23 March 2013
After watching An Inconvenient Truth and becoming aware of the push for renewable energy, I questioned the efficacy of renewable energy sources meeting global energy needs. I thought thermodynamics held the key in being able to understand this. Thus my quest began in January 2007. Today, I can report meaningful progress on this subject.
To build the appropriate model, I started with some publicly available fine grain data from the Bonneville Power Administration. I used data from January 1, 2007 00:00 to February 28, 2011 12:05 PST. The data is segregated into 5 minute blocks of the average power within that 5 minute period. Here is the excel file of the BPA wind power/capacity and grid load. You can verify this data by comparing the previous links. The date format is from Mathematica and is in “Absolute Time” : each full integer is 1 second. As a reference, 3376598400 is January 1, 2007 00:00:00 PST. The data is posted here in a parsed format only for your convenience and to aid in your analysis as the entirety of the modeling can readily be done in Excel if so desired.
The generalization of statistical mechanics to all measurable space done here provided the necessary logical framework and methodology to conduct the technical analysis. When we look at the grid as a market of consumers and the wind being supplied as a market of suppliers we can put the two sources in context with each other under an ideal arbitrage cycle. We can see that wind with an average temperature of 638 MW and the grid at 137 MW is a temperature ratio of 4.66 leading to a significant exergy loss. To minimize losses and the subject of a future post is that when the supply and demand are placed in contact with each other the most efficient outcome occurs when they are in diathermal contact with zero temperature difference. This is directly analogous to exergy analysis of a heat exchanger. The process of communication between the two markets is not ideal and carries with it some non trivial losses. The impact is that in order to make this market function a price premium has to be placed on wind in order to bring it to the market.
When playing around with the Maxwell relations to find the maximum of , minimum of , we set resulting when of installed capacity. Thus wind can improve its performance to a point, and that after that point it’s performance degrades.
The next post will calculate the temperatures of a number of various generating assets. I am still working out some technical difficulties in the analysis so this is all very preliminary. As always, I look forward to any feedback on this subject.
Statistical Economics is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
Based on a work at statisticaleconomics.org.