Small modular reactors for green remote mining: A multi-objective optimization from a sustainability perspective

Abstract

Nuclear energy sources, in particular the small modular reactor (SMR), are being recognized as low-emission clean energy sources. The development of SMRs has focused on minimizing their carbon footprint while ensuring that energy is generated in a safe and cost-competitive manner, which leads to a worldwide growing interest in SMR deployments. In addition, there is a strong demand for renewable energy sources; however, their output can be influenced significantly by weather and location. Therefore, integration of renewable energy sources with SMRs and an energy storage system, which results in a nuclear renewable hybrid energy system (NR-HES), is required to improve power generation reliability. NR-HES represents a promising energy system solution for off-grid communities and industries, especially those involving remote mining sites. In the present study, an off-grid NR-HES to meet electrical and thermal demands is considered for the Victor mine site in northern Ontario, Canada. Off-grid remote mines in Canada primarily rely on diesel generators. The proposed NR-HES is composed of SMR units with a thermal capacity of 15 MWth each, two wind turbines with a capacity of 2.3 MWe each, a two-tank molten salt thermal storage system, a hydrogen storage facility, and peaking diesel generators. To achieve a near-zero-emission mining site, electric vehicles are proposed in the mining site energy demand structure. A transient thermodynamics model is developed to simulate the NR-HES and generate hourly variation of sustainability performance parameters that include power cycle efficiency, SMR capacity factor, round-trip efficiency of the storage systems, thermal storage state of charging, and greenhouse gas emissions. In addition, an economic analysis is performed to determine the additional annualized cost of the system relative to a baseline site. The simulation has a test matrix of 16 runs with various combinations of design input parameters, namely SMR thermal power, storage duration (e.g., daily, seasonally, or monthly), and thermal storage system volume. Grey relational analysis (GRA) is used as a level-based multi-objective optimization technique to determine the nearest optimal design parameter (e.g., decision variable) combination within the test matrix and thus the optimal configuration. In addition, the GRA ranks the design input parameters based on how significantly each parameter can affect the overall performance of the NR-HES. Results revealed that optimal sustainable perspectives have a significant influence on the overall system parameters and performance. For instance, the results showed that there is an opportunity to achieve a reduction in greenhouse gas emissions by 94.8% with a minimal impact to system annualized cost if the cost and environmental effect objectives were prioritized over other sustainability performance parameters.