With the rise of various renewable energy sources, comes the possibility for combining the different type of sources together to balance their shortcomings. The goal is to find a renewable energy system that can be reliable year-round and be accessible for everyone. This research
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With the rise of various renewable energy sources, comes the possibility for combining the different type of sources together to balance their shortcomings. The goal is to find a renewable energy system that can be reliable year-round and be accessible for everyone. This research tries to model such a system.
A model of a grid-tied PV-battery-electrolyser-fuel cell power system, which is based on a continuation of a series of master thesis projects, was expanded to include a neighbourhood with a fully electrical load or a combination of electrical and hydrogen loads. This model was developed to answer the following question.
What is the techno-economic feasibility of a grid-tied PV-battery-electrolyser-fuel cell power system for a household area in the Netherlands which is either fully electrical or hydrogen integrated?
This hybrid system is simulated by using the graphical interface program TRNSYS. The system size of the PV, batteries, electrolyser, fuel cell and hydrogen gas storage tank are optimised by the GenOpt, an add-on for TRNSYS. The optimisation algorithm will try to find the lowest levelised cost of energy(LCOE) while keeping the system self-sufficiency ratio(SSR) around 1 [%]. This will mean that only 1 [%] of the load is allowed to be extracted from the grid.
The simulation is based on a neighbourhood that consists of 630 houses located in Pijnacker Netherlands. All houses will be equipped with a roof mounted solar PV system with centralised batteries, electrolyser, fuel cell and a hydrogen storage tank. If needed the model can be extended to include a small solar park next to the neighbourhood.
The model will simulate two scenarios for a simulation time of one year, the first being that the neighbourhood is fully electrical and the second for a neighbourhood with integrated hydrogen gas in its consumption. The first one is the base, with only the electrical load demand of houses. Then the load profile will be extended by adding vehicle to the neighbourhood, including the heat demand of the house. These additional load profiles will either be electrical energy based for the fully electrical scenario or hydrogen gas based for the integrated hydrogen scenario.
To estimate the economic development of this hybrid system, a price projection of PV, battery, electrolyser, fuel cell, hydrogen heating, heat pumps and inverters components were determined for the years 2020, 2030, 2040 and 2050. a, the cases will all be simulated for these years. The economic analysis will be over the systems lifetime, which is 25 years.
Before the cases were simulated the model undertook a sensitivity analysis. From this resulted that the simulation start time can be moved from the 1st of January to the 2nd of March to relief the storage tank of getting depleted at the start of the simulation. A battery discharge constraint was lifted and this led the batteries to provide more energy. A forecasting method was applied to the system that effectively reduced the electrolyser on/off cycles by 60 [%], which increased the lifetime of the electrolyser component.
From a technical feasibility analysis of the cases, it resulted that the integrated hydrogen scenario was not technical feasible with the PV system (roof mounted with the PV park) of this model. All the integrated hydrogen scenario cases resulted in a depleted hydrogen storage tank, which forced the system to buy the hydrogen demand externally. The system will rely on an external source more than the allowed 1 [%] (hydrogen gas SSR >> 1 [%]) of the load demand. From the fully electrical scenario the 2020 C-E-(V+H) case resulted not be technical feasible with a SSR value of 2.1 [%]. All the other cases were technical feasible.
From an economic and cost perspective, the cases resulted that the LCOE reduced with the years. The lowest LCOE value found was for the C-E-(Base) case, which reduced from 0.44 [€/KWh] in 2020 to 0.21[€/KWh] in 2050.
The cost breakdown of the cases resulted in the PV system and the storage tank to be the most expensive components of this system. Due to the fact that the C-H2-(H) case had to buy a significant amount of hydrogen from an external source, this became a significant expensive cost of the system.
Comparing the two scenarios resulted that the integrated hydrogen scenario system sizes were smaller, but this is an effect of the system being more eager to buy hydrogen gas then to expand the hydrogen production components. As both scenarios had different SSR values of their respected energy demands, a conclusion of which scenario is more beneficial will be inadequate.
