Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/C31FBEE7-AD8D-4E7E-8120-0D185A09F7F6 | ||
| Year | 2021 | ||
| Type of Item | Diploma Work | ||
| License |
|
||
| Bibliographic Citation | Dimitra Kyriakou, "Optimal operation scheduling of microgrids of large building complexes", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2021 https://doi.org/10.26233/heallink.tuc.90367 | ||
| Appears in Collections |
Microgrids constitute a developing area of the energy industry, representing a paradigm shift from centralized power plants to more localized, distributed generation, particularly in cities, communities and campuses. Microgrids provide efficient and low-cost energy, improve the local robustness and the regional electric grid operation and stability. Moreover, plug-in vehicles (PEVs) are expected to play an important role in the operation of electric power systems as electric vehicle technology is rapidly developing.In this thesis, a method for optimal operation scheduling of microgrids of large building complexes is proposed. It is based on a hierarchical multi-agent system (MAS) comprising a group of large office buildings, considering their thermal and electrical loads, and plug-in electric vehicles (EVs). A significant advantage of the proposed algorithm is that at the time periods of the day that the electric grid is not available, the microgrid is able itself to supply a satisfactory percentage of the buildings’ electric power demand exploiting only the hosted PEVs. The main target of the method is to minimize the total operation cost of the microgrid while satisfying at the same time a large number of technical and operation constraints. The examined optimization problem is solved using the particle swarm optimization (PSO) method. The efficiency of the method is proved by detailed simulation results of different operation scenarios, showed that cost savings in range of 27% can be achieved.
Copyright © DIAS 2013
