Low-Carbon Robust Planning for PIESs with Multi-Time-Scale Uncertainties and Elastic DR Regulation
多時間スケール不確実性と弾力的DR調整を考慮したPIESの低炭素ロバスト計画 (AI 翻訳)
Xin Huang, Shucan Zhou, Jian Xiong, Keteng Jiang, Hao Yu, Haibo Li
🤖 gxceed AI 要約
日本語
本論文は、パーク統合エネルギーシステム（PIES）に対し、冷熱・熱・電力・ガス・水素を含むエネルギーフレームワークを構築し、価格弾力性に基づく動的DRメカニズムと情報ギャップ理論を用いた長期不確実性を考慮したロバスト計画手法を提案する。ケーススタディでは、ライフサイクルコストを55.24%、CO2排出量を47.75%削減できることを示した。
English
This paper proposes a robust planning method for park integrated energy systems (PIESs) considering multi-time-scale uncertainties and an elastic demand response mechanism. It models an energy flow framework including cooling, heating, electricity, gas, and hydrogen, and uses information gap decision theory for long-term load growth uncertainty. Case studies show 55.24% cost reduction and 47.75% carbon emission reduction, with the proposed DR method further cutting costs and emissions.
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
日本の地域エネルギー計画や水素社会実現に向けて、複数エネルギーキャリアの統合と需要応答の弾力化は重要である。本手法は、日本におけるゼロカーボン街区やスマートコミュニティの計画に活用可能な知見を提供する。
In the global GX context
As integrated energy systems gain traction globally, this paper offers a robust optimization approach that handles both short-term and long-term uncertainties, which is relevant for grid decarbonization and demand-side flexibility. The inclusion of hydrogen carriers aligns with global hydrogen strategies.
👥 読者別の含意
🔬研究者:Provides a novel method for joint optimization of multi-energy systems under multi-timescale uncertainties, with demand response modeling.
🏢実務担当者:Offers a planning tool for park-level energy systems that can significantly reduce costs and emissions, useful for energy service companies.
📄 Abstract（原文）
With the widespread application of park integrated energy systems (PIESs), challenges of multi-energy coupling, high investment costs, and multi-type uncertainties have become increasingly prominent. Existing research often employs typical scenario generation or robust optimization for short-term uncertainties but struggles with long-term load growth uncertainties and fails to fully utilize the flexibility of demand-side resources during the planning phase. This paper proposes a robust planning method for PIESs considering dynamic demand response and multi-timescale uncertainties. First, an energy flow framework encompassing cooling, heating, electricity, gas, and hydrogen is constructed. To overcome the limitations of traditional fixed-boundary DR, a dynamic elastic DR mechanism featuring transferable, substitutable, and curtailable types is established. Transferable demand boundaries are defined by a price–demand elasticity matrix, and actual responses are dynamically adjusted in synergy with system power balance conditions for optimal configuration. Second, multivariate dynamic time warping and hierarchical clustering algorithms derive typical daily scenarios accounting for short-term uncertainties. Finally, information gap decision theory characterizes long-term load growth uncertainty, constructing a robust planning model addressing both timescales. Case studies show that flexible resources and demand response reduce lifecycle cost by 55.24% and carbon emissions by 47.75%. The proposed demand response method further cuts costs by 153,800 yuan and emissions by 11.36%. The robust planning method synergistically addresses multi-timescale uncertainties, ensuring economy while maximizing resilience to uncertain fluctuations.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/en19133207first seen 2026-07-09 05:15:17
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