Fuzzy Inference-Based Power System Planning Considering Collaborative Decarbonization on the Supply-Demand Side

As power systems worldwide still heavily rely on fossil fuels, the power sector remains a major source of carbon emissions, making its decarbonization a critical societal concern. However, the absence of a unified approach that simultaneously addresses emission reductions on both the supply and demand sides presents a significant barrier to achieving deep decarbonization. To bridge this gap, this paper proposes a novel Fuzzy Inference System (FIS)-based two-stage decarbonization framework that integrates supply-side retrofitting with demand-side carbon response strategies. On the supply side, the framework involves retrofitting Coal-Fired Power Plants (CFPPs) with Carbon Capture, Utilization, and Storage (CCUS) technologies. The captured carbon is further processed through power-to-gas (P2G) conversion and the generated natural gas is stored in gas energy systems, thereby enhancing the overall efficiency of carbon mitigation. On the demand side, an enhanced carbon emission flow model is developed to reflect the spatial and temporal impacts of emissions, enabling Large Industrial Loads (LILs) and Internet Data Centers (IDCs) to respond dynamically to carbon intensity variations. To improve the practical feasibility of the proposed framework, the FIS is used to model external factors that affect both supply-side and demand-side decisions, including candidate site selection for retrofitting and realistic estimations of flexible demand capacity. The proposed method has been validated on the IEEE 24-bus and 118-bus systems, confirming the effectiveness in reducing system-wide emissions while maintaining operational reliability.