Anybody who remembers the effects of the “rolling blackouts” of 2000 and 2001—when Californians suffered through an energy crisis largely created by market manipulation—would probably prefer not to repeat the experience.
Unlike a conventional blackout—which is caused by an unexpected system failure—a rolling blackout actually is controlled by the utility, according to the California Energy Commission (News - Alert). It is an organized and planned event that typically occurs between the hours of 3 p.m. to 7 p.m.—when the demand for energy has reached a peak and there is insufficient capacity to meet customers’ needs.
These planned blackouts help prevent "uncontrolled" electrical power outages during peak periods of demand; so that there are minimal, if any, interruptions in the electricity supply. However, even with sufficient warning, frequent rolling blackouts are an inconvenience to the healthy—and a danger to the elderly and infirm.
Now, a University of California–Riverside assistant professor of Electrical Engineering and several of his colleagues have created a new measurement tool that could help avoid an energy crisis like the one California endured during the early 2000s and better prepare the electricity market for the era of the smart grid.
The tool calculates all of the factors that affect market power/pricing—including systems that can dynamically affect the ways in which consumers use the smart grid, such as large-scale storage, renewable power generation and demand response.
“In the coming years, as we move toward a smart grid, a tool like this [will be] crucial, so that we can quantify the impact of emerging smart grid phenomena, such as demand response and renewable energy, on [the ability of] generation firms to manipulate the prices in a deregulated electricity market,” said Hamed Mohsenian-Rad, an assistant professor at UC–Riverside’s Bourns College of Engineering.
Mohsenian-Rad outlined the research in a paper entitled “A Unifying Approach to Assessing Market Power in Deregulated Electricity Markets” that he co-authored in collaboration with Professor Adam Wierman of California Institute of Technology and two graduate students, Chenye Wu and Subhonmesh Bose.
Above, photograph from the International Space Station of California at night (courtesy of NASA).
Manipulating the Market
For example, in the case of the California energy crisis, the manipulative factor was demand response. A demand-supply gap was created by energy companies—the chief offender being the energy trading company, Enron Corporation of Houston— in order to artificially create a power shortage; which then led to higher prices and rolling blackouts.
How did the power companies do it? Energy traders took power plants offline for maintenance on days of peak demand to increase the price. In this way, traders were able to sell power at premium prices—sometimes up to a factor of 20 times its normal value. Generally speaking, they were successful in doing so because they had “market power,” due to their dominance either in the overall service territory or in a particular region in the market where transmission lines had limited capacities.
Indeed, on February 3, 2005, CNN published proof positive, in the form of transcribed audiotapes, which demonstrated that Enron had plotted to take a power plant offline in 2001 in order to jack up electric prices in the western United States.
The tapes were released to CNN by Snohomish Public Utility District in Everett, Washington—which stated that an Enron employee and a worker at a power plant in Las Vegas made up phony repairs so that they could rationalize taking the plant offline on January 17, 2001.
"We want you guys to get a little creative ... and come up with a reason to go down," an Enron worker tells a plant employee on one of the tapes. "Anything you want to do over there? ... Cleaning, anything like that?" the Enron employee asks.
"Yeah, yeah," the other replies. "There's some stuff we could be doing."
To prevent rolling blackouts in the future, a number of experts have made suggestions, such as adding transmission lines or increasing generation capacities. However, collectively, “these measures are confusing and in some cases may conflict,” according to Mohsenian-Rad and his colleagues.
Instead, the UC-Riverside team has created a measure called “transmission constrained network flow.” It is a combination of three large classes of market power measures: residual supply based, network flow based and minimal generation based. Transmission constrained network flow is fundamentally different from the existing methods because it proposes a “function” to assess market power under varying operating conditions, instead of giving a single measure that is designed for a normal operating condition.
“The existing literature on assessing market power is a confusing landscape with no unified view,” Mohsenian-Rad said “Our tool unifies this diverse set of measures, and it also takes into account the smart grid.”
Edited by Rachel Ramsey