Team: 4
School: Capital High
Area of Science: Clean Energy
Interim: Team Number: 04
School Names: Capital High School, Santa Fe High School, Santa Fe Prep
Participant Names: Shrey Poshiya, Lucas Blakeslee, Valentin Ornelas, Ian Olson
Optimizing the Geographic Location of Renewable Energy Sources
Problem Definition: Renewable energy is widely accepted as the next step to take toward slowing the warming of our planet. Photovoltaic energy is one of the fastest growing and most widely utilized forms of renewable energy sources; however, there are environmental limitations. Even with the recent improvement in solar panel technology, there are a myriad of variables that negatively affect their energy output (solar irradiance and solar zenith angle). Most photovoltaic models only take into account these environmental variables, but a photovoltaic farm’s location relative to the electrical grid is also a variable that greatly affects a farm’s viability. The goal of this project is to analyze all of these variables, mainly the electrical grid, within the contiguous United States and determine the best location for solar farms in order to maximize their efficiency and cost.
Proposed Computational Solution: In order to solve this we plan to begin by creating a dataset that contains our environmental variables for the whole of the US. Once we have data for individual data points, a fitness function that takes into consideration all of the variables will be applied to find the efficiency of solar farms in certain locations. As time progresses, we will add more variables into our model in order to create a more accurate model.
Current Progress: Thus far we have spent much of our time finding possible datasets and researching variables which need to be accounted for. We have now found a reliable dataset from NREL. We began the process by creating a proof of concept using data from the state of Colorado: we chose Colorado as our candidate as it has a very simple shape, therefore making the process of data collection easier. Additionally, it seemed most prudent to try to retrieve a small amount of data before trying to analyze data for the entire contiguous US (comparatively — we still have nearly 500 data points total after we took the mean of roughly 9000 hourly data points per coordinate pair). We have yet to apply our fitness function; however, we are confident that our proof of concept will work as we have all the necessary data compiled.
Expected Results: At the end of this project we believe that we will have created a model that will be able to evaluate the fitness of a photovoltaic farm at a certain location in the US. We plan to check our results by comparing them to the efficiency of solar farms throughout the country. If our predictions match real-world data, it would allow us to say with reasonable certainty that our model is accurate, and could potentially have applications for the future of solar power and the placement of solar farms. In addition, the sheer number of variables in this problem makes it such that in the time frame provided we can only account for the most important ones; however, this means that even after the official end to this project, it will have significant room for improvement and expansion.
Clack, C. T. M. (2017). Modeling Solar Irradiance and Solar PV Power Output to Create a Resource Assessment Using Linear Multiple Multivariate Regression, Journal of Applied Meteorology and Climatology, 56(1), 109-125. Retrieved Feb 2, 2022, from https://journals.ametsoc.org/view/journals/apme/56/1/jamc-d-16-0175.1.xml
https://nsrdb.nrel.gov/
Mokarram, M., Mokarram, M.J., Khosravi, M.R. et al. Determination of the optimal location for constructing solar photovoltaic farms based on multi-criteria decision system and Dempster–Shafer theory. Sci Rep 10, 8200 (2020). https://doi.org/10.1038/s41598-020-65165-z
Mierzwiak, Michal & Calka, Beata. (2017). Multi-Criteria Analysis for Solar Farm Location Suitability. Reports on Geodesy and Geoinformatics. 104. 10.1515/rgg-2017-0012.
Labrini, H. (2018). Graph-Based Model For Distribution Systems: Application To Planning Problem. Core.
Team Members:
Valentin Ornelas
Lucas Blakeslee
Ian Olson
Shrey Poshiya
Sponsoring Teacher: Irina Cislaru