Smart Grids from a Global Perspective

The accessibility of small scale renewable sources, the emergence of electric vehicles, and a need for sustainability are fueling a change in the way electricity is produced and consumed. This is happening together with the digitalization of the electric infrastructure, something that is providing for vast amount of data and control opportunities. We overview the current and promised change in the electricity distribution grid from the perspective of Information and Communication Technology, taking the points of the smart meter, the user, the utility, and the ICT service provider.

The promise of smart grids is very attractive. However, it is not yet clear what the future smart grid will look like. Although most researchers acknowledge that users will play a more prominent role in smart grids, there is a lot of uncertainty on this issue. To counter the strong technological bias in smart grid research and literature, we propose that research should focus more on the social and business dimension of smart grid developments. The main elements of such a research agenda are:More general, on the role of users in smart grids, the main lesson is that user roles should be taken more seriously in relation to smart grids: experts should no longer regard users exclusively and/or simply as potential barriers to smart grid innovation but also as important stakeholders and potential participants in the innovation process.

A transition to smart grids requires a wide range of changes in household energy behaviour. In this chapter we discuss four key issues important for understanding and promoting behaviour in smart grids. First, we need to identify which behaviour needs to be changed. A transition to smart grids involves changes in a wide range of energy behaviours, including the adoption of sustainable energy resources, energy-efficient technologies, and automated control technology; investments in energy efficiency measures in buildings such as insulation; and user behaviour. Second, we need to know which factors influence behaviour in smart grids. We discuss the role of motivations and contextual factors. Third, it is important to test effects of interventions aimed to promote smart energy behaviours. Interventions can be aimed at changing the actual costs and benefits of behaviour, or at changing people’s perceptions and evaluations of different costs and benefits of behavioural options. Fourth, we need to understand which factors influence the acceptability of energy policies and energy systems changes aimed to promote smart grids. In this chapter we address important findings from psychological studies on these topics.

Smart grids are defined in a variety of ways that are more or less continuous with current energy systems and technologies. Since their emergence in the first decade of the 21st century, a number of trends have become visible in the way smart grids are defined, from revolutionary break, to additive (‘adding an ict layer’), to enabling the energy transition. Smart Grids as a term is increasingly accused of being a rather vague label for a variety of innovations. This scepticism around the term indicates that it may be moving from being the latest buzzword to being decried as ‘hype’. But this multiplicity is in itself interesting. Closer consideration of what we talk about when we talk about smart grids provides insights into the current paths to innovation that are emerging and into the changing requirements to energy systems. In this chapter, I put forth three ways of looking at definitions of smart grids and the functions they fulfill: as promissory work, as creation of new objects and as boundary work. By considering the functional value of definitions beyond description, a richer, more critical discussion can arise. Shedding this light on the definitions of smart grids provides a tool for interdisciplinary interaction and a useful analytic basis for collaborative work on smart grids.

This chapter discusses anomalies which may not be attributed to expected operational deviations and/or mishaps associated with component failure and/or environmental conditions. The question here is: what are known cyber-security vulnerabilities which could be used to aid in the detection of patterns and signatures associated with various types of attacks and intrusions in the system which need to be detected and analyzed using Smart Grid’s sensory data, such as Smart meter’s and/or PMU’s data, to help differentiate between “cyber-attacks in progress” as opposed to “expected system anomalies” due to operational failures of its components?

This chapter addresses the balancing problem that arises in smart energy grids. Because power generation from renewable energy resources is tied to environmental factors, supply is often fluctuating and decentralised. Minimising the imbalance between supply and demand is important for grid stability, as well as for economic considerations. Flexible appliances propose a means to achieve supply-demand matching by shifting their production or consumption in time. We take a distributed optimal control point of view: we formulate the problem as an optimal control problem and suggest solutions based on distributed model predictive control (MPC) methods. In particular, we aim to minimise the imbalance using demand response regulation and via Power-to-Gas facilities that offer energy storage. Furthermore, we discuss how demand response regulation can be embedded in the market structure of the Universal Smart Energy Framework. We present example simulations to demonstrate the viability of our approaches.

The growing attention for the environmental effects of using fossil energy calls for an evaluation of current regulatory regimes of energy networks. In the past, tariff regulation of energy networks was mainly meant to foster competition and to improve efficiency in order to achieve lower prices for energy users. Currently, it is generally believed that regulation also has to facilitate the process of decarbonisation. In order to deal, for instance, with the growing significance of distributed generation, distribution-network operators have to upgrade their networks. The key question now is whether the existing regulatory frameworks should be adapted in order to enable these types of developments. This chapter focuses on yardstick regulation, which is a form of tariff regulation in which the allowed revenues of network operators are based on the average costs of all operators. The chapter concludes that several mechanisms exist by which yardstick regulation fosters efficient investments directed at making the grids smarter. However, such a regulatory framework may also include mechanisms potentially hindering efficient investments. These negative effects of regulation on the development of smart grids occur if the regulated firms operate in different circumstances and when externalities exist. The chapter ends by presenting a number of options to deal with such regulatory shortcomings.

This chapter studies the problem of frequency regulation in power grids in the presence of unknown and uncontrollable generation and demand. We propose distributed controllers such that frequency regulation is achieved, while maximising the ‘social welfare’, i.e. maximising the utility of consuming power minus the cost of producing power. The controllable generation and loads are modeled as the output of a first-order system, which includes a widely used model describing the turbine-governor dynamics. We formulate the problem of frequency regulation as an output agreement problem for distribution networks and address it using incremental passivity, enabling a systematic approach to study convergence to the steady state with zero frequency deviation. In order to achieve optimality, the distributed controllers are utilising a communication network to exchange relevant information. The academic case study provides evidence that the performance of the controllers is good.

High level challenges that motivate the evolution towards smart grids include (i) the anticipated electrification of transportation, including electrical vehicles (EVs), and (ii) the increasing penetration of distributed renewable energy sources (DRES). This chapter will discuss how the extra grid load stemming from the EVs can be handled, including the context of reduced control over power generation in light of DRES adoption (especially solar and wind power). After a basic introduction to common EV charging technology, we give two illustrative examples of controlling EV charging: avoiding peaks, and balancing against renewable generation. We then qualitatively present possible demand response (DR) strategies to realize such control. Finally, we highlight the need for, and underlying principles of, (smart grid) simulation tools, e.g., to study the effectiveness of such DR mechanisms.

This chapter assesses the challenges that the introduction of smart meters in the European Union creates for the right to privacy and data protection of individuals in those situations in which the transmitted data are used by law enforcement authorities for surveillance purposes. In presenting the potential risks and the limitations of the existing safeguards for the protection of the individuals by State interferences, this analysis takes a human rights approach based on the existing European legal framework, case law and doctrine. The legal analysis is augmented by evidence collected from technical/engineering studies that show the interest that smart meter data has for law enforcement authorities. It is argued that the current legal framework is not adequate for addressing the challenges that surveillance via smart meter data creates for the rights of the individuals and that the existing legal gap must be taken into account and used in favour of the protection of the fundamental rights of the individuals.

A smart grid is about the delivery of power, but there is power in and behind a smart grid as well. This chapter takes stock of the current debate and the power relations behind smart grids by analysing it through two insights from the French philosopher Michel Foucault, in particular on the relation between ‘power/knowledge’ and his understanding of indirect government through ‘the conduct of conduct’. Based on these insights this chapter makes two arguments. First, that debates about smart grids are hardly about electricity at all but mainly about the infrastructure to gather, analyse and problematize consumption data. In other words, they are about knowledge and in line with a simplified power/knowledge nexus of Foucault this knowledge relates to power and vice versa. Second, while smart grids are favoured to increase consumer choice, they are actually geared towards a particular way of life by organizing the circulation of electricity towards an impeccably behaving consumer. The choices offered to consumers are hence indirectly governed as companies and governments are conducting the conduct of consumers. Based on such an interpretation, there is cause to question the current conduct of smart grids not only from a privacy standpoint, but from a wider understanding on power as well. While smart grids might decentralise and thus democratise electricity production, the centralisation of information inherently negates this decentralisation of production.

This chapter examines the emergence and development of smart grids from a sociological perspective. In particular we draw on ‘social practice theory’ to understand the dynamics of domestic energy consumption and production in emerging smart energy configurations. There are two focal points in the analysis. First, we will concentrate on a specific type of social practices, so called ‘e-practices’. This is a term that we coin to refer to all those practices in and around the home that involve the consumption, conservation, monitoring, generation and storage of energy. Second, we incorporate ‘information flows’ as a key element in our understanding of the emergence of new e-practices. Although the term “smart” has been defined in various ways, a common denominator is that the generation, handling and use of data, information and knowledge is part of what makes a system smart. After introducing both concepts, we outline a conceptual framework around e-practices and information flows that can guide social scientific research on smart energy systems. We also illustrate how this framework can be put to use empirically, based on data that have been gathered in the Netherlands. The chapter is concluded with a research agenda that outlines theoretical and methodological challenges for future smart grid research.

This chapter describes lessons learned from smart grid demonstration projects, which can provide common understanding for large-scale implementation of smart grids across borders. Although this paper is focusing on smart grid demonstration projects in The Netherlands, the analysis is executed from an international perspective. It turns out that each region requires tailor-made solutions depending on local conditions, such as local culture, electricity markets and regulations. Subsequently, this chapter focuses on smart grid projects in the Netherlands. The feasibility project PowerMatching City has demonstrated that it was possible to create smart grids with the associated market models using existing technologies. Small scale demonstration projects, such as PowerMatching City II and ‘Smart Grid: Rendement voor Iedereen’ (Smart Grid: Benefits for All) showed that smart grid services can balance the energy demand and supply in grids with renewable energy sources. Smart grids will only be successful when energy consumers embrace new technologies. Commercially attractive business models, timely standardization and secure IT solutions are required to realize a smart grid at affordable costs. Hence, a fair distribution of the benefits among all the stakeholders (consumers, energy providers and network operators) is essential. The next step is the large-scale demonstration of smart grids and standardization in order to lower the costs for implementation. Smart grid concepts should become available as a more common means of energy supply or even as value adding energy services. Hence, energy targets should be expressed in consumer benefits related with activities such as heating, cooking, washing and watching TV; and attractive incentives should seduce consumers to participate in services offering flexibility. New initiatives such as the Green Deal Smart Energy Cities and the Universal Smart Energy Framework have the objective to stimulate the application of repeatable solutions to increase the implementation of smart grids.

The Danish path to a sustainable energy system focuses on increasing energy efficiency and flexible consumption via smart grid technologies. Information and communication technology is fundamental for achieving these goals by enabling among others new methods and systems for data collection and decision support. This book chapter covers new data collection options exemplified in the concrete case of a living lab for smart grid technologies. Furthermore, the chapter covers the use of visualisation to design decision support for such collected data. We formulate energy management based on energy data as a visualisation problem in the nested model for information visualisation. We prototype a visualisation tool chain to produce a rich set of visualisations based on energy data from five commercial and industrial buildings. Finally, we present qualitative study results for the value of visualisations as an analytical tool. Building on the results we identify important information needs for users of data analysis tools.

In PowerMatching City, the leading Dutch smart grid project, 40 households participated in a field laboratory designed for sustainable living. The participating households were equipped with various decentralized energy sources (PV and micro combined heat-power units), hybrid heat pumps, smart appliances, smart meters, and an in-home display. Stabilization and optimization of the network was realized by trading energy on the market. To reduce peak loads on the smart grid and to be able to make optimal use of the decentralized energy sources, two energy services were developed jointly with the end users: Smart Cost Savings enabled users to keep the costs of energy consumption as low as possible, and Sustainable Together enabled them to become a sustainable community. Furthermore, devices could be controlled automatically, smartly, or manually to optimize the energy use of the households. Quantitative and qualitative studies were conducted to provide insight into the experiences and behaviours of end users. In this chapter, these experiences and behaviours are described. The chapter argues that end users: (1) prefer to consume self-produced energy, even when it is not the most efficient strategy to follow, (2) prefer feedback on costs over feedback on sustainability, and (3) prefer automatic and smart control, even though manual control of appliances felt most rewarding. Furthermore, we found that experiences and behaviours were fully dependent on trust between community members, and on trust in both technology (ICT infrastructure and connected appliances) and the participating parties.