A Multi-Scale method for combined design and dispatch optimization of nuclear hybrid energy systems including storage

Abstract

Reliable, carbon-free power generation is increasing in importance. Nuclear-renewable hybrid energy systems (NHES) are a potential solution for current generation challenges, but design and dispatch optimization for these systems remains challenging particularly when stochastic effects, long time horizons and nonlinear modeling are needed. This work presents a multi-scale method for combining the design and dispatch optimization problems for nonlinear NHES over long time horizons. Rather than treating the entire horizon as a single optimization problem, a large number of combined optimization problems are solved for shorter samples of the horizon resulting in distributions of optimal capacities for each of the capacities being optimized. The distributions are then aggregated, and the techno-economics of each aggregate is analyzed. The result is a contextual knowledge about the techno-economic tradeoffs for the system rather than a single set of optimal capacities. This method is applied to two NHES of varying complexity. Results indicate that unit capacities for a grid-connected district energy system can be reduced by at least 18.9% by allowing for 3 periods of power import over the course of a 2000 day dispatch. The algorithm is used to solve combined optimization problems with dispatch horizons 112.5 times longer than are solvable directly.