The idea for this website and analysis was formed after working on a project with the Office of Sustainability at Washington University in St. Louis. The University was interested in receiving more of their energy from renewable resources. While Washington University is putting in some on-campu solar, even if every inch of roof space on campus was covered with solar panels, they would only be able to provide a small fraction of the university's total energy needs. So we began looking into off-campus wind power as an option.
Studying how to get involved in off-campus wind (either through a utility or with a third-party operator) is a complicated process. In order to get a better handle on the economics of the situation, I began to delve into MISO's historical energy price data. After spending an entire day sifting through the large amounts of data to do an analysis for a single site for one year, it was clear that there was a need for a better analysis tool that would allow for easy access to historical prices trends from an area and comparison between node points. There was a wealth of data, but it was just too hard to get useful information out of it
The tools designed for the Energy Price Analysis Tools site have hopefully changed that. You now have easy access to compare historical prices through the northern MISO region and compare how prices have varied between nodes. Additionally a more thorough wind analysis tool was built that coupled the energy price data with local wind data. This tool is very useful in analyzing not just the physical output from wind farms, but also how valuable the energy they produce is. I hope these tools lead you to a better understandin of energy in the MISO RTO. If you have any questions about the tools or the site please contact me at my e-mail adress listed below. Additionally, if you would like consultation or a more thorough study on a particular project or site please reach out.
All of the data used in this project is freely available online. For the node pricing data used in all of the tools, we obtained Historical Annual Real-Time Energy Prices from MISO's Market Reports . The formatting for this data is a csv file with columns for Market_Day, Node, Type, Value, Price Hour 1, Price Hour 2,...,Price Hour 24.
The Type refers to the category of the node. Each node has three lines for each day corresponding to one of three values being reported: LMP, MCC, MLC. These refer to the Location Marginal Price, Marginal Congestion Cost, and the Marginal Loss Cost. The LMP is the actual price paid at any given point. All node points within the MISO region start at the same Base Price. The MLC and MCC are subtracted or added to that price based on local constraints. The MLC is similar across all node points and accounts for physical losses in the system. The MCC varies significantly and has the biggest effect on a local LMP. The MCC accounts for congestion in the grid, which factors in how much generation, demand, and available transmission capacity exist.
For the tools in this website, only the LMP data was used. Additionally only sites in the northern section of MISO were used because the Southern section was added to MISO in 2013 and only has two years of full data. For our tools we further selected sites down to just over 100 geographically disperse nodes for which we could find lat, long measurements. Nearby nodes will have similar prices as long as they are not transmission constrained therefore the node points shown in these analyses are representative of the complete grid.
In additon to determining the nodes to be used for study, the dates had to be properly formatted and the data was transformed so that each hour price could be inserted into a SQLite database for easy access in the web platform. For more details about the data cleaning and the transformation, check out my github account with all of the code for this project (full databases not included).
To do a more thorough analysis on possible revenue generated by wind at each node site, we needed to collect wind speed. While the Energy Price data from MISO was fairly clean and already put together, the wind data for rural sites in Iowa and Illinois, was not. In order to get comparable wind speeds for each of the 16 node points used in the wind study, NOAA's Quality Controlled Local Climatological Data . The interface did not allow for easy access to large longitudinal sets for a site and as shown in the image below "Data gap might (DO) Exist".
In order to obtain the required wind data in a reasonable fashion, I built a web scrapper that would send post requests to the website to indivdually grab each month of data for the sites and port them into a SQLite database for use with the EPAT tools. Each site took about 2 minutes to get 60 post requests (5 years, 12 months), clean/transform the data and put it into the wind Sites where choosen that were the closest wind stations to a node point. The meta data connecting wind stations to Node points was stored in a table in the wind database.
There were several transformations and cleaning that were done to the data to get it into useable format. First, each site takes measurements at different time intervals (see below). However, each site would take at least one measurement at a regular interval within 10 minutes of the hour mark. For the two sites below, Fort Dodge had a measurement at the 55th minute of every hour and Marshalltown had a measurement at the 53rd minute of every hour. So only the wind data from that measurement each hour was used. Additionally, some data gaps existed. Currently they are filled in with the most recent available wind speed prior to the gap (most gaps only lasted one to two hours). I would like to change that process to an interpolation between the prior and post wind speed values.
For the wind analysis, revenue estimates for each Wind Farm were produced based on both wind speeds and energy prices. In order to accomplish this task, we needed to convert the ground wind speed into an Energy Output from wind turbines. First, wind turbines are very tall and the wind at the altitude of the blades is generally faster than the wind at ground level. A wind speed graident approach is used to convert the ground wind speed, to the wind speed at 100m which is around the height of a typical wind turbine. Future tools will allow you to change the height of the wind turbine in a report. The ground wind speed is assumed to be measured at 10m. The equation below is used to convert the wind speed.
Where vw is the wind speed at turbine height, h is the turbine height, hg is the ground measurement height, vg is the ground wind speed, and alpha is the Hellmann exponent which is a function of the type of terrain. We use 0.45 for alpha which is between the common values for Neutral and Stable air above human inhabited areas.
Once the wind speed at the proper height is found, it must be used to determine energy output from the wind turbine. The power generated by wind will vary with the cube of wind speed (wind_speed^3). Therefore, doubling the wind speed will create 8 times as much power. However, there are some limitation when it comes to wind turbines. There is a cut-in wind speed. Below the cut-in speed (usually between 5-10mph), no power will be generated. Once wind speed reaches the cut-in speed, the turbine will begin to produce power. However, a 1MW turbine will not immeaditaly generate 1MW, it start generating a small amount of power and follow the cube law until reaching it's rated power. Once it reaches the minimum wind speed to reach rated power, higher wind speeds will not increase the power output. The minimum speed to reach rated power usually falls between 25-40mph. The turbine will produce at the rated power level until reaching a shut-off wind speed. At this speed operation of the turbine is not safe and energy generation will be halted. This value can range from 40mph for small turbine up to 100mph for very large ones. The values used for our conversion of wind speed to turbine energy production can be seen in the table below.
|Cut-in speed||7 mph|
|Minimum rated power speed||30 mph|
|Shut-off speed||80 mph|
|Default Farm Size||20 MW|
For each hour the ground wind speed was converted to the and energy output based on the method outlined above. Since the time is one hour if the farm operated at 15 MW for the hour it would generate 15MWh for that hour. If you would like a more thorough explaination of the difference between power and energy, you can read this blog post . Other factors such as air density at each site was not accounted for in these calculations.
Matthew T. Lawder