A Parallel Hybrid Forecasting Framework for Economic Dispatch: With Applications for China's Electricity Market Operations
経済的負荷配分のための並列ハイブリッド予測フレームワーク：中国の電力市場運営への応用 (AI 翻訳)
Wen Zhang, Dengfeng Li, Zhibin Wu, Xiaojun Zeng
🤖 gxceed AI 要約
日本語
中国の双炭目標達成のため、変動再生可能エネルギーと排出権取引の導入が進む中、経済ディスパッチの課題を解決する並列ハイブリッド予測フレームワークを提案。予測精度と計算効率を両立し、バッテリー貯蔵や補助サービスを考慮した動的経済ディスパッチモデルを開発。中国のノードシステムと実炭素価格系列を用いた検証で、VRES予測誤差の影響や炭素価格の有効性などを明らかにした。
English
This paper proposes a parallel hybrid forecasting framework for economic dispatch in China's electricity market, addressing challenges from VRES variability and carbon price volatility. It integrates dynamic battery storage and ancillary services into day-ahead and real-time dispatch models. Using China-based node data and real carbon prices, it finds that ancillary service expansion alone cannot mitigate dispatch cost increases from VRES forecasting errors, and carbon pricing is more effective at lower VRES penetration.
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
日本でも再生可能エネルギー導入拡大や排出権取引の検討が進む中、本論文の予測フレームワークや経済ディスパッチモデルは応用可能性がある。特にVRES予測誤差と炭素価格の相互作用に関する知見は、日本のGX政策立案に示唆を与える。
In the global GX context
This paper provides empirical evidence from China's evolving electricity market, offering insights for other countries integrating high shares of renewables and carbon pricing. The parallel hybrid forecasting framework is novel and could be adapted for other markets. The findings on the interplay between VRES forecasting, battery storage, and carbon pricing contribute to the global discourse on decarbonizing power systems.
👥 読者別の含意
🔬研究者:This paper offers a novel forecasting-optimization framework and empirical insights on the interaction between VRES, carbon pricing, and ancillary services in China's electricity market.
🏢実務担当者:The proposed framework can help grid operators and power companies improve economic dispatch under uncertainty from renewables and carbon prices.
🏛政策担当者:Policymakers can learn about the conditions under which carbon pricing is most effective for emission reductions in power systems with VRES.
📄 Abstract（原文）
China's “Dual Carbon” targets demonstrate a strategic commitment to addressing energy security and climate change. To achieve these decarbonization goals, China has implemented two initiatives: accelerated deployment of variable renewable energy sources (VRESs) and a nationwide emissions trading system for the power sector. The demand fluctuations, carbon price volatility, and inherent variability of VRES generation introduce complex challenges for economic dispatch in the developing electricity markets. This study alleviates the uncertainties by proposing a parallel hybrid dynamic self-learning forecasting framework that balances prediction accuracy with computational efficiency. The impacts of VRES generation forecasting are analyzed by utilizing a random error generation method. Incorporating the dynamic battery storage state and ancillary services, this study elucidates the operational interdependences between the electric energy markets and the ancillary service market by developing a dynamic economic dispatch (DED) model for the day-ahead market and a revised DED model for the real-time market. The robustness of the proposed forecasting-optimization framework is verified based on the China-based node system and the real carbon price series. Key findings reveal the following: Ancillary service expansion alone cannot sufficiently mitigate dispatch cost escalations caused by significant VRES forecasting errors; strategics between energy market bidding and ancillary service participation are critical for battery storage cost recovery; and utilities of carbon pricing on emission reductions are more evident when there are lower VRES penetration rates, more competitive battery cost, and larger storage capacities.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tem.2026.3655323first seen 2026-05-15 17:25:00
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