The rapid transition towards a renewable-based electricity system in the Netherlands creates significant challenges for the national transmission grid. With increasing shares of wind and solar power, the variability in generation leads to mismatches between electricity supply and demand, causing congestion and curtailment risks. This thesis investigates to what extent Short-Duration Energy Storage (SDES) and Multi-Day Energy Storage (MDES) technologies can contribute to deferring costly transmission grid expansion while supporting system flexibility. A quantitative modelling approach was applied using the PyPSA-Eur cost-optimisation framework to simulate a 2040 scenario. In this scenario, the installed renewable generation capacity is optimised such that it can meet an electricity demand that is estimated to be three times higher than 2023 levels. The model optimises investments in generation, transmission, and storage to minimise total system costs. The modelling results indicate that deploying a combined capacity of 19.4 TWh of SDES and MDES significantly reduces the required expansion of the Dutch transmission grid from 86.2GW (in a no-storage case) to just 11.8GW. This translates into grid investment savings ofe1613 billion. Annual system costs are also reduced from e1 934 billion to e321 billion, not only due to avoided transmission investments but also by reducing curtailment, avoiding overdimensioned generation capacity, and improving utilisation of renewable output. Importantly, the analysis confirms the complementary roles of SDES and MDES. Lithium-ion batteries (SDES) are well-suited for addressing intra-day fluctuations, particularly solar variability, due to their fast response and high power output. In contrast, iron-air batteries (MDES) manage multi-day and seasonal supply deficits, especially aligned with wind variability, by providing long-duration energy storage at lower energy capital costs. The joint deployment of both technologies maximises system efficiency by matching storage characteristics with different forms of renewable variability. While the quantitative modelling provides valuable system-level insights, its validity is inherently limited by simplifications. A sensitivity analyses was employed to test robustness across a range of technology parameters and shows greater sensitivity to external factors such as grid expansion costs, CO2 targets, and future demand levels. Moreover, the model abstracts from real-world actor behaviour, regulatory barriers, and market dynamics. To complement these quantitative findings, qualitative interviews were conducted with employees from TenneT and ACM. The qualitative insights reveal that the ownership and operation of storage assets should primarily rest with market parties, as they can optimise asset utilisation across multiple markets such as the day-ahead or intra-day markets. Allowing Transmission System Operators (TSOs) to directly own storage would likely result in underutilisation, as regulatory restrictions prevent them from participating in energy markets. However, the interviews also identify several persistent barriers to realising large-scale storage deployment for congestion relief. Chief among these are potentially insufficient financial incentives for market actors to prioritise congestion management, complex regulatory requirements to guarantee grid-supportive behaviour without limiting flexibility, and a lack of spatial coordination guiding optimal siting of flexible assets.