  Modeling to generate alternatives (MGA) is an increasingly popular method in
energy system optimization. MGA explores the near-optimal space, namely, system
alternatives whose costs are within a certain fraction of the globally optimal
cost. Real-world stakeholders may prefer these alternatives due to intangible
factors. Nonetheless, widespread MGA adoption is hampered by its additional
computational burden. Current MGA methods identify boundary points of the
near-optimal space through repeated, independent optimization problems.
Hundreds of model runs are usually required, and such individual runs are often
inefficient because they repeat calculations or retrace previous trajectories.
In this study, we transcend such limitations by introducing a novel algorithm
called Funplex, which uses methods from multi-objective Simplex to optimize
many MGA objectives with minimal computational redundancy. For a simple
linear-programming energy hub case study, we show that Funplex is five times
faster than existing methods and yields higher-quality near-optimal spaces.
Furthermore, sensitivity analyses suggest that Funplex scales well with the
number of investment variables, making it promising for capacity planning
models. The current proof-of-concept implementation based on a full
multi-objective tableau may face memory and stability limitations for large
models. Nonetheless, future developments based on more advanced versions of
Simplex may overcome such barriers, thereby making MGA more accessible and
standard among modeling teams.
