Managing a hybrid energy smart grid with a renewable energy source

The aim of this work is to develop tools for optimal power flow management control in a micro grid (MG). The latter consists of a wind turbine, energy storage system, two gas turbines (GTs), and the main grid. Unlike the traditional approach, which is limited to the distribution of active power, thi...

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Bibliographic Details
Published in:Asian journal of control 2019-07, Vol.21 (4), p.2060-2073
Main Authors: Chakir, Houssam Eddine, Hamid, Ouadi, Fouad, Giri
Format: Article
Language:English
Subjects:
Algorithms
Bellman algorithm
Computer simulation
distributed generation
Dynamic programming
Electric power distribution
Energy management
energy management system
Energy storage
Gas turbines
hybrid system
optimal power flow
Optimization
Power flow
Reactive power
Smart grid
Storage units
Weather forecasting
Wind turbines
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Summary:The aim of this work is to develop tools for optimal power flow management control in a micro grid (MG). The latter consists of a wind turbine, energy storage system, two gas turbines (GTs), and the main grid. Unlike the traditional approach, which is limited to the distribution of active power, this paper models an electrical system to coordinate and optimize the flow of both active and reactive power using discrete controls. The proposed optimal power distribution strategy has two objectives. First, it aims at forecasting over a time horizon of 24 hours the optimal distribution of the active and reactive power required for each power source connected to the MG. The proposed management incorporates the forecasts of consumption, weather, and tariffs. Second, it aims at reducing the CO2 emissions rate by optimizing both the operating point of the two GTs and the usage of the storage unit. The proposed optimization problem for the energy management system is solved using the Bellman algorithm through dynamic programming. Its effectiveness is illustrated with various simulations carried out in the Matlab environment. The supremacy of the proposed management algorithm is highlighted by comparing its performance with conventional (restricted) management.
ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.1995