Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/1591DEA9-140A-4147-849B-CD0E999EC731 | ||
| Year | 2025 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Michail Keramianidis, "Reliability optimization of a microgrid with Renewable Energy Sources", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2025 https://doi.org/10.26233/heallink.tuc.104905 | ||
| Appears in Collections |
In this thesis a hybrid energy system is simulated, that combines photovoltaic arrays, wind turbines, tidal generators, and a lithium-ion battery storage system to power supply a given electrical load. The load corresponds to a cluster of dwellings located within the same geographic area, not connected to the utility grid, and supplied exclusively by the hybrid system, for which the optimal topology is investigated with the aim of maximizing coverage while simultaneously minimizing unserved energy.Optimization of delivered energy and minimization of the Loss of Load Probability (LOLP), subject to economic and siting constraints, were carried out using the Particle Swarm Optimization (PSO) algorithm. On the other hand, the simulation resolves in detail both generated and delivered energy and the operational behavior of each subsystem, analyzing charge/discharge cycles, the evolution of battery state of charge, and each subsystem’s contribution to meeting the load. The optimal configurations returned by the PSO algorithm are then re-injected into the simulation model and verification simulations are performed to confirm the results. At the same time, failure rates were evaluated for the components comprising each subsystem and for the system as a whole as functions of air temperature, solar irradiance, and wind speed. Based on these rates, the expected failure rate and the expected downtime, during which the system does not deliver energy, are determined.The above optimization workflow, combined with the failure-rate analysis, enables a systematic comparison of alternative layouts in terms of delivered energy and total cost, and supports an evidence-based selection of the appropriate solution for off-grid microgrids, achieving demonstrably higher reliability and reduced unserved energy. In conclusion, the proposed approach integrates realistic simulation, reliability analysis via failure rates, and constrained optimization, providing a coherent framework for quantitative assessment and justified selection of the system optimal topology.
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