Cost Optimization for Combined Small Modular Reactors and Renewables: A Genetic Algorithm-Based Approach

The transition to renewable resources has been hindered by the inherent intermittence of power production from those resources. This has necessitated the use of battery storage systems (BSS) to be able to meet the load constantly by charging when there is an excess generation and discharging its power when there is a lack of power to follow the electric demand. While currently essential to operating a fully clean energy system, the BSS is prohibitively expensive. The high cost of the BSS has severely discouraged the transition to completely clean energy systems. Recent advancements in the small modular reactor (SMR) technology have allowed it to increase and decrease its power output to act as a load follower and become a possible replacement for the far more expensive BSS. The goal of this research is to investigate the difference in SMR size and cost when using two and five-percent ramp rates for the NuScale Small Modular Reactor (SMR) using a genetic algorithm for optimization. This was done to understand a relationship between the ramping rate of the SMR and its effects on levelized cost. Four case study locations were chosen, and tests were run with combinations of SMR and BSS where a model was created to find the globally optimized battery and SMR size.