NATIONAL ACADEMIES PRESS
The electric grid is an indispensable critical infrastructure that people rely on every day. The Department of Energy (DOE) envisions that by 2030, the grid will have evolved into an intelligent energy system, a smart grid. By “smart,” DOE anticipates that the grid will have the characteristics of (1) customer participation, (2) integration of all generation and storage options, (3) new markets and operations, (4) power quality for the 21st century, (5) asset optimization and operational efficiency, (6) self-healing from disturbances, and (7) resiliency against attacks and disasters. The next-generation electric grid must be more flexible and resilient than today’s. For example, the mix of generating sources will be more heterogeneous and will vary with time (e.g., contributions from solar and wind power will fluctuate), which in turn will require adjustments such as finer-scale scheduling and pricing. The availability of real-time data from automated distribution networks, smart metering systems, and phasor data hold out the promise of more precise tailoring of services and of control, but only to the extent that large-scale data can be analyzed nimbly.
Today, operating limits are set by off-line (i.e., non-real-time) analysis. Operators make control decisions, especially rapid ones after an untoward event, based on incomplete data. By contrast, the next-generation grid is envisioned to offer something closer to optimized utilization of assets, optimized pricing and scheduling (analogous to, say, time-varying pricing and decision making in Internet commerce), and improved reliability and product quality. In order to design, monitor, analyze, and control such a system, advanced mathematical capabilities must be developed to ensure optimal operation and robustness; the envisioned capabilities will not come about simply from advances in information technology. Within just one of the regional interconnects, a model may have to represent the behavior of hundreds of thousands of components and their complex interaction affecting the performance of the entire grid. While models of this size can be solved now, models where the number of components is many times larger cannot be solved with current technology. As the generating capacity becomes more heterogeneous due to the variety of renewable sources, the number of possible states of the overall system will increase. While the vision is to treat it as a single interdependent, integrated system, the complete system is multi-scale (in both space and time) and multi-physics, is highly nonlinear, and has both discrete and continuous behaviors, putting an integrated view beyond current capabilities. In addition, the desire to better monitor and control the condition of the grid leads to large-scale flows of data that must in some cases be analyzed in real time. Creating decision-support systems that can identify emerging problems and calculate corrective actions quickly is a nontrivial challenge. Decision-support tools for non-real-time tasks—such as pricing, load forecasting, design, and system optimization—also require new mathematical capabilities.
Mathematical modeling and control of the electric grid has been an active area of research for decades. However, in 1996 a major outage that affected 11 Western states and 2 Canadian provinces—coupled with emerging concerns that computers would malfunction after December 31, 1999—increased awareness of a lack of complete understanding of the overall system and its frailties. For several decades the Electric Power Research Institute funded a program of research to develop tools for recognizing early signs of instability and means to counter them. That research was largely of a mathematical nature.
More recently, DOE has been supporting research to develop the analytical and computational tools that will be necessary for the next-generation grid. Many frontier areas of the mathematical sciences are represented in that body of research. For example, the 2011 DOE conference Computational Needs for the Next Generation Electric Grid identified seven computational challenges associated with the operation and planning of the electric power system:
- Cloud computing,
- Hierarchical models,
- Analysis and planning for contingencies,
- Modeling of infrastructure interdependencies,
- Modeling and controlling multi-time-scale and multidimensional power systems,
- Optimization under uncertainty, and
- Unit commitment and economic dispatch.
Other than the first of these, all require or could benefit from new tools from the mathematical sciences. In short, the future grid will rely on integrating advanced computation and massive data to create a better understanding that supports decision making. That future grid cannot be achieved simply by using the same mathematics on more powerful computers. Instead, the future will require new classes of models and algorithms, and those models must be amenable to coupling into an integrated system.
To complement this research specifically focused on tools for the next-generation grid, a range of potentially applicable research exists. Examples include research into the general topics of uncertainty quantification, simulation and analysis of complex adaptive systems, simulation and analysis of multi-time-scale systems, and methods for characterizing and controlling resilience and reliability. This research is taking place in a range of science and engineering disciplines. More generally, complex adaptive systems have been studied for several decades, and a good deal of “mathematical machinery” has been developed.
While many of the necessary tools are inherently mathematical, the best progress in these complex areas is achieved through multidisciplinary efforts, involving a community with diverse strengths and perspectives. In order to develop the next generation of tools required for the challenges of the smart grid, DOE commissioned the National Research Council (NRC)3 to engage in a study with the following charge:
What are the critical areas of mathematical and computational research that must be addressed for the next-generation electric transmission and distribution (grid) system? Identify future needs. In what ways, if any, do current research efforts in these areas (including non-U.S. efforts) need to be adjusted or augmented?
Because this research frontier is best approached by a community that is truly multidisciplinary—including not only a cutting-edge knowledge of mathematics, statistics, and computation, but also a deep understanding of the emerging electric grid and of the questions that need answering to realize its potential—How can DOE help to effectively build this community? What mix of backgrounds is needed and how can the community be developed? How can DOE extend its reach beyond its existing ties?
To address this charge, the NRC assembled a committee of 15 members who collectively have academic, industrial, and national laboratory experience in both power systems and the relevant mathematical areas. In addition to meeting five times over the course of the study, a subset of the committee planned and ran a workshop on February 11-12, 2015, at the Arnold and Mabel Beckman Center of the National Academies in Irvine, California, to gain outside perspectives. The agenda of that workshop is appended to this report, and a published summary of that workshop is available at http:///www.nap.edu/21808.
The grid itself and the conditions under which it operates are changing, and the end state is uncertain. For example, new resources, especially intermittent renewable energy such as wind and solar, are likely to become more important, and these place new demands on controlling the grid to maintain reliability. At the same time, the increasing affordability of storage technology may ease controllability. Many technical improvements could be made to the grid, such as those noted below, but this report does not aim to cover them all nor does it presume one possible future grid scenario over another. The next-generation grid will require the efforts of many other scientific disciplines, including economics, social science, market planning, and risk analysis, to name a few, and some of these have significant mathematical content. After discussions with the study’s sponsor, the committee interpreted its charge to focus on those mathematical research directions with broad impact, and which must be advanced in order to enable the next-generation grid, rather than to discuss the full range of possible improvements to the grid or mathematics that may play a secondary role in the next-generation grid’s planning or management.
About the National Academies Press
The National Academies Press (NAP) was created by the National Academy of Sciences to publish the reports of the National Academies of Sciences, Engineering and Medicine, operating under a charter granted by the Congress of the United States. The NAP publishes more than 200 books a year on a wide range of topics in science, engineering, and medicine, providing authoritative information on important matters in science and health policy. The institutions served by the NAP are unique in their ability to attract leading experts in many fields to join panels and committees charged with providing policy advice on some of the nation’s most pressing scientific, technical, and health-related issues.