Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/1A5EF96D-8DA7-468F-9C82-C9BC68D36675 | ||
| Year | 2019 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Marios Papanikolaou, "Optimal coordination control for improved power flow of a Smart Grid using smart metering based on genetic algorithm", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2019 https://doi.org/10.26233/heallink.tuc.82431 | ||
| Appears in Collections |
The conventional electric power grid has proven inadequate. Because the grid technology currently implemented is outdated, there is manifestation of reliability problems. In this thesis an Intelligent Energy Management System (EMS) for end consumers has been proposed. This system develops an optimal power flow control of a Smart grid using real time measurements in a grid offering energy at invariable price and Genetic Algorithm (GA) for a smart meter which is integrated between distribution grid and end consumers. GA methodology is an evolutionary process, which imitating evolution process of nature. The smart meter determines when to draw the energy from grid or the storage unit for consumption. Furthermore, the EMS provides the ability to end consumers for smart charging in Electric Vehicles (EVs). The first objective of using this algorithm is to reduce the cost for end consumers by charging energy storage unit (ESU) from the grid during the low cost periods. The second objective of this system is to prevent grid overload by shifting the power drawn from high demand period to low demand period. The algorithm use two parameters, the hourly price and the load demand of the grid. It demonstrated that the GA approach is much better in cost saving and grid load reduction compare to conventional grid and fuzzy logic approach. The implementation of GA strategy showed 26.1% cost saving rate in comparison with conventional grid for one year period. In addition, the above strategy is 2.4% better than fuzzy logic approach in cost saving rate.
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