The generation and transmission maintenance scheduling (GTMS) problem, in a competitive electricity market environment, presents electricity utilities scheduling their facilities for maintenance to improve productivity and maximize profits, and an independent system operator (ISO) pushing for maintenance schedules (MS) of generators and transmission facilities that keep the system reliability and minimizes operation cost. Thus, the GTMS is inherently a high-dimensional, non-linear, non-convex, and multi-objective optimization problem that contains mixed integer-real variables and conflicting objectives related to the goals of the different parties in the market.

The GTMS problem is crucial in power systems operation and planning due to the increasing complexity of today’s power grid, the aging of current operating electricity facilities, and the increasing share of renewable energy in the network and the market. In that sense, this thesis proposes to solve the GTMS problem using hybrid models that combine in a novel way multi-objective evolutionary algorithms (MOEAs) and classical optimization techniques to obtain a set of feasible non-dominated MS solutions.

These hybrid models solve the GTMS problem in systems with thermal, hydro, and wind generation, handling maintenance and operation variables separately and sequentially, considering transmission congestion and losses, the opportunity cost in the future of water stored in reservoirs, the stochastic nature of wind generation and the impact of MS in electricity prices in the market. The models used match accepted industry maintenance practices with cutting-edge optimization techniques developed in the academia. The models are evaluated in the IEEE-RTS 24 test system, complemented with hydro units and wind farms belonging to two Bolivian electricity utilities. GENCO’s profits, system adequacy, and operation costs are used as objective functions, and their conflicting relationships are evaluated in the obtained set of MS solutions. Finally, the models allow the ISO to use this set to identify the best MS solution using the technique for ordering preferences according to similarity to an ideal solution (TOPSIS) decision-making tool.