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Carbon Assessment and Carbon Reduction Potential Analysis of Rural Integrated Energy System Based on Reinforcement Learning

Year: 2025

Type: article

Abstract: Recent years have seen Reinforcement Learning (RL) excel in real-time decision-making and coordinated optimization of complex energy systems. Scholars have applied RL to carbon emission optimization in urban microgrids and industrial parks, but a gap exists in rural integrated energy systems (RIES) regarding carbon assessment and reduction potential analysis—rural energy data is coarse and variable, and rural social-technical coupling is complex, demanding RL for dynamic trade-offs. This study introduces an RL framework to build a "data-driven-model collaboration-strategy evolution" system, uses heuristic algorithms for RIES carbon accounting, scenario simulation and reduction evaluation, compares with algorithms like greedy to find the optimal one, and supports precise carbon reduction and rural revitalization. Results show that the proposed heuristic algorithm reduces costs by 32.3% and carbon emissions by 32.1% compared to the pure diesel algorithm, and balances economic efficiency and low-carbon benefits more effectively than the greedy algorithm.

Source: Applied and Computational Engineering

DOI: 10.54254/2755-2721/2025.gl27034

Author: Leqi Zhao

Institution: Beijing University of Civil Engineering and Architecture

Language: English

Topic: Integrated Energy Systems Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Climate action, Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4414478144

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