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Google Searches and Your Carbon Footprint: A Tempest in a Teacup? By Charles River Associates Nov 24, 2009 - 10:32:44 AM
By Aleksandr Rudkevich
The tempest in a teacup occurred on January 11 of this year when Times Online published an article by Dr. Alexander Wissner-Gross, a physicist and Environmental Fellow at Harvard University. In it he stated: “We have calculated that each Google search generates an estimated 5-10 g of CO2” – or about the amount of carbon dioxide as required to prepare a cup of tea. This did not go unnoticed by Google, which vehemently protested this estimate. A few days later Times Online made the following clarification: “We are happy to make clear that this does not refer to a one-hit Google search taking less than a second, which Google says produces about 0.2g of CO2, a figure we accept.”
Google’s own estimate states that a simple Google search takes, on average, 0.0003 kWh of electric energy and that this translates to 0.2g of CO2 emissions. There is no reason to dispute the first number; after all, Google should know how much electricity its own servers require. But should we accept the second? Although Google’s 0.2g estimate is accurate on average, it is generally inaccurate in real-time. But it is real-time that matters for the impact on the carbon footprint of electricity consumption. This real-time impact can be measured by the Marginal Carbon Intensity (MCI) of electricity demand, and presents a new dimension on which utilities and customers can interact through a Smart Grid marketplace.
Smart Grid and Carbon Footprints
We envision the Smart Grid as a means for dramatically increasing the efficiency of electricity use by giving consumers real-time information about changes in the electricity market and tools to react to these changes, thereby providing feedback signals to the market itself. In this context we often talk about electricity prices as the most important informational components of the state of the market, and rightfully so. However, price is not the only consumer concern; many at both the residential and corporate level are increasingly concerned about their carbon footprint. And in the same way consumers can react to prices with real-time information, they may choose to react to the state of their carbon footprint. To do that, they will need to monitor real-time carbon effects. The Smart Grid can be a platform to deliver these price and carbon signals and provide consumers with tools to react to them.
For the same reason the price of electricity depends on the time of use and a consumer’s location on the electrical grid, carbon emissions caused by electricity consumption also depend on time and location. Both are due to the changing blend of generation sources used throughout the day and year, including the effects of locational dispatch that is necessary during periods of transmission constraints. Furthermore, in the same way the price of electricity reflects the cost of the marginal generating unit (or units) required to meet incremental demand for power, the carbon impact of that incremental demand is directly attributable to the level of CO2 emissions by this marginal generating unit (or units).
This real-time impact on carbon could be defined as the mass of incremental CO2 released into the atmosphere of the entire electrical grid in response to small incremental changes in consumption of electricity. This incremental mass of CO2, measured for example in g(CO2)/kWh, represents what I call the Marginal Carbon Intensity (MCI) of electricity demand.
Like prices, MCIs are determined by the physics and economics of the operation of the power grid – which generating units are on the margin at any given point in time, which transmission lines are congested, and what is the “electrical path” on the power grid connecting marginal generators with the location of the consumer. In the absence of transmission congestion, the value of MCI is determined by the CO2 emission rate of the marginal generator in the system. For example, when the marginal generator is coal-fired, each additional kWh of electricity consumption will result in approximately 1 kg of incremental CO2 emissions.
Applying this to the Google spate earlier this year, if the Google search is powered by coal-fired generation, the 0.0003 kWh of electricity it requires will result in about 0.3g of CO2 emissions, or 50% above Google’s average estimate. When the marginal generator is an efficient combined cycle gas-fired plant, the carbon footprint of a Google search powered by this generator will be only 0.12g, or 40% below Google’s average estimate.
The behavior of MCI in the presence of transmission congestion is more complicated and sometimes counter-intuitive, because in some circumstances MCI could be negative; reducing electricity consumption at some locations could increase CO2 emissions and vice versa. To understand this phenomenon, imagine that the system has two marginal generators – a coal-fired and a gas-fired. In the presence of transmission congestion, in order to deliver an additional kWh to a particular location, it could be necessary to increase generation of the gas-fired plant by 2 kWh and reduce output of the coal-fired plant by 1 kWh. By doing so, the system will release an extra 0.8 kg of CO2 from the gas plant, but reduce CO2 emissions from the coal plant by a whole 1 kg. The net result of this would be total reduction of CO2 emissions by 0.2 kg. A Google search conducted at such a location would actually have a negative carbon footprint of -0.06 g.
Taking action to reduce the carbon footprint of electricity consumption requires timely and precise monitoring. Smart Grid technologies can provide the means to deliver this signal to electricity consumers and let them respond in real-time. However the power industry will need to provide this signal in the first place. My recent paper with Pablo Ruiz, entitled “Analysis of Marginal Carbon Intensities in Constrained Power Networks,” offers the exact definition of the MCI concept and derives precise mathematical formulas which grid operators could use to compute real-time values of MCI for every location. If and when this will be done is another matter!
Analysis of Marginal Carbon Intensities in Constrained Power Networks (pdf)
How You Can Help Reduce the Footprint of the Web
Revealed: The Environmental Impact of Google Searches (with clarification)
Aleksandr Rudkevich is Vice President in the Energy & Environment Practice of Charles River Associates. He manages projects and directs studies involving complex modeling of energy systems with applications to valuation of generation and transmission assets and related issues.
The views expressed in this article are those of the author and do not necessarily reflect the opinion of Charles River Associates or its Energy & Environment Practice.
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