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Energy system and computable general equilibrium (CGE) models play vital roles in climate change mitigation studies. These models have advantages and disadvantages, and attempts have been made to integrate them. This study aimed to describe the method for integrating energy system and CGE models and demonstrate the new model that captures the strengths of both models. The method developed in this study ensured the detailed convergence of the energy system by exchanging the results iteratively. We demonstrated the model integration by adopting the method to MESSAGEix-GLOBIOM and AIM/Hub and estimating a mitigation scenario that limits the temperature rise to below 2 °C under the middle-of-the-road socioeconomic projection in Shared Socioeconomic Pathways. As a result of the integration, the index showing the difference between the two models proposed in this study decreased from 1.0 to 0.066. Therefore, we confirmed that these models estimated consistent scenarios. The diagnostic indicators showed that compared to its counterpart CGE model, the newly-developed model was characterized by a higher contribution of demand-side reductions, a lesser alteration in the primary energy supply composition, and lower abatement costs. Given the convergence and advantages of the integrated framework, the proposed method is useful for further application to mitigation studies. • Proposed a new method to integrate energy system models and CGE models. • Integrated MESSAGEix-GLOBIOM and AIM/Hub using the newly proposed method. • AIM/Hub provided multi-sectoral and goods representation. • MESSAGEix-GLOBIOM enabled estimates in consideration of detailed technologies. • Integrated model was characterized by demand-side effort and lower abatement costs. [ABSTRACT FROM AUTHOR]

Increasing energy demand and the industrialization of communities have been the leading causes of climate change. As a result, natural disasters have increased blackouts in power systems. Climate change mitigation should be a global goal of communities. Since natural disaster occurrence has risen recently, rapid system restoration after natural disasters is essential. Mitigation of global warming is inextricably linked to the mechanisms and architecture of energy systems. Energy resilience is a multi-dimensional solution to this problem. In this research, resilient strategies for dealing with natural disasters have been examined. Implementing distributed energy resources (DERs), smart distribution networks, and defining load priority based on consumers' preferences have made a viable solution to the post-disaster operation. Previous efforts cared more about the generation and transmission sectors. This study focused on active distribution systems (ADSs) because distribution systems, as a vital part of energy systems, have not sufficiently contributed to crisis management. A framework for resilience enhancement to smartening conventional distribution systems against natural disasters has been proposed. Three-stage socio-technical optimization was investigated, including optimal allocation of battery storage systems (BESSs), intentional islanding, and optimal power flow (OPF) in MATLAB and DIgSILENT. This optimization presents a decision-making tool for distribution system operators to quickly prepare the system against natural disasters. By applying eight scenarios in the proposed system, in the best scenario (CS4), the recovered power value (RPV) has increased from 38% to more than 99% (more than 61% increase in the resilience index). The amount of supplied load increased from 56% to 92%. Furthermore, prioritizing crucial consumers and reconfiguration are two primary crisis management tools. This approach creates a solid ground for avoiding the social consequences of natural disasters like electrocution and power outages of priority consumers. Failure to apply these two techniques would result in a 21% reduction in the RPV. • Determining load priorities based on consumers' preference. • Optimal placement of batteries considering cost and load priority. • Intentional islanding of the system to enhance resilience. • In the best scenario (CS4), recovered power value (RPV) has increased to 99.35%. • Failure to apply load priority and reconfiguration has reduced RPV to 77.85% (CS5). [ABSTRACT FROM AUTHOR]

Renewable energy resources have garnered considerable attention owing to concerns regarding climate change mitigation and sustainability. The performance of renewable energy resources varies based on weather conditions, which is an important consideration in power system planning as renewable energy penetration increases. In this study, a probabilistic approach for the potential estimation of renewable energy resources based on augmented spatial interpolation was proposed. The proposed algorithm was verified using empirical data obtained from wind farms in Jeju Island. Wind power output scenarios were modeled through ordinary kriging and Monte Carlo simulations. Moreover, the point and cause of line overload according to the seasonal wind power output and power demand were analyzed through transmission security analysis, and the frequency and scale of the curtailments were estimated. This can be used to estimate potential renewable energy resources and establish a power system operation plan. Further study includes the development of stable power system operation plans for large-scale renewable energy resource-integrated power systems. [ABSTRACT FROM AUTHOR]