This work explores the potential of quantum computing to optimize energy systems. With the rise of energy demand power generation has also increased and the integration of renewable sources of energy has increased the complexity of finding the optimal solution which minimizes the production cost. This work focuses on solving the Unit Commitment Problem by using quantum and classical techniques. The fundamentals of quantum computers for both available architectures are discussed. This work proposes to formulate a Unit Commitment Problem as MINLP and then implement it on a Quantum Computer using Hybrid Annealing and on a Conventional Computer using a Gurobi solver using real-world data and varying constraints. This work draws a comparison between the solutions obtained by both quantum and classical computing techniques and discusses the trade-off between them based on a set of benchmarks.