Pumped Hydroelectric Storage Model
This project asked teams to develop a line of reasoning for decision making in large scale engineering projects, specifically a pumped hydroelectric storage facility. A model was required to take these decisions as inputs and output the overall cost and efficiency of the system. Furthermore, teams were asked to take human factors into the decision making process. Afterwards, teams presented and justified their conclusions to a panel of stakeholders.
Poster used for a presentation and oral defense of the team's conclusions
November 2022
For this challenge, I contributed a Python program which accepts multiple parameters as input, such as reservoir height, reservoir elevation, pump speed, turbine speed, and pipe friction coefficient, and then outputs the system efficiency, fill time, and empty time of the reservoir.
​
The model performed an optimization on the system in question in order to maximize the system efficiency with two specific issues in mind:
-
The system should fill within 8 hours
-
The system should empty within 12 hours
After the initial calculation for system efficiency, the program would optimize the pump and turbine flow rates in order to reach the best possible efficiency given the time frames.
​
I then worked alongside my team to iterate through multiple tests to determine optimal parameters for increasing cost effectiveness of the design. I used this information to contribute to the cost-impact analysis of the project and the conclusions and recommendations.