The Merits of Smart Grid Technology

Srikanth Iyer
October 24, 2010

Submitted as coursework for Physics 240, Stanford University, Fall 2010

User Average Cost of a 1 Hour Blackout
Residential $2.70
Commercial $886
Industrial $3,253
Table 1: Estimated cost of one hour power interruption per customer in U.S. 2002 CPI weighted dollars. [1]

Individual and corporate demand for power is rapidly increasing as the human race develops new ways to leverage energy. What has been neglected, however, is the development of a more efficient and reliable method of distributing power given this trend of increasing power consumption. The ultimate consequence of the void in effective power distribution technology is the loss of $80 billion each year by businesses in the United States nearly one-third of the $249 billion total revenue of retail electricity (2002 estimates). [1,2] The proposed solution to this problem is the development of a "smart" grid i.e. a distribution mechanism that is designed for dynamic loads and is capable of adjusting power transmission in real time based on sensor data from devices, homes and generators.

The term smart grid technology refers to the collective application of modern technology to electric power distribution issues. There are four key technologies that motivate the design of smarter grids: sensors, communication, information systems and electronic control. I speculate that the main intent of a smart grid is to stabilize the load of the system, and the main motivator for doing this is dynamic energy pricing.

One of the major challenges engineers who are designing this smart grid are facing is dealing with the uncertainty associated with both energy use and energy production, due to a predicted increase in the number of energy producers. In the U.S., wind energy production is predicted to increase from 31 TWh (2008) to 1160 TWh in 2030. [4,5] Similarly, cumulative solar energy production is predicted to read 16 GW in 2020. [6] Both these renewable energy sources are associated with high variability, littler correlation with the load profile, and increased transmission congestion. [3]

One of the ways smart grid technology will level loads is through the expansion of voluntary load rejection. Even in today's grid it is a common practice to use load rejection as an emergency procedure to protect the grid. [3] Essentially, the practice of load rejection involves the utility company providing some sort of price compensation in exchange for the right to reduce power delivery to a large energy consumer in times of peak demand. In typical systems, contractual participation in load rejection is less than 5% of the peak load power use. [7] The hope is that increased availability of real-time pricing data will increase the incentive to participate in price based load rejection. More complicated contracts can be drawn up to allow for automatic load rejection based on price signaling. [3] This aspect of smart grid technology is an excellent example of the essence of smart grid technology, which I believe is using advanced control techniques to increase the ability to interpret large amounts of data in order to create more options for both consumers and producers of energy.

© Srikanth Iyer. The author grants permission to copy, distribute and display this work in unaltered form, with attribution to the author, for noncommercial purposes only. All other rights, including commercial rights, are reserved to the author.


[1] K. H. LaCommare and J. H. Eto, "Understanding the Cost of Power Interruptions to U.S. Electricity Consumers," Lawrence Berkeley National Laboratory, LBNL-55718, September 2004.

[2] "Electricity," U.S. Energy Information Administration.

[3] K. Moslehi and R. Kumar, "Smart Grid - a Reliability Perspective," IEEE Transactions on Smart Grid 1, No. 1, 57 (2010).

[4] "20% Wind Energy by 2030," U.S. Department of Energy, DOE/GO-102008-2567, July 2008.

[5] "Annual Report on US Wind Power Installation, Cost and Performance Trends: 2007," U.S. Department of Energy, DOE/GO-102007-2433, May 2008.

[6] B. Vanderzwaan and A. Rabi, "The Learning Potential of Photovoltaics: Implications for Energy Policy," Energy Policy 32, 1545 (2004).

[7] " Harnessing the Power of Demand," IRC Council, 16 Oct 07.