Studies of prosumer decision making in the smart grid have focused on a single decision within the framework of expected utility theory (EUT) and behavioral theories such as Prospect Theory. This chapter studies prosumer decision making in a more natural market situation in which a prosumer has to decide whether to make a sale of solar energy units generated at her home every day or hold (store) the energy units in anticipation of a future sale at a better price. Specifically, it proposes a new behavioral model that extends EUT to take into account bounded horizons (in terms of the number of days) that prosumers implicitly impose on their decision making in arriving at “hold” or “sell” decisions of energy units. The new behavioral model assumes that humans make decisions that will affect their lives within a bounded horizon regardless of how far into the future their units may be sold. Modeling the utility of the prosumer using parameters such as the offered price on a day, the available energy units on a day, and the probabilities of the forecast prices, both traditional EUT and the proposed behavioral model with bounded horizons are fit to prosumer data.

Fast and accurate unveiling of power-line outages is of paramount importance not only for preventing faults that may lead to blackouts but also for routine monitoring and control tasks of the smart grid. This chapter presents a sparse overcomplete model to represent the effects of (potentially multiple) power line outages on synchronized bus voltage angle measurements. Based on this model, efficient compressive sensing algorithms can be adopted to identify outaged lines at linear complexity of the total number of lines. Furthermore, the effects of uncertainty in synchronized measurements will be analyzed, along with the optimal placement of measurement units.

This chapter studies the impact of the deployment of electric vehicles (EVs) at a large scale on the existing and future energy networks. The impact on the grid is assessed in terms of residential distribution network (DN) costs. Essentially, the main goal to optimize the battery charging schedules of EVs to minimize a cost that takes into account residential distribution transformer aging and the distribution energy losses. Within this context, the underlying mathematical tool is a static noncooperative game that describes the interaction between all EVs and the DN operator. An equilibrium analysis is conducted for this game in both its atomic and nonatomic versions.

This chapter describes methods to detect and identify power system transmission line outages in near real time. These methods exploit statistical properties of the small random fluctuations in electricity generation as well as energy demand to which a power system is subject to as time evolves. To detect and identify transmission line outages, a linearized incremental small-signal power system model is used in conjunction with high-speed synchronized voltage phase angle measurements obtained from phasor measurement units. By monitoring the statistical properties of voltage phase angle time-series, line outages are detected and identified using techniques borrowed from the theory of quickest change detection. Several case studies are considered for the cases of detecting and identifying single- and double-line outages in an accurate and timely fashion.

Dictionary learning has emerged as a powerful method for data-driven extraction of features from data. The initial focus was from an algorithmic perspective, but recently there has been increasing interest in the theoretical underpinnings. These rely on information-theoretic analytic tools and help us understand the fundamental limitations of dictionary-learning algorithms. We focus on theoretical aspects and summarize results on dictionary learning from vector- and tensor-valued data. Results are stated in terms of lower and upper bounds on sample complexity of dictionary learning, defined as the number of samples needed to identify or reconstruct the true dictionary underlying data from noiseless or noisy samples, respectively. Many analytic tools that help yield these results come from information theory, including restating the dictionary-learning problem as a channel-coding problem and connecting analysis of minimax risk in statistical estimation to Fano’s inequality. In addition to highlighting effects of parameters on the sample complexity of dictionary learning, we show the potential advantages of dictionary learning from tensor data and present unaddressed problems.

The stability of the electric power grid is maintained through real-time balancing of generation and demand. Grid-scale energy storage systems are increasingly being deployed to provide grid operators the flexibility needed to maintain this balance. Energy storage also imparts resiliency and robustness to the grid infrastructure. Over the last few years, there has been a significant increase in the deployment of large-scale energy storage systems. This growth has been driven by improvements in the cost and performance of energy storage technologies and the need to accommodate distributed generation, as well as incentives and government mandates. Energy management systems (EMSs) and optimization methods are required to effectively and safely utilize energy storage as a flexible grid asset that can provide multiple grid services. The EMS needs to be able to accommodate a variety of use cases and regulatory environments. This chapter provides a brief history of grid-scale energy storage, an overview of EMS architectures, and a summary of the leading applications for storage. Subsequently, EMS optimization methods and designs are discussed.

We discuss the question of learning distributions over permutations of a given set of choices, options or items based on partial observations. This is central to capturing the so-called “choice’’ in a variety of contexts. The question of learning distributions over permutations arises beyond capturing “choice’’ too, e.g., tracking a collection of objects using noisy cameras, or aggregating ranking of web-pages using outcomes of multiple search engines. Here we focus on learning distributions over permutations from marginal distributions of two types: first-order marginals and pair-wise comparisons. We emphasize the ability to identify the entire distribution over permutations as well as the “best ranking’’.

Techno-romantic thought has been with us for at least two hundred fifty years. Every wave of technological innovation during the industrial, technological, and most recently the digital revolution has engendered new iterations of the same paradoxical response: Technological progress calls forth hubristic fantasies of unlimited, quasi-magical powers, while also triggering deep-seated anxieties about subjugation, dehumanization, and annihilation. This tension manifests, for instance, in Mary Shelley's Frankenstein (1818), where the eponymous hero's command of fantastical technology allows him to overcome death and assume the godlike status of a “modern Prometheus.” At the same time, he renders his creature a victim to cruel oppression and thus turns it into a lethal danger. Techno-romantic perspectives help articulate and mitigate fears about modernity. Rendering it possible to savour the fascinating aspects of technology while grappling with its threats, techno-romantic thought is neither inherently technophile nor technophobic, but can be found in the context of technological utopias like Ian M. Banks's The Culture series (1987-2012) as well as dystopias like the Wachowskis’ The Matrix franchise (1999-).

The techno-romantic paradigm construes modern technology as a force that can reach beyond the limits of physical reality. This, on the one hand, magnifies technology's powers and thus its perils, but also envisions it as a means to safeguard the human soul against modern reification. Associated with emotional, imaginary, or spiritual qualities, technology then corroborates the primacy of human consciousness and facilitates retreats to an immaterial realm. As Mark Coeckelbergh has suggested, “As children of twentieth-century romantic counterculture, we seamlessly fuse technology and romanticism.