The electricity grid is rapidly evolving to accommodate various new assets and energy storage is one of the key enabling technologies in this transition. Solar, wind, decentralised grids, different ways of storing energy, increasing electricity demand and new Electric vehicle (EV) charging infrastructures are all unfolding, leading to new challenges to keep the grid reliable and stable. Overcoming these challenges leads to different energy storage requirements in terms of energy, power, duration and cycle frequency. To define these requirements, the following issues need to be addressed:
• How to select and combine different batteries with different power, energy, voltage and lifetime?
• How to integrate the batteries with different voltages with appropriate power electronics in an efficient, reliable and cost-effective way?
• How to optimally use the entire system in terms of efficiency, reliability and cost, and determine when which battery has to be used, for which service?
Power electronic converters and their control enable many system functionalities and form the interface with the batteries but are often neglected. With the market growing into many different use-cases, there is a need for reliable, modular, and universal power electronics interface solutions that seamlessly integrate with all system components and the electricity grid. Also, the usage of the batteries is critical in terms of system lifetime, and enabling an optimal monitoring and control of their operation based on detailed aging information and needed grid services is crucial.
Solving these aspects will help society to facilitate the energy transition, increasing societal acceptance of renewable and make Europe even more autonomous in terms of energy supplies.
iSTORMY addresses these challenges by developing an interoperable and modular Hybrid Battery Energy Storage System (HESS), including high-energy and high-power batteries with advanced SoX estimation and Remaining Useful Life (RUL) algorithm. Also high-efficiency power electronics are targeted, together with universal and machine-learning-based energy management, enabling the demonstration of various use cases and seamlessly interfacing the grid to provide multiple services, such as a combination of load levelling, frequency regulation, provision of backup power, at minimum cost and with reduced environmental impact.
