Abstract
The modeling of micro-grid’s PV units’ and loads’ size and distribution along its network could severely affect the accuracy of power losses calculation and; thus the amount of energy to be imported from the main-grid to balance the load. Also, it will affect the micro-grid’s optimal energy management results. Therefore, a comprehensive analysis is carried out to assess the impact of the PV’s and the load’s sizing and sitting (either lumped or distributed). Four case studies are carried out to illustrate the impact of the modeling on the micro-grid’s losses and the imported energy from the main-grid and their costs. The obtained results are used to implement the proposed energy management two-single objective optimization functions applying Genetic Algorithm. Then, a fifth case study is carried out to optimize the micro-grid energy management process through an optimal chagrining - discharging scheduling of a storage module. The obtained results, recommendations, and evaluations for choosing a proper sizing and siting modeling and the chagrining - discharging scheduling of a storage module under seasonal variations are reported and discussed.
Highlights
The integration of Distributed Energy Recourses (DER) such as wind turbines, Photovoltaic (PV), storage systems, and electric vehicles have increased the complexity of the distribution system’s operation
To avoid the centralized disadvantages research is being directed at micro-grids because of their ability to enhance the reliability of a power system and reduce its environmental impact [3]
Function 2: The second objective is to minimize the total amount of energy received by the micro-grid from the main grid represented by its total energy cost
Summary
The integration of Distributed Energy Recourses (DER) such as wind turbines, Photovoltaic (PV), storage systems, and electric vehicles have increased the complexity of the distribution system’s operation. The centralized energy management systems focus on controlling all energy activities in the whole system to reach an optimal energy management operation Their main disadvantage is reduced flexibility, as modifications are needed for each additional component (Generators, Storage systems...etc) to be installed in the system and the extensive computational requirements [2]. While [11], GA was implemented to minimize energy cost and emissions of a micro-grid with different DGs and energy storage systems in both winter and summer seasons It did not go into the details of each of the micro-grid’s decision variables. The energy management problem of a PV– energy storage-based grid connected micro-grid is investigated through a proposed optimization process to minimize energy loss and cost. A detailed description of the system under study and the decision-making tool are presented
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