REMix-NZ energy system optimisation dataset: five scenarios for electricity, heat and transport decarbonisation, 2020 to 2050

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🔬研究者:Energy system modellers can use this open dataset for benchmarking or to extend scenario analysis in other regions.

🏢実務担当者:Utility and energy planners can gain insights into the trade-offs between electrification, biomass, and hydrogen in decarbonizing heat and transport.

🏛政策担当者:Policymakers can reference the scenario framework to understand the implications of different technology pathways for achieving net-zero emissions.

This dataset contains the input data, model build scripts, and optimisation results for a multi-sector energy system model of Aotearoa New Zealand covering the period from 2020 to 2050. The model is developed using the open-source REMix framework and solved at hourly temporal resolution across 11 regional nodes, capturing the full electricity grid as well as heat and transport energy demands. Five long-term scenarios are included: GP  (Global Projections) : energy demand and technology cost assumptions consistent with global trend projections for Aotearoa New Zealand NT (National Targets) : a pathway aligned with (and limited to) Aotearoa New Zealand's official emissions reduction targets ELEC+ (Electrification+) : an accelerated direct electrification scenario for heat and transport end-uses BIO+ (Biomass+) : a pathway with expanded bioenergy carriers and biomass-based fuel conversion H2+ (Hydrogen+) : a pathway with more indirect electrification with green hydrogen and derivatives The model includes electricity generation (hydro, geothermal, solar PV, onshore and offshore wind, gas turbines, biomass, coal, diesel), hydropower reservoirs, battery storage, and an extensive multi-energy system with electrolysers, methanisers, Fischer-Tropsch synthesis, direct air capture (DAC), and hydrogen fuel cells. Each scenario is optimised for system cost minimisation subject to a net-zero CO₂ constraint in 2050.   Contents of this repository: Python scripts for building model input data ( build.py ), running the GAMS optimisation ( run.py ), and post-processing results ( evaluate_scenarios.py ) Input data: regional power plant database, hourly electricity demand profiles (8,760 h/year), renewable energy potential and capacity factors, multi-sector fuel demand projections GAMS result files (.gdx) for all five scenarios across seven model years (2020, 2025, 2030, 2035, 2040, 2045, 2050) Software requirements: Python ≥ 3.10, REMix framework, GAMS with Gurobi or CPLEX solver.

• Zenodo https://zenodo.org/records/20593095first seen 2026-06-09 04:16:01