Optimizing the Implementation of Small Modular Reactors into Ontario's Future Energy Mix

• Published in: Nuclear technology Vol. 210; no. 1; pp. 23 - 45
• Main Authors: Colterjohn, C., Nagasaki, S., Fujii, Y.
• Format: Journal Article
• Language: English
• Published: La Grange Park Taylor & Francis 02.01.2024; American Nuclear Society
• Subjects: Energy; learning curve; linear programming; Ontario energy mix; recursive optimization; Small modular reactors
• ISSN: 0029-5450; 1943-7471
• DOI: 10.1080/00295450.2023.2217390

This paper performs a detailed analysis of the optimized Ontario power mix under impending load and emissions constraints with the consideration of small modular reactor (SMR) deployment. The target of minimizing the total cost of the 2055 power mix while retaining real-world energy requirements was achieved using a semidynamic, recursive linear optimization model with hourly time resolution for the accurate consideration of wind and photovoltaic variable renewable energy. Utilizing IBM's ILOG CPLEX Optimization Studio's Flow Control method, dynamic factors such as forecasted demand growth, increasing capacity installations, learning curve applications, and reactor refurbishment and decommissioning schedules were applied to the modeling scenarios. Optimized scenarios have demonstrated that SMR-based capacity should play a vital role in the provincial energy mix in order to minimize cost while meeting emissions reduction goals and responding to increasing demand. Simulations show ideal cost reductions when approximately one-third of generated energy is produced by SMRs in the future energy mix and that the absence of SMRs may lead to up to 29% higher spending. Additional cases have considered the benefits of early SMR investment and direct SMR-CANDU cost comparisons.