Development of control algorithm and conditioning monitoring for peak load balancing in smart grids with energy storage system

Renewable energy investments are increasing day by day in our country and in the world. Rising the number of micro grids such as wind and solar, and their inclusion in the grid system requires studies on new grid control methods. However, it is inevitable to include new energy sources in the power system in order to ensure the continuity of energy as well as energy efficiency in order to meet the increasing power demand. In this context, it is important to establish micro grids that allow more distributed generation and energy storage by the end-user in smart grids. The search for solutions for the imbalances that can be caused by mobile and variable loads on the network, such as electric vehicles, is being continued. In this context, energy storage technologies and solar power plants are among the prominent issues to ensure the operating flexibility of the distribution system operator in the power system in terms of both sustainability of energy and peak load balancing. This study aims to develop a condition monitoring and control algorithm for the peak load and supply-demand balancing in a smart grid system by using an energy storage unit. For this purpose, the energy storage unit has been integrated into the smart grid system, designed by the De Lorenzo Company, which is installed in the Manisa Celal Bayar University, Faculty of Engineering, Department of Electrical and Electronics Engineering. A battery pack is integrated into the smart grid system as an energy storage unit. Condition monitoring and control software for the photovoltaic system and battery pack are developed in the LabVIEW™ program. Moreover, an algorithm is developed that provides the conditions for the integration of the battery pack into the system. For the tests of the algorithm, daily load data belongs to the year 2020, of Manisa province is taken as the sample. Also, different demand response programs providing operational flexibility for the distribution system operator in accordance with the scenarios created are considered.