Re-identification of Smart Meter data

The Smart Grid approach enhances the power grid with information technology. Smart Meters are an important part of the Smart Grid. They record the energy consumption of households with a high-resolution and transfer consumption records to the energy provider in real time. Since they allow to infer personal information like the daily routine of the household members, Smart Meters are also a promising source for lifelogging. However, in liberalized energy markets, many different parties have access to these data. This puts the privacy of consumers at risk. In this paper, we analyze to which degree Smart Meter data, as collected by our industry partner, can be linked to its producer, using simple statistical measures. We devise features of the energy consumption, for example, the first peak of demand in the morning, and we describe an analytical framework that quantifies how well these features can identify households. Finally, we conduct a study with 60,480 energy-consumption records from 180 households. Our study shows that 68 % of the records can be re-identified with simple means already. This insight is important for Smart Grids, as it emphasizes the need for research and use of anonymization techniques for the Smart Grid.     

Cyber security in the Smart Grid: Survey and challenges

The Smart Grid, generally referred to as the next-generation power system, is considered as a revolutionary and evolutionary regime of existing power grids. More importantly, with the integration of advanced computing and communication technologies, the Smart Grid is expected to greatly enhance efficiency and reliability of future power systems with renewable energy resources, as well as distributed intelligence and demand response. Along with the silent features of the Smart Grid, cyber security emerges to be a critical issue because millions of electronic devices are inter-connected via communication networks throughout critical power facilities, which has an immediate impact on reliability of such a widespread infrastructure. In this paper, we present a comprehensive survey of cyber security issues for the Smart Grid. Specifically, we focus on reviewing and discussing security requirements, network vulnerabilities, attack countermeasures, secure communication protocols and architectures in the Smart Grid. We aim to provide a deep understanding of security vulnerabilities and solutions in the Smart Grid and shed light on future research directions for Smart Grid security.     

Socio-technical smart grid optimization via decentralized charge control of electric vehicles

The penetration of electric vehicles becomes a catalyst for the sustainability of Smart Cities. However, unregulated battery charging remains a challenge causing high energy costs, power peaks or even blackouts. This paper studies this challenge from a socio-technical perspective: social dynamics such as the participation in demand-response programs, the discomfort experienced by alternative suggested vehicle usage times and even the fairness in terms of how equally discomfort is experienced among the population are highly intertwined with Smart Grid reliability. To address challenges of such a socio-technical nature, this paper introduces a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness. In contrast to related work, a novel autonomous software agent exclusively uses local knowledge to generate energy demand plans for its vehicle that encode different battery charging regimes. Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced system-wide. Evaluation with real-world data confirms the improvement of drivers’ comfort and fairness using the proposed planning method, while this improvement is assessed in terms of reliability and cost reduction under a varying number of participating vehicles. These findings have a significant relevance and impact for power utilities and system operator on designing more reliable and socially responsible Smart Grids with high penetration of electric vehicles.     

Designing dependable and sustainable Smart Grids – How to apply Algorithm Engineering to distributed control in power systems

In this work, we present the Smart Grid Algorithm Engineering (SGAE) process model for application-oriented research and development in information and communication technology (ICT) for power systems. The SGAE process model is motivated by the main objective of contributing application-oriented research results for distributed control concepts on a sound methodological background. With this process model, we strive for an engineering aspiration within the domain of Smart Grids. The process model is set up with an initial conceptualisation phase followed by an iterable cycle of five phases with both analytical and experimental parts, giving detailed information on inputs and results for each phase and identifying the needed actors for each phase. Simulation of large-scale Smart Grid scenarios is a core component of SGAE. We therefore elaborate on tooling and techniques needed in that context and illustrate the whole process model using an application example from a finished research and development project.     

Characterizing Grids: Attributes, Definitions, and Formalisms

Grid systems and technologies have evolved over nearly a decade; yet, there is still no widely accepted definition for Grids. In particular, the essential attributes that distinguish Grids from other distributed computing environments have not been articulated. Most approaches to definition adopt a static view and consider only the properties and components of, or the applications supported by, Grids. The definition proposed in this paper is based on the runtime semantics of distributed systems. Rather than attempt to simply compare static characteristics of Grids and other distributed computing environments, this paper analyzes operational differences, from the viewpoint of an application executing in both environments. Our definition is expressed formally as an Abstract State Machine that facilitates the analysis of existing Grid systems or the design of new ones with rigor and precision. This new, semantical approach proposes an alternative to the currently accepted models for determining whether or not a distributed system is a Grid.     

Algorithm for cataloging topologies in the Common Information Model (CIM)

This paper presents a novel algorithm for finding the catalog of topologies in a power system’s model in the Common Information Model (CIM) format. The algorithm prepares the models of complex, large-scale power systems (e.g., Smart Grids with renewable energy sources) and allows analytic Distribution Management System (DMS) functions to achieve high performance and optimize the power system operation in real time. It utilizes the Ullmann graph isomorphism algorithm to find unique topologies. In addition, it is optimized for parallel execution on 64-bit, multi-processor computers. Its ability to handle large amounts of data was verified on detailed, real-life electric power system data.     

低碳经济的发展建设推动智能电网

阐述了低碳经济催生下的智能电网发展现状,以及智能电网建设对清洁、可再生能源和节能减排的重要意义。然后本文从电网构架出发分别研究了智能电网发电、输配电、用电环节的低碳发展模式。最后从智能电网技术角度出发讨论了支撑智能电网推动低碳经济发展的关键技术。     

Improving data partition schemes in Smart Grids via clustering data streams

Data mining techniques are traditionally divided into two distinct disciplines depending on the task to be performed by the algorithm: supervised learning and unsupervised learning. While the former aims at making accurate predictions after deeming an underlying structure in data—which requires the presence of a teacher during the learning phase—the latter aims at discovering regular-occurring patterns beneath the data without making any a priori assumptions concerning their underlying structure. The pure supervised model can construct a very accurate predictive model from data streams. However, in many real-world problems this paradigm may be ill-suited due to (1) the dearth of training examples and (2) the costs of labeling the required information to train the system. A sound use case of this concern is found when defining data replication and partitioning policies to store data emerged in the Smart Grids domain in order to adapt electric networks to current application demands (e.g., real time consumption, network self adapting). As opposed to classic electrical architectures, Smart Grids encompass a fully distributed scheme with several diverse data generation sources. Current data storage and replication systems fail at both coping with such overwhelming amount of heterogeneous data and at satisfying the stringent requirements posed by this technology (i.e., dynamic nature of the physical resources, continuous flow of information and autonomous behavior demands). The purpose of this paper is to apply unsupervised learning techniques to enhance the performance of data storage in Smart Grids. More specifically we have improved the eXtended Classifier System for Clustering (XCSc) algorithm to present a hybrid system that mixes data replication and partitioning policies by means of an online clustering approach. Conducted experiments show that the proposed system outperforms previous proposals and truly fits with the Smart Grid premises.     

Multi-Grid, Multi-User Workflows in the P-GRADE Grid Portal

Computational Grids connect resources and users in a complex way in order to deliver nontrivial qualities of services. According to the current trend various communities build their own Grids and due to the lack of generally accepted standards these Grids are usually not interoperable. As a result, large scale sharing of resources is prevented by the isolation of Grid systems. Similarly, people are isolated, because the collaborative work of Grid users is not supported by current environments. Each user accesses Grids as an individual person without having the possibility of organizing teams that could overcome the difficulties of application development and execution more easily. The paper describes a new workflow-oriented portal concept that solves both problems. It enables the interoperability of various Grids during the execution of workflow applications, and supports users to develop and run their Grid workflows in a collaborative way. The paper also introduces a classification model that can be used to identify workflow-oriented Grid portals based on two general features: Ability to access multiple Grids, and support for collaborative problem solving. Using the approach the different potential portal types are introduced, their unique features are discussed and the portals and Problem Solving Environments (PSE) of our days are classified. The P-GRADE Portal as a Globus-based implementation for the classification model is also presented. The work described in this paper is supported by the Hungarian Grid project (IHM 4671/1/2003), by the Hungarian OTKA project (No. T042459) and a collaboration project with the University of Reading.     

SFINA - Simulation Framework for Intelligent Network Adaptations

The introduction of ICT in techno-socio-economic systems, such as Smart Grids, traffic management, food supply chains and others, transforms the role of simulation as a scientific method for studying these complex systems. The scientific focus and challenge in simulations move from understanding system complexity to actually prototyping online and distributed regulatory mechanisms for supporting system operations. Existing simulation tools are not designed to address the challenges of this new reality, however, simulation is all about capturing reality at an adequate level of detail. This paper fills this gap by introducing a Java-based distributed simulation framework for inter-connected and inter-dependent techno-socio-economic system: SFINA, the Simulation Framework for Intelligent Network Adaptations. Three layers outline the design approach of SFINA: (i) integration of domain knowledge and dynamics that govern various techno-socio-economic systems, (ii) system modeling with dynamic flow networks represented by temporal directed weighted graphs and (iii) simulation of generic regulation models, policies and mechanisms applicable in several domains. SFINA aims at minimizing the fragmentation and discrepancies between different simulation communities by allowing the interoperability of SFINA with several other existing domain backends. The coupling of three such backends with SFINA is illustrated in the domain of Smart Grids and disaster mitigation. It is shown that the same model of cascading failures in Smart Grids is developed once and evaluated with both MATPOWER and InterPSS backends without changing a single line of application code. Similarly, application code developed in SFINA is reused for the evaluation of mitigation strategies in a backend that simulates the flows of a disaster spread. Results provide a proof-of-concept for the high modularity and reconfigurability of SFINA and puts the foundations of a new generation of simulation tools that prototype and validate online decentralized regulation in techno-socio-economic systems.     

A survey of communication/networking in Smart Grids

Smart Grid is designed to integrate advanced communication/networking technologies into electrical power grids to make them “smarter”. Current situation is that most of the blackouts and voltage sags could be prevented if we have better and faster communication devices and technologies for the electrical grid. In order to make the current electrical power grid a Smart Grid, the design and implementation of a new communication infrastructure for the grid are two important fields of research. However, Smart Grid projects have only been proposed in recent years and only a few proposals for forward-looking requirements and initial research work have been offered in this field. No any systematic reviews of communication/networking in Smart Grids have been conducted yet. Therefore, we conduct a systematic review of communication/networking technologies in Smart Grid in this paper, including communication/networking architecture, different communication technologies that would be employed into this architecture, quality of service (QoS), optimizing utilization of assets, control and management, etc.     

Using a Simple Prioritisation Mechanism to Effectively Interoperate Service and Opportunistic Grids in the EELA-2 e-Infrastructure

Grids currently in production can be broadly classified as either service Grids, composed of dedicated resources, or opportunistic Grids that harvest the computing power of non-dedicated resources when they are idle. While a service Grid provides high and well defined levels of quality of service, an opportunistic Grid provides only a best-effort service. Nevertheless, since opportunistic Grids do not require resources to be fully dedicated to the Grid, they have the potential to assemble a much larger number of resources. Moreover, these Grids cater very well to the execution of the so-called embarrassingly parallel applications, a type of application that is frequently found in practice, and that comprises the largest portion of the typical workload processed in production Grid systems. The EELA-2 e-infrastructure is comprised of a service Grid and an opportunistic Grid that federates computing resources from scientific institutions in both Europe and Latin America. Due to the complementary characteristics of these two types of Grids, a lot of attention has recently been placed in how to interoperate them. In this paper we focus on the less studied problem of assessing the feasibility of such interoperation. We analyse different prioritisation policies that define when the resources of one Grid can be used to run jobs originating from the other. Our results show that in the absence of a suitable prioritisation policy, the benefits that the users of one Grid may have, frequently come with an important negative impact on the users of the other Grid. We also show that a simple reciprocation mechanism is capable of arbitrating the interoperation in such a way that, whenever possible, users profit from the interoperation and, in no case, this benefit leads to a noticeable reduction on the quality of service that the users would experience were the Grids not to interoperate. We conclude discussing how we have implemented, in the context of the EELA-2 project, this prioritisation mechanism, allowing the effective interoperation of a service Grid based on the gLite middleware with an opportunistic Grid that uses the OurGrid middleware.     

A software tool for semi-automatic gridification of resource-intensive Java bytecodes and its application to ray tracing and sequence alignment

Computational Grids deliver the necessary computational infrastructure to perform resource-intensive computations such as the ones that solve the problems scientists are facing today. Exploiting Computational Grids comes at the expense of explicitly adapting the ordinary software implementing scientific problems to take advantage of Grid resources, which unavoidably requires knowledge on Grid programming. The recent notion of “gridifying” ordinary applications, which is based on semi-automatically deriving a Grid-aware version from the compiled code of a sequential application, promises users to be relieved from the requirement of manual usage of Grid APIs within their source codes. In this paper, we describe a novel gridification tool that allows users to easily parallelize Java applications on Grids. Extensive experiments with two real-world applications - ray tracing and sequence alignment - suggest that our approach provides a convenient balance between ease of gridification and Grid resource exploitation compared to manually using Grid APIs for gridifying ordinary applications.     

Towards supporting multiple virtual private computing environments on computational Grids

Grid computing now becomes a practical computing paradigm and solution for distributed systems and applications. Currently increasing resources are involved in Grid environments and a large number of applications are running on computational Grids. Unfortunately Grid computing technologies are still far away from reach of inexperienced application users, e.g., computational scientists and engineers. A software layer is required to provide an easy interface of Grids to end users.To meet this requirement HEAVEN (Hosting European Application Virtual ENvironment) upperware is proposed to build on top of Grid middleware. This paper presents HEAVEN philosophy of virtual computing for Grids – a combinational idea of simulation and emulation approaches. The concept of Virtual Private Computing Environment (VPCE) is thereafter proposed and defined. The design and current implementation of HEAVEN upperware are discussed in detail. Use case of Ag2D application justifies the philosophy of HEAVEN virtual computing methodology and the design/implementation of HEAVEN upperware.     

Key management for Smart Grid based on asymmetric key-wrapping

Cyber security is considered as a critical issue which must be addressed early during the transition from the current aging electric power grid into the expected Smart Grid. Key management is the fundamental supporting cryptographic technologies for Smart Grid cyber security. This paper focuses on remote key generation and distribution for Smart Grid, especially computation-saving mechanisms for meters. Specifically, we specialize asymmetric key-wrapping to the Smart Grid application scenario, and further suggest an instantiation of remotely generating and distributing keys for Smart Grid, which is an issue raised by National Institute of Standards and Technology. In this way, we achieve a key-wrapping adapter, retrofitting an encryption system to work with an incompatible key-management architecture and seamlessly supporting different options of cryptosystems for smart meters. The proposed scheme has low computational cost at weak smart meters, because of the weaker security assumption of the wrapping algorithm and the transfer of computational cost from meters to servers.     

Identification of vulnerable node clusters against false data injection attack in an AMI based Smart Grid

In today׳s Smart Grid, the power Distribution System Operator (DSO) uses real-time measurement data from the Advanced Metering Infrastructure (AMI) for efficient, accurate and advanced monitoring and control. Smart Grids are vulnerable to sophisticated data integrity attacks like the False Data Injection (FDI) attack on the AMI sensors that produce misleading operational decision of the power system (Liu et al., 2011 [1]). Presently, there is a lack of research in the area of power system analysis that relates the FDI attacks with system stability that is important for both analysis of the effect of cyber-attack and for taking preventive measures of protection.In this paper, we study the physical characteristics of the power system, and draw a relationship between the system stability indices and the FDI attacks. We identify the level of vulnerabilities of each AMI node in terms of different degrees of FDI attacks. In order to obtain the interdependent relationship of different nodes, we implement an improved Constriction Factor Particle Swarm Optimization (CF-PSO) based hybrid clustering technique to group the nodes into the most, the moderate and the least vulnerable clusters. With extensive experiments and analysis using two benchmark test systems, we show that the nodes in the most vulnerable cluster exhibit higher likelihood of de-stabilizing system operation compared to other nodes. Complementing research is the construction of FDI attacks and their countermeasures, this paper focuses on the understanding of characteristics and practical effect of FDI attacks on the operation of the Smart Grid by analysing the interdependent nature of its physical properties.     

Large scale distributed visualization on computational Grids: A review

Advances in science and engineering have put high demands on tools for high performance large-scale data exploration and analysis. Visualization is a powerful technology for analyzing data and presenting results. Todays science and engineering have benefited from state-of-the-art of Grid technologies and modern visualization systems. To visualize the large amount of data, rendering technologies are widely used to parallelize visualization tasks over distributed resources on computational Grids. It raises the necessity to balance the computational load and to minimize the network bandwidth requirements. This article explains in Grid environments how new approaches of visualization architecture and load-balancing algorithms address these challenges in a principled fashion. The Grid infrastructure that supports large scale distributed visualization is also introduced. Some typical visualization systems on Grids are referenced for discussions.     

Achieving efficiency, quality of service and robustness in multi-organizational Grids

Scalability, flexibility, quality of service provisioning, efficiency and robustness are the desired characteristics of most computing systems. Although the emerging Grid computing paradigm is scalable and flexible, achieving both efficiency and quality of service provisioning in Grids is a challenging task but is necessary for the wide adoption of Grids. Grid middleware should also be robust to uncertainties such as those in user-estimated runtimes of Grid applications. In this paper, we present a complete middleware framework for Grids that achieves user satisfaction by providing QoS guarantees for Grid applications, cost effectiveness by efficiently utilizing resources and robustness by intelligently handling uncertain runtimes of applications.     

A Launch-time Scheduling Heuristics for Parallel Applications on Wide Area Grids

Large and dynamic computational Grids, generally known as wide-area Grids, are characterized by a large availability, heterogene- ity on computational resources, and high vari- ability on their status during the time. Such Grid infrastructures require appropriate schedule mechanisms in order to satisfy the application performance requirements (QoS). In this paper we propose a launch-time heuristics to schedule component-based parallel applications on such kind of Grid. The goal of the proposed heuristics is threefold: to meet the minimal task computation- al requirement, to maximize the throughput between communicating tasks, and to evaluate on-the-fly the resource availability to minimize the aging effect on the resources state. We evaluate the proposed heuristics by simulations applying it to a suite of task graphs and Grid platforms randomly generated. Moreover, a further test was conducted to schedule a real application on a real Grid. Experimental results shown that the proposed solution can be a viable one.     

Grid Economics: A Selective Discussion of Two Research Problems

The last 5 years have seen considerable discussion of various types of Grids—compute Grids, storage Grids, and data Grids. Using the checklist given in Foster (, 2002) to define a Grid, two important problems that arise in the context of resource sharing in Grid computing environments are discussed. First, the well documented problem in compute Grid environments that arises from the inability of consumers to accurately estimate their resource requirements is presented. This results in incorrect scheduling of requests for Grid resources and social welfare loss. To address this problem, two research proposals are briefly described. The first approach argues for the design of decision support tools to help users with resource estimation while the second approach studies the design of resource allocation mechanisms that can work with stochastic specifications of resource requirements. This is in contrast to the traditional point estimates of resource required by extant mechanisms. Next, resource provisioning and pricing problems that arise in data storage and retrieval Grids are described. These Grids differ fundamentally from compute Grids but share some economic characteristics with P2P file sharing networks. Drawing on this connection, pricing mechanisms and resource provisioning research is briefly discussed.