Implementation Experiences from Smart Grid Security Applications and Outlook on Future Research

Experiences from smart grid cyber-security incidents in the past decade have raised questions on the applicability and effectiveness of security measures and protection mechanisms applied to the grid. In this chapter we focus on the security measures applied under real circumstances in today’s smart grid systems. Beginning from real world example implementations, we first review cyber-security facts that affected the electrical grid, from US blackout incidents, to the Dragonfly cyber-espionage campaign currently focusing on US and European energy firms. Provided a real world setting, we give information related to energy management of a smart grid looking also in the optimization techniques that power control engineers perform into the grid components. We examine the application of various security tools in smart grid systems, such as intrusion detection systems, smart meter authentication and key management using Physical Unclonable Functions, security analytics and resilient control algorithms. Furthermore we present evaluation use cases of security tools applied on smart grid infrastructure test-beds that could be proved important prior to their application in the real grid, describing a smart grid intrusion detection system application and security analytics results. Anticipated experimental results from the use-cases and conclusions about the successful transitions of security measures to real world smart grid operations will be presented at the end of this chapter.

Electric vehicles and opportunities to displace tailgate emissions using Smart Charging controls

Under the European Union Renewable Energy Directive each Member State is mandated to ensure that 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020. The Irish Government intends to achieve this target with a number of policies including ensuring that 10% of all vehicles in the transport fleet are powered by electricity by 2020. This paper investigates the impact of the 10% electric vehicle target in Ireland in 2020 using a dynamic programming based long term generation expansion planning model. The model developed optimizes power dispatch using hourly electricity demand curves up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. Two distinct scenarios are analysed based on a peak and off-peak charging regimes in order to simulate the effects of the electric vehicles charging in 2020. The importance and influence of the charging regimes on the amount of energy used and tailgate emissions displaced is then determined.

Reductive photocatalysis and smart inks

Semiconductor-sensitised photocatalysis is a well-established and growing area of research, innovation and commercialisation; the latter being mostly limited to the use of TiO2 as the semiconductor. Most of the work on semiconductor photocatalytic systems uses oxygen as the electron acceptor and explores a wide range of electron donors; such systems can be considered to be examples of oxidative photocatalysis, OP. OP underpins most current examples of commercial self-cleaning materials, such as: glass, tiles, concrete, paint and fabrics. OP, and its myriad of applications, have been reviewed extensively over the years both in this journal and elsewhere. However, the ability of TiO2, and other semiconductor sensitisers, to promote reductive photocatalysis, RP, especially of dyes, is significant and, although less well-known, is of growing importance. In such systems, the source of the electrons is some easily oxidised species, such as glycerol. One recent, significant example of a RP process is with respect to photocatalyst activity indicator inks. paiis, which provide a measure of the activity of a photocatalytic film under test via the rate of change of colour of the dye in the ink coating due to irreversible RP. In contrast, by incorporating the semiconductor sensitiser in the ink, rather than outside it, it is possible to create an effective UV dosimeter, based on RP, which can be used as a sun-burn warning indicator. In the above examples the dye is reduced irreversibly, but when the photocatalyst in an ink is used to reversibly photoreduce a dye, a novel, colourimetric oxygen-sensitive indicator ink can be created, which has commercial potential in the food packaging industry. Finally, if no dye is present in the ink, and the semiconductor photocatalyst-loaded ink film coats an easily reduced substrate, such as a metal oxide film, then it can be used to reduce the latter and so, for example, clean up tarnished steel. The above are examples of smart inks, i.e. inks that are active and provide either dynamic information (such as UV dose or O2 level) or a useful function (such as tarnish removal), and all work via a RP process and are reviewed here

Assessment of the Activity of Photocatalytic Paint Using a Simple Smart Ink Designed for High Activity Surfaces

The use of an acid violet 7 (AV7) smart ink to assess the activity of photocatalytic paint is demonstrated. A linear correlation is established between the change in oxidized dye concentration, as measured by diffuse reflectance, and the change in the green component of the RGB color values, obtained using a portable hand-held scanner, suggesting that such tests can be monitored easily using an inexpensive piece of hand-held office equipment, as opposed to an expensive lab-based instrument, such as a diffuse reflectance UV/vis spectrophotometer. The bleaching of the AV7 follows first order kinetics, at a rate that is linearly dependent upon the UVA irradiance (0.30–3.26 mW cm–2). A comparison of relative rate of bleaching of the AV7 ink with the relative rate of removal of NOx, as determined using the ISO test (ISO 22197-1:2007), established a linear relationship between the two sets of results and the relevance of this correlation is discussed briefly.

A smart ink for the assessment of low activity photocatalytic surfaces

The azo dye, basic blue 66 (BB66) is used in a photocatalyst activity indicator ink (paii) to assess the activity of low activity photocatalytic surfaces, such as commercial photocatalytic tiles and silicone contaminated self-cleaning glass. The BB66 paii is shown to respond much faster than a previously reported, resazurin (Rz) based paii, i.e. the use of a BB66 paii on low activity self-cleaning tiles was found to be >6 times faster than the Rz paii. The BB66 paii is also shown to be effective at assessing the activity of piece of commercial self-cleaning glass contaminated with a coating of silicone, on which the Rz ink, in contrast, failed to show any significant change in colour over the same time period.

Smart, Reusable labels for assessing self-cleaning films

Novel, reversible (reusable) photocatalyst activity indicator labels, which undergo a rapid colour change when in contact with a photocatalytic film via the photoreduction of methylene blue contained within the label’s adhesive, are explored as a method for assessing the activity of self-cleaning glass in situ and the laboratory, using digital photography.

Open Source Telecommunications Framework for Time-Series Data from Smart Grid Sensors.

This paper presents a framework for a telecommunications interface which allows data from sensors embedded in Smart Grid applications to reliably archive data in an appropriate time-series database. The challenge in doing so is two-fold, firstly the various formats in which sensor data is represented, secondly the problems of telecoms reliability. A prototype of the authors' framework is detailed which showcases the main features of the framework in a case study featuring Phasor Measurement Units (PMU) as the application. Useful analysis of PMU data is achieved whenever data from multiple locations can be compared on a common time axis. The prototype developed highlights its reliability, extensibility and adoptability; features which are largely deferred from industry standards for data representation to proprietary database solutions. The open source framework presented provides link reliability for any type of Smart Grid sensor and is interoperable with existing proprietary database systems, and open database systems. The features of the authors' framework allow for researchers and developers to focus on the core of their real-time or historical analysis applications, rather than having to spend time interfacing with complex protocols.

Towards a Cyber-physical Resilience Framework for Smart Grids

As modern power grids move towards becoming a smart grid, there is an increasing reliance on the data that is transmitted and processed by ICT systems. This reliance introduces new digital attack vectors. Many of the proposed approaches that aim to address this problem largely focus on applying well-known ICT security solutions. However, what is needed are approaches that meet the complex concerns of the smart grid as a cyber-physical system. Furthermore, to support the automatic control loops that exist in a power grid, similarly automatic security and resilience mechanisms are needed that rely on minimal operator intervention. The research proposed in this paper aims to develop a framework that ensures resilient smart grid operation in light of successful cyber-attacks.

Demand-side characterisation of the smart city for energy modelling.

This paper presents a new methodology for characterising the energy performance of buildings suitable for city-scale, top-down energy modelling. Building properties that have the greatest impact on simulated energy performance were identified via a review of sensitivity analysis studies. The methodology greatly simplifies the description of a building to decrease labour and simulation processing overheads. The methodology will be used in the EU FP7 INDICATE project which aims to create a master-planning tool that uses dynamic simulation to facilitate the design of sustainable, energy efficient smart cities.

Cybersecurity Test-Bed for IEC 61850 based Smart Substations

With the development and deployment of IEC 61850 based smart substations, cybersecurity vulnerabilities of supervisory control and data acquisition (SCADA) systems are increasingly emerging. In response to the emergence of cybersecurity vulnerabilities in smart substations, a test-bed is indispensable to enable cybersecurity experimentation. In this paper, a comprehensive and realistic cyber-physical test-bed has been built to investigate potential cybersecurity vulnerabilities and the impact of cyber-attacks on IEC 61850 based smart substations. This test-bed is close to a real production type environment, and has the ability to carry out end-to-end testing of cyber-attacks and physical consequences. A fuzz testing approach is proposed for detecting IEC 61850 based intelligent electronic devices (IEDs) and validated in the proposed test-bed.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.