This chapter provides an overview of the theory of controlled sensing, and its application to the sequential design of data-acquisition and decision-making processes. Based on the theory, it provides an overview of the applications to the quickest detection and localization of anomalies in power systems. This application is motivated by the fact that agile localization of anomalous events plays a pivotal role in enhancing the overall reliability of the grid and avoiding cascading failures. This is especially of paramount significance in the large-scale grids due to their geographical expansions and the large volume of data generated. Built on the theory of controlled sensing, the chapter discusses a stochastic graphical framework for localizing the anomalies with the minimum amount of data. This framework capitalizes on the strong correlation structures observed among the measurements collected from different buses. This framework, at its core, collects the measurements sequentially and progressively updates its decision about the location of the anomaly.

This chapter reviews the methods used to estimate the state of a power system and its network model based on the measurements provided by supervisory control and data acquisition (SCADA) systems and/or by phasor measurement units (PMU). Initially, it provides an overview of the commonly implemented SCADA-based state estimators. Network observability and bad data processing functions are briefly described. This is followed by a description of the changes in the problem formulation and solution introduced by the incorporation of PMU measurements as well as the associated opportunities and challenges. Finally, detection, identification, and correction of network model errors and impact of such errors on system reliability as well as market operations are presented.

This chapter focuses on distributed control and learning for electric vehicle charging. After a brief survey, it covers three related sets of algorithms: (i) distributed control for electric vehicle charging based on a basic formulation; (ii) distributed control for an extension of the basic setting to include network capacity constraints; and (iii) distributed learning for an extension of the basic setting with limitations in the information flow. The chapter ends with a brief summary of open problems.

This chapter introduces mean field games to capture the mutual interaction between a population and its individuals.Within this context, a new equilibrium concept called mean field equilibrium replaces the classical Nash equilibrium in game theory. In a mean field equilibrium each individual responds optimally to the population behavior. In other words, no individuals have incentives to deviate from their current strategies. This new way of modeling the interactions among members of large populations is used to study dynamic demand response management in electricity grids. Moreover, some generalizations of the classical idea of mean field games are introduced to embrace the situations in which the whole population can be divided into classes of members.

The problem of tracking the system frequency is ubiquitous in power systems. However, despite numerous empirical comparative studies of various algorithms, the underlying links and commonalities between frequency tracking methods are often overlooked. To this end, we show that the treatment of the two best known frequency estimation methodologies: (i) tracking the rate of change of the voltage phasor angles, and (ii) fixed frequency demodulation, can be unified, whereby the former can be interpreted as a special case of the latter. Furthermore, we show that the frequency estimator derived from the difference in the phase angle is the maximum likelihood frequency estimator of a nonstationary sinusoid. Drawing upon the data analytics interpretation of the Clarke and related transforms in power system analysis as practical Principal Component Analyzers (PCA), we then set out to explore commonalities between classic frequency estimation techniques and widely linear modeling. The so-obtained additional degrees of freedom allow us to arrive at the adaptive Smart Clarke and Smart Park transforms (SCT and SPT), which are shown to operate in an unbiased and statistically consistent way for both standard and dynamically unbalanced smart grids. Overall, this work suggest avenues for next generation solutions for the analysis of modern grids that are not accessible from the Circuit Theory perspective.

Solar irradiance is the source of exergy for all living organisms. Photosynthesis in primeval organisms generates the food for the other species. It also provides the chemical energy in biomass that is used as fuel. Energy conversions in humans produce mechanical work using food as the exergy source. The food intake; the metabolic and thermic processes in the human body; the production of adenosine triphosphate (ATP); and the conversion of the ATP energy into mechanical work are analyzed using the principles of thermodynamics. An interesting conclusion is that humans have evolved as inefficient energy conversion systems, with food-to-work exergetic efficiencies close to 10%. The analyses and a number of examples in this chapter elucidate the application of thermodynamics to biological processes including: production and use of biomass; exergy value of nutrients; exergy spent for vital processes, such as respiration, blood circulation, and maintenance of body temperature; and exergy spent in sports, such as weight-lifting, walking races, the marathon, and bicycling. The chapter also surveys the relationship between exergy destruction, the state of health, aging, and life expectancy.

Data injection attacks serve as the hallmark example of the security concerns posed by the incorporation of advanced sensing and communication capabilities in power systems. Data injection attacks arise when one or several malicious attackers compromise a subset of the meters used by the state estimation procedure with the aim of manipulating the estimate obtained by the network operator. This chapter surveys the main data injection attacks that are formulated under the assumption that the state variables do not posses a probabilistic description and, therefore, the network operator implements unbiased state estimation procedures. Data injection attacks without this assumption are also studied. In particular, when the network operator perform minimum mean square error (MMSE) estimation, a fundamental trade-off is established between the distortion induced by the attacker and the achievable probability of attack detection. Within this setting, optimal attack strategies are described. The chapter also describes stealth attack constructions that simultaneously minimize the amount of information obtained by the network operator and the probability of attack detection.

Our society does not need energy per se. We use the various forms of energy to accomplish desired actions – commuting to work, keeping the interior of homes at comfortable temperatures, producing industrial goods, etc. The so-called “minimum energy” requirement for processes is actually a thermodynamic maximum, defined by exergy. The application of the exergy methodology determines the benchmark for the minimum energy resources that arerequired to perform the desired actions and tasks. The minimum energy benchmark is determined for several processes including: natural gas transportation, refrigeration, liquefaction, drying, water desalination, and petroleum refining. The energy requirements for the lighting, heating and air-conditioning of buildings are also calculated as well as the minimum energy for the transportation of goods and the commuting of persons in conventional and electric vehicles. Given their importance for the transition to renewable energy forms, the exergy method is applied to energy storage systems. Several examples in this chapter offer assistance and resources for the application of the exergy methodology to energy-consuming systems and processes.

Graph Signal Processing (GSP) is a general theory, whose goal is to bring about tools for graph signals analysis that are a direct generalization of Digital Signal Processing (DSP). The goal of this chapter is understanding the graph-spectral properties of the signals, which are typically explained through the linear generative model using graph filters. Are PMU a graph signal that obeys the linear generative model prevalent in the literature? If so, what kind of graph-filter structure and excitation justifies the properties discussed already? Can we derive new strategies to sense and process these data based on GSP? By putting the link between PMU data and GSP on the right footing, we can determine to what extent GSP tools are useful, and specify how we can use the basic equations for gaining theoretical insight that support the observations.

This chapter focuses on critical infrastructures in the power grid, which often rely on Industrial Control Systems (ICS) to operate and are exposed to vulnerabilities ranging from physical damage to injection of information that appears to be consistent with industrial control protocols. This way, infiltration of firewalls protecting the control perimeter of the control network becomes a significant tread. The goal of this chapter is to review identification and intrusion detection algorithms for protecting the power grid, based on the knowledge of the expected behavior of the system.

The electric power system is evolving toward a massively distributed infrastructure with millions of controllable nodes. Its future operational landscape will be markedly different from existing operations, in which power generation is concentrated at a few large fossil-fuel power plants, use of renewable generation and storage is relatively rare, and loads typically operate in open-loop fashion. This chapter provides an overview of the technical developments that aim to leverage advances in optimization and control to develop distributed control frameworks for next-generation power systems that ensure stability, preserve reliability, and meet economic objectives and customer preferences.