检索结果-内蒙古大学图书馆

data-driven distributionally robust optimization for Day-Ahead Operation Planning of a Smart Transformer-Based Meshed Hybrid AC/DC Microgrid Considering the Optimal Reactive Power Dispatch

引用

ENERGIES 2024年第16期17卷 4036页

作者： Nunez-Rodriguez, Rafael A. Unsihuay-Vila, Clodomiro Posada, Johnny Pinzon-Ardila, Omar Unidades Tecnol Santander Sch Elect Engn Bucaramanga 680005 Colombia Univ Fed Parana Dept Elect Engn BR-81531980 Curitiba Brazil Univ Autonoma Occidente Dept Elect Engn Cali 760030 Colombia Univ Pontificia Bolivariana Sch Elect Engn Floridablanca 681007 Colombia

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a data-driven distributionally robust optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model's efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter's flexibility for optimal energy management under uncertain conditions.

关键词： AC/DC microgrid data-driven distributionally robust optimization Duality-Free Decomposition Meshed Hybrid Microgrids uncertainty Smart Transformer

来源：评论

学校读者我要写书评

暂无评论

A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources

引用

ENERGY 2021年 216卷 119171-119171页

作者： Zhang, Yachao Liu, Yan Shu, Shengwen Zheng, Feng Huang, Zhanghao Fuzhou Univ Sch Elect Engn & Automat Fuzhou 350116 Peoples R China

The increasing expansion of wind and gas turbine installation has intensified the interdependency between power system and natural gas network, which poses great challenges to the coordination scheduling of the multi-energy coupled system (MECS). The data-driven robust optimization (DDRO) model is proposed for the energy coupled system. In this model, the temporal-spatial correlation of wind power can be considered based on the minimum volume enclosing convex hull uncertainty set, and the confidence set about the probability distribution for wind power scenarios with the form of the norm-1 and norm-inf constraints is constructed to handle wind power uncertainty. Moreover, to describe natural gas transient characteristics, the hydrodynamic model for gas flow represented as a series of partial differential equations is transformed by the Wendroff difference scheme and linearization technique. And then the master-subproblem framework and tri-level duality-free decomposition method is developed to solve the above model. Finally, the proposed model and solving method are carried out on two test systems with different scale, and the robust optimization models and distributionally optimization models in the existing literatures are implemented for comparison. Simulation results demonstrate the effectiveness and superiority of the proposed model for solving the coordination scheduling problem of MECS. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Multi-energy coupled system data-driven distributionally robust optimization Temporal-spatial correlation Distribution uncertainty Coordination scheduling

来源：评论

学校读者我要写书评

暂无评论

Distributed data-driven distributionally robust Volt/Var control for distribution network via an accelerated alternating optimization procedure

引用

ENERGY REPORTS 2023年 9卷 532-539页

作者： Li, Peishuai Wu, Zaijun Yin, Minghui Shen, Jiawei Qin, Yingming Nanjing Univ Sci & Technol 200 Xiaolingwei St Nanjing 210094 Peoples R China Southeast Univ 2 Sipailou Nanjing 210000 Peoples R China

This paper proposes a distributed data-driven distributionally robust volt/var control (DDDR-VVC) approach which schedules on-load-tap changer (OLTC), capacitor banks (CBs) and Photovoltaic (PV) inverters coordinately. With integration of distributed optimization and data-driven distributionally robust optimization, the DDDR-VVC model is constructed to reduce power losses and maintain voltage within allowable range under uncertainty. To fully address the uncertainty impacts, construction of uncertain set is improved through that the probability distribution sets and trapezoidal fuzzy functions are developed to model PV supply and load demand respectively. An accelerated alternating optimization procedure which integrates the alternating direction method of multipliers and the column-and-constraint generation algorithm is proposed. This method improves the dual information update via Nesterov's accelerated gradient method, thus solving the DDDR-VVC model at a high convergence rate. The effectiveness of proposed method is verified through numerical simulations using IEEE 123 bus test system. (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： Distribution network Volt Var control Distributed optimization data-driven distributionally robust optimization Accelerated alternating optimization procedure

来源：评论

学校读者我要写书评

暂无评论

Transmission and Generation Expansion Planning Considering Virtual Power Lines/Plants, Distributed Energy Injection and Demand Response Flexibility from TSO-DSO Interface

引用

ENERGIES 2025年第7期18卷 1602-1602页

作者： Ferreira, Flavio Arthur Leal Unsihuay-Vila, Clodomiro Nunez-Rodriguez, Rafael A. Univ Fed Parana UFPR Dept Elect Engn BR-81531980 Curitiba Brazil Un Tecnol Santander Sch Elect Engn Bucaramanga 680005 Colombia

This article presents a computational model for transmission and generation expansion planning considering the impact of virtual power lines, which consists of the investment in energy storage in the transmission system as well as being able to determine the reduction and postponement of investments in transmission lines. The flexibility from the TSO-DSO interconnection is also modeled, analyzing its impact on system expansion investments. Flexibility is provided to the AC power flow transmission network model by distribution systems connected at the transmission system nodes. The transmission system flexibility requirements are provided by expansion planning performed by the connected DSOs. The objective of the model is to minimize the overall cost of system operation and investments in transmission, generation and flexibility requirements. A data-driven distributionally robust optimization-DDDRO approach is proposed to consider uncertainties of demand and variable renewable energy generation. The column and constraint generation algorithm and duality-free decomposition method are adopted. Case studies using a Garver 6-node system and the IEEE RTS-GMLC were carried out to validate the model and evaluate the values and impacts of local flexibility on transmission system expansion. The results obtained demonstrate a reduction in total costs, an improvement in the efficient use of the transmission system and an improvement in the locational marginal price indicator of the transmission system.

关键词： expansion planning energy storage virtual power line data-driven distributionally robust optimization

来源：评论

学校读者我要写书评

暂无评论

distributionally robust optimization Based Model Predictive Control for Stochastic Mixed Traffic Flow

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第2期25卷 1491-1502页

作者： Gao, Fengkun Yang, Bo Chen, Cailian Guan, Xinping Tang, Yuliang Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Ind Intelligent Control & Ma Shanghai 200240 Peoples R China Xiamen Univ Dept Informat & Commun Engn Xiamen 361005 Peoples R China

In this paper, we investigate a mixed-traffic control problem considering uncertainties of HDVs flow. The challenges mainly lie in modeling the stochastic characteristics of mixed-traffic flow and developing less-conservative algorithm to deal with the uncertainties. To tackle the problem, we propose a stochastic model predictive control (MPC) strategy based on data-driven distributionally robust optimization (DRO). First, a stochastic mixed-traffic model, extended from cell transmission model, is proposed to describe the traffic dynamics. Then, utilizing historical traffic data, an incremental principal component analysis (IPCA) based method is given to construct ambiguity set and incorporate generalized moment information of uncertainties. Based on the above predictive model and ambiguity set, a DRO-based MPC problem is formulated and further converted into an equivalent dual form for efficient solutions, i.e., ramp metering and variable speed limit control. Finally, simulation results based on real data collected in Shanghai, China, demonstrate that our proposed strategy can significantly reduce traffic congestion, achieving 5.74% total travel time reduction compared to robust MPC.

关键词： Mixed-traffic flow ramp metering variable speed limit control data-driven distributionally robust optimization stochastic model predictive control

来源：评论

学校读者我要写书评

暂无评论

PolieDRO: a novel classification and regression framework with non-parametric data-driven regularization

引用

MACHINE LEARNING 2024年第8期113卷 5807-5846页

作者： Gutierrez, Tomas Valladao, Davi Pagnoncelli, Bernardo K. Pontificia Univ Catolica Rio de Janeiro Ind Engn Dept Rio de Janeiro Brazil Univ Cote Azur SKEMA Business Sch Lille France Lamps Co Rio De Janeiro Brazil

PolieDRO is a novel analytics framework for classification and regression that harnesses the power and flexibility of data-driven distributionally robust optimization (DRO) to circumvent the need for regularization hyperparameters. Recent literature shows that traditional machine learning methods such as SVM and (square-root) LASSO can be written as Wasserstein-based DRO problems. Inspired by those results we propose a hyperparameter-free ambiguity set that explores the polyhedral structure of data-driven convex hulls, generating computationally tractable regression and classification methods for any convex loss function. Numerical results based on 100 real-world databases and an extensive experiment with synthetically generated data show that our methods consistently outperform their traditional counterparts.

关键词： data-driven distributionally robust optimization Hyperparameter-free Regression Classification Machine learning

来源：评论

学校读者我要写书评

暂无评论

Day-Ahead Economic Dispatch of AC/DC Hybrid Distribution Network Based on Cell-Distributed Management Mode

引用

FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Wei, Wei Hao, Tonglin Xu, Tao Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin Peoples R China

Current development trends suggest that future networks will be AC/DC hybrid distribution networks (AC/DC HDNs) operating under the Cell-distributed management mode (CDMM). Cross-Cell transaction in the economic dispatch of CDMM-based distribution networks is necessary to ensure Cell demand, improve the level of renewable energy generation outpower consumption, and enhance the economic efficiency of the whole system. To realize adjacent Cell and cross-Cell transactions in this management mode, this paper proposes a day-ahead economic dispatch model of AC/DC HDNs based on CDMM. The proposed model includes a power transaction stage and a security check stage. In the power transaction stage, a Cell-to-Cell transaction model based on the alternating direction method of multipliers is established. This model forms a consistent transaction plan between different Cells on the basis of information exchange between the Cell operator and the Cell system operator. In the security check phase, a security check model based on data-driven distributionally robust optimization is developed to reduce the transfer power according to various security considerations. Alternate iteration of these two stages enables the day-ahead economic dispatch of AC/DC HDNs based on CDMM for adjacent Cell and cross-Cell transactions. Finally, the effectiveness of the proposed model is verified through a numerical example. The differences among four dispatch models are analyzed to illustrate the need for cross-Cell transactions, and the impact of the confidence level on the dispatch results is discussed.

关键词： AC DC hybrid distribution network cell-distributed management mode alternating direction method of multipliers data-driven distributionally robust optimization day-ahead economic dispatch

来源：评论

学校读者我要写书评

暂无评论

Cooperative optimization scheduling of the electricity-gas coupled system considering wind power uncertainty via a decomposition-coordination framework

引用

ENERGY 2020年第0期194卷 116827-000页

作者： Zhang, Yachao Huang, Zhanghao Zheng, Feng Zhou, Rongyu Le, Jian An, Xueli Fuzhou Univ Sch Elect Engn & Automat Fuzhou 350116 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China China Inst Water Resources & Hydropower Res Beijing 100038 Peoples R China

The extensive installation of gas -fired units and the integration of large-scale wind power connected to power grid have strengthened the interdependency between power system and natural gas network. Consequently, wind power uncertainty accompanying with the coupling interaction by gas-fired units has brought new challenges to the safe and economic operation of electricity-gas coupled system. A decomposition-coordination framework is developed to study the cooperative optimization operation of the integrated electricity-gas coupled system. In this framework, a data-driven distributionally robust optimization (DDRO) model is proposed to solve the power system scheduling problem with wind power uncertainty. Aiming to minimize the expectation of the operation cost under the worst-case distribution, DDRO combines the advantages of the traditional robust optimization (RO) and stochastic optimization. Case studies are implemented on two electricity-gas coupled systems of different scales to verify the effectiveness of the proposed decomposition-coordination framework with DDRO. Specifically, compared with the distributionally robust optimization (DRO) model based on moment information, the solution obtained by DDRO can save 1765.01 $ for the 6-bus power system and 2331.18 $ for the IEEE 24-bus power system, respectively. It is demonstrated that DDRO can achieve less conservative and more economic scheduling solutions compared to DRO. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： data-driven distributionally robust optimization Wind power uncertainty Electricity-gas coupled system Decomposition-coordination framework

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：