Online disturbance prediction for enhanced availability in smart grids

A gradual move in the electric power industry towards Smart Grids brings new challenges to the system's efficiency and dependability. With a growing complexity and massive introduction of renewable generation, particularly at the distribution level, the number of faults and, consequently, disturbances (errors and failures) is expected to increase significantly. This threatens to compromise grid's availability as traditional, reactive management approaches may soon become insufficient. On the other hand, with grids' digitalization, real-time status data are becoming available. These data may be used to develop advanced management and control methods for a sustainable, more efficient and more dependable grid. A proactive management approach, based on the use of real-time data for predicting near-future disturbances and acting in their anticipation, has already been identified by the Smart Grid community as one of the main pillars of dependability of the future grid. The work presented in this dissertation focuses on predicting disturbances in Active Distributions Networks (ADNs) that are a part of the Smart Grid that evolves the most. These are distribution networks with high share of (renewable) distributed generation and with systems in place for real-time monitoring and control. Our main goal is to develop a methodology for proactive network management, in a sense of proactive mitigation of disturbances, and to design and implement a method for their prediction. We focus on predicting voltage sags as they are identified as one of the most frequent and severe disturbances in distribution networks. We address Smart Grid dependability in a holistic manner by considering its cyber and physical aspects. As a result, we identify Smart Grid dependability properties and develop a taxonomy of faults that contribute to better understanding of the overall dependability of the future grid. As the process of grid's digitization is still ongoing there is a general problem of a lack of data on the grid's status and especially disturbance-related data. These data are necessary to design an accurate disturbance predictor. To overcome this obstacle we introduce a concept of fault injection to simulation of power systems. We develop a framework to simulate a behavior of distribution networks in the presence of faults, and fluctuating generation and load that, alone or combined, may cause disturbances. With the framework we generate a large set of data that we use to develop and evaluate a voltage-sag disturbance predictor. To quantify how prediction and proactive mitigation of disturbances enhance availability we create an availability model of a proactive management. The model is generic and may be applied to evaluate the effect of proactive management on availability in other types of systems, and adapted for quantifying other types of properties as well. Also, we design a metric and a method for optimizing failure prediction to maximize availability with proactive approach. In our conclusion, the level of availability improvement with proactive approach is comparable to the one when using high-reliability and costly components. Following the results of the case study conducted for a 14-bus ADN, grid's availability may be improved by up to an order of magnitude if disturbances are managed proactively instead of reactively. The main results and contributions may be summarized as follows: (i) Taxonomy of faults in Smart Grid has been developed; (ii) Methodology and methods for proactive management of disturbances have been proposed; (iii) Model to quantify availability with proactive management has been developed; (iv) Simulation and fault-injection framework has been designed and implemented to generate disturbance-related data; (v) In the scope of a case study, a voltage-sag predictor, based on machine- learning classification algorithms, has been designed and the effect of proactive disturbance management on downtime and availability has been quantified

Networked world: Risks and opportunities in the Internet of Things

The Internet of Things (IoT) – devices that are connected to the Internet and collect and use data to operate – is about to transform society. Everything from smart fridges and lightbulbs to remote sensors and cities will collect data that can be analysed and used to provide a wealth of bespoke products and services. The impacts will be huge - by 2020, some 25 billion devices will be connected to the Internet with some studies estimating this number will rise to 125 billion in 2030. These will include many things that have never been connected to the Internet before. Like all new technologies, IoT offers substantial new opportunities which must be considered in parallel with the new risks that come with it. To make sense of this new world, Lloyd’s worked with University College London’s (UCL) Department of Science, Technology, Engineering and Public Policy (STEaPP) and the PETRAS IoT Research Hub to publish this report. ‘Networked world’ analyses IoT’s opportunities, risks and regulatory landscape. It aims to help insurers understand potential exposures across marine, smart homes, water infrastructure and agriculture while highlighting the implications for insurance operations and product development. The report also helps risk managers assess how this technology could impact their businesses and consider how they can mitigate associated risks

Internet of Things (IoT) and the Energy Sector

Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.Peer reviewe

Internet of Things (IoT) and the Energy Sector

Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.Peer reviewe

Privacy Enhancing Mechanisms in the Smart Grid

The Smart Grid constitutes a hot research topic, nowadays, due to the potential that is has to further improve and optimize the power generation, delivery and consumption. The set of components it is comprised of, such as the smart meters, as well as the advanced communication technologies it incorporates, renders it capable of bringing significant societal benefits and high reliability in reference with its orderly operation. An interesting technology in the context of the Smart Grid is the Demand Response. This technology attempts to change the way that the electricity customers used to perceive the power consumption by engaging them in an interaction with the energy producer. Essentially, the customers are asked to adapt their power needs based on the state of the power grid. In that way, the energy capacity or resources could be shared more efficiently and unpleasant incidents, such as power outages, could be prevented. In return, the utility company offers monetary incentives, rewarding in this way the customers' power curtailment efforts. Nevertheless, the fulfillment of the DR goals requires the exchange of information between the utility company and the customers. From the customers' point of view this interaction might be privacy invasive. Consequently, DR programs could not be widely accepted by the public before the privacy concerns are alleviated. This thesis investigates the trade off between the privacy and the efficiency of a DR mechanism by simulating the stakeholders and their interactions in a mock DR environment

New electric utility management and control systems : proceedings of conference, held in Boxborough, Massachusetts, May 30-June 1, 1979

"This work was supported by the Center for Energy Policy Research and the Electric Power Systems Engineering Laboratory of the Massachusetts Institute of Technology.

Securing CAN-Based Cyber-Physical Systems

With the exponential growth of cyber-physical systems (CPSs), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lacks a systematic review of the CPS security literature. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it difficult to study the problem with one generalized model. As the first component of this dissertation, existing research on CPS security is studied and systematized under a unified framework. Smart cars, as a CPS application, were further explored under the proposed framework and new attacks are identified and addressed. The Control Area Network (CAN bus) is a prevalent serial communication protocol adopted in industrial CPS, especially in small and large vehicles, ships, planes, and even in drones, radar systems, and submarines. Unfortunately, the CAN bus was designed without any security considerations. We then propose and demonstrate a stealthy targeted Denial of Service (DoS) attack against CAN. Experimentation shows that the attack is effective and superior to attacks of the same category due to its stealthiness and ability to avoid detection from current countermeasures. Two controls are proposed to defend against various spoofing and DoS attacks on CAN. The first one aims to minimize the attack using a mechanism called ID-Hopping so that CAN arbitration IDs are randomized so an attacker would not be able to target them. ID-Hopping raises the bar for attackers by randomizing the expected patterns in a CAN network. Such randomization hinders an attacker’s ability to launch targeted DoS attacks. Based on the evaluation on the testbed, the randomization mechanism, ID-Hopping, holds a promising solution for targeted DoS, and reverse engineering CAN IDs, and which CAN networks are most vulnerable. The second countermeasure is a novel CAN firewall that aims to prevent an attacker from launching a plethora of nontraditional attacks on CAN that existing solutions do not adequately address. The firewall is placed between a potential attacker’s node and the rest of the CAN bus. Traffic is controlled bi-directionally between the main bus and the attacker’s side so that only benign traffic can pass to the main bus. This ensures that an attacker cannot arbitrarily inject malicious traffic into the main bus. Demonstration and evaluation of the attack and firewall were conducted by a bit-level analysis, i.e., “Bit banging”, of CAN’s traffic. Results show that the firewall successfully prevents the stealthy targeted DoS attack, as well as, other recent attacks. To evaluate the proposed attack and firewall, a testbed was built that consisted of BeagleBone Black and STM32 Nucleo- 144 microcontrollers to simulate real CAN traffic. Finally, a design of an Intrusion Detection System (IDS) was proposed to complement the firewall. It utilized the proposed firewall to add situational awareness capabilities to the bus’s security posture and detect and react to attacks that might bypass the firewall based on certain rules