检索结果-内蒙古大学图书馆

Multi-time scale optimization scheduling of microgrid considering source and load uncertainty

ELECTRIC POWER SYSTEMS RESEARCH 2023年 216卷

作者： Hou, Jianmin Yu, Weijie Xu, Zhihao Ge, Quanbo Li, Zhi Meng, Ying Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China

With the increase of renewable energy penetration in microgrid and the stochasticity of customer load, microgrid faces new difficulties in maintaining the smooth power of contact lines and system economy when achieving optimal scheduling. In order to balance the accuracy, economy and robustness of microgrid scheduling better, a multi-time scale optimal scheduling strategy for microgrids considering the uncertainty of source and load is proposed. In the day-ahead scheduling stage, a two-stage distributionally robust optimal scheduling model is established with the objective of minimizing the comprehensive day-ahead scheduling cost of the microgrid, and the optimal day-ahead scheduling solution is found under the probability distribution of the worst scenario. In the intra-day scheduling stage, based on the prediction value of source and load with higher accuracy, a distributed model predictive control method is used to build an intra-day rolling optimization and real-time adjustment optimal scheduling model, which ensures the effective implementation of the day-ahead plan and fully suppresses power fluctuation of the contact line. The simulation results show that the proposed multi-time scale optimal scheduling method can not only maintain the smooth power of contact lines, but also achieve robust and economic operation of the microgrid.

关键词： Multi -time scale Microgrid Optimal scheduling Distributionally robust optimization distributed model predictive control

来源：评论

学校读者我要写书评

暂无评论

distributed MPC-Based Secondary control for Energy Storage Systems in a DC Microgrid

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第6期36卷 5633-5644页

作者： Yang, Qiufan Zhou, Jianyu Chen, Xia Wen, Jinyu Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

In this paper, a novel distributed model predictive control (DMPC) strategy based on voltage observer for multiple energy storage systems (ESs) is firstly proposed to achieve a tradeoff between voltage regulation and power sharing. In order to reduce the impact of communication delay on voltage observer, an improved DMPC consensus algorithm is proposed. This scheme improves the robustness of the system to the delay. Besides, a small-signal dynamic model of a DC microgrid with the improved DMPC controllers is established to analyze its dynamic performance. The time-domain simulation results are presented to verify that, under the constraints of the DC bus voltage, a flexible trade-off between voltage regulation and power distribution can be achieved. Besides, the communication delay problems can be reduced.

关键词： Voltage control Microgrids Observers predictive control Delays predictive models Heuristic algorithms DC microgrid distributed model predictive control secondary control power sharing

来源：评论

学校读者我要写书评

暂无评论

distributed Cooperative Path Planning for Tracking Ground Moving Target by Multiple Fixed-wing UAVs via DMPC-GVD in Urban Environment

引用

INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2021年第2期19卷 823-836页

作者： Hu, Chaofang Meng, Zhuo Qu, Ge Shin, Hyo-Sang Tsourdos, Antonios Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Cranfield Univ Sch Aerosp Transport & Mfg Cranfield Beds England

In this paper, a real-time distributed path planning method is developed for cooperatively tracking ground moving target in urban by multiple fixed-wing unmanned aerial vehicles (UAVs). For reasons of changeable movement of target, the commanded speed and turning rate of each UAV are both taken as control input variables. In urban environment, buildings may occlude the line of sight of on-board sensor. Hence the target coverage degree is proposed as objective function instead of distance. To save energy of UAV system as much as possible, the control input cost and sensor energy consumption are also taken as objectives. For preemptive priority requirement, the objective functions are fuzzified and the satisfactory degree order is designed to model priority. To guarantee the feasibility of solution, the varying domain is introduced to replace the strict order constraint. On this basis, generalized varying domain (GVD) method is developed to balance optimization and priority. In terms of the maneuverability of UAVs, the diverse constraints are considered, including real speed and turning rate, control input saturation, collision avoidance between UAVs, and obstacle avoidance between UAV and buildings. Consequently, distributed model predictive control (DMPC) strategy is designed to calculate the optimal path of each UAV, where the state information in finite period of UAV is transferred to the adjacent ones. The simulations show the effectiveness of proposed method by comparing with hierarchical optimization (HO).

关键词： distributed model predictive control path planning priority unmanned aerial vehicle

来源：评论

学校读者我要写书评

暂无评论

Robust Data predictive control Framework for Smart Multi-Microgrid Energy Dispatch Considering Electricity Market Uncertainty

引用

IEEE ACCESS 2021年 9卷 32390-32404页

作者： Brahmia, Ibrahim Wang, Jingcheng Xu, Haotian Wang, Hongyuan Turci, Luca De Oliveira Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Xian Technol Univ Autonomous Syst & Intelligent Control Int Joint R Xian 710021 Peoples R China

With the emerging technologies for Energy Intent (EI) and data-driven applications, the conventional power grid network is undergoing a radical modernization. An efficient energy management and electricity price forecasting remains a challenging task. In this paper, a new Robust Data predictive control framework for Energy Management System (RDPC-EMS) is developed to overcome the uncertainty of the electricity retail price market and minimize the total operating costs for the multi-microgrids (MMG) system. The proposed framework solves the economic energy dispatch based on an accurate Electricity Price Forecasting (EPF) by an Outlier-Robust Extreme Learning Machine (OR-ELM) algorithm and a two layers cooperative distributed model predictive control (DMPC). The First level provides an optimal energy scheduling between the Distribution System Operator (DSO) and cooperative microgrids systems to minimize the operating cost based on the forecasted electricity price. In contrast, second level maintains the supply-demand balance by applying the optimal energy scheduling from the first layer through an adjustment of the distributed energy resources (DER). The electricity retail price prediction is assessed using real dataset from the Iso New England electricity market. The OR-ELM regression method shows a significant forecasting performance in terms of error metrics. For instance, the mean absolute error in the training stage 2.05% for OR-ELM with a comparison of 4.17% and 6.29% for Support Vector Regression (SVR), and Artificial Neural Network (ANN) models respectively. Finally, simulation results demonstrate the efficiency RDPC-EMS for daily operating cost reduction, with decrease of 15% for MG 1 and 16% for MG 2.

关键词： Microgrids Energy management Optimization Forecasting predictive control Electricity supply industry Batteries distributed model predictive control energy management outlier robust extreme learning machine renewable energy sources retail electricity price market

来源：评论

学校读者我要写书评

暂无评论

Coalitional model predictive control on Freeways Traffic Networks

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第11期22卷 6772-6783页

作者： Chanfreut, Paula Maestre, Jose Maria Camacho, Eduardo F. Univ Seville Dept Syst & Automat Engn Seville 41092 Spain

This paper discusses the application of coalitional model predictive control (MPC) to freeways traffic networks, where the goal is reducing the time spent by the drivers through a dynamic setting of variable speed limits (VSL) and ramp metering. The prediction model METANET is used to represent the traffic flows evolution. The system behavior and objective function lead to a non-convex and non-linear optimization problem, which can only be solved in a centralized fashion for small networks. The underlying motivation of this paper is the continued advance of clustering methods in the control of large-scale and spatially distributed systems. The global freeway system is partitioned into a set of coupled sub-stretches, which in turn are assigned to the different agents involved in the control problem. These local controllers can dynamically assemble into coalitions to take coordinated measures. In this work, a top-down approach is considered: the bottom layer consists of the set of controllers that compute the VSL and ramp-metering across time;and the supervisory layer changes periodically the information exchange structure to promote coalitions of those controllers that bring greater performance to the global system. In this way, a balance is sought between optimality and efficiency. Finally, the coalitional approach is simulated on a stretch of traffic freeway where cooperation with adjacent sub-stretches is allowed.

关键词： Mathematical model Traffic control Roads predictive control predictive models Vehicle dynamics distributed model predictive control coalitional control control by clustering traffic systems

来源：评论

学校读者我要写书评

暂无评论

Rolling self-triggered distributed MPC for dynamically coupled nonlinear systems☆

引用

AUTOMATICA 2024年 160卷

作者： Wang, Tao Kang, Yu Li, Pengfei Zhao, Yun-Bo Tang, Hao Hefei Univ Technol Dept Automat Hefei 230009 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei 230000 Peoples R China

The mutual influences caused by dynamic couplings in large-scale systems increase the difficulty in the design and analysis of distributed model predictive control (DMPC), and require information exchange among subsystems which calls for a scheduling strategy to save communication resources in communication-limited environments. To circumvent the two problems, we design a rolling self -triggered DMPC strategy for large-scale dynamically coupled systems with state and control input constraints. First, the optimal control problem where the cost is subject to the coupled dynamic and the constraints are subject to the uncoupled counterpart is proposed, forming the dual-model DMPC that is simple in design and analysis but yields good control performance. Second, the information exchange only occurs at some specified triggering instants determined by a rolling self-triggered mechanism, saving communication resources more significantly. The effectiveness of the designed strategy is verified by numerical simulations. (c) 2023 Published by Elsevier Ltd.

关键词： Dynamically coupled systems distributed model predictive control Self -triggered control Constrained nonlinear systems

来源：评论

学校读者我要写书评

暂无评论

Cooperative distributed MPC via decentralized real-time optimization: Implementation results for robot formations

引用

control ENGINEERING PRACTICE 2023年第1期138卷

作者： Stomberg, Goesta Ebel, Henrik Faulwasser, Timm Eberhard, Peter TU Dortmund Univ Inst Energy Syst Energy Efficiency & Energy Econ D-44227 Dortmund Germany Univ Stuttgart Inst Engn & Computat Mech D-70569 Stuttgart Germany

distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While the stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion with nominal convergence guarantees. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.

关键词： distributed model predictive control Mobile robots Decentralized optimization Alternating direction method of multipliers Formation control Hardware experiment

来源：评论

学校读者我要写书评

暂无评论

distributed MPC based on robustly controllable sets for PWA systems

引用

AUTOMATICA 2023年第1期154卷

作者： Ma, Aoyun Li, Dewei Xi, Yugeng Shanghai Jiao Tong Univ Shanghai Engn Res Ctr Intelligent Control & Manage Dept Automat Key Lab Syst Control & Informat ProcMinist Educ C Shanghai 200240 Peoples R China

The optimization problem in model predictive control (MPC) algorithm for piecewise affine (PWA) systems often contains substantial logic variables, which requires extensive computing power for online implementation. This paper proposes a one-step distributed MPC algorithm for spatially interconnected PWA systems with both input and state constraints, where the algorithm is based on a series of r-step robustly controllable sets. Algorithms for offline computing terminal sets and a series of r-step robustly controllable sets are developed using the step-by-step backward approach. The states that lie in the robustly controllable sets are steered into the terminal set in finite steps and ultimately converge into a robustly positively invariant set. The proposed algorithm is demonstrated to significantly reduce the online computational burden. Simulation results are provided to showcase the effectiveness of the algorithm. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Spatially interconnected systems Piecewise affine systems Robustly controllable sets

来源：评论

学校读者我要写书评

暂无评论

A two-dimensional design and synthesis method for coordinated control of flexible-operational combined cycle of gas turbine

引用

ENERGY 2023年第1期284卷

作者： Lu, Nianci Pan, Lei Pedersen, Simon Arabkoohsar, Ahmad Southeast Univ Natl Engn Res Ctr Power Generat Control & Safety Sch Energy & Environm Nanjing 210096 Peoples R China Aalborg Univ Dept Energy Niels Jernes Vej 10 DK-9220 Aalborg Denmark Tech Univ Denmark Dept Civil & Mech Engn Lyngby Denmark

The conventional combined cycle of gas turbine (CCGT) coordinated control strategy cannot quickly track the increasingly frequent and rapidly changing automatic generation control (AGC) commands due to the high penetration of renewable energy. In this study, a novel CCGT coordinated control strategy is proposed based on a two-dimensional design method consisting of time-scale decomposition and energy-flow coupling analysis. First, some critical design parameters of coordinated control, such as the power assignment ratios of gas turbine and steam turbine, and the energy storage coefficients of the heating networks are derived from energy-flow coupling analysis. Then, the AGC commands are decomposed into peak-shaving signals, low-frequency signals, and high -frequency signals from time-scale dimensional. As a result, a novel CCGT coordinated control strategy with precise dynamic heat-power conversion compensation is proposed and synthesized using distributed model predictive control to solve the large inertia, constrained, multivariable control problems. Simulation results show that the absolute integral error of the AGC tracking decreases to 1736 and the maximum ramping rate of the gas turbine is decreased by 20% without affecting the extraction steam flow under the proposed control strategy. All three indicators demonstrate the advantages of the coordinated control strategy designed by the proposed two-dimensional method.

关键词： Combined cycle of gas turbine distributed model predictive control Coordinate control system Automatic generation control Two-dimensional design

来源：评论

学校读者我要写书评

暂无评论

Grouping consistency control strategy based on DMPC and energy storage unit constraints

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年第1期148卷

作者： Zhang, Jing Long, Benjin He, Yu Cheng, Rong Fan, Luqing Li, Bowen Gu, Tingyun Fu, Xiaofan Li, Hongqian Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China Guizhou Power Grid Co Kaili Power Supply Bur Kaili 556000 Peoples R China Elect Power Res Inst Guizhou Power Grid Co Ltd Guiyang 550002 Peoples R China

This paper proposes a distributed packet consistency control strategy to solve the distributed coordinated control problem among multiple battery storage units. In order to ensure that the distributed power allocation results meet the power constraints of each energy storage unit and reduce the communication requirements of distributed control, this paper proposes a weighted consistency algorithm based on distributed model predictive control and state constraints. A grouping control strategy of energy storage units is proposed. The weight of energy storage unit is set according to SOC information to improve consistency of SOC of energy storage unit. Meanwhile, based on the proposed consistency algorithm, an inter-group coordination control strategy and an efficiency improvement strategy of energy storage units are developed to improve the regulation ability of energy storage system, extend the life of energy storage system and improve the energy conversion efficiency. Finally, the proposed algorithm and control strategy are simulated and analyzed by building a microgrid system with eight battery storage units in Matlab. The simulation results show that the proposed algorithm and control strategy have certain advantages in improving convergence speed, optimizing control effect, prolong energy storage life and improve the operation efficiency of the energy storage system.

关键词： Microgrid Grouping of battery unit distributed model predictive control Weighted consensus algorithm

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：