Development of a multi-regional factorial optimization model for supporting electric power system's low-carbon transition – A case study of Canada

Abstract

In this study, a multi-regional factorial optimization model (MRFO) has been developed for supporting nation-wide transitions to low-carbon electric power systems (EPS) under the commitment to reduce greenhouse gas emissions. Through integrating non-deterministic optimization methods (interval linear, chance-constrained, and mixed-integer linear programming) with factorial analysis, MRFO can address multiple uncertainties stated as intervals and probability distribution in system parameters and objectives; it can also unveil the effects of multiple uncertain parameters and their interactions on system performance. A Canadian case study is provided to demonstrate the applicability of the proposed approach. Optimal schemes of electricity generation, capacity expansion, and inter-regional trades at different risk levels are examined with the objective of minimizing the total system costs. Results indicate that renewable energy (i.e., wind and solar) would play an important role in facilitating the transitions to low-carbon EPS, which would contribute to approximately 13% of the total national electricity generation by 2050. In addition, increasing the collaboration among regional EPS would have positive effects on the national penetration of low-carbon power generation in the light of the diversities in regional generation mixes. The findings can support the national efforts in formulating desired long-term power generation expansion plans and emission reduction policies.