While the renewable energy sources are rising more and more throughout the years, and the effects of climate change starting to become more visible in our everyday life, some actions need to be taken. Combining different renewable energy sources gives the opportunity to build systems that can help limit these effects and provide energy in different types. The main goal of this project is to model a system that can offer electricity and hydrogen to a steel industry, in this case Tata Steel, and provide an insight for their next steps towards sustainability not only to cover their electricity needs but also investigate alternative steel making routes for their primary processes which are the iron making and further the steel making with hydrogen injection. The models developed for this project aim to answer the following research question: ”What is the feasibility to cover with a solar - wind - hydrogen system, the loads of the different steel making routes of Tata Steel ?” Different models were build through the software TRNSYS for the scenarios investigated that are linked with different combinations of components and loads so more sustainable steel making routes can be followed in the future. The steel making routes refer to the iron making process and the electrification of the industry with green electricity. For the year of 2030 a fuel mix of 70% natural gas and 30% of hydrogen and for the year of 2050, 100% of hydrogen will be used for the iron making process while for both of them 100% of the electricity loads were investigated to be covered by renewable energy sources. The scenarios were divided in a combining structure of local - non local generation and the loads corresponding to the different steel making routes. The local scenario refers to generation in the Netherlands while the non local scenario refers to the generation of hydrogen in the Arabic Peninsula and the generation of the electricity loads of the processes in the Netherlands. The components that were used were wind turbines, solar panels, batteries and electrolyzers. Each system was optimized with the help of GenOpt, an add - on of TRNSYS, to which was set to minimize the levelized cost of electricity, considering also the load coverage both for the hydrogen and electricity loads not to deviate more than 1% from the full coverage. Further than the optimization, a sensitivity analysis with the Sobol method through the programming environment of python and more specifically the SALib library and the parametric analysis of TRNSYS was conducted for all the different scenarios investigated to give an insight how the amount of the different components affects the levelized cost of electricity. Combining different metrics that were calculated as the Levelised cost of electricity (LCOE), the Self Sufficiency Ratio (SSR), the SSR of hydrogen, the total cost, the avoided emissions per MWh, the area ratio and the avoided emissions per area, a final comparison was done through the different scenarios. The most attractive choices both in a feasibility and economical perspective were the scenarios for local generation of hydrogen and electricity for the projected steel making routes of the years of 2030 and 2050. For the local generation scenario of 2030 an LCOE of 0.383 AC/kWh with an electricity coverage of 99.007 % and a hydrogen load coverage of 99.135% were resulted. On the other hand, for the scenario of the local generation of 2050, an LCOE of 0.421 AC/kWh, with an electricity load coverage of 99.875% and a hydrogen load coverage of 99.207% were calculated. The scenario that was the most attractive throughout the different metrics was the one for the local generation of 2050 while the next to come was the one for the local generation of 2030. Given the aforementioned study and its respectful results, it is a first step to evaluate the impact that such a system can have not only in the emissions reduction of such an intensively emitting industry but also to bring into perspective all the different aspects needed to be considered to realize such a project and evaluate them in more depth in the future.