The uninterrupted growth in energy demand, environmental concerns, and technological advancements requires an effective energy system with high reliability. Therefore, in this thesis, we propose optimal strategies for the Integrated Maintenance Scheduling (IMS) problem of energy systems considering generating units, power flow, and transmission network constraints. The maintenance schedule is incorporated with a scenario-based model for uncertain power generation from renewable energy sources encompassing photovoltaic panels and wind turbine generators. The realistic data set collected in Taiwan, including wind speed records associated with offshore wind turbines and solar radiation records with PV module specifications, are utilized to validate the proposed model. Energy Storage (ES) is also implemented to compensate and mitigate the potential energy deficit during peak load or maintenance outages. This research aims to minimize total operation and maintenance costs and Energy Non-Supply (ENS) without compromising its safety and reliability regulations.
The IMS model in this work is addressed as an NP-Hard problem. BARON solves small-scale problems to optimize results. Moreover, a Genetic Algorithm-based Matheuristic is also developed to decompose large-scale problems into subproblems and solve them with smaller computational efforts. Results are compared with exact solutions to assess precision and authenticity. Moreover, the thesis presents optimal schedules and reduction of ENS achieved by integrating ES.

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