Fuzzy Inference-Based Power System Planning Considering Collaborative Decarbonization on the Supply-Demand Side
ファジィ推論に基づく需給両面の協調的脱炭素化を考慮した電力系統計画 (AI 翻訳)
Chenxi Zhang, Yi Yang, Jing Qiu, Jiafeng Lin, Jiawei Zhang
🤖 gxceed AI 要約
日本語
本論文は、供給側の石炭火力発電所へのCCUS・P2G導入と需要側の炭素排出フローモデルによる大規模産業負荷・データセンターの動的応答を統合した、2段階の電力系統脱炭素化フレームワークを提案。ファジィ推論システムを用いて実用的な制約条件をモデル化し、IEEEテスト系統でシステム全体の排出削減と運用信頼性の両立を確認した。
English
This paper proposes a two-stage decarbonization framework for power systems that integrates supply-side retrofitting of coal plants with CCUS and power-to-gas, and demand-side carbon response using an enhanced emission flow model for industrial loads and data centers. A fuzzy inference system models practical constraints. Validation on IEEE 24- and 118-bus systems shows effective emission reduction while maintaining reliability.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX政策では、石炭火力の段階的削減と需要側の柔軟性活用が重要課題。本フレームワークはCCUSと需要応答を統合する点で、日本の電源構成転換と需要家参加型の脱炭素施策に示唆を与える。
In the global GX context
Globally, integrating supply-side CCUS with demand-side flexibility is a key challenge for deep decarbonization. This framework offers a systematic approach that could inform integrated resource planning and carbon pricing mechanisms in various power markets.
👥 読者別の含意
🔬研究者:Provides a novel fuzzy inference-based method for coordinated supply-demand decarbonization planning, applicable to future power system models.
🏢実務担当者:Offers a practical framework for utilities and system operators to evaluate retrofitting CCUS and demand response programs jointly.
🏛政策担当者:Demonstrates a potential pathway for policy design that incentivizes both supply-side investment and demand-side carbon responsiveness.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tia.2025.3605893first seen 2026-05-06 00:02:16
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