IEEE has been at the forefront of activities to help build a global smart grid. Over the years, it has created a Web portal, launched several publications, and developed standards. But now IEEE is taking a more aggressive approach by compiling a comprehensive collection of smart-grid documents that offer a broad view of how it expects the technology to evolve. IEEE Smart Grid Research contains materials written by IEEE experts that cover projections about where the technology should head and the challenges it faces. Also outlined are research opportunities in five key areas: power, computing, communications, control systems, and vehicular technology.
“A lot of technologies are converging to become the smart grid, which is really a ‘system of systems,’ and we chose the five areas most involved,” says Bill Ash, strategic program manager for the IEEE Standards Association (IEEE-SA), in Piscataway, N.J., which is leading the effort. “IEEE is known for the quality of its technical information, so it makes a lot of sense for the organization to play a defining role.”
IEEE-SA collaborated with the IEEE Communications, Computer, Control Systems, Intelligent Transportation, and Power & Energy societies on the research website. Each society is responsible for providing materials, including so-called “vision documents” for its area that look at issues 20 to 30 years into the future. They also provided road maps for getting there, along with research papers addressing more immediate concerns.
Several vision documents, along with their road maps and reference materials, are already available. They cover power and energy, control systems, computing, and communication.
“These documents present the technical challenges regarding the gaps or inadequacies that must be overcome,” says Senior Member Georges Simard, editor in chief of IEEE Grid Vision 2050, which addresses the power system infrastructure. “We hope they’ll give direction to academics, researchers, product developers, and investors on where work is needed and where innovation is likely to be rewarded.”
The challenges are not only technical, however. The merging of technologies will stimulate coordinated research and standards activities among different technical societies and organizations, according to Simard. The sharing of expertise will be crucial, he says.
IEEE Grid Vision 2050 describes the future infrastructure based on scenarios from the International Energy Agency, which defines energy availability, production, and usage into 2050. The vision also covers electricity generation, transmission and distribution, and usage, as well as issues related to operations and control.
IEEE Vision for Smart Grid Controls: 2030 and Beyond provides an overview from the control systems perspective of research needed for the integration of renewable sources, reliability, self-healing, energy efficiency, and resilience to physical and cyber attacks on the smart grid.
“The document also discusses the breadth of roles control systems will play, which has been significantly enlarged because of drivers from consumers, environmental concerns, growing energy demand, and increasingly electrified transportation, as well as aging infrastructures,” says editor in chief and IEEE Fellow Anuradha Annaswamy. “It’s a ‘must read’ for all smart-grid stakeholders and control researchers because it covers the scenarios, opportunities, and challenges for delivering reliable and affordable power, anywhere and at any time.”
The IEEE Smart Grid Vision for Computing: 2030 and Beyond describes where computing technologies still on the horizon are likely to impact various smart grid architectures the most, according to its editor in chief, IEEE Member Dan McCaugherty.
“The vision targets the computing professional interested in helping solve smart-grid problems by leveraging future computing technologies,” he says. “It maps 22 computing concepts to 27 smart-grid functional concepts and discusses their relationships.” Topics include information security, autonomous control, self-integrating systems, modeling and simulation, and stochastic analysis. Future road maps will consider computing-technology evolution and computing reference models.