Alexandr Vasenev

Towards a collaborative framework to improve urban grid resilience

Two trends will help to ensure resilient electricity supply in Smart Cities: a) the ongoing deployment of Smart Grid technology and b) the adoption of distributed energy resources. Unfortunately, the increased reliance on ICT in the Smart Grid will expose new threats that could result in incidents that might affect urban electricity distribution networks by causing power outages. Diverse specialists will need to cooperate to address these threats. This position paper outlines a methodology for establishing a collaborative framework that supports the definition of response strategies to threats. We consider the ongoing evolution of the electricity grids and the threats emerging while the grid evolves. After outlining possible scenarios of urban grid development, we highlight several threats and the strategies of attackers. Finally, we introduce a framework that aims to foster the collaboration of stakeholders involved in city resilience planning taking into account grid vulnerability and criticality from a citys perspective.

A distributed data collection and management framework for tracking construction operations

Construction work typically means producing on shifting locations. Moving materials, equipment and men efficiently from place to place, in and in between projects, depends on good coordination and requires specialized information systems. The key to such information systems are appropriate approaches to collect de-centralized sensor readings and to process, and distribute them to multiple end users at different locations both during the construction process and after the project is finished. This paper introduces a framework for the support of such distributed data collection and management to foster real-time data collection and processing along with the provision of opportunities to retain highly precise data for post-process analyses. In particular, the framework suggests a scheme to benefit from exploiting readings from the same sensors in varying levels of detail for informing different levels of decision making: operational, tactical, and strategic. The sensor readings collected in this way are not only potentially useful to track, assess, and analyse construction operations, but can also serve as reference during the maintenance stage. To this extent, the framework contributes to the existing body of knowledge of construction informatics. The operationality of the framework is demonstrated by developing and applying two on site information systems to track asphalt paving operations

Assessing loss event frequencies of smart grid cyber threats: Encoding flexibility into FAIR using Bayesian network approach

Assessing loss event frequencies (LEF) of smart grid cyber threats is essential for planning cost-effective countermeasures. Factor Analysis of Information Risk (FAIR) is a well-known framework that can be applied to consider threats in a structured manner by using look-up tables related to a taxonomy of threat parameters. This paper proposes a method for constructing a Bayesian network that extends FAIR, for obtaining quantitative LEF results of high granularity, by means of a traceable and repeatable process, even for fuzzy input. Moreover, the proposed encoding enables sensitivity analysis to show how changes in fuzzy input contribute to the LEF. Finally, the method can highlight the most influential elements of a particular threat to help plan countermeasures better. The numerical results of applying the method to a smart grid show that our Bayesian model can not only provide evaluation consistent with FAIR, but also supports more flexible input, more granular output, as well as illustrates how individual threat components contribute to the LEF.

Threat navigator: grouping and ranking malicious external threats to current and future urban smart grids

Deriving value judgements about threat rankings for large and entangled systems, such as those of urban smart grids, is a challenging task. Suitable approaches should account for multiple threat events posed by different classes of attackers who target system components. Given the complexity of the task, a suitable level of guidance for ranking more relevant and filtering out the less relevant threats is desirable. This requires a method able to distill the list of all possible threat events in a traceable and repeatable manner, given a set of assumptions about the attackers. The Threat Navigator proposed in this paper tackles this issue. Attacker profiles are described in terms of Focus (linked to Actor-to-Asset relations) and Capabilities (Threat-to-Threat dependencies). The method is demonstrated on a sample urban Smart Grid. The ranked list of threat events obtained is useful for a risk analysis that ultimately aims at finding cost-effective mitigation strategies.

Tangible modelling to elicit domain knowledge: an experiment and focus group

Conceptual models represent social and technical aspects of the world relevant to a variety of technical and non-technical stakehold- ers. To build these models, knowledge might have to be collected from domain experts who are rarely modelling experts and don’t usually have the time or desire to learn a modelling language. We investigate an app- roach to overcome this challenge by using physical tokens to represent the conceptual model. We call the resulting models tangible models. We illustrate this idea by creating a tangible representation of a socio- technical modelling language and provide initial evidence of the relative usability and utility of tangible versus abstract modelling. We discuss psychological and social theories that could explain these observations and discuss generalizability and scalability of the approach.

Employing a virtual reality tool to explicate tacit knowledge of machine operations

The quality and durability of asphalted roads strongly depends on the final step in the road construction process; the profiling and compaction of the fresh spread asphalt. During compaction machine operators continuously make decisions on how to proceed with the compaction accounting for projectspecific aspects as: ambient conditions, road geometry, roller type, asphalt mixture characteristics and behavior of other machines. In discussions over quality improvement in road construction it is often suggested to robotize rollers. To operate such robots would of course require operational strategies and routines. The reality of this moment is that these operational strategies and routines are not documented. To identify the existing best working practices and, ultimately, to proceed with developing automated robotized compactors, the knowledge of machine operators should at first be explored and described in a formalized way. Unfortunately operators have difficulty verbalizing their expertise. Observation of behavior, as machine movement patterns, could be helpful to extract the operational strategies, but such observations are time and labor consuming. To overcome this burden we developed a Virtual Reality (VR) environment. In this VR environment operators perform compaction virtually, and their operational choices are traced and analysed. This paper describes this VR development and explains how it helps in following machine operators and extracting their (tacit) professional knowledge. The road geometry and the working conditions are visually represented; the operators show - rather than explain - how they would proceed with the compaction process in the given conditions. Movements of virtual machines are documented, analysed, visualised and discussed. This VR approach is expected to contribute to learning, to more formal description of operational strategies; stepping stones towards compaction algorithms for roller robotisation.

Prediction of the in-asphalt temperature for road construction operations

During the construction of new asphalt roads, compaction is the final step. Proper compaction is crucial for the roads lifetime. The temperature of the asphalt mixture directly impacts on the compactability and therefore the construction process strategy. Ideally compaction should be done within a certain in-asphalt temperature window, with lower and higher temperature boundaries, to achieve high quality road surface. But, as there are no available systems to predict in-asphalt temperature, roller operators have to guess the actual temperatures. This paper describes a method and proposes an implementation of this method to predict in-asphalt temperature at any given position. Calculations are based on an initial asphalt mix temperature during paving operations and the automated computing of a cooling function for a specific mix within certain ambient weather conditions. The implementation of the method was tested using position and temperature information collected by following a real paving project. Outcome of the method - the resulted visualization - aims to provide information about in-asphalt temperature to support decisions of machine operators when to start and stop rolling process to obtain the high quality road surface more reliable.

Load shifting of domestic water heaters under double price tariffs: Bringing together money savings and comfort

Demand Side Management (DSM) programs can offer residential electricity consumers opportunities to cut their energy bills. However, if such programs significantly downgrade comfort of consumers, they can choose to opt them out. This impedes the DSM implementation in practice and declines the efficiency of DSM in overall. Finding ways how consumers can reduce their money expenses with least impact to their comfort is thus desirable. This paper focuses on tank electric water heaters (WHs) under double-price tariffs as a case of energy storage devices under simplified variable pricing. We investigate whether or not the WH load shifting can bring money savings while maintaining the user comfort based on the introduced expenses-comfort balancing approach. The proposed approach is based on day-night energy rates and the energy-comfort model suggested earlier. The refined energy model constitutes the first contribution of this paper. As the second contribution, we reformulate the energy-comfort balancing problem into the problem of ‘expenses-comfort balancing’. By simulating diverse hot water usage we show that the proposed mechanism can enable monetary savings without significant drop of comfort. Specifically, the customers can reach up to 20% of daily money savings compared to the regular operation of the heater during weekdays. The reported research can be of interest to utilities that focus on improving consumer uptake of DSM.

A hierarchical scheme for balancing user comfort and electricity consumption of tank water heaters

Demand response (DR) programs offered by utility companies to residential consumers can negatively affect user comfort because of altering a regular operation of domestic devices. Advanced DR solutions need to account for flexibility of device usage in the form of energy savings that the customers can agree with. Specialized control schemes should inform utility companies about the level of such possible savings. This paper presents a hierarchical scheme that allows to control a domestic hot water storage system according to user comfort requests. The scheme uses a multi-objective optimization approach to interconnect comfort and electricity consumption of the tank water heater. The simulations show that the proposed control can not only significantly (by 95.6%) improve the user thermal comfort during hot water activities, but can also reduce energy consumption by 16.4% as compared to the weekly regular operation of the heater.

Analysing non-malicious threats to urban smart grids by interrelating threats and threat taxonomies

A comprehensive study of the smart grid threat landscape is important for designing resilient urban grids of the future. To this end, an analysis could first cross reference threat categorizations and interrelate treat events on the basis of threat lists that complement each other. This paper show how to cross-relate threat taxonomies and analyze relations between threats and system components to reasonably link diverse threats to a smart grid. We illustrate how one can look beyond a specific threat by (1) relating threat sources from one taxonomy to threat lists from other taxonomies; (2) analyzing how threats can be cross-related to identify possible scenarios of undesirable events; and (3) assigning threat categories to system components. These steps in sequence or individually aim to provide input to threat identification and (thus) risk assessment tasks. This paper focusses on threats listed in the IRENE research project and relates them to threat taxonomies used in the AFTER and SESAME projects which focused on smart grids as well.