The world has entered a new era in the field of energy due to reasons such as eliminating the disadvantages arising from the use of fossil fuels, ensuring the supply-demand balance, quality requirements, increasing demand for the use of renewable energy sources and the advancement of technology in this direction, security needs and the expansion of the network network. 

Intelligent energy management systems have an important place among energy storage technologies to identify opportunities for cost reduction, decarbonisation and resilience. Energy management technologies are increasingly being adopted and are effective systems for utilizing on-site resources such as renewable energy and energy storage.

It is very important for the energy field to prevent the changes and fluctuations that may occur in the production and distribution of energy by using energy storage systems. An monitoring system can be developed by transferring the measured parameters to a cloud server, which monitors the usable capacity (SoC), Health status (SoH) and aging status of all storage systems to be produced with smart energy management systems. This system can examine life expectancy and aging by using deep learning, artificial intelligence, and pattern recognition methods. Intelligent energy management systems, which include the balancing circuits of energy storage systems that need stabilizers such as batteries and supercapacitors, and which will ensure the safe operation of the energy storage system, can work in this context. Moreover, DC/DC and DC/AC converters needed for the operation of the developed energy storage systems, their hardware and embedded system software can be realized. In order to effectively manage energy storage systems integrated into the electrical power system at different levels, an optimization-based efficient energy management system is developed and renewable energy sources integrated at both the power plant and distributed generation level and with various levels of flexibility are taken into account in the system operation and the new generation Optimum actions for energy storage systems can be determined in case of demand, where the effect of loads will also be examined. The effect of these axioms on energy storage system planning can also be observed through sensitivity analysis.