and decrease reliance on imported fossil fuels, Europe is pushing for greater integration of renewables in the mix of energy production sources. In combination, both put great pressure on the capacity of Europe’s pre-existing energy distribution network infrastructure, which cannot currently scale up to meet expected demands, at least not if managed in traditional ways. European countries have two options: invest in upgrading energy infrastructure networks or optimize the use of the existing infrastructure capacity by utilizing smart information systems (SIS).

On the supply side, improved efficiency can derive from better management of volatile renewable energy generation solutions, improved maintenance of the energy grid infrastructure, and even enhancing modelling of demand needs and thereby infrastructure investments. On the demand side, improved efficiency can derive from shifting energy consumption patterns through real-time demand-response pricing and load balancing across the grid.

While the use of SIS in energy distribution, i.e., in smart grids, holds the promise that countries will be able to ensure affordable and sustainable energy for the ever-increasing energy demands of smart living, it presents a number of ethical challenges.

This case study reviews the social, ethical, and human rights issues arising from the utilization of SIS (artificial intelligence [AI] technologies and big data analytics) in the energy sector, and in particular in smart energy grids. The next section explains the use of SIS in the energy sector and in particular in energy distribution via energy grids. The section after, a review of the current implementation of big data and AI-powered analytics in the energy sector and the ethical issues that may arise as a result are reviewed. The section focuses on the types of SIS technologies being used and highlights the range of social and ethical issues surrounding their use. The third section will analyse a Dutch distribution system operator (DSO) company, while the fourth will explore and critically evaluate ethical issues arising from the introduction of SIS technologies in smart grids in practice, through interviews conducted with staff members. This section will evaluate whether there are policies and procedures in place to tackle these issues and what the protocol is for addressing concerns.

The use of SIS in smart grids

The use of SIS in energy promises to ensure sustainable affordable energy for the ever-increasing demands of smart living without big investments in the energy distribution systems in two ways. First, SIS allow for the optimizations of energy supply and demand management from existing resources. Smart grids involve a host of intelligent technologies to improve the management of the energy distribution network that connects energy producers with consumers. These include:

• sensors that collect real-time information about energy quality at different points along the distribution network;
• sensors that collect information about consumption via smart meters installed in people’s houses;
• a mechanism to analyze all collected data in order to better predict energy needs, optimize supply and demand, and swiftly respond to unpredictable changes in either; and
• a means to provide the necessary insights to design incentivization programs to change energy consumption behaviors.

Second, smart grids enable the safe incorporation of renewables and green electricity into the grid. While renewables are a key component of Europe’s sustainability goals, their integration poses a challenge for traditional power grids. Surges of power generated by renewables may overcharge the grid leading to power cuts and costly maintenance work, or compromise the reliability and quality of the electricity provided. SIS allow to safely manage the risks from integrating renewables into the energy production mix. According to Liang, quality issues arise from:

• voltage and frequency fluctuations that can be caused by the intermittent nature of renewable energy production due to changing weather conditions, and
• harmonic (wavelength) distortions introduced by electronic devices utilized in renewable energy generation.

Such risks introduced by renewable energy technologies may affect the performance of electrical equipment, particularly sensitive electronic devices. A number of problems can compromise the performance and reliability of electronic systems, such as equipment shutoff, errors or memory loss, loss of data and burned circuit boards, reader errors, and the like. To handle such volatility requires the monitoring and control of electricity from the point of production to the point of consumption, as well as real-time adjustments in energy distribution depending on fluctuations in weather conditions, fluctuations in energy generation and demand, and other factors that can affect the quality of the energy supply. Hence, the use of smart grid technologies can help solve the World Energy Council's "Energy Trilemma": how to secure (energy security) affordable energy for all (energy equity) in a sustainable manner (environmental sustainability).

Fig. 1 The three dimensions of the Energy Trilemma. Source: World Energy Trilemma Index 2018, World Energy Council 2018

Energy security: Effective management of primary energy supply from domestic and external sources, reliability of energy infrastructure, and ability of energy provide to meet current and future demand.

Energy equity: Accessibility and affordability of energy supply across the population.

Environmental sustainability: Encompasses achievement of supply- and demand-side energy efficiencies and development of energy supply from renewable and other low-carbon sources.

The application of SIS in the energy sector in Europe is deployed within the wider context of accelerating the European Energy System transformation set out by the Integrated Strategic Energy Technology Plan (SET-Plan). The SET plan was originally conceived in 2008 and amended in 2015 to introduce steps towards a pan-European Energy Union. It seeks to accelerate knowledge and technology transfer and adoption, foster research and development (R&D), and drive the uptake of low-carbon energy technologies to reach energy and climate change goals and achieve the transition to a low-carbon economy by 2050. The plan makes provisions for dealing with the technological challenges posed by renewables, their volatility in energy production, and their distributed nature. It also makes provisions for the integration of the upcoming electric transport systems and IoTs in an integrated energy system, which will require new protocols of data exchange and collaboration across the energy, transport, and ICT sectors and regulators. The plan sets out a number of priorities to facilitate innovation in the sector with the view to develop and implement solutions that can help (a) maximize the value and lifetime of the existing grid to defer large lump sum investments in costly new infrastructure, and (b) integrate power from renewable generation sources and distribute it at a local or regional level (see Figure 2 below).

Fig. 2 Top 10 priorities of the SET plan ratified in Tallinn 2016

Smart grids involve a host of intelligent technologies to improve monitoring and control of energy consumption, and communication technologies to address operational issues around distribution and production, but also collect real-time information about energy consumption from consumers via smart meters. It is worth noting that such technologies do not substitute but complement traditional grids. Such technologies comprise:

• HAN (Home Area Networks), which ensure the communication between smart meters and smart appliances;
• WASA (Wide Area Situational Awareness), which provides monitoring of performance and ensures dynamic prevention and response services when necessary;
• SCADA (Substation Supervisory Control and Data Acquisition) systems, which are used to monitor and control energy plants or equipment, as well as transportation;
• AMI (Advanced Metering Infrastructure), which allows smart meters to communicate with the grid;
• PMUs (Phasor Measurement Units), which allow the concurrent, real-time monitoring of energy supply systems by measuring electricity current and voltage by amplitude and phase across selected locations (stations) of the grid;
• WAMPAC (Wide Area Monitoring Protection and Control), which ensures the security of the power system;
• IEDs (Intelligent Electronic Devices), smart devices which can communicate with each other and with SCADA to enable fault detection and rectification; and
• FACTS (Flexible AC Transmission Systems, such as Unified Power Flow Controllers), which enable long-distance transport and integration of renewable energy sources.

Energy data from a variety of sources is combined and analysed, in order to:

• Develop a responsive power grid to achieve appropriate levels of reliability, resilience, and economic efficiency in the face of the fluctuations of renewable power generation. Smart power grids optimize not only the seamless integration of sustainable power, but also its storage, its connection with other networks (e.g., heat and cold, transport), and the inter-regional exchange of power.
• Develop local and regional energy systems to facilitate the integration of renewables in the local or regional supply by 2030, and enable the inter-regional exchange of spare energy, as well as the security and resilience of European energy systems (see Figure 3 below).

Fig. 3 Current Innovation Priorities of SET plan

Smart grid management systems require the analysis of real-time energy consumption data and energy production data. AMI collect household energy consumption data, as well as data relating to voltage quality, power quality, active energy, and reactive power, as well as diagnostics information about the condition and control of the smart meter itself (pinging the meter) and operational status (indicators, alarms, and error messages) from the meter.

The use of artificial intelligence and big data analytics in the energy sector is nascent. It is contingent upon the widespread adoption of smart meters by the public. According to a study by the European Commission, there were 45 million electricity smart meters installed in Finland, Italy, and Sweden, representing only 25% of the potential market penetration in these countries. This number has increased to around 60% by 2018, but it still lags behind the expected level of 80% for smart meter penetration, which is necessary to require and justify the use of such systems for energy management. Nevertheless, progress towards this goal is likely to be rapid. In 16 European Member States (Austria, Denmark, Estonia, Finland, France, Greece, Ireland, Italy, Luxemburg, Malta, Netherlands, Poland, Romania, Spain, Sweden, and the U.K.) rollouts of smart meters by 2020 or earlier are planned, and Poland and Romania have already seen consumer benefits.

In Germany, Latvia, and Slovakia, smart metering was found to be economically justified, but only for particular groups of customers, while the business case in terms of consumer benefits across the population was either negative or inconclusive in Belgium, the Czech Republic, Germany, Latvia, Lithuania, Portugal, and Slovakia. No roll out plans were available in Bulgaria, Cyprus, Hungary, and Slovenia. In 15 of the 16 Member States, DSOs are responsible for installing smart meters in households which are to be financed via network tariffs. DSOs are responsible for energy distribution infrastructure (mainly electricity and gas piles, exchanges, etc.). DSOs are responsible for not only installing the meters but also for data analytics in most countries, with the exception of Denmark, Estonia, Poland, and the U.K., where data is handled by an independent central data hub; and with the exception of Czech Republic, Germany, and Slovakia, where alternative options for data handling are being considered. The European Commission requires that companies advise customers on how best to balance their energy consumption and enable new energy related services and products. This hinges on access to real-time customer information and raises issues of profiling due to the gathering and storing of sensitive information on the household energy footprint, and stored data in the light of privacy and confidentiality policies.

The European Energy Union aspires to connect the networks of its various countries into an integrated energy system. This will require data and knowledge exchange, as well as collaborations across the energy, transport, and ICT sectors from experts and regulators transnationally. The stakeholder landscape in the energy sector is beginning to change, giving rise to the development of cross-sector and cross-country collaborations, such as the European Technology and Innovation Platform on Smart Networks for the Transition, which includes the participation of industry representatives, research, academia, and users (see Figure 4 below). The development of cross-border groups is an interesting development in that there is an international collaboration between DSOs to tackle common issues, which is of particular interest to the case below. Platforms for a public debate with the participation of all types of future energy providers might be more useful in voicing concerns, getting public commitments towards agreed courses of action, and informing policy and regulations.

Fig. 4 The smart grid innovation landscape: The European Technology and Innovation Platform on Smart Networks for the Transition involves industry representatives, research, academia, and users. Source.

Not only is the relationship between companies within the energy supply chain beginning to change, but also the relationship between companies and customers. Smart utilities promise consumers greater control over their energy consumption choices by collecting and providing customers with real-time information related to energy use and pricing. In addition, the role of end users in the energy value chain is likely to change. With the advent of household renewables solutions, end users will increasingly generate their own power to use, give back to the grid, or exchange at a local level. Hence their role will increasingly change from that of passive consumers of energy to that of an energy prosumer. This will require the development of smart household energy management and billing systems that can become an extension of the existing energy grid.

Typically, end users receive their household energy from energy providers (gas and electricity companies) with whom they have a contract agreement. Many such utilities struggle to garner their customers’ support for the installation of smart meters in households, particularly in Europe and the USA. The two primary concerns fueling the resistance to smart meters relate to health and data privacy issues. Customers, in general, are uncomfortable with commercial organizations, including utilities, possessing such fine-level data that can give away intimate information about one’s lifestyle. Aside from privacy, a number of other ethical issues relate to the use of SIS in smart grids.

Ethical issues of using SIS in smart grids

Despite the wide range of articles on smart grids, there has been very little research on the ethical and legal implications of using SIS technologies in energy. The installation of smart meters in the mainstream is not completed. Consumer research that has taken place relies mostly on pilots with interested parties and looks at the response and use of such technologies, from a functional rather than an ethical perspective. There have been few articles that have cohesively addressed ethical issues of SIS technology in the energy sector. A key issue receiving little attention at present is the implications of energy grids for energy justice and transitional justice to smart grids. Smart grids and their management are only a part of the new energy ecosystem and will likely become the key customer platform and key industry gatekeeper collecting customer insight. There seems to be an underlying mistrust towards energy players and governments about their positions and commitments towards practices that benefit society as a whole once implementation takes place.

Anticipated consumer benefits, such as energy savings, are predicated on the collection and analysis of granular information on household energy usage via smart meters, but these can be used to reveal detailed information about people’s private lives within the home, raising serious questions at a technical and policy level about in-home surveillance and how to address consumers’ privacy interests. These issues have been particularly controversial for gaining user acceptance in the U.S. and Europe. In addition, energy systems are strategic targets for economically-motivated cybercrime, cyberterrorism, and even cyberwar. Smart grids have ICT dependencies that make them more prone to additional security risks (perpetuated by deficiencies in system configurations, network design, software and platform vulnerabilities, or lack of standards and policies). Hence, the use of SIS in the energy sector brings into question societal norms around competing priorities and around the deliberation processes for resolving conflicts and reaching consensus.

The following subsections explore the ethical tensions related to the smart grid. First, ethical issues arising from the impact of smart grids on people’s health are discussed. Next, the implications around eliciting informed consent with privacy and industry practices is addressed. Finally, cybersecurity risks threatening energy security are elaborated upon.

Health and safety: Does the smart grid make us unhealthy?

The wireless communication between smart appliances and the smart grid raises health and safety concerns relating to radio frequency radiation and its possible carcinogenic effects. Similar concerns are associated with cell phone usage. Research conducted by the Electric Power Research Institute on the health implication of the radio-frequency exposure of smart grids indicated that levels fall within acceptable thresholds as defined by the Federal Communications Commission (FCC) for the general public. Yet, adverse public perceptions persist. This has led to various levels of resistance and disputes. For example, in California, citizens and municipalities have resisted the rollout of smart meters, taking the case to the Federal court. The matter also remains open for debate by the International Agency for Research on Cancer. While the case was recently resolved in court, negative perceptions about potential health risks still persist.

Privacy and informed consent: Does the smart grid give away household privacy?

Privacy concerns relate to the granularity of electricity consumption data that can be collected about a household and the intimate lifestyle information that can be inferred from it. For instance, smart meters can tell when someone is at home, whether they are cooking, taking a shower, or watching TV based on monitoring the energy consumption patterns (loads) of such appliances. Customers in general are uncomfortable with commercial organizations, including utilities, possessing such fine-level information. Some people also object to the mandate of being told to have a smart meter installed, unaware of how this decision was made and who were involved in this decision. Objections are then related to trust and transparency. To mitigate such concerns, and in response to Europe's General Data Protection Regulation (GDPR), utilities have embarked on educating customers regarding their privacy policies, the reasons behind data collection and sharing, accessing of data, and consumer rights.

Ofgem, the government regulator for gas and electricity markets in Great Britain, for example, informs citizens of their rights and suggests that energy suppliers and network companies access smart meter data no more than once every 30 minutes to ensure accurate billing, carry out other essential tasks, and share data (with customer consent) with third parties to offer them new products/services, such as dynamic billing options (see Smart Meters: A Guide to Your Rights at Ofgem.gov.uk). The British Chartered Trading Standards Institute (CTSI) have, however, received complaints from citizens about being misinformed and pressured to install smart meters, potentially breaching Consumer Protection from Unfair Trading Regulations 2008 and raising doubts about the industry’s overall ethical code.

New market players such as aggregators and storage operators are expected to offer dynamic consumption advice as a service to residential customers, contingent upon customers’ consent to share their household consumption data. The company Efficiency 2.0, for example, delivers energy efficiency via customer engagement programs, combining personalized technology portfolios and energy conservation actions with rewards and loyalty incentives to optimize energy savings. This raises the question as to who will be able to afford such services and at what cost. Given the high cost of energy as a percentage of discretionary income for poorer families, it also raises the question as to whether poorer families can afford to opt out from giving their consent if this is the only way to get cheaper energy, or will they be indirectly coerced into it? Furthermore, those who live in, e.g., government-assisted accommodation where electricity is paid by the stateor are occupants in shared private housing where the landlord controls the energy supply may not be given the option to consent individually.

Cyber-risks and security: Do we jeopardize energy security?

Cyber-attacks on the smart grid can do significant damage, yet they are still inadequately addressed due to their low probability of occurrence. Using coordinated, distributed resources, cyber-attacks could potentially target sufficient critical power control equipment simultaneously to originate cascading effects and eventually cause the system to collapse. This would not only be harmful to system integrity but also poses huge risks to human safety. The use of sensors and IoT networks opens up the energy grid to cyber-attacks that can cause disruption to energy distribution flows and even to the distribution infrastructure itself. As energy is fundamental for all aspects of modern living (from cooking to heating to telecoms), such disruptions can directly affect people’s well-being.

To date, cyber-attacks on the energy grid have been sparse but raise significant concerns. In 2015, and again in 2016, hackers launched a cyber-attack on West Ukraine’s power grid. The Industroyer malware hijacked standardized industrial communication protocols, which are used in most critical infrastructure systems, to take direct control of electricity substation switches and circuit breakers to cut electricity in 250,000 households in Western Ukraine for several hours. Lying dormant until instructed otherwise, malware can be reactivated remotely to overwrite documents with random data or make the operating system unbootable. Cyber-attacks on the energy grid have economic implications for citizens at a national level; they have cost the U.K. 545 million euro in losses alone, while global losses were estimated to be 1.69 billion euro in 2018.

Affordability and energy equity: A new criteria to set society apart

One of the key drivers for the smartification of energy grids is to create energy abundance, or at least sufficiency, without the need for costly infrastructure investments, in order to maintain energy affordability for all. The introduction of smart grids and active demand systems that monitor and incentivize alternative energy consumption habits can enable dynamic consumption of energy by enabling customers to shift their consumption to take advantage of dynamic pricing.

Industrial and commercial organizations have experts who work on optimizing energy requirements for their organizations. Citizens, however, lack the expertise, drive, and flexibility to change their lifestyles according to dynamic pricing. The same holds for most SMEs and their energy consumption patterns. Energy aggregators and storage operators are expected to offer alternative services to facilitate dynamic consumption and manage the energy production of individual or community owned renewables. Nevertheless, smart energy systems will pose energy equity dilemmas around energy justice. For example, while the cost of the energy grid is funded via taxation and hence shared between citizens, the benefits from transition to smart grids are not equally shared. Affluent consumers (e.g., those who can afford electric cars or to invest in photo-voltaic energy production) will reap the benefits of smart grids earlier and to a larger extent. Smart grids also raise questions about the potential of algorithmic bias in managing energy distribution. For example, how can we ensure that energy distribution algorithms will not be designed to favor charging an affluent person’s electric car over the washing machine of a poorer family?

While smart grids are seen as one of the solutions to effecting energy justice or equity, what they in fact try to achieve is energy abundance, such that there is enough supply to satisfy the disproportionate increases in energy demand. One could argue that via dynamic pricing, such technologies promote social engineering of the energy consumption patterns of large sections of the population for whom spending on energy consumption is a considerable part of their discretionary income, to support the ever-expanding list of electronic gadgets and (soon) electric vehicles available to the more affluent members of society. Poorer socio-economic strata would be the most motivated to save on their energy costs, but they might find it difficult to benefit from dynamic pricing as their energy use is frugal to begin with. There is also a concern that dynamic pricing will leave consumers who are unable to shift their energy consumption worse off, as companies will try to "penalize" energy use during peak times by raising prices.

Sustainability: Doing our bit for climate change

Smart grids are part of the E.U.’s green energy strategy to tackle CO₂ emissions and climate change, in cost efficient ways. The more accurate and real-time the modelling that matches energy production with consumption can be, the more responsive the grid will be in managing electricity flows. Hence, the more reliably renewables can be incorporated into the energy production mix. The transition to sustainable energy resources is a key part of the climate change agenda and closely linked to issues of intergenerational justice. Another key contribution of smart grids towards the E.U.’s sustainability goals is the transition of transportation systems from fossil-fuels to electric. While this will exponentially increase the demand for electricity over the coming years, it enables greater freedom as to which sources this electricity will come from. On the other hand, smart meters and SIS technologies come at an energy cost, as they themselves use electricity to function. Hence, they contribute to a country’s overall energy consumption by the public sector as they become operational part of the energy distribution network, a critical infrastructure funded by taxation.

The case of a large Dutch distribution system operator using SIS in smart grids

This section will focus on a specific Dutch energy company. The company is a large Dutch DSO and as such responsible for introducing smart grid technologies (including smart meters) in the geographical areas of the Netherlands where it operates. According to Foss Ballo (2015), in the Netherlands the introduction of smart meters was met with resistance or indifference by the public for two key reasons: (i) unresolved privacy issues and (ii) lack of transparency, since decision-making about this policy happened "behind closed doors."

Market dynamics and their impact on progress and implementation were also explored. DSOs are transitioning from market facilitators to energy platforms with multiple providers, both traditional and innovative, relying on the customer insights of DSOs. This has created rivalry within the sector and a lack of clarity between energy players.

The aim of this section is to understand the company’s perspective on the ethical issues arising from use of SIS by the organization and by the sector overall. The section is informed by background research on the company’s use of SIS technology, and interviews with staff members on their experiences and use of SIS technologies and their views on the current and anticipated ethical issues pertaining to the use of SIS by both the company and the energy sector more generally.

Description of company

The company is a typical DSO operating in the Netherlands. It manages the networks that transport electricity and gas from energy producers to customers. The company was founded in 2000 and it is based in the middle of the Netherlands. It is one of the largest DSOs and part of a energy provider group. There are seven DSOs in the Netherlands, among which three large DSO’s each servicing a different geographical area. The company services 3.3 million households in the middle band of the Netherlands in the provinces of Gelderland and Noord-Holland, and parts of Flevoland, Friesland, and Zuid-Holland. Much like other DSOs, it is publicly funded and is expected to maintain its financial independence from its holding company to ensure that it provides a level ground for all utilities relying on its distribution network.

According to its website, the company is “implementing Smart Grids to create an intelligent electricity supply system: substations and mid-voltage units are equipped with ICT and sensor technology to make the network intelligent, while also raising its capacity from 10 kV to 20 kV.” This is in response to the environmental sensibilities of modern society around energy use and the need to detach from fossil fuels, which will become increasingly scarce and expensive. As part of that, the holding company has invested in a LiveLab to bring together managers, engineers, and procurement staff to experiment with new technologies, equipment, and processes that can improve the management and maintenance of energy networks.

The company is tasked by the Dutch government to install smart meters for at least 80% of their customer base by 2020. To date, around 60% of its customer base has adopted smart meters. There is hence an urgency to reach out to another 20% of the market by 2020. Installation of smart meters is considered Phase I of the Energy Transition Plan, where meters are mainly used to raise customers' insights into their own energy usage and drive behavioral changes or purchasing decisions that can lead to lower energy consumption. Phase II of the Energy Transition plan, to commence after 2020, is the use of smart meters as a means to support the smarter management of grids and enable innovation in the energy ecosystem. For example, this could enable energy suppliers to develop and deliver new services and flexible tariffs for electricity; allow for more than one electricity provider to serve the same household; and facilitate transactions in renewable microgrids with the main grid.

The company is involved in various pilot initiatives to experiment with smart grid technologies. For example, it has been involved in microgrid projects with citizens and aggregators, such as citizens investing together in generating energy via solar panels, and in collective purchasing of energy facilitated by aggregators (companies which specialize in the automation of high- and middle-voltage stations), as well as the production of smart sensors. The holding company has also created a living lab, a mid-voltage network dedicated to live testing of innovative technologies, equipment, and processes, with collaboration from asset managers, network managers, engineers, and procurement staff (see Figure 5 below).

Fig. 5 The smart grid innovation landscape: The European Technology and Innovation Platform on Smart Networks for the Transition involves industry representatives, research, academia, and users. Source.

Joint interviews were conducted with members of the data protection team within the company. While data protection officers (DPOs) are formally an independent role, the DPOs interviewed reside within the customer data domain (departments smart-metering and customer markets). The DPO team is responsible for ensuring the company’s compliance with GDPR and enabling the organization to utilize and make data available to private organizations in order to facilitate the development of utility services to customers. GDPR is seen by the interviewees as a factor halting progress towards the further adoption of technology by new customers by raising suspicion about corporations’ use of smart meter data. Hence, the company has been proactive in coordinating its effort towards lifting barriers to smart meter adoption and utilization of smart meter data and advising on how to use personal data within the limits of the law.

Description of SIS technologies used in the company

The company does not yet currently use AI as a means to run and manage energy distribution, as envisioned by industry and academic literature, and makes limited use of existing smart meter data for smart grid purposes. Smart meter data collection and analysis is a prerequisite for the development and testing of an operating model that could utilize AI to run a smart grid autonomously or semi-autonomously, and hence the installation of smart meters to the majority of the customer basis is a first step in this direction. Yet, while data from smart meters are getting collected by smart meters in approximately 60% of the company’s customer base, they remain mostly unused for smart grid purposes (see more on this in section 3.3 below) primarily due to lack of clarity in the implementation of privacy laws in combination with energy laws. Despite these tensions, different types of data can legitimately be collected via smart meters:

a) Consumption data collected in 15-minute intervals (electricity) and 60-minute intervals (gas) currently stored within the smart meters

b) Power quality voltage data are not hard related to energy consumption behavior of individual households. Power quality data relate to voltage spikes and dips and are made available through smart meters because of their less privacy sensitive character (voltage spikes and dips have no hard relation to specific energy usage of households). In addition, such data cannot be requested by third parties.

c) Power quality data (current related) is directly related to energy consumption behaviors. This type of data is treated the same as consumption data. Power quality current data is not lightly made available via the smart meters and also cannot be requested by third parties.

d) Pinging the meter, to ensure control of and access to the meter by the company

e) Event data, i.e., status information (indicators, alarms, and error messages) from the meter

Once the legal issues around the use of data get clarified, data from smart meters can be used for the management of the energy grid and, in the future, could also support the provision of personalized services at the household level. The granularity of data collection and analysis depends on the purpose of data analytics. In order to monitor grid performance, the company usually requires aggregated data for smart meters connected to the same energy line, which can involve several hundred meters. When a household experiences a problem with their electricity supply, a specific smart meter needs to be engaged with to diagnose energy leakages. Energy leakages can occur due to a fault in the energy distribution network, or due to energy theft, resulting in unaccounted energy consumption being compensated by the DSO and hence by taxpayers money. The company aspires to use smart meter data for various purposes such as:

• Error, failure and hazard detection, analysis, and prediction
• Power quality monitoring to ensure quality standards set out by law
• Network capacity planning to inform investments
• Visualization of performance and adherence to quality standards
• Status and events recording

To monitor the functioning of the energy grid and detect any malfunction, one needs to combine smart meter readings of events and check the accessibility of the meters and the power quality voltage. This is in line with the company’s legal obligations from the energy law and no analysis of energy consumption data is required. Malfunctions of the energy grid are automatically linked to a compensation fee to the customer, hence it is important to accurately record the duration of the malfunction. To do so, a post-hoc analysis is performed by reading smart meter data on power quality.

Consumption data are used for predicting and projecting energy demand and detecting possible fraud. Consumption data, along with power quality, can be read for a specific period in order to monitor the capacity of the network for the purposes of planning, so that potential bottlenecks can be solved or limited. Consumption data can indicate possible cases of energy theft (as in the case of drug farms, or vacant property squatting, for example) or loss of energy due to technical issues. According to the company’s drafted code of conduct, personal data can be used when:

(a) the data subject has given consent for the processing of personal information for one or more specific purposes;

(b) the processing of personal data is necessary for the execution of an agreement in which the party concerned is involved, or to take measures at the request of the person concerned before the conclusion of an agreement;

(c) the processing of personal data is necessary in order to comply with a legal obligation, which rests on the system operator;

(d) the processing of personal data is necessary to protect the vital interests of the person concerned or of another natural person;

(e) the processing of personal data is necessary for the performance of a task of general interest or of a task within the framework of the exercise of the public authority entrusted to the processor responsible;

(f) the processing of personal data is necessary for the protection of the legitimate interests of the network operator or of a third party, except where the interests or fundamental rights and freedoms of the person concerned which protect personal data require more weight than those interests, especially when the person concerned is a child.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and punctuation. In some cases important information was missing from the references, and that information was added. To more easily differentiate footnotes from references, the original footnotes (which where numbered) were updated to use lowercase letters. The original article has an inline citation for Servapali et al. 2012 but nothing listed in the sources for it. An educated guess was made concerning the source and was added for this version. The original article has an inline citation for ENSI, 2016 but nothing in the sources to match it. The source is presumably the 2018 "Regulatory Innovation Zones for Smart Energy Networks" presentation referenced in the description of Figure 3 and is used in this version for the citation. In the original, Footnote 3 and 8 are found inline in the text but are not expounded upon; they have been omitted for this version. The original has an inline citation for Eder-Neuhauser et al. 2017 but doesn't include the detailed citation in the references; the presumed reference has been added for this version.