FEDECOM - D4.2 システムモデリングライブラリとモデル予測制御
FEDECOM - D4.2 System modelling library and model predictive controllers (原題)
Zabala, Laura, Arandia, Nerea, Osorio García, Andoni, Martin Bizkarra Belategi, Guisasola Iparraguirre, Ignacio, Decuyper, Jan, Funez, Carlos, Rohner, Tobias, Diedrich, Hannes, Tzavikas, Spyridon, Trbovich, Ana
🤖 gxceed AI 要約
日本語
本報告書は、FEDECOMプロジェクトにおけるエネルギー資産のシミュレーションモデルと、地域エネルギーシステムの最適制御のためのモデル予測制御(MPC)アルゴリズムの開発を詳述する。3つのパイロットサイト(グリーン水素、住宅用小水力、eモビリティ)を対象に、各資産のモデルとMPCを実装し、コスト・エネルギー削減を実現する。モデルは実データで検証され、適応制御に活用される。
English
This deliverable details the development of energy asset simulation models and Model Predictive Control (MPC) algorithms for optimized control of local energy systems within the FEDECOM project. It covers three pilot sites: green hydrogen, residential hydropower, and e-mobility, implementing asset models and MPCs to achieve cost and energy savings. Models are validated with real-world data for adaptive control.
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
日本のエネルギーコミュニティや需給調整市場において、再生可能エネルギー導入拡大に伴う需給変動への対応が課題となる中、本報告書のMPC技術は地域エネルギー管理システム(CEMS)やVPPの高度化に応用可能。特に水素製造やEVのV2G制御は、日本の水素社会実現や運輸部門の脱炭素に示唆を与える。
In the global GX context
This work contributes to the global discourse on energy community optimization and demand-side flexibility, aligning with EU's clean energy package and the broader energy transition. The MPC and modeling approaches for hydrogen, heat pumps, and EV integration offer transferable insights for decentralized energy systems and grid stability, relevant to international efforts on sector coupling and digitalization.
👥 読者別の含意
🔬研究者:Provides validated models and MPC frameworks for federated energy communities, useful for advancing research in optimal control and sector coupling.
🏢実務担当者:Offers practical modeling and control solutions for energy communities, potentially applicable to virtual power plants and microgrids.
🏛政策担当者:Demonstrates technical feasibility of federated energy systems, informing policies on energy community support and grid integration.
📄 Abstract(原文)
This deliverable describes the work performed in T4.2 “Energy asset and system-level modelling and simulation” and T4.3 “MPC algorithms for optimized control of local energy systems” within the FEDECOM project. It focuses on the development of energy system simulation models and field-level Model Predictive Control (MPC) services for the three distinct pilot sites, each representing different types of federated energy communities. Pilot 1 (Virtual Green H2 Federation) includes specific models for condensing boilers with efficiency depending on return temperature and partial load, heat storage relating temperature to the filling volume, conventional boilers based on efficiency values at different return temperatures, biomass boilers that account for the inertia of the asset, and electrolyzers for green hydrogen production facilities. In addition, models developed for the optimization tool, used in Puertollano and TMB plant models are presented, enabling more efficient hydrogen production scheduling. Pilot 2 (Residential Hydropower Federation) includes models for building simulations, which consider the thermal envelope, internal heat gains, and heat losses. These models are built using a grey-box model approach guaranteeing a balance between detail and computational cost, facilitating their application in optimization. The building models in Lugaggia are enriched by the implementation of a Moving Horizon Estimator (MHE), used to integrate self-learning capabilities. This technique is used to recalibrate the model parameters before each execution of the MPC. Other modelled assets include: 1) Storage aging models, especially for electrical storage, which consider various parameters influencing aging to determine degradation with high accuracy; 2) Heat pumps created using performance curves and calibrated with measured data and calibrated with standard least squares linear regression technique and 3) Water tanks, modelled through a first order model. Pilot 3 (Cross-country e-Mobility Federation) focuses on the electrical domain with models for electrical vehicles (EVs) and battery systems.. The EVs are created using hybrid models and include V2G scenarios. The battery models are simplified for use in an MPC framework to balance accuracy and computational efficiency. These models utilize time series data to describe operational states and historical performance. The modelling library is completed by presenting the results and discussion of simulation and calibration efforts, with models validated using real-world data and real-time measurements for adaptive control. The report also highlights the development and implementation of Model Predictive Control (MPC) algorithms for optimized control of local energy systems. These MPC algorithms enable the execution of optimal control of assets and systems, minimizing the tracking error with respect to optimal energy demand profiles while achieving optimization objectives such as cost and energy savings. The MPCs leverage the First Principle Reduced Order (FPRO) models from the system modeling library. The report details MPCs for the three pilot sites. The MPC also incorporates a mechanism to integrate recommendations from a cross-vector optimiser, acting as an Energy Hub. Additionally, Grid Singularity integrated and tested a Tekniker-developed grey-box heat pump coefficient of performance (COP) model as an additional option for configuring heat pump digital twins in the Grid Singularity Exchange.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/22026642first seen 2026-08-26 04:13:04 · last seen 2026-08-27 04:34:26
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