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 CO2 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 CO2 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 CO2 emissions in coal-dominant cities, particularly in future power systems with higher renewable energy penetration.