Measurement and Co-optimisation Model of Electricity Carbon Emission Factors Considering Time-Series Characteristics

Urbanisation and rising living standards have led to rising electricity demand, posing challenges to the balance between supply and demand of the power system and environmental sustainability. The aim of this paper is to propose a time-series characteristic-based model for measuring and co-optimising the carbon emission factors of electricity in order to improve energy efficiency and reduce carbon emissions through an in-depth analysis of the electricity consumption characteristics of urban residents. The study first explores the time-series characteristics of residents' electricity consumption behaviour and its influencing factors, and then constructs a Bayesian network model to analyse the time-series state of electricity loads. On this basis, the paper further develops an electricity consumption optimisation model considering carbon emission factors, and verifies the effectiveness of the model with actual data. The results show that the model can effectively reduce the carbon emission of residential electricity consumption and lower the cost of electricity consumption, which provides a new perspective and strategy for the low-carbon operation and load management of the power system, and has important practical application value and theoretical significance.