Redundancy Optimization in Power Distribution Networks
In this study, a two-stage stochastic mixed integer linear programming (MILP) model is developed
for the optimized design of a distributed power system, with components that are prone to errors.
When a component fails is uncertain. The model will optimize the topology of a network based
on costs minimization while dealing with this failure uncertainty. The total costs depend on the
investment costs, operational costs as well as penalty costs for not delivering energy. When an
energy demand is not met this amount will be multiplied by a penalty factor to determine
the penalty costs. The penalty factor depends on the building category and is arbitrary. The
height of the penalty factor appears to a very important factor in the optimized network topology.
Mathematical programming proved to be an efficient way to optimize energy networks based on
redundancy. It can be concluded that in the Dutch distributed power system for low demand
buildings redundancy is not feasible. Only at extreme high penalty factors local options (converter
or renewable storage) are benecial. Higher demand buildings (commercial or industrial) can
benet from redundant lines at a reasonable penalty factor according to the model. When a
the sustainable solution is demanded the optimized network will shift to the renewable storage for any