Energy usage by an exponentially increasing human population has created environmental problems that are stressing several ecosystems on earth. The concept of eco-exergy (which is not equivalent to mechanical work) has been used to explain the relationship between energy use and the formation of complex organisms in ecosystems. The harmonious co-existence in ecosystems has inspired the notion of industrial ecology as a paradigm for the improvement of exergetic efficiencies and complete utilization of resources. The exergy-environment nexus and the implications of exergy analyses on sustainable development are critically examined in this chapter. Environmental exergonomics, exergoenvironmental analysis that includes eco-indicators, life-cycle exergy analysis, and sustainability indices are theoretical tools that use exergy and other thermodynamic variables to define the state of the environment and to recommend industrial practices that would alleviate the detrimental effects of energy use and would promote global environmental stewardship and sustainability.

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.

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.

Chapter 6 presents industrial case histories using VSA to diagnose inner and outer race faults in bearings of SCIMs.

Chapter 8 presents industrial case histories using VSA to diagnose miscellaneous faults in rolling element bearings in SCIMs.