The efficient, economical, and sustainable generation and distribution of power represents a major global challenge with significant technological and social implications. A common approach to solving this complex problem involves formulating it within a mathematical optimization framework, which typically incorporates both discrete and continuous decision variables. Due to the inherent complexity of these optimization problems, conventional practice often necessitates the use of a problem decomposition scheme to enable solution derivation. While this decoupling procedure facilitates the computation of solutions, the removal of intrinsic problem interactions inherently leads to a solution that is, at best, suboptimal. To address this limitation and seek optimal solutions, this work investigates the application of the Quantum Computing paradigm for solving a realistic, albeit reduced, version of the Mexican energy system. In particular, we address the QUBO¿QAOA solution of a deterministic UC/ED instance under fixed hourly demand and deterministic generator parameters. The primary objectives of this work are to identify suitable optimization formulations amenable to quantum algorithms, determine the potential advantages and disadvantages of using Quantum Computing for this specific domain and examine the scalability and characteristic requirements imposed by power generation and distribution systems. As the availability of quantum processors and robust, noise-tolerant quantum systems increases, quantum computing is anticipated to become capable of providing optimal solutions to some of the most challenging scientific and technological problems across diverse fields, including materials design, computational fluid dynamics, and logistics.
