Two-stage Stochastic Optimization Scheduling of an Electro-thermal Integrated System for an Energy-Intensive Industrial Park Considering Uncertainty
不確実性を考慮したエネルギー多消費型工業団地の電気熱統合システムの二段階確率的最適化スケジューリング (AI 翻訳)
Zikang Yi, Yulong Ying, Jingchao Li, Bin Zhang, Haojie Lv, Xiaoxia Zhou
🤖 gxceed AI 要約
日本語
本論文は、再生可能エネルギーの高普及に伴う不確実性に対処するため、エネルギー多消費型工業団地における電気熱統合システムの二段階確率的最適化モデルを提案する。コレスキー分解を用いて風力、太陽光、負荷の時間的相関を捉え、期待純運営コスト最小化の下で日前ユニットコミットメントと周波数制御入札、リアルタイムの電気熱ディスパッチを調整する。CVaRを用いて極端な悪条件下の財務リスクを評価し、提案方式が経済性とリスク耐性において最適であることを示した。
English
This paper proposes a two-stage stochastic optimization scheduling model for an electro-thermal integrated energy system in an energy-intensive industrial park under multiple uncertainties. It uses Cholesky decomposition to capture temporal correlations of wind, solar, and load, and coordinates day-ahead unit commitment and frequency regulation with real-time dispatch. Conditional Value-at-Risk is integrated to assess tail financial risks. Results show the scheme achieves optimal economic efficiency and risk resilience, revealing an economic substitution effect between battery storage and high-energy loads, and a trade-off between economic maximization and low-carbon targets.
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
日本の産業団地でも再エネ導入と需給調整が課題となっており、本モデルはコスト最適化と低炭素化の両立に資する。特に、電気熱連携による柔軟性活用やCVaRによるリスク評価手法は、日本の電力市場設計や産業政策への示唆を含む。
In the global GX context
Globally, increasing renewable penetration demands demand-side flexibility. This paper provides a stochastic optimization framework that integrates electro-thermal coordination and CVaR-based risk management, offering insights for industrial park energy management and electricity market design in regions pursuing energy transition.
👥 読者別の含意
🔬研究者:The two-stage stochastic model with CVaR provides a methodological reference for researchers working on uncertainty-aware dispatch and integrated energy systems.
🏢実務担当者:Industrial park operators and energy managers can apply the proposed optimization scheme to reduce costs and improve flexibility under renewable uncertainty.
🏛政策担当者:The identified trade-off between economic maximization and low-carbon targets informs policy design for carbon pricing and flexibility incentives.
📄 Abstract（原文）
To address the uncertainties brought by high renewable energy penetration, exploiting demand-side flexibility is crucial for the economic and stable operation of energy-intensive industrial parks. This paper proposes a two-stage stochastic optimization scheduling model for an electro-thermal integrated energy system (IES) considering multiple uncertainties. The model utilizes Cholesky decomposition to capture the temporal correlation of wind, solar, and load profiles. Aiming to minimize the expected net operating cost, it coordinates day-ahead unit commitment and frequency regulation bidding with intra-day real-time electro-thermal dispatch. Conditional Value-at-Risk (CVaR) is integrated to quantitatively evaluate tail financial risks under extreme adverse scenarios. Simulation results demonstrate that: (1) The proposed electro-thermal decoupled IES scheme achieves optimal economic efficiency and risk resilience, with an expected daily net profit of 4.189 CNY and a 95% CVaR of 3.899 CNY; (2) An "economic substitution effect" between battery storage and high-energy loads under current price signals is quantitatively revealed; (3) The inherent trade-off between economic maximization and low-carbon emission targets is explicitly identified. This research provides scientific decision support for the optimal dispatch and policy design of diverse flexibility resources in electricity markets.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/net-lc70284.2026.11605783first seen 2026-07-25 05:22:16
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