An optimization scheduling model of multi-energy virtual power plants considering uncertainty constraints and multi-energy coupling characteristics.
不確実性制約とマルチエネルギー連携特性を考慮したマルチエネルギー仮想発電所の最適スケジューリングモデル (AI 翻訳)
Jia Lu, Junjie Wang, Jijun Liu, Youwu Liu
🤖 gxceed AI 要約
日本語
本論文は、不確実性下でのマルチエネルギー仮想発電所（MEVPP）の最適運用スケジューリングモデルを提案。バイオマス混焼炭素回収発電所や電力-アンモニア、尿素合成を統合し、電力・熱・水素・炭素の連携を実現。シミュレーションでは、炭素回収なしのベースラインと比較して、CO2排出量38.5%削減、総コスト75.1%削減を達成。中国のカーボンニュートラル目標に資する知見を提供。
English
This paper proposes a stochastic optimization scheduling model for a multi-energy virtual power plant (MEVPP) integrating biomass co-combustion carbon capture, power-to-ammonia, and urea synthesis. It accounts for wind and solar uncertainties. Results show 38.5% CO2 reduction and 75.1% cost reduction compared to baseline. Carbon trading price sensitivity shows 30-38% emission reductions. Provides insights for China's dual-carbon goals.
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
本論文は、炭素回収と多エネルギー連携を組み合わせたVPPモデルを提案しており、日本におけるCCUSやアンモニア混焼の推進、さらにカーボンプライシングの導入検討に示唆を与える。特に、不確実性下での最適運用は、日本の再生可能エネルギー導入拡大に伴う需給調整に役立つ可能性がある。
In the global GX context
This paper advances the modeling of virtual power plants by integrating carbon capture, power-to-ammonia, and urea synthesis into a stochastic optimization framework. It demonstrates significant emission and cost reductions, providing a replicable approach for low-carbon energy system planning. The carbon trading sensitivity analysis offers insights for designing carbon pricing mechanisms.
👥 読者別の含意
🔬研究者:Provides a novel optimization model for multi-energy VPPs that integrates carbon capture and chemical production, useful for further research on integrated energy systems under uncertainty.
🏢実務担当者:Offers a scheduling tool for operators of multi-energy systems to optimize revenue and emissions, with sensitivity analysis on carbon prices.
🏛政策担当者:Demonstrates the potential of combining carbon capture with multi-energy systems to achieve deep decarbonization, informing policy on carbon trading and technology incentives.
📄 Abstract（原文）
Existing research on virtual power plants (VPPs) has not fully integrated the coupling relationships among electricity, heat, hydrogen, and carbon, and scheduling strategies under uncertainty conditions remain imperfect. To address this gap, this paper proposes an optimization scheduling model for a multi-energy virtual power plant (MEVPP) that incorporates uncertainty constraints and multi-energy coupling characteristics. The proposed model integrates biomass co-combustion carbon capture power plants (BCCPP), power-to-ammonia (P2A), and low-carbon chemical production (urea synthesis) within a unified stochastic VPP scheduling framework, achieving multi-energy synergy and flexible coupled operation involving electricity, heat, hydrogen, and carbon. A scenario generation method based on Latin hypercube sampling (LHS) is adopted to formulate a stochastic scheduling model aimed at maximizing the expected total system revenue under wind and solar uncertainties. Simulation results demonstrate that compared to the baseline scenario without carbon capture, the proposed model reduces CO₂ emissions by 38.5% (from 10,000 t to 6,150 t) and total costs by 75.1% (from $800,000 to $199,200) in the optimal scenario. Carbon trading price sensitivity analysis shows that emission reductions can reach 30-38% through constraint adjustments. These findings provide practical insights for system operators and policymakers in advancing low-carbon energy transitions, particularly for China's dual-carbon goals.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1371/journal.pone.0343212first seen 2026-05-15 17:36:11
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。