To use the multi objective optimization a genetic algorithm is implemented to minimized cost, green house gases etc. Defined by the numbers of input, output variables and dimensions, the algorithm will compare the solution to other breeds of the LCA analysis.
Regarding the utilized fitness function in this assignment, we consider three different geometrical variables as input for our genetic algorithm. These variables are:
- The width of the lift bridge span: The width varies between 10 and 30 meters.
- The distance between poles in the railway system: This distance varies between 50 and 60 meters.
- The height of the monopile steel for the wind turbine varies between 30 and 50 meters.
With that in mind the optimization will be processed for two integrated parts of the systems individually. First, we consider the energy system which is represented by the nuclear power station and the off shore wind farm.
The fitness function produces as output the best possible solution for the maintenance strategies, the amount of the emissions, energy produced, and their costs. The optimal maintenance strategy implies finding the minimum distance between two successive intervention events.
Optimization of the Energy System
The energy system comprehend two individual systems which power up the whole state of Schleswig-Holstein. Its important that for maintenance only one of them needs to shut down for maintenance or for a structural renewal of the system, after life time expectations were met.
In the figure above it can be seen which maintenance frequency should be taken into account to reduce the cost of emissions. Clearly, different possibilities are creating different paths which lead to a diverse output. The lower and upper bound are shown on the x-axis. The red graph represents an optional solution.
The following interplay is from an optimization point of view the best solution for the integrated maintenance schedule combined with the system design.
One of the best solution is for example with a duration of 166.0 days intervention distribution of 1, energy consumption with 399,118,370,034 kWh, and emissions like 10,274,084 t of CO2, 41,683.62 t of NOx , 90,146.26 t of SO2 and 51.36 billion of cost.
Optimization of the Infrastructure System
The infrastructure system comprehends three individual systems with critical infrastructure components in that area because of the geographical location and the limitation of the river and the shore. The infrastructure is important to deliver materials and skilled workers to the designed areas like the harbour, port, and the manufacture plants around Brunsbüttel. It is significant for general maintenance activities that only one of the infrastructure shuts down during one of these activities or go through a structural renewal of the system. This renewal can occur after lifetime expectations were met (such as the renewal of the wind turbine).
One of the best solution is for example with a duration of 246 days intervention distribution of 1, an energy consumption with 4662654276co2 kWh, and emissions like 1559723 t of CO2, 6816.605 t of NOx , 14230.89 t of SO2 and 0.6388685 billion of cost.
