Risk-based electric power system planning for climate change mitigation through multi-stage joint-probabilistic left-hand-side chance-constrained fractional programming: A Canadian case study

Abstract

Climate change mitigation by reducing greenhouse gas emissions is one of the major challenges for existing electric power systems. This study presents a multi-stage joint-probabilistic left-hand-side chance-constrained fractional programming (MJCFP) approach to help tackle various uncertainties involved in typical electric power systems and thus facilitate risk-based management for climate change mitigation. The MJCFP approach is capable of solving ratio optimization problems associated with left-hand-side random information by integrating multi-stage programming method, joint-probabilistic chance-constrained programming, fractional programming into a general framework. It can balance dual-objectives of two aspects reflecting system optimal ratio and analyze many of possible scenarios due to various end-user demand situations during different periods. The MJCFP approach is implemented and applied to the provincial electric power system of Saskatchewan, Canada to demonstrate its effectiveness in dealing with the tradeoff between economic development and climate change mitigation. Potential solutions under various risk levels are obtained to help identify appropriate strategies to meet different power demands and emission targets to the maximum extent. The results indicate that the MJCFP approach is effective for regional electric power system planning in support of long-term climate change mitigation policies; it can also generate more alternatives through risk-based management, which allows in-depth analysis of the interrelationships among system efficiency, system profit and system-failure risk.