Efficient Structures of the Bottom-up Energy System Model for Assessing Greenhouse Gas Reduction Policies in the Road Transport Sector
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Linear programming (LP) is widely used for development of energy and emission reduction analysis models because of its simplicity and ease of operation, but actual model users sometimes do not understand all the structures of models based on linear programming. The reason for this is that the bottom-up models have become more complicated with decision variables and constraints while subdividing the space, time, and operating modes. Besides LP's core constraints, various user-defined constraints added to reflect various restrictions of reality can be another obstacle for users. This paper summarizes efficient structures of LP formulation which have core decision variables and constraints specific to the road transport sector and minimal types of additional constraints for bottom-up energy system models. It shows the process of systematically extending from the most basic formulation to the case of technology with vintage and multiple activities. It also defines three types of constraints and their combinations that can encompass all kind of constraints often used by energy modelers. This work will contribute to better understanding of bottom-up energy models developed with LP and efficient building-up of the models for the road transport sector.
Abstract
Linear programming (LP) is widely used for development of energy and emission reduction analysis models because of its simplicity and ease of operation, but actual model users sometimes do not understand all the structures of models based on linear programming. The reason for this is that the bottom-up models have become more complicated with decision variables and constraints while subdividing the space, time, and operating modes. Besides LP's core constraints, various user-defined constraints added to reflect various restrictions of reality can be another obstacle for users. This paper summarizes efficient structures of LP formulation which have core decision variables and constraints specific to the road transport sector and minimal types of additional constraints for bottom-up energy system models. It shows the process of systematically extending from the most basic formulation to the case of technology with vintage and multiple activities. It also defines three types of constraints and their combinations that can encompass all kind of constraints often used by energy modelers. This work will contribute to better understanding of bottom-up energy models developed with LP and efficient building-up of the models for the road transport sector.
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