Research on energy structure optimisation and carbon emissions assessment based on electricity big data

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a robust optimization method integrating big data and multi-objective algorithms for power system decarbonization studies.

🏢実務担当者:Power companies can apply this model to optimize generation mix and reduce emissions while maintaining grid stability.

🏛政策担当者:Offers quantitative evidence for designing energy policies that balance carbon targets and supply security.

As the global energy transition and carbon neutrality objectives advance, the electricity sector plays a pivotal role in optimising energy structures and controlling carbon emissions. This study explores methods for energy structure optimisation and carbon emissions assessment based on electricity big data, aiming to achieve low-carbon, efficient, and sustainable energy management. By constructing a multi-objective optimisation model, it analyses the trade-offs between energy structure, carbon emissions, and power supply reliability. An improved multi-objective optimisation algorithm based on genetic algorithms (GA) and particle swarm optimisation (PSO) is proposed to provide decision support for the green transition of power systems. The research first collected and cleaned power system big data to analyse the spatio-temporal characteristics of load fluctuations, renewable energy output, and carbon emissions. Subsequently, an energy structure optimisation model was designed using multi-objective optimisation methods, with its effectiveness validated through case studies. Findings indicate that the optimised energy structure significantly reduces carbon emissions while enhancing power supply reliability.

• openalex https://doi.org/10.1117/12.3111317first seen 2026-06-25 04:55:25