Designing a Model for the Global Energy System—GENeSYS-MOD: An Application of the Open-Source Energy Modeling System (OSeMOSYS)

Abstract

This paper develops a path for the global energy system up to 2050, presenting a new application of the open-source energy modeling system (OSeMOSYS) to the community. It allows quite disaggregate energy and emission analysis: Global Energy System Model (GENeSYS-MOD) uses a system of linear equations of the energy system to search for lowest-cost solutions for a secure energy supply, given externally defined constraints, mainly in terms of CO 2 -emissions. The general algebraic modeling system (GAMS) version of OSeMOSYS is updated to the newest version and, in addition, extended and enhanced to include e.g., a modal split for transport, an improved trading system, and changes to storages. The model can be scaled from small-scale applications, e.g., a company, to cover the global energy system. The paper also includes an application of GENeSYS-MOD to analyze decarbonization scenarios at the global level, broken down into 10 regions. Its main focus is on interdependencies between traditionally segregated sectors: electricity, transportation, and heating; which are all included in the model. Model calculations suggests that in order to achieve the 1.5-2 • C target, a combination of renewable energy sources provides the lowest-cost solution, solar photovoltaic being the dominant source. Average costs of electricity generation in 2050 are about 4 €cents/kWh (excluding infrastructure and transportation costs).

Global Transportation Demand Development with Impacts on the Energy Demand and Greenhouse Gas Emissions in a Climate-Constrained World

“…Research on transition dynamics indicate that the speed of transition assumed in this research is possible and not out of reach. Several research teams [169][170][171][172] find similar results to this research for the transition in the transport sector as summarized in Table 27. Smith et al [198] apply socio-economic constraints for an overall energy transition and conclude that a phase-out of carbon-intensive infrastructure at the end of its design lifetime enables a 1.5 • C scenario at a 64% chance, which is close to the 67% probability used typically, and in agreement to this research.…”

“…Löffler et al [171] show the most ambitious scenario with the least final energy demand for the transport sector among all scenarios of around 10,410 TWh in 2050. The final energy share of electricity and synthetic fuels is 41% and 44%, respectively, and the rest is supplied by biofuels (15%).…”

Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system

“…Another option to reduce computation times is to include multiple sectors and/or multiple countries, but reduce the number of representative demand and weather situations to several typical days [38][39][40][41][42]. A lower intra-annual resolution allows optimisation of investment paths over multiple decades, but does not allow enough resolution to assess the variability and flexibility requirements for high shares of wind and solar power [43,44].…”

Determinants of energy futures—a scenario discovery method applied to cost and carbon emission futures for South American electricity infrastructure

“…Because energy infrastructure outlast any electoral or administrative cycle, such transparent information is critical for stakeholders including the public, that is, taxpayers and voters, and support organisations, like development banks. To this end, the analysis uses the Open Source Energy MOdelling SYStem (OSeMOSYS) which is an open source energy model generator that uses linear optimization techniques, and has global application (Fattori et al 2016, Löffler et al 2017, Niet et al 2017, Pfenninger et al 2018, Taliotis et al 2016, UN DESA 2016. It determines the cost-optimal long-term investment and operation required to satisfy an exogenously defined energy demand (Howells et al 2011).…”