Integrating hourly quasi-input-output into multi-city power system planning considering spatio-temporal emission variations

Decarbonisation increases the complexity of the spatio-temporal dynamics in CO 2 emission distributions within the power system . Existing power system planning studies have not thoroughly explored the spatio-temporal allocation of emission responsibility, consequently leading to inefficiencies for certain stakeholders. This research innovatively integrates the hourly quasi-input-output (QIO) approach into the multi-city capacity expansion planning (CEP) model, thus enabling an allocation of carbon emissions with high spatio-temporal resolutions. Using Anhui Province, China, with its 16 cities as a case study, the model is validated and reveals that the traditional annual emission accounting approach is projected to either underestimate or overestimate CO 2 emissions in 2040, as hourly emission factors could vary sixfold due to day-night variations in photovoltaic power output. Emission factors tend to be underestimated in cities dominated by renewables, while coal-dominant cities face the opposite issue due to the substantial transmission of high-embodied-emission electricity to other cities at nighttime. To mitigate the unfair allocation of emission burdens among cities, renewable-dominant cities need to take greater responsibility for managing CO 2 emissions in coal-dominant cities, particularly in future power systems with higher renewable energy penetration. • Capture spatio-temporal CO 2 emissions dynamics in power systems. • Integrate hourly quasi-input-output with multi-city power planning. • Optimise regional power planning with city-specific strategies. • Reveal significant high day-night CO 2 emission factor variances. • Enhance accuracy of spatial emission burden allocations.