A Distributionally Robust Control Strategy for Frequency Safety based on Koopman Operator Described System Model
Koopman演算子で記述されたシステムモデルに基づく周波数安全性のための分布的ロバスト制御戦略 (AI 翻訳)
Cao Q, Shen C
🤖 gxceed AI 要約
日本語
本論文では、再生可能エネルギーの導入拡大に伴う周波数安定性問題に対し、データ駆動型の分布的ロバスト制御フレームワークを提案。予測誤差の不確実性をWasserstein距離に基づくあいまい集合でモデル化し、ミニマックス最適化問題として定式化。Value at Risk制約の解析近似により計算効率を向上させ、シミュレーションで制御コスト低減と周波数安定性の確保を実証。
English
This paper proposes a distributionally robust emergency frequency control (DREFC) framework for power systems with high renewable penetration. The approach models uncertainty in prediction errors via a Wasserstein-distance-based ambiguity set and formulates a min-max optimization problem. By analytically approximating Value at Risk constraints, it achieves computational efficiency. Simulations demonstrate effective frequency stability with minimal control costs.
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
日本でも再生可能エネルギーの大量導入が進み、周波数制御の重要性が増している。本手法はデータ駆動型で不確実性にロバストな制御を提供し、日本の電力系統運用者にとって実用的な知見となりうる。
In the global GX context
As renewable energy penetration grows globally, frequency stability becomes critical. This data-driven, distributionally robust control method addresses prediction uncertainties and offers a computationally efficient solution for grid operators, relevant to modern power systems undergoing decarbonization.
👥 読者別の含意
🔬研究者:A novel application of distributionally robust optimization to frequency control using Koopman operator modeling, offering a computationally tractable approach for uncertainty handling.
🏢実務担当者:Provides a robust control strategy that can be implemented for emergency frequency regulation in grids with high renewable energy share, balancing stability and cost.
🏛政策担当者:Supports grid reliability amid renewable integration, informing regulatory standards for frequency control mechanisms.
📄 Abstract（原文）
As renewable energy and power electronics become more prevalent in power systems, modeling system frequency dynamics under power deficits presents increasing complexity. While data-driven approaches can alleviate some of these difficulties, they are exposed to noise and training inaccuracies, which introduce uncertainty in modelling and prediction. To tackle the uncertainty and limited statistical knowledge of prediction errors, we propose a distributionally robust, data-driven emergency frequency control (DREFC) framework. The framework focuses on achieving a high probability of frequency stability while providing flexibility for decision-makers to adjust control conservativeness. Specifically, DREFC formulates a min-max optimization problem to determine an optimal control strategy that is robust to variations in prediction errors, modeled within a Wasserstein-distance-based ambiguity set. By analytically approximating the Value at Risk (VaR) constraints, we achieve a computationally efficient reformulations. Simulation results show that DREFC effectively guarantees frequency stability, minimizes control costs and low computation time.
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.22541/authorea.15003145/v1first seen 2026-05-14 21:21:11
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