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frequency constrained unit commitment considering reserve provision of wind power

APPLIED ENERGY 2024年 361卷

作者： Jiang, Boyou Guo, Chuangxin Chen, Zhe Zhejiang Univ Coll Elect Engn 38 Zheda Rd Hangzhou 310027 Zhejiang Peoples R China Aalborg Univ DOE Fredrik Bajers Vej 7K DK-9220 Aalborg Denmark

Large-scale wind power integration not only requires extra flexibility for power system operation but also leads to declining system inertia and raises concerns regarding frequency stability. Pertinent studies have substantiated the capability of wind turbines (WTs) to provide reserves via pitch angle control (PAC) and rotor speed control (RSC). In this study, a comprehensive modeling approach is employed for the first time to capture WTs' reserve capacities while accounting for the exogenous uncertainty associated with wind speed and the decision -dependent uncertainty regarding the control decisions including pitch angle and rotor speed. Subsequently, a two -stage frequency constrained stochastic unit commitment model incorporating WTs' reserve provision is formulated to jointly optimize the unit commitment, generation, and reserves from both conventional generating units (CGUs) and WTs. To enhance computational tractability, a deep neural network based framework is adopted in combination with piece -wise linearization to linearize the nonlinear terms regarding PAC and RSC. Furthermore, two solution acceleration strategies tailored to the model's characteristics are proposed. Case studies show that (i) the proposed model effectively develops the reserve potential of WTs, leading to a reduction in reserve cost and wind curtailment;(ii) the proposed acceleration strategies significantly improve the solution efficiency, reducing the solution time by 62.88% and 15.71% in the IEEE 9 -bus and 118 -bus systems, respectively.

关键词： Wind power reserve Pitch angle control Rotor speed control Decision-dependent uncertainty frequency constrained unit commitment

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Enhancing frequency security for renewable-dominated power systems via distributionally robust frequency constrained unit commitment

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 239卷

作者： Xu, Danyang Wu, Zhigang Liu, Yanling Zhu, Lin South China Univ Technol Sch Elect Power Guangzhou 510640 Peoples R China

The insufficient inertia and reserve in renewable energy source (RES)-dominated power systems significantly challenge frequency security. This paper introduces a distributionally robust frequency constrained unit commitment (DR-FCUC) scheme at the system operation level to tackle this issue, optimizing day-ahead unit commitment, generation dispatch, and demand-side reserve procurement. The proposed DR-FCUC accounts for frequency dynamic constraints under the most extensive power disturbance scenarios and employs a DR chance constrained (DRCC) approach using a Wasserstein-metric ambiguity set to manage RES uncertainty. Furthermore, we utilize alternate support vector machine decision trees (ASVMTREE) to convert the high-dimensional frequency nadir constraint into a set of linear constraints and introduce a two-stage sampling method to enhance the ASVMTREE training dataset. Consequently, the proposed DR-FCUC is formulated as a mixed-integer linear programming (MILP) model. Case studies on modified IEEE 39-bus and IEEE 118-bus test systems demonstrate the necessity of incorporating frequency constraints into dispatch schemes, the critical role of demand-side frequency support, the effectiveness of the proposed DR-FCUC scheme, and the accuracy of the constraint convexification method.

关键词： frequency constrained unit commitment Demand response Distributionally robust optimization Machine learning

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A novel frequency constrained unit commitment considering VSC-HVDC's frequency support in asynchronous interconnected system under renewable energy Source's uncertainty

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 238卷

作者： Xu, Danyang Wu, Zhigang Zhu, Lin Guan, Lin South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Peoples R China

The increasing share of renewable energy source (RES) poses a challenge to the frequency security of the power system. frequency constrained unit commitment (FCUC) serves as an effective measure to address this challenge at the operational level. This paper introduces a novel FCUC model applicable to the asynchronous interconnected system connected by voltage source converter based HVDC (VSC-HVDC), fully taking into account the frequency support capability of VSC-HVDC in order to reduce the demand for synchronous generators' inertia and reserve while ensuring frequency security, thereby lowering operating costs. Additionally, the uncertainty of RES is also considered. The constraint expressions for three frequency indicators are derived based on a system frequency response model that includes frequency support from VSC-HVDC. The proposed model optimizes unit commitment, generation and reserve dispatch and VSC-HVDC transmission power and frequency response parameters, while addressing RES uncertainty using the distributionally robust chance constrained approach. In response to the highly nonlinear characteristics of the maximum frequency derivation (MFD) constraints, we propose an intelligent sampling-based support vector machine to convexify the MFD constraints and introduce a two-stage decomposition algorithm for solving the model. The effectiveness of the proposed model is demonstrated based on a modified IEEE RTS-79 system.

关键词： Power system frequency constrained unit commitment VSC-HVDC Inertia Asynchronous interconnected system

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Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2023年 36卷

作者： Rajabdorri, Mohammad Kazemtabrizi, Behzad Troffaes, Matthias Sigrist, Lukas Lobato, Enrique Comillas Pontif Univ IIT ICAI Sch Engn Madrid Spain Univ Durham Durham England

As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To address this issue frequency constraints are being included in the scheduling process, which ensure a tolerable frequency deviation in case of any contingencies. In this paper, a method is proposed to integrate the non-linear frequency nadir constraint into the unit commitment problem, using machine learning. First, a synthetic training dataset is generated. Then two of the available classic machine learning methods, namely logistic regression and support vector machine, are proposed to predict the frequency nadir. To be able to compare the machine learning methods to traditional frequency constrained unit commitment approaches, simulations on the power system of La Palma island are carried out for both proposed methods as well as an analytical linearized formulation of the frequency nadir. Our results show that the unit commitment problem with a machine learning based frequency nadir constraint is solved considerably faster than with the analytical formulation, while still achieving an acceptable frequency response quality after outages.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Data -driven method Mixed integer linear programming frequency constrained unit commitment Machine learning

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