Mechanical energy is the most useful energy form to humans. This motivates the question: given an energy resource – fossil or nuclear fuel, wind, solar, geothermal – what is the maximum mechanical energy one may extract? A similar question is: what is the difference between the low-temperature waste heat of a nuclear power plant and the high-temperature heat in the nuclear reactor? The combination of the first and second laws of thermodynamics, in conjunction with the characteristics of the environment where energy conversion processes occur, offers a definitive answer to these and similar questions: exergy is the thermodynamic variable that describes the maximum mechanical work that may be extracted from energy resources, the concept that quantifies the quality of energy. This chapter elucidates the concept of exergy and its relationship to the energy resources. It derives useful expressions for the exergy of primary energy sources including: fossil fuels, geothermal, solar, wind, hydraulic, tidal, wave, and nuclear. The effects of the environment on the exergy of energy sources, the energy conversion processes, and the exergetic efficiencies of the processes are also elucidated.

When uncontrollable resources fluctuate, optimal power flow (OPF), routinely used by the electric power industry to redispatch hourly controllable generation (coal, gas, and hydro plants) over control areas of transmission networks, can result in grid instability and, potentially, cascading outages. This risk arises because OPF dispatch is computed without awareness of major uncertainty, in particular fluctuations in renewable output. As a result, grid operation under OPF with renewable variability can lead to frequent conditions where power line flow ratings are significantly exceeded. Such a condition is considered undesirable in power engineering practice. Possibly, it can lead to a risky outcome that compromises grid stability – line tripping. A chance-constrained (CC) OPF approach is developed, which corrects the problem and mitigates dangerous renewable fluctuations with minimal changes in the current operational procedure. Assuming availability of a reliable wind forecast parameterizing the distribution function of the uncertain generation, this CC-OPF approach satisfies all the constraints with high probability while minimizing the cost of economic redispatch.

The large amount of synchrophasor data obtained by Phasor Measurement Units (PMUs) provides dynamic visibility of power systems. As the data is being collected from geographically distant locations facilitated by computer networks, the data quality can be compromised by data losses, bad data, and cybernetic attacks. Data privacy is also an increasing concern. This chapter, describes a common framework of methods for data recovery, error correction, detection and correction of cybernetic attacks, and data privacy enhancement by exploiting the intrinsic low-dimensional structures in the high-dimensional spatial-temporal blocks of PMU data. The developed data-driven approaches are computationally efficient with provable analytical guarantees. For instance, the data recovery method can recover the ground-truth data even if simultaneous and consecutive data losses and errors happen across all PMU channels for some time. This approach can identify PMU channels that are under false data injection attacks by locating abnormal dynamics in the data. Random noise and quantization can be applied to the measurements before transmission to compress the data and enhance data privacy.

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.

This chapter introduces the fundamental elements of random matrix theory and highlights key applications in line outage detection using actual data recovered from existing power systems around the globe. The key mathematical component is a novel concept referred to as the mean spectral radius (MSR) of non-Hermitian random matrices. By analyzing the changes of the MSR of random matrices, grid failure detection is reliably achieved. Several studies and simulations are considered to observe the performance of this new theoretical approach to line outage detection.

Smart grids (SGs) promise to deliver dramatic improvements compared to traditional power grids thanks primarily to the large amount of data being exchanged and processed within the grid, which enables the grid to be monitored more accurately and at a much faster pace. The smart meter (SM) is one of the key devices that enable the SG concept by monitoring a household’s electricity consumption and reporting it to the utility provider (UP), i.e., the entity that sells energy to customers, or to the distribution system operator (DSO), i.e., the entity that operates and manages the grid. However, the very availability of rich and high-frequency household electricity consumption data, which enables a very efficient power grid management, also opens up unprecedented challenges on data security and privacy. To counter these threats, it is necessary to develop techniques that keep SM data private, and, for this reason, SM privacy has become a very active research area. The aim of this chapter is to provide an overview of the most significant privacy-preserving techniques for SM data, highlighting their main benefits and disadvantages.

The application of the exergy methodology reveals the system components where high exergy dissipation occurs and improvements may be accomplished to conserve energy resources. The method is applied to several systems: heat exchangers, including boilers and condensers; vapor and gas power cycles, including cogeneration units; jet engines; and geothermal units. Calculations on exergy dissipation identify the processes and components where improvements would save energy resources. The calculations reveal that combustion processes waste a great deal of exergy, leading to the conclusion that direct energy conversion devices, such as fuel cells, utilize fossil fuels in a sustainable way. The exergy method is also applied to photovoltaic cells and thermal solar power plants, as well as to solar collectors that deliver heat. Significant exergy destruction occurs in wind turbines because of Betz’s limit and the wind turbine characteristics. A large number of examples in this chapter elucidate the exergy calculations and provide guidance and resources for the application of the exergy methodology to power and heat generation systems and processes.

This chapter presents a game-theoretic solution to several challenges in electricity markets, e.g., intermittent generation; high levels of average prices; price volatility; and fundamental aspects concerning the environment, reliability, and affordability. It proposes a stochastic bi-level optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile energy-only electricity market. The decision on storage capacities is made in the upper-level problem and the operation of strategic/regulated generation, storage, and transmission players is modeled in the lower-level problem using an extended stochastic (Bayesian) Cournot-based game.

Deep learning (DL) has seen tremendous recent successes in many areas of artificial intelligence. It has since sparked great interests in its potential use in power systems. However, success from using DL in power systems has not been straightforward. Even with the continuing proliferation of data collected in the power systems from, e.g., synchrophasors and smart meters, how to effectively use these data, especially with DL techniques, remains a widely open problem. This chapter shows that the great power of DL can be unleashed in solving many fundamentally hard high-dimensional real-time inference problems in power systems. In particular, DL, if used appropriately, can effectively exploit both the intricate knowledge from the nonlinear power system models and the expressive power of DL predictor models. This chapter also shows the great promise of DL in significantly improving the stability, resilience, and security of power systems.

The use of energy has defined our civilization and governs our lives. Throughout the day and night modern humans consume enormous quantities of energy resources for: food preparation; transportation; lighting, heat, ventilation and air-conditioning of buildings; entertainment; and a myriad other applications that define modern life. A gigantic global energy industry transports and inconspicuously transforms the energy resources to convenient forms (gasoline, diesel, electricity) that are vital to the functioning of the modern society. This introductory chapter surveys the types of the global primary energy sources, how they are transformed to useful energy, and how they are used. The chapter introduces the two laws of thermodynamics that govern the conversion of energy from one form to another; explains the methodology of thermodynamics, which is essential for the understanding of energy conversion processes; and delineates the limitations on energy conversion. The thermodynamic cycles for the generation of power and refrigeration are reviewed and the thermodynamic efficiencies of the cycles and energy conversion equipment (turbines, compressors, solar cells, etc.) are defined.

Mathematical optimization has been used since the early 20th century to improve the profitability of systems and processes. The time-value of money that leads to the concepts of net present value, annual worth, and annual cost of capital investment, is paramount in the optimization of energy systems that typically operate for very long periods. The method of thermoeconomics (which was formulated in the 1960s) and the similar method of exergoeconomics (which emerged in the 1990s) are two cost-analysis methods extensively used for the optimization of energy systems, components, and processes. Calculus optimization and the Lagrange undetermined multipliers are similarly used tools. This chapter begins with an exposition of the basic concepts of economics and optimization theory, and continues with the critical examination of the mathematical tools for the optimization of energy conversion systems using the exergy concept. The uncertainty of the optimum solution, which is an important consideration in all economic analyses, is clarified and an uncertainty analysis for exergy-consuming systems is presented.

The increasing penetration of renewable resources has changed the characteristics of power system and market operations, from one relying primarily on deterministic and static planning to one involving highly stochastic and dynamic operations. In such new operation regimes, the ability of adapting changing environments and managing risks arising from complex scenarios of contingencies is essential. To this end, an operation tool that provides probabilistic forecasting that characterizes the underlying probability distribution of variables of interest can be extremely valuable. A fundamental challenge in probabilistic forecasting for system and market operations is the scalability. As the size of system and the complexity of stochasticity increase, standard techniques based on direct Monte Carlo and machine learning techniques become intractable. This chapter outlines an alternative approach based on an online learning to overcome barriers of computation complexity.