データ・モデルハイブリッド駆動による製造パーク仮想発電所のマルチタイムスケール低炭素経済運用
Data-Model Hybrid-Driven Multi-Timescale Low-Carbon Economic Dispatch for Manufacturing-Park Virtual Power Plants (原題)
Ruosong Hou, Wei Guo, Ziheng Zhao, Xiaolin Tan, Yuan Cao
🤖 gxceed AI 要約
日本語
製造パークの仮想発電所（VPP）を対象に、データ駆動とモデル駆動を融合した多時間スケールの低炭素経済運用手法を提案。不確実性シナリオを実データから生成し、生産制約を考慮した3段階確率計画モデルを解く。ケーススタディでは運用コスト22.6%削減、炭素排出18.0%削減、再エネ吸収率94.8%を達成。
English
This study proposes a data-model hybrid method for multi-timescale low-carbon economic dispatch of manufacturing-park virtual power plants. It integrates empirical uncertainty scenarios with a production-constrained stochastic program, achieving 22.6% cost reduction, 18.0% carbon emission reduction, and 94.8% renewable absorption in case studies.
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
日本の製造業は再エネ導入と生産計画の両立が課題であり、本手法は工場群のVPP運用やカーボンフットプリント削減に貢献。SSBJ開示やサプライチェーン排出量算定の実務にも示唆を与える。
In the global GX context
This research addresses the integration of renewable energy and production scheduling in manufacturing parks, relevant to global efforts on industrial decarbonization and virtual power plants. It offers a methodological framework that can inform corporate sustainability strategies and grid integration policies.
👥 読者別の含意
🔬研究者:Provides a novel hybrid data-model approach for multi-timescale dispatch with production constraints, useful for advancing VPP and low-carbon optimization research.
🏢実務担当者:Offers a practical framework for manufacturing parks to reduce costs and emissions while maintaining production feasibility, applicable to energy management systems.
🏛政策担当者:Highlights the potential of VPPs in industrial parks for renewable integration and carbon reduction, informing policy on distributed energy resources and industrial decarbonization.
📄 Abstract（原文）
INTRODUCTION: Manufacturing parks combine energy-intensive production equipment, auxiliary systems, distributed renewables, and flexible loads, creating coupled fluctuations between production schedules and electricity demand. OBJECTIVES: This study develops an auditable data-model hybrid method that links empirically updated uncertainty scenarios with a production-constrained, low-carbon, three-stage dispatch model for manufacturing-park virtual power plants. METHODS: The data module estimates wind-speed, irradiance, manufacturing-load, and price distributions from rolling historical records, generates joint scenarios, and updates operating states. The model module solves a coupled day-ahead-intraday-real-time stochastic program with process-feasibility, carbon-flow, storage, and inter-park constraints; reduced scenarios and measured deviations form the interface between the two modules. RESULTS: Case studies show a 22.6% reduction in operating cost, an 18.0% reduction in carbon emissions, a renewable-energy absorption rate of 94.8%, and a 48.8% reduction in power-fluctuation standard deviation compared with single-time-scale dispatch. CONCLUSION: The proposed framework coordinates energy and production decisions while maintaining production feasibility and improves economic, low-carbon, and operational performance under high renewable penetration.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.4108/eetsis.13894first seen 2026-08-28 04:42:00
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