As the share of renewable energy generation increases, the need for energy storage also increases. Therefore, there is a need for better storage representation in the current energy modelling tools. In the present day,
the longer-term energy storage systems are not fully represented since, for existing storage systems, the self-serving nature of these leads to participation in multiple energy markets. This is because participating in other markets, like the balancing markets, can lead to higher overall profits than a storage system only participating in the wholesale market.
This thesis investigates different energy storage technologies and multiple prominent storage applications for grids. Furthermore, an overview of the European energy markets will be examined, and different design options will be discussed. These markets include frequency containment reserve (FCR), frequency regulation reserves (aFRR/mFRR) and the wholesale markets. The review of storage technologies, applications, and available markets has led to the development and simulation of single-purpose energy storage models fulfilling grid applications.
By combining the specific purpose models, a complete energy market and energy storage model representation could be created. The model created is unique since the complete energy system model allows energy storage systems to optimally dispatch over multiple markets while at the same time also influencing these markets. Multiple cases were investigated using this model, such as the influence of increasing storage capacity on the wholesale and balancing market and the influence of storage systems just performing one service, so only regulation, arbitrage or peak-shaving. Based on the model results, recommendations are made on improving the current energy market designs and how to better represent storage systems in existing energy system models.

This report discusses the design and implementation of a subsystem required for an energy system integration demonstrator. The purpose of this subsystem is emulating energy systems using physical table-top model representations. This system has been realised using a Raspberry Pi 3B+, which communicates information using the IኼC protocol to the table-top models. The physical models can actuate information regarding the energy systems from the Raspberry Pi with DACs, and sense realtime information with ADCs.