A Factorial Fractional Chance-Constrained Programming Model for Regional Electricity Systems Management under GHG Emission Mitigation — A Case Study of Saskatchewan, Canada

Abstract

In this study, a factorial fractional chance-constrained programming model (FFCC) is developed for managing Saskatchewan’s electricity systems under the pressure of greenhouse gas (GHG) emissions reduction. Through integrating multiple programming methods (i.e., linear fractional, mixed-integer linear, and chance-constrained) with factorial analysis into an optimization framework, FFCC could effectively (1) tackle multi-objective problems; (2) manage stochastic features of system parameters expressed as probability distributions and facilitate constraint-violation analysis; (3) reflect the impacts of various economic and environmental factors and their interactions on system response. Optimal electricity generation schemes, capacity expansion plans, and electricity import/export strategies under different policy scenarios and risk levels are explored with the objective of maximizing low-carbon power generation per unit of system cost. Results find that small modular nuclear reactor power would have the potential to replace fossil fuel-fired technologies and aid Saskatchewan in achieving net-zero carbon emissions by 2050. It is expected that the modelling results can support regional efforts in proposing effective power generation capacity expansion plans and relevant environmental policies.

Keywords: electricity systems optimization, linear fractional programming, chance-constrained programming, factorial analysis, greenhouse gas emissions