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The rapid expansion of low-cost renewable electricity combined with end-use electrification in transport, industry, and buildings offers a promising path to deep decarbonisation. However, aligning variable supply with demand requires strategies for daily and seasonal balancing. Existing models either lack the wide scope required for long-term transition pathways or the spatio-temporal detail to capture power system variability and flexibility. Here, we combine the complementary strengths of REMIND, a long-term integrated assessment model, and PyPSA-Eur, an hourly energy system model, through a bi-directional, price-based and iterative soft coupling. REMIND provides pathway variables such as sectoral electricity demand, installed capacities, and costs to PyPSA-Eur, which returns optimised operational variables such as capacity factors, storage requirements, and relative prices. After sufficient convergence, this integrated approach jointly optimises long-term investment and short-term operation. We demonstrate the coupling for two Germany-focused scenarios, with and without demand-side flexibility, reaching climate neutrality by 2045. Our results confirm that a sector-coupled energy system with nearly 100% renewable electricity is technically possible and economically viable. Power system flexibility influences long-term pathways through price differentiation: supply-side market values vary by generation technology, while demand-side prices vary by end-use sector. Flexible electrolysers and smart-charging electric vehicles benefit from below-average prices, whereas less flexible heat pumps face almost twice the average price due to winter peak loads. Without demand-side flexibility, electricity prices increase across all end-users, though battery deployment partially compensates. By integrating hourly power system dynamics into multi-decadal energy transition pathways, our approach addresses the fundamental trade-off between the wide scope needed for climate policy analysis and the spatio-temporal detail needed for power system planning.
