Broad ranges of investment configurations for renewable power systems, robust to cost uncertainty and near-optimality

Achieving ambitious [Formula: see text] emission reduction targets requires energy system planning to accommodate societal preferences, such as transmission reinforcements or onshore wind parks, and acknowledge uncertainties in technology cost projections among many other uncertainties. Current models often solely minimize costs using a single set of cost projections. Here, we apply multi-objective optimization techniques in a fully renewable European electricity system to explore trade-offs between system costs and technology deployment for electricity generation, storage, and transport. We identify ranges of cost-efficient capacity expansion plans incorporating future technology cost uncertainties. For example, we find that some grid reinforcement, long-term storage, and large wind capacities are important to keep costs within 8% of least-cost solutions. Near the cost optimum a technologically diverse spectrum of options exist, allowing policymakers to make trade-offs regarding unpopular infrastructure. Our analysis comprises 50,000+ optimization runs, managed efficiently through multi-fidelity surrogate modeling techniques using sparse polynomial chaos expansions and low-discrepancy sampling.