During the night of January 31 to February 1, 1953, the southwest coast of the Netherlands experienced a ferocious storm, killing over 1800 people, causing untold suffering and a major economic loss. As a consequence, the Dutch government initiated the Delta Project, which, through a combination of engineering works, should make the country safe for years to come. As part of this project, risk measures were introduced, like the so-called Dutch standard of a 1 in 10 000 years safety measure. Their statistical estimation was worked out and embedded in major engineering projects. These resulted in the construction of numerous new dikes along the coast. Through this example, we highlight several aspects of hazard protection. First, mathematics has an important role to play. Second, interdisciplinarity is key. Third, with such major and costly projects, spanning several generations, a clear communication to politicians as well as the public is both demanding as well as necessary.

Part one gives a description of the characteristics of the wind field over the ocean, including wind shear, turbulence and coherence. It shows how these parameters are modeled and used as an input to wind turbine analyses. The long-term statistics of the mean wind speed are discussed as well as the most common principles for wind speed measurements. In part two, the kinematics and dynamics of ocean waves are given in a form which in subsequent chapters is used in computing wave loads on structures, both in time and frequency domain. Long- and short-term wave statistics are discussed.

This chapter provides an overview of the main infrastructure systems that are the focus of this book as well as describing fundamental concepts and information about network theory, reliability and availability, and disruptive events that are also applicable to the rest of this book.

This chapter outlines the basic knowledge required from the reader in order for them to follow the narrative in the book. Key terms and concepts are introduced with brief descriptions. The chapter also lists books, articles, and papers by the author, which deal with the subject matter covered in the book in a more detailed fashion.

This concise and self-contained introduction builds up the spectral theory of graphs from scratch, with linear algebra and the theory of polynomials developed in the later parts. The book focuses on properties and bounds for the eigenvalues of the adjacency, Laplacian and effective resistance matrices of a graph. The goal of the book is to collect spectral properties that may help to understand the behavior or main characteristics of real-world networks. The chapter on spectra of complex networks illustrates how the theory may be applied to deduce insights into real-world networks.

The second edition contains new chapters on topics in linear algebra and on the effective resistance matrix, and treats the pseudoinverse of the Laplacian. The latter two matrices and the Laplacian describe linear processes, such as the flow of current, on a graph. The concepts of spectral sparsification and graph neural networks are included.

This chapter is about measuring voltage and current. The use of instrument transformers and also an emerging alternative option to measure current and voltage without electric contact are discussed. Basic concepts, such as sampling rate, reporting rate, measurements accuracy, measurement aliasing, and the impact of averaging filters are covered. Root-Mean-Square (RMS) voltage and current profiles are examined; as time series and in histograms and scatter plots. RMS voltage and current transient responses, which can be caused by faults, equipment actuation, and load operation are discussed. RMS voltage and current oscillations are studied, such as in wide-area oscillations. Three-phase RMS voltage and current measurements; and some applications, such as measuring phase unbalance and phase identification are discussed. The fundamental subject of events in smart grid measurements is partly covered in this chapter. Events can be caused by a change in any component in the power system. Methods for event detection are discussed. Other subjects that are covered in this chapter include measuring frequency, the frequency responses of the electric grid; and measuring frequency oscillations.

This chapter starts by reviewing important concepts from probability theory and stochastic processes. Subsequent chapters on probabilistic input and structural uncertainty make heavy use of random vectors and vector-valued stochastic process, so the reader should be familiar with the material included on these concepts. Next, the chapter provides a review of set-theoretic notions. The material on sets in Euclidean space included in this part is key to understanding the set-theoretic approach to input uncertainty modeling. The chapter concludes with a review of several fundamental concepts from the theory of discrete- and continuous-time linear dynamical systems.

In this chapter, we specify the nature of network infrastructures from our alignment perspective. We first pay attention to the expected services that network infrastructures intend to provide: they are the backbones of the economy and deliver services essential to its citizens. We show how the infrastructures and the services they are expected to deliver are embedded in societal values. We then discuss the two dimensions of network infrastructures, the technological and the institutional dimensions, and analyze the characteristic of complementarity that underlies their components. Complementarities require tight coordination. Furthermore, we discuss in this chapter the core of our argument: the modalities providing technological coordination, on the one hand, and institutional coordination, on the other hand, should be well aligned; otherwise, the fulfillment of critical functions is endangered. We need to better understand how network infrastructures operate and under which conditions they can achieve the expected performance. We focus on the interdependencies between the technological and the institutional dimensions; on the critical functions as requirements for the system to provide the expected services; and on the necessity to align the coordination arrangements in both dimensions, in order to fulfill these critical functions. Otherwise, expected services cannot be delivered.

The basics of game theory, which are necessary for understanding the rest of the book, are provided in this chapter. Specifically, typical game compoments, solution concepts, and their applications are explained.

This chapter covers basics on microgrid operation, distributed energy resources modeling, microgrid control, and virtual synchronous generator. The main topics are hierarchical control principle, droop control, and other advanced controls.

Identifying arbitrary topologies of power networks in real time is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. The potential of recovering the topology of a grid using only the publicly available data (e.g., market data) provides an effective approach to learning the topology of the grid based on the dynamically changing and up-to-date data. This enables learning and tracking the changes in the topology of the grid in a timely fashion. A major advantage of this method is that the labeled data used for training and inference is available in an arbitrarily large amount fast and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time topology identification.

How humanity is fed now and how we can do this better in the future. What can be done and what can everyone do?