Profit Maximizing Scheduler for a Power Generator

Rice University Computational And Applied Mathematics Senior Design

Team Members: Julio Ledesma, Jonathan Celaya, Margaret Gacheru

Summary

We designed a dynamic programming algorithm to produce profit maximizing schedules for a power generator subject to various physical constraints.

While this problem suffers from the curse of dimensionality, using dynamic programming leaves a relatively simple approach to adding new constraints that produce a more realistic schedule.

Thus, the algorithm we have developed appears to be tractable as a fast, heuristic solution to the one generator power generator scheduling problem that is relatively easy to implement and modify based on the exact physical constraints of the generators.

Algorithm Summary

Source files

powergenDP.py -- Implementation for two constraints

powerGen3cons.py -- Implementation for three constraints

Power Generation Report.pdf -- Report with problem statement, solution, and results