Optimization of hybrid energy storage systems for the integration of renewable energy sources into the power grid and off-grid systems

The ultimate motivation behind this PhD project is to develop technology within energy storage that will speed up the transition to renewable energy sources like solar and wind in electrical power systems. One of the challenges related to many renewable energy sources, e.g. solar and wind, is that the delivered energy and power from these energy sources is intermittent and dependent on the time of day, season, clouds, wind speed, etc. In addition to this, the power needs of the end users vary, but these variations don?t ?fit? with the variations in power production. Consequently, these energy systems are completely dependent on good energy storage solutions to exploit the produced energy in an optimal way. When the energy system produces more energy than the end users need, the surplus energy must be stored in the best possible way, so that it can be delivered to the end users when there is no sun or wind. Energy storage is a field with many different technologies, each with its own advantages and disadvantages. Technologies include various batteries, hydrogen storage, compressed air and others. In many cases the optimal storage solution will be a hybrid system that combines two or more of these technologies, e.g. batteries for short-term storage and hydrogen for long-term storage. However, this results in more complex systems. To meet this challenge, this PhD project will develop computer models that can simulate such hybrid energy storage systems to optimize the system for different cases, both grid-connected systems and off-grid energy systems. Algorithms that use machine learning/artificial intelligence will also be developed that will predict energy production from the renewable energy sources several days ahead. This will make it easier to estimate the required energy storage capacity and consequently further optimize the whole energy system.

The main objective of this Ph.D. project is to model the energy management of a novel hybrid energy storage system for the integration of renewable energy sources (RES) into the power grid. The Ph.D. candidate will develop simulation models for the storage system and verify the simulation results in a real pilot test platform that will be built in collaboration with another Ph.D. project at the University of Oslo. Collaboration with national and international research institutes and industry will be an integral part of the project. Energy storage systems for large-scale renewable energy applications are poorly understood and there is an urgent need to offer solutions to this challenge.The candidate will evaluate the energy management of the novel hybrid energy storage system using both actual data as well as appropriate simulation models and machine learning. The candidate will perform analysis of individual components and their system integration in hybrid configurations including batteries and hydrogen based energy storage. The system will demonstrate power smoothing and energy management by utilizing simulation technologies, forecasting of power generation several hours in advance using machine learning, and different storage solutions. The Ph.D. project will also maximize the lifetime and performance of commercial batteries by developing high-voltage cobalt-free Nickel Metal Hydride (NiMH) batteries that avoids the cost limitations of lithium and cobalt, as well as the safety issues related to lithium batteries. Short- and long-term energy storage will be combined by using a hybrid energy storage system. A new paradigm for energy storage R&D will be introduced with the aim of generating ground-breaking results on the effect of RES integration into decentralized and/or centralized electric grids.