  We suggest a new methodology for designing robust energy systems. For this,
we investigate so-called near-optimal solutions to energy system optimisation
models; solutions whose objective values deviate only marginally from the
optimum. Using a refined method for obtaining explicit geometric descriptions
of these near-optimal feasible spaces, we find designs that are as robust as
possible to perturbations. This contributes to the ongoing debate on how to
define and work with robustness in energy systems modelling. We apply our
methods in an investigation using multiple decades of weather data. For the
first time, we run a capacity expansion model of the European power system (one
node per country) with a 3-hourly temporal resolution with 41 years of weather
data. While an optimisation with 41 weather years is at the limits of
computational feasibility, we use the near-optimal feasible spaces of single
years to gain an understanding of the design space over the full time period.
Specifically, we intersect all near-optimal feasible spaces for the individual
years in order to get designs that are likely to be feasible over the entire
time period. We find significant potential for investment flexibility, and
verify the feasibility of these designs by simulating the resulting dispatch
problem with four decades of weather data. They are characterised by a shift
towards more onshore wind and solar power, while emitting up to 50% less $CO_2$
than a cost-optimal solution over that period. Our work builds on recent
developments in the field, including techniques such as Modelling to Generate
Alternatives and Modelling All Alternatives, and provides new insights into the
geometry of near-optimal feasible spaces and the importance of multi-decade
weather variability for energy systems design. We also provide an effective way
of working with a multi-decade time frame in a highly parallelised manner.
