One of the goals outlined during the Paris Agreement in 2015 aimed at 'holding the increase in global average temperature to well below 2C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5C'. In conjunction with this, the Klimaatakkord of the Netherlands aims to 'reduce greenhouse gas emissions in the Netherlands by 49% compared to 1990 levels' To achieve this goal, a rapid decarbonisation of our economy and energy system is needed. Currently, residential usage accounts 20.4% of Dutch energy consumption.. To reach these targets, the integration of renewable energy sources in Dutch households will be a needed.

Solar energy is already one of the most affordable renewable energy sources available and is currently being integrated into newly built households across the Netherlands. However, as the renewable capacity of the Netherlands expands, so will the need for energy storage to meet the mismatch between renewable generation and demand. A battery bank is usually adopted to supply this mismatch on a daily basis and the production and consumption of hydrogen the chosen technology for a seasonal one. Thus, future households and neighbourhoods in the Netherlands must incorporate both in order to maximise self sufficiency from the grid. The high costs of these components make it unsuitable for implementation in a single household, but scaling up to provide for an entire neighbourhood is a more feasible approach. This results in a so called grid-connected hybrid PV-Battery-Electrolyser-FC energy system.

This final thesis project models and optimises a grid-connected hybrid PV-Battery-Electrolyser-FC energy system to asses its feasibility, both economically and technologically, for utilisation on a neighbourhood in the Netherlands. The simulation model of the hybrid energy system is designed TRNSYS. The model is optimised to minimise the levelised cost of eletricity (LCOE) and to maintain a self-sufficiency ratio of 1\% for the hybrid energy system in TRNOPT. Several scenarios are optimised based on the overall system layout and cases dependant on the electrical, heat and mobility demand. The particle swarm optimisation (PSO) and Hooke-Jeeves optimisation algorithms are used for the optimisation process in GenOpt. In addition, a literature study on the learning curves of different components in the hybrid energy system was performed to predict their costs in 2030. The results of this were used to optimise the system as if it were built in 2030.

The simulated hybrid PV-Battery-Electrolyser-FC energy systems are technically feasible for most scenarios and load profiles for a Dutch neighbourhood. The one exception to this is heat load demand with de-centralised PV generation, which saw an energy deficit at the end of the year. The lowest LCOE of 0.749 €/kWh was found for the centralised scenario implementing smart load management in the load demand. It is found that de-centralising the PV-system to the roofs of houses and the battery storage system each increases of the LCOE of the system due to larger installations costs and a different battery technology. The preliminary results of the future scenarios suggest the results will follow the same trends as was seen in 2020. The LCOE reduces by 21% - 28% compared to the LCOE of 2020. However, more research is needed on this topic to draw conclusive results.