Establishing the Economic Impacts of Kevin F. Forbes Research Supported by the United States National Science Foundation Did Geomagnetic Activity Challenge Electric Power Reliability During Solar Cycle 23? Evidence from the Balancing Market in England and Wales Kevin F. Forbes The Catholic University of America Washington, DC USA O.C. St. Cyr Department of Physics The Catholic University of America and NASA-Goddard Space Flight Center 32 nd USAEE/IAEE North American Conference Anchorage, Alaska July 2013 Research Supported by the National Science Foundation

Electricity is produced at relatively low voltages but is generally transported at high voltages so as to reduce transmission losses Source: U.S.-Canada Power System Outage Task Force

 Retail expenditures on electricity were approximately $370 billion in 2011, the most recent year for which data are available.  The economic contribution of this industry is much higher than this because electricity is critical to almost everything we do and thus gives rise to a very large “consumer surplus”

Area “A” Represents consumer expenditures on electricity Area “B” represents the consumer surplus that society receives from electricity B A Hypothetical Demand for Electricity Quantity of Electricity Price P1P1 Q1Q1 A Hypothetical Demand Function for Electricity, Expenditures on Electricity, and the Consumer Surplus from Electricity.

 While the wholesale price of electricity may be about $40 per megawatt-hour, the economic literature reports that the “value of lost load” is about $5,000- $10,000 per megawatt-hour  Indicative of the high value of “lost load,” it is not unheard of for the real-time price of electricity in today’s restructured electricity industry to be close to $1,000 per MWh.

 The power system is almost exclusively an alternating current system  There is a target level of system frequency, i.e. a desired level of voltage and current oscillations each second.  The desired level of system frequency is 50 times per second in most of the world and 60 times per second in North America.  Maintaining the desired levels of frequency requires that electricity supply equal demand at all times, not just on average.

 System frequency falls when demand exceeds supply and rises when demand is less than supply. In either case, reliability is compromised.  System operators offset these electricity imbalances by dispatching balancing power. These deployments can be large in magnitude

 Geomagenetic storms are disturbances in the Earth’s magnetic field that are largely caused by explosions in the Sun’s corona that spew out solar particles.

Source: NASA

 Power Grids are vulnerable to geomagnetic Storms because the power transmission grid acts as an ‘‘antenna’’ of sorts, picking up geomagnetically induced currents (GICs).  These currents have the potential to impair the performance of transformers

Source: Alan Thompson of the BGS

 GICs have been found to be statistically related with various measures of real-time operations in 12 power grids including PJM, NYISO, New England, England and Wales, New Zealand, Australia, Ireland, and the Netherlands.  It may also be relevant to note that the Hydro-Quebec system collapsed in 1989 during a geomagnetic storm.

The Day-Ahead and Real-Time Reference Price in the New York ISO, January

 Net Imbalance Volume (NIV) – the quantity of electricity that the system operator uses to balance the system.  Positive NIV values for a market period indicate that the system was “short”  Negative NIV values indicate that there was excess supply

NIV tends to be negative because market participants are significantly penalized for being ‘short”

 Ambient Temperature  The GIC proxy  Explanatory variables that reflect expected operating conditions such as forecasted load, the level of scheduled generation relative to forecasted load, and scheduled imports and exports  Binary variables for the hour of the day and the day of the week

 The model was first estimated using ordinary least squares. A number of the coefficients are highly statistically significant.  Unfortunately, the OLS residuals are highly autocorrelated which renders the results open to question.

 The goal of this estimation is to achieve “white noise” in the residuals.  There is no reason to have any confidence in the estimates in the absence of white noise,

 The GIC/NIV relationship is highly statistically significant.  The relationship is highly contingent on terrestrial based system conditions.  In short, the GIC/NIV relationship is more robust the smaller the level of available generation relative to load.

 The out of sample evaluation period: 1 Jan – 31 March 2005  The control area was expanded to include Scotland on 1 April 2005.

The forecast is less accurate if the estimated effect of the GICs is removed from the forecast equation

 The research reported here strongly supports the view that space weather had electricity market effects during solar cycle 23 even though there is no published evidence of a major space weather induced blackout.  The research also indicates that electricity market imbalances have a degree of predictability. Thus, it may not insurmountable for a system operator to forecast the terrestrial based vulnerability of its power system.  Such forecasts may have the potential to enhance reliability even when the role of space weather is minor.