Carbon Reduction Potential and Sensitivity Analysis of Rural Integrated Energy System with Carbon Trading and Coordinated Electric-Thermal Demand Response

TL;DR

This paper tackles carbon reduction potential in rural integrated energy systems (RIES) and evaluates how external carbon trading and demand response (DR) interact with device-level parameters. It develops a bi-level macro–micro framework: a macro-level multi-energy low-carbon operation with coordinated electric–thermal DR and carbon trading, and a micro-level carbon-emission model with sensitivity analysis across 28 parameters. Case studies using typical operation data from northern China show that coordinating electric–thermal DR with carbon trading achieves the highest carbon-reduction potential and lowers operating costs. The analysis highlights four highly sensitive parameters (gas-turbine efficiency, gas-boiler heating efficiency, pyrolysis furnace efficiency, and pyrolysis power generation) that strongly drive emission reductions, providing actionable guidance for decarbonizing RIES.

Abstract

Constructing clean and low-carbon rural integrated energy system (RIES) is a fundamental requirement for supporting China's rural modernization and new-type urbanization. Existing research on RIES decarbonization primarily focuses on the optimal low-carbon operation of system-level energy devices at the macro level, while the synergistic carbon-reduction effects of demand-side flexible loads and external carbon trading mechanisms have not been fully explored. Meanwhile, at the micro level, the carbon sensitivity of device parameters and their potential contribution to emission reduction remain insufficiently investigated. To address these gaps, this study integrates macro- and micro-level analyses. At the macro level, a multi-energy-coupled low-carbon optimal operation framework is developed, incorporating coordinated electric-thermal demand response (DR) and carbon trading. At the micro level, a carbon emission model for RIES components is established, and sensitivity analysis is conducted on 28 carbon-related parameters to identify highly sensitive determinants of emission reduction. Case studies based on typical operation data from a rural region in northern China demonstrate that coordinated electric-thermal DR and carbon trading can achieve maximum carbon-reduction potential. Furthermore, the identified high-sensitivity parameters provide essential theoretical guidance for enhancing the decarbonization potential of RIES.

Figures (19)

• Figure 1: Electric-thermal RIES structure
• Figure 2: Electric and thermal RIES structure
• Figure 3: Framework of the carbon reduction optimization model and solution algorithm for RIES
• Figure 4: Wind and Solar Power Output Curve
• Figure 5: Electric Load Balance Diagram of Scenario 1