Benchmarks for Grid Energy Management with Python Gekko

Recent grid energy problems in California and Texas highlight the need for optimized grid design and operation. The complexity of the electric grid presents difficult control problems that require powerful solvers and efficient formulations for tractable solutions. The Gekko Optimization Suite is a machine learning and optimization package in Python for optimal control with differential algebraic equations and is capable of solving complex grid design and control problems. A series of non-dimensional benchmark cases are proposed for grid energy production. These include (I) load following, (II) cogeneration, (III) tri-generation, and energy storage with (IV) constant production, (V) load following, and (VI) cogeneration. Individual case studies include ramp rate constraints, power production, and energy storage operation as design variables. The tutorials demonstrate methods to solve control problems with sequential and simultaneous solutions of the objective and dynamic constraints. While these tutorials are specific to grid energy system optimization, the tutorials also demonstrate how to efficiently solve large-scale nonlinear dynamic systems with a trade-off analysis between sequential and simultaneous methods.