Multi-level energy-environmental-economic modeling for supporting low-carbon transition of power systems under uncertainty

Abstract

To combat climate change, the low-carbon transition of electricity systems is of critical significance. Such transition is a complex and long-term process, involving many social, economic, environmental, technical and political factors, and requires a complete analysis for every aspect of the transition pathways. In this dissertation, low-carbon transition strategies of electricity systems were studied from three levels. A set of identification, optimization, and simulation models were developed to facilitate the analysis. For the efforts made at the technical level, the necessity and feasibility of introducing an emerging low-carbon power generating technology (i.e., SMRs) have been studied, with a focus on suitable site selection and environmental impact analysis. For the power systems level, optimized low-carbon transition pathways have been identified, taking into account the effects of multiple uncertainties and the associated risks. In addition, given the tight ties that exist between socio-economic systems and power systems, corresponding impacts of the transition strategy made in the second level on socioeconomic systems have been explored, such that the performance of the entire economy can be assessed. The major contribution of this research is the development of a set of innovative models to aid in the management of power system transition under uncertainty. Overall, the proposed models outperformed the previous modeling approaches due to their advantages in complexity characterization, uncertainty representation, impact analysis, and policy formulation. In detail, the proposed models and related contributions are: (1) climate-oriented SMR site recognition model (CSSR), which is capable of taking long term variation of climate conditions into consideration while conducting siting studies; (2) SMR-induced environmental input-output model (SEIOM), which can quantify contributions of having an emerging power-generating technology as an alternative energy supplier to emission mitigation and related impacts on other sectors; (3) stochastic multistage lifecycle programming model (SMLP), through which detailed environmental and economic profiles of each power generation technology were systematically investigated within lifecycle frameworks and were considered in power system optimization modeling, such that the robustness of the resulting decision support can be enhanced; (4) coupled non-deterministic optimization and mixed-level factorial analysis model (NOMFA), which is an attempt to integrate the system optimization methods with mixed-level factorial design under various uncertainties, such that effects of various external interferences and their interactions on the systems can be investigated; (5) nondeterministic optimization-driven factorial CGE model (NFCGE), which is an integration of “economy-wide” equilibrium models and “technology-rich” energy system optimization approaches, and can help investigate the responses of various economic sectors to alternative transition strategies as identified through the optimization efforts. The developed models were applied to a number of Canadian and Chinese cases to demonstrate the applicability and superiority. The results can assist decision-makers in identifying the most efficient and feasible low-carbon transition pathways for power systems and in achieving a more balanced energy-environment-economic structure.