Distributed Model Predictive Control Strategy for Multi-Energy Virtual Power Plant Based on Digital Twin
デジタルツインに基づくマルチエネルギーバーチャルパワープラントの分散型モデル予測制御戦略 (AI 翻訳)
Yang Gao, Shuangqi Li, Tianhua Xu, Subhash Lakshminarayana, Siqi Bu, Chenghong Gu, Qian Ai
🤖 gxceed AI 要約
日本語
本論文は、デジタルツインと段階的動的炭素取引を統合したマルチエネルギーバーチャルパワープラント（MEVPP）の分散型モデル予測制御戦略を提案する。深層ニューラルネットワークとクラスタリングを用いた動的集約モデルにより、再生可能エネルギーの不確実性を考慮し、電気自動車のV2G機能を活用する。多段階ローリング最適化により、前日計画と当日運用の乖離を抑制し、経済性と炭素排出削減の両立を実現する。
English
This paper proposes a distributed model predictive control strategy for a multi-energy virtual power plant (MEVPP) integrating digital twin and stepwise dynamic carbon trading. It uses deep neural networks and clustering for dynamic aggregation, accommodates renewable uncertainty, and leverages vehicle-to-grid (V2G) flexibility. Multi-stage rolling optimization reduces day-ahead and intra-day deviations, achieving lower costs and carbon emissions while keeping operational variance within 1.2%.
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
日本でもバーチャルパワープラントやカーボンプライシングの導入が進む中、デジタルツインと炭素取引を組み合わせた本手法は、次世代電力システムの効率的運用に示唆を与える。特に、需要側リソースの柔軟性活用と炭素排出の正確な計測は、日本のGX政策にも応用可能である。
In the global GX context
This paper offers a novel integration of digital twin technology and dynamic carbon trading for multi-energy virtual power plants, addressing renewable intermittency and load flexibility. It contributes to the global discourse on carbon pricing and optimization in next-generation power systems, with potential applications in markets like the EU or US.
👥 読者別の含意
🔬研究者:Provides a detailed optimization framework combining digital twin and carbon trading, useful for advancing VPP and carbon market research.
🏢実務担当者:Offers a practical strategy for VPP operators to enhance flexibility and reduce emissions using real-time pricing and V2G.
🏛政策担当者:Demonstrates how carbon trading mechanisms can be integrated into operational dispatch, informing carbon market design and smart grid policies.
📄 Abstract（原文）
Multi-energy virtual power plant (MEVPP) faces significant challenges stemming from the inherent intermittency of renewable energy, the underutilized flexibility of distributed load-side resources, and the intricate complexities associated with optimizing multi-energy coupling and dynamic carbon emission management. This paper proposes a multi-timescale digital twin rolling optimization strategy incorporating vehicle-to-grid (V2G) interaction and stepwise dynamic carbon trading to address these issues. First, a digital twin dynamic aggregation modeling approach is developed by combining deep neural networks with an enhanced k-means spectral clustering algorithm. This enables accurate dynamic characterization of wind and solar generation uncertainties and equivalent aggregation of distributed resources, thereby unlocking greater flexibility on the demand side. Second, considering electricity-carbon coupling and the unique operational characteristics of electric vehicle charging and discharging, a real-time pricing strategy based on time-of-use tariffs and incentive/penalty mechanisms is introduced. Simultaneously, a stepwise dynamic carbon trading mechanism is proposed to facilitate more precise measurement of carbon emissions from various devices and enable coordinated optimization of electricity and carbon flows. Finally, a multi-stage rolling optimization model is constructed within a distributed model predictive control framework to mitigate deviations between day-ahead scheduling and intra-day operation caused by prediction errors and changing weather conditions. Multi-scenario comparative analyses demonstrate that the proposed strategy can effectively reduce the peak-to-valley difference of the electric load, lower the total system economic cost, and decrease carbon emissions while maintaining operational cost variance within 1.2%. These findings offer theoretical and practical guidance for the efficient and sustainable operation of MEVPP within next-generation power systems, particularly under evolving market conditions and increasing renewable penetration.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tsg.2025.3635089first seen 2026-05-15 17:22:39
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