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.

在这项研究中，开发了一个多区域因子优化模型 (MRFO)，以支持全国范围内向低碳电力系统 (EPS) 的过渡，承诺减少温室气体排放。通过将非确定性优化方法（区间线性、机会约束和混合整数线性规划）与因子分析相结合，MRFO 可以解决系统参数和目标中表示为区间和概率分布的多种不确定性；它还可以揭示多个不确定参数的影响及其相互作用对系统性能的影响。提供了一个加拿大案例研究来证明所提议方法的适用性。以最小化总系统成本为目标，研究了不同风险级别的发电、容量扩展和区域间交易的最佳方案。结果表明，可再生能源（即风能和太阳能）将在促进向低碳 EPS 过渡方面发挥重要作用，到 2050 年，这将占全国总发电量的 13% 左右。此外，鉴于区域发电组合的多样性，加强区域 EPS 之间的合作将对低碳发电在全国的普及产生积极影响。这些发现可以支持国家制定所需的长期发电扩张计划和减排政策的努力。