Smart grid design for efficient and flexible power networks operation and control

James A. Momoh

2009 · 271 citations · 12 references

Concepts

TL;DR

The US electric power infrastructure is aging and overstressed, prompting national concern over modernization amid shifting policies, new vulnerabilities, and consumer demands for higher power quality and reliability, which has spurred interest in developing the smart grid of the future. This paper proposes smart grid intelligent functions—integrating telecommunication, control, and optimization—to enable adaptability, self‑healing, efficiency, and reliability, and highlights dynamic stochastic optimal power flow as a key tool for future power system design. The authors discuss integrating DSOPF with advanced distribution management system capabilities to achieve these design goals.

Abstract

The modernization of the US electric power infrastructure, especially in lieu of its aging, overstressed networks; shifts in social, energy and environmental policies, and also new vulnerabilities, is a national concern. Our system are required to be more adaptive and secure more than every before. Consumers are also demanding increased power quality and reliability of supply and delivery. As such, power industries, government and national laboratories and consortia have developed increased interest in what is now called the smart grid of the future. The paper outlines smart grid intelligent functions that advance interactions of agents such as telecommunication, control, and optimization to achieve adaptability, self-healing, efficiency and reliability of power systems. The author also presents a special case for the development of dynamic stochastic optimal power flow (DSOPF) technology as a tool needed in smart grid design. The integration of DSOPF to achieve the design goals with advanced DMS capabilities are discussed herein. This reference paper also outlines research focus for developing next generation of advance tools for efficient and flexible power systems operation and control.

References

12