Development of An Interval Chance-Constrained Mixed-Integer Linear Programming Model for Electric Power System Planning — A Case Study for the Province of Alberta, Canada

Abstract

Reducing carbon emissions from power systems is essential for meeting increasingly stringent decarbonization require- ments while maintaining reliable electricity supply and economic performance. This study develops an interval chance-constrained mixed-integer linear programming (ICM) model to maximize total system profit and support capacity expansion and generation planning under uncertainty. The proposed ICM framework integrates mixed-integer programming, chance-constrained programming, and interval linear programming to represent both risk preferences and interval-type uncertainties in key inputs, and it considers eight planned power generation technologies. The model generates optimal technology-specific capacity expansion plans and electricity generation strategies that satisfy end-user demand while complying with carbon dioxide (CO2) emission targets under three risk levels. The approach is demonstrated through a provincial-scale case study in Alberta, Canada, where uncertainties and risks are quantified and trade-offs among multiple system criteria are examined. Results indicate that the share of installed capacity for small modular reactors (SMRs) will increase rapidly, clean energy generation will rise to 66% of total electricity production, and total CO2 emissions will decrease by approximately 34%. The proposed framework provides decision-makers with a practical tool for optimizing provincial power systems and advancing long-term environmental and economic sustainability.

Keywords: SMRs, system optimization, uncertainty, power system planning, carbon emission, clean energy