Transparent regional energy modelling for data-scarce arctic bidding zones: diagnostics for Northern Norway

The transition toward low-carbon energy systems requires planning approaches capable of representing the spatial, climatic, and infrastructural heterogeneity of peripheral Arctic regions, where national-scale models often smooth over locally binding constraints and data limitations. This study investigates whether high-resolution regional energy system optimization can provide substantially different transition diagnostics and infrastructure priorities compared with aggregated national analyses, while also identifying the methodological implications of regional data scarcity. A transparent open-source optimization model was developed for Northern Norway for the period 2020–2050 using five-year planning intervals, 96 hourly seasonal time-slices, explicit interregional electricity trade representation, and reproducible regional downscaling procedures for buildings, transport, industry, heating, and power generation sectors. The results show that regional modelling reveals significantly stronger electricity demand dynamics than population-based national indicators suggest, with Northern Norway representing about 9% of the national population but approximately 13% of electricity consumption in the buildings sector, highlighting limitations of conventional downscaling approaches. The optimization further identifies hydropower as the structural backbone of the regional system while offshore wind becomes a major expansion technology option under constrained hydropower growth and sustained export assumptions; however, removing future export growth approximately halves total wind deployment and largely eliminates storage requirements, demonstrating the strong sensitivity of infrastructure outcomes to trade assumptions. Sensitivity analysis on offshore wind capacity factors additionally shows storage charging increases approaching 60% under higher renewable availability conditions, emphasizing the importance of temporal resolution for flexibility assessment. The study also demonstrates that the principal limitation for detailed regional industrial decarbonization modelling is not optimization capability but the absence of transparent and disaggregated industrial datasets. These findings show that transparent regional optimization models can expose infrastructure bottlenecks, reveal the consequences of modelling assumptions, and provide decision-relevant diagnostics that aggregated national energy models cannot capture.