Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence

Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Digitalisation For Sustainable Infrastructure: The Road Ahead

In today’s tumultuous and fast-changing times, digitalisation and technology are game changers in a wide range of sectors and have a tremendous impact on infrastructure. Roads, railways, electricity grids, aviation, and maritime transport are deeply affected by the digital and technological transition, with gains in terms of competitiveness, cost-reduction, and safety. Digitalisation is also a key tool for fostering global commitment towards sustainability, but the race for digital infrastructure is also a geopolitical one. As the world’s largest economies are starting to adopt competitive strategies, a level playing field appears far from being agreed upon. Why are digitalisation and technology the core domains of global geopolitical competition? How are they changing the way infrastructure is built, operated, and maintained? To what extent will road, rail, air, and maritime transport change by virtue of digitalisation, artificial intelligence, and the Internet of Things? How to enhance cyber protection for critical infrastructure? What are the EU’s, US’ and China’s digital strategies?Publishe

Real-time pricing algorithms with uncertainty consideration for smart grid

In today modern life smart electrical devices are used to make the human lives more comfortable. Actually, this is the combination of electronics and communications that provides the opportunity for real time communication while the measured electricity by smart meters is sent to the energy provider. In this way smart meters in residential areas play an important role for two way interaction between several users and energy provider. Solving an optimization problem with regard to consideration of satisfaction of both sides of users and energy providers tends to achieve the optimum price that is sent to the users to optimize their consumption in peak demand periods that is the main goal of demand response management programs. As nowadays the renewable energy plays an important role in providing the request of the users specially in residential areas consideration of the concept of uncertainty is an important issue that is considered in this thesis. Therefore, solving the optimization problem in presence of load uncertainty is important topic that is investigated. Another interesting issue is consideration of users' number variation in presence of load uncertainty in dynamic pricing demand response programs which gives the advantage of having good estimation of optimum consumption level of users according to the optimum announced price. In this thesis these issues are considered for solving an Income Based and Utility Base optimization problems that are further explained in upcoming chapters. In chapter III ,which provides the first contribution of the thesis a novel algorithm called Income Based Optimization (IBO) is defined and compared with previously proposed Utility Based Optimization problem (UBO). The price, users' consumption versus provided energy capacity by energy provider in 24 hours period are simulated and analyzed. The effect of variation in other parameters dependent to the cost imposed to the energy provider and the parameters that affect the users level of satisfaction is also evaluated. In Chapter IV, existence of load uncertainty is considered in proposed UBO algorithm when it is assumed that number of users in each time slot is varying based on different distributions such as Uniform or Poison. The results for the average gap between energy provider's generating capacity and consumption of the users are compared with when number of users kept constant in presence of load uncertainty in 24 hours period. Moreover, the effect of different distributions on the gap between generating capacity and the users consumption is evaluated assuming the number of users are increasing and following the distributions. The results for the announced price in 24 hours period is also evaluated and further is extended to the average announced price with respect to increase in number of users when it is assumed that user entry and departure type is varying based on different distributions and the load uncertainty also is existed. In chapter V, the proposed IBO algorithm in chapter three is further extended to the Uncertain IBO and is called UIBO. Therefore, it is assumed that bounded uncertainty is added to the users consumption. This algorithm is further extended in a way that variation in number of users is considered based on different distributions. The results are evaluated for the average gap between generating capacity and users consumption in 24 hours period and is further extended with respect to consideration of the increasing pattern for the number of users in presence of load uncertainty and different types of distributions for the users number variation. With respect to consideration of UIBO algorithm the price in 24 hours period is evaluated and the results are further extended to evaluate the average price with respect to increasing pattern for number of users that are varying based on different distributions when the bounded uncertainty is added to the users consumption. Moreover, the achieved gain of the proposed algorithm based on the ratio of the variation of the announced price to the varying number of users is evaluated. Finally chapter VI provides the conclusion and suggestion for future work

Building a National IoT Plan: Policy Recommendations and the Case of Brazil

The Internet of Things (“IoT”) is an expression that refers to a whole set of new services and devices that includes at least three fundamental aspects: connectivity, use of sensors or actuators, and computational capacity for data processing and storage. The Internet of Things goes beyond connecting objects to each other; it also gives them the power to process data (thereby making them "smart"). For developing countries such as Brazil, the opportunities offered by the Internet of Things can compensate for shortcomings in infrastructure and services, and can improve innovation, quality of life, productivity, and even the economic complexity of our basket of export products. However, the way in which each country will seize this opportunity will depend on its specific aspirations and strategies. The broader economic, social, political, and legal context of the country should be considered, as well as the local development of information and communication technologies. For this reason, the National Bank for Economic and Social Development (BNDES), in partnership with the Ministry of Science, Technology, Innovation and Communications (MCTIC), has commissioned this study, "Internet of Things: An Action Plan for Brazil." This study, mapped by a consortium comprised by McKinsey & Company, the CPqD Foundation, and Pereira Neto | Macedo Law Firm, outlines the local technological and economic challenges related to the topic, as well as well as how to address legal issues inherent to the development of IoT in Brazil

The dichotomy between smart metering and the protection of consumer’s personal data in brazilian law

This paper investigates the intersection between the pursue of the goal of energy efficiency, by means of the installation and use of smart meters, and the protection of power consumers’ personal data, according to Brazilian Law. Since energy data will be collected and processed by distribution utilities, in order to optmize power supply and power consumption, there is a growing and urgent need to verify in what measure the right to privacy is fullfiled in that metter. The Brazilian legal framework still lacks an extensive and detailed regulation regarding the protection of personal data. On the other hand, the Brazilian regulatory agency for electric energy has issued a Normative Resolution on smart meters, which is, nevertheless, too general and leaves to interpretation important questions related to the legal treatment of energy data. While two Draft Bills on smart metering can be regarded as insufficient to address the matter highlighted in this paper, one Draft Bill on personal data’s protection might fill the regulatory gap. Special attention should be drawn towards the relation between the data minimization principle and the efficiency of smart metering systems, since the efficiency of these technology depends on the amount of data collected and processed. It still remains as an open question, which energy data and which amount of energy data from power consumers suits the data minimization principle

Methods to Attack and Secure the Power Grids and Energy Markets

The power grid is a highly complex control system and one of the most impressive engineering feats of the modern era. Nearly every facet of modern society critically relies on the proper operation of the power grid such that long or even short interruptions can impose significant economic and social hardship on society. The current power grid is undergoing a transformation to a Smart Grid, that seeks to monitor and track diagnostic and operational information so as to enable a more efficient and resilient system. This significant transformation, however, has made the grid more susceptible to attacks by cybercriminals, as highlighted by several recent attacks on power grids that have exposed the vulnerabilities in modern power systems. Motivated by this, this thesis aims at analyzing the effect of three classes of emerging cyberattacks on smart grids and a set of possible defense mechanisms to prevent them or at least reduce their damaging consequences in the grid. In the first part of the thesis, we analyze the security of the power grid against the attacks targeting the supervisory control and data acquisition (SCADA) network. We show that the existing techniques require some level of trust from components on SCADA system, rendering them vulnerable to sophisticated attacks that could compromise the entire SCADA system. As a viable solution to this issue, we present a radio frequency-based distributed intrusion detection system (RFDIDS) that remains reliable even when the entire SCADA system is considered untrusted. In the second part of the thesis, we analyze the performance of the existing high-wattage IoT botnet attacks (Manipulation of Demand IoT (MaDIoT)) on power grids and show they are ineffective in most of the cases because of the existence of legacy protection schemes and the randomness of the attacks. We discuss how an attacker can launch more sophisticated attacks in this category which can cause a total collapse of the power system. We illustrate that by computing voltage instability indices, an attacker can find the appropriate time and locations to activate the high-wattage bots, causing (with very high probability) a complete voltage collapse and blackout in the bulk power system; we call these new attacks MaDIoT 2.0. We also propose novel effective defenses against MaDIoT 2.0 attacks by modifying the way classical protection algorithms work in the power networks. In the third part of the thesis, we discuss how an smart attacker with access to high-wattage IoT botnet can indirectly manipulate the energy prices in the electricity markets. We name this attack as Manipulation of Market via IoT (MaMIoT). MaMIoT is the first energy market manipulation cyberattack that leverages high-wattage IoT botnets to slightly change the total demand of the power grid with the aim of affecting the electricity prices in the favor of specific market players. Using real-world data obtained from two major energy markets, we show that MaMIoT can significantly increase the profit of particular market players or financially damage a group of players depending on the motivation of the attacker. We discuss a set of effective countermeasures to reduce the possibility and effect of such attacks.Ph.D