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检索条件"主题词=Distributed Model Predictive Control"

共 587 条记录，以下是371-380 订阅

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State of Charge Equalization of Battery Modules Using Single-Phase Cascaded Multilevel Converters

State of Charge Equalization of Battery Modules Using Single...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Ashkan Pirooz Yousef Firouz Joeri Van Mierlo Electric Engineering and Energy Technology(ETEC) Vrije Universiteit Brussel Brussels Belgium

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

This paper aims to use cascaded multilevel converters for connecting several battery modules to a single-phase AC grid or load. Electrical power is exchanged either from storage to AC side to discharge battery modules, or from AC side to the storage to charge battery modules. Charging and discharging operations are performed while battery modules' state of charges get equalized. AC side current is also controlled to follow its reference while high differential-mode dv/dt on load are avoided using adjacent voltage levels assignment. For these purposes, no additional controlling circuit is implemented and only one controlling block is defined to handle AC current reference tracking, and state of charge balancing tasks. model predictive control, as a technique capable of multi-parameter controlling, is used to achieve beforementioned system objectives in a simple, efficient, and scalable manner. MATLAB Simulink simulations demonstrate the feasibility of proposed controlling strategy for this sort of application.

关键词： cascaded multilevel converters battery balancing state of charge model predictive control CHARGE STATES model predictive control predictive control ALTERNATING CURRENT Batteries distributed model predictive control nonlinear model predictive control cascades Converters Electric power

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Sensitivity to Forecast Errors in Energy Storage Arbitrage for Residential Consumers

Sensitivity to Forecast Errors in Energy Storage Arbitrage f...

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IEEE International Conference on Communications, control, and Computing Technologies for Smart Gridss

作者： Diego Kiedanski Md Umar Hashmi Ana Busic Daniel Kofman Telecom Paristech INRIA DI ENS Ecole Normale Superieure CNRS PSL Research University

ISBN: (纸本)9781538681008

With the massive deployment of distributed energy resources, there has been an increase in the number of end consumers that own photovoltaic panels and storage systems. The optimal use of such storage when facing Time of Use (ToU) prices is directly related to the quality of the load and generation forecasts as well as the algorithm that controls the battery. The sensitivity of such control to different forecast techniques is studied in this paper. It is shown that good and bad forecasts can result in losses in, particularly bad days. Nevertheless, it is observed that performing model predictive control (MPC) with a simple forecast that is representative of the pasts can be profitable under different price and battery scenarios. We observe that performing MPC at a faster sampling time with a receding optimization horizon makes arbitrage less sensitive to uncertainties in forecasting. We use real data from Pecan Street and ToU price levels with different buying and selling price for the numerical experiments.

关键词： Sensitivity Arbitrage Smart grids Battery Load forecast model predictive control Smart grids Batteries load forecasting model predictive control predictive control Error of prediction distributed model predictive control nonlinear model predictive control Prices distributed energy resources Cache Memory System

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distributed MPC for Automatic Generation control of Power System Including Renewable Energy Sources

Distributed MPC for Automatic Generation Control of Power Sy...

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第30届中国过程控制会议（CPCC 2019）

作者： LIU xiang-jie LUO ying School of Control and Computer Engineering North China Electric Power University

Automatic generation control(AGC) is crucial for the operation and design of modern electric power ***,a high level of penetration of renewable energy sources is integrated to the power grids,bringing great challenge to *** paper presents a distributed model of AGC that contains thermal power plant,wind turbines and PV(photovoltaic) power plants.A distributed model predictive control(DMPC) method is designed for this hybrid power *** means of the proposed method,the renewable power output is controlled to keep the system *** proposed method is compared with a maximum power point tracking control-based *** performance of the proposed method is demonstrated better through simulations.

关键词： distributed model predictive control Automatic generation control Wind turbines PV power plants

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Relaxed collision constraints based on interval superposition principle in a DMPC scheme 24

Relaxed collision constraints based on interval superpositio...

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24th IEEE International Conference on Parallel and distributed Systems (ICPADS)

作者： Sprodowski, Tobias Pannek, Juergen Univ Bremen Fac Prod Engn D-28359 Bremen Germany Univ Bremen Bremer Inst Prod & Logist BIBA D-28359 Bremen Germany

ISBN: (纸本)9781538673089

Within this paper, we consider a group of mobile robots in a shared operation space controlled by a distributed model predictive control scheme, where each robot is assigned with individual start and target positions. Each robot solves an Optimal control Problem, based on information about the predictions of the other robots, to calculate an optimal trajectory over a fixed horizon length. The information exchange among the robots is quantised by projecting the trajectory onto a grid. To reduce the communication overhead, the interval superposition principle is used by transmitting only the minimum and maximum coordinates of the trajectory, yielding a static interval of reserved cells. To attenuate the effect of many reserved cells, the dynamics and directional movement of the robot is incorporated to determine, which of the reserved cells are not needed by this robot anymore. This additional information can be transmitted without necessary overhead. Furthermore, the time instant can be excluded from the communicated cells. To this end, numerical simulations based on holonomic dynamics of mobile robots are presented and compared with the conservative approach.

关键词： distributed model predictive control Robotics Non-cooperative control

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Life lessons from and for distributed MPC - Part 2: Choice of decision makers

Life lessons from and for distributed MPC - Part 2: Choice o...

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18th International-Federation-of-Automatic-control (IFAC) Conference on Technology, Culture and International Stability (TECIS)

作者： Olaru, S. McNamara, P. Canizares, J. C. Farina, M. Maestre, J. M. Trodden, P. Negenborn, R. R.

This paper and an accompanying paper (McNamara et al., 2018) revisit the distributed predictive control (DMPC) literature and seek to establish links with the social behaviour, focusing in particular on ways in which DMPC could be used to provide insights into the mechanisms of group regulation in social systems. It will be noted that there are major differences between the way in which DMPC algorithms and Social Human Participants (SHPs) form their respective decisions. Whereas in a first paper (McNamara et al., 2018) we concentrated on the dynamics of the cooperation and the weightings in the agents' decision, the present paper extends the discussion to the arrangements in the group of decision makers. This paper concludes with some caveats as regards further analyses of social system using the methods proposed in these two papers. (C) 2018, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Social Systems

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model predictive Self-healing control Scheme for Dual Active Bridge Converter

Model Predictive Self-healing Control Scheme for Dual Active...

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Workshop on Smart Grid and Renewable Energy (SGRE)

作者： Mohsen Hosseinzadehtaher Ahmad Khan Matthew W. Baker Mohammad B. Shadmand Power Electronics & Autonomous Systems Research Group Kansas State University Manhattan KS USA

ISBN: (数字)9781728129600

ISBN: (纸本)9781728129617

This paper presents a model predictive self-healing control (MPSC) of dual active bridge (DAB) converter for battery energy storage system (BESS) application. The main feature of the proposed MPSC scheme is an autonomous corrective action at the prediction stage in comparison to conventional model predictive control (MPC) approach. This feature is required in particular for predictive controlled DAB converter to ensure and validate the predicted AC link inductor's current n-step ahead in horizon of time. The proposed MPSC for DAB converter doesn't require substantial tuning effort in comparison to conventional control schemes for DAB converters. The MPSC cost function is developed based on the desired phase shift between the primary and secondary sides' voltages of the DAB transformer. The self-healing loop utilizes the feasible range of power transfer in conjunction with the AC link inductor's voltage status i.e. profile, to validate the predicted current and phase shift for the cost function. The AC link inductor voltage profile in DAB converter could cause high error in predicted current, thus the proposed self-healing loop autonomously corrects potential error in current prediction. The theoretical concepts are validated via several case studies for the considered BESS application. The performance and design effort of the controller is compared with conventional PI-based control schemes for DAB.

关键词： Dual active bridge converter model predictive control Self-healing control Autonomous control Audio recording autonomous control Phase shift Multiprotocol Serial controller Converters AC link model predictive control distributed model predictive control nonlinear model predictive control predictive control design effort

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Life lessons from and for distributed MPC - Part 1: Dynamics of cooperation

Life lessons from and for distributed MPC - Part 1: Dynamics...

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18th International-Federation-of-Automatic-control (IFAC) Conference on Technology, Culture and International Stability (TECIS)

作者： McNamara, P. Negenborn, R. R. Canizares, J. C. Farina, M. Maestre, J. M. Trodden, P. Olaru, S.

This paper and a second accompanying paper (Olaru et al., 2018) explore the potential of distributed predictive control (DMPC) literature to provide valuable insights into social behaviour. In particular this first paper focuses on the mechanisms of group regulation in social systems. It will be noted that there are major differences between the way in which DMPC algorithms and Social Human Participants (SHPs) form decisions. DMPC can make optimal decisions but these are only optimal with respect to a given objective and model, both of which must be explicit. SHPs operate, by and large, with only vague, implicit objectives and models which can be surprisingly accurate but often make sub-optimal decisions both individually (because of irrationality or poor anticipation and due to a short horizon, bad model or misjudgement of objectives) and in a group sense (for the previous reasons plus selfishness). Thus while SHPs' decisions would typically be suboptimal, with respect to their desired goals, for the aforementioned reasons, it can be expected that SHPs' decision making would evolve towards an optimal solution as groups of SHPs develop more experience within the system they're operating in. (C) 2018, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Social Systems

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A distributed predictive control Strategy Based on State Estimator for islanded microgrid 8

A Distributed Predictive Control Strategy Based on State Est...

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8th International Power Electronics Conference (IPEC-Niigata) / Energy Conversion Congress and Exposition (ECCE Asia)

作者： Dong, Mi Li, Li Tian, Xiaoyu Cent S Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

ISBN: (纸本)9784886864055

The traditional droop control is usually used to share real and reactive power without communication for fully decentralization power system. Because of that the traditional droop control has its inherent limitations, this paper presents a modified droop controller based on Kalman filter to reduce load effect, and then utilizes a distributed consensus algorithm to transmit error voltage to other distributed generation (DG) with sparse communication network, and last make use of distributed model predictive control (MPC) to achieve accurately load power sharing between multi-parallel inverters. Simulation results is verified the reliability of the proposed algorithm in realizing load sharing and the voltage-frequency restoration.

关键词： droop control Kalman filter load effect distributed consensus algorithm distributed model predictive control

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Stochastic model predictive control for Scheduling Charging of Electric Vehicle Fleets with Market Power

Stochastic Model Predictive Control for Scheduling Charging ...

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International Conference on Connected Vehicles and Expo (ICCVE)

作者： Arec L. Jamgochian Mykel J. Kochenderfer Stanford University Stanford CA

ISBN: (纸本)9781728101439

With the penetration of electric vehicles in local markets, vehicle-induced electricity demand can cause power grid instability. Collaborative smart charging can help stabilize grid demand and mitigate those issues. This paper formulates charge scheduling when connected vehicles constitute a large portion of instantaneous demand. Allowing coordinated charging to sway electricity price, we formulate a multi-objective stochastic optimization problem to minimize cost while maximizing charge in each car. We model stochastic base electricity demand using a Gaussian Mixture model (GMM) and solve the certainty-equivalent stochastic optimization problem. We then implement a stochastic model predictive control (SMPC) algorithm and compare performance between a naive policy, a certainty-equivalent optimized policy, and SMPC on a dataset derived from California ISO-serviced demand.

关键词： Stochastic programming model predictive control Electric vehicles Charge scheduling Electric vehicles predictive control Stochastic models vehicle fleet SMPC distributed model predictive control nonlinear model predictive control CHARGING(ACCOUNTTNG) stochastic programming model predictive control power markets

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Hybrid trajectory planning approach for roundabout merging scenarios

Hybrid trajectory planning approach for roundabout merging s...

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International Conference on Connected Vehicles and Expo (ICCVE)

作者： Carlos Hidalgo Ray Lattarulo Joshué Pérez Estibaliz Asua Electricity and Electronics Department University of the Basque Country Bilbao Spain Industry and Transport Division Tecnalia Research & Innovation Bilbao Spain

ISBN: (纸本)9781728101439

Despite the progress made in the Intelligent Transportation Systems (ITS) field, there is still a large amount of scenarios to be solved, such as urban roundabouts. In particular, the entrance must be emphasized due to most of accidents take place in this part. In such way, the article presents a method to solve the roundabout merging considering a nominal trajectory generated through Bézier curves combining with a model predictive control (MPC) to assure safe future states. Tests are made using a real platform to execute the maneuver and a virtual platform to represent the other participant on the scenario. Experimental results demonstrate the good performance of the approach under merging scenarios.

关键词： Bezier curves model predictive control Roundabouts Merging Intelligent transportation systems Roundabout Intelligent transportation systems merger predictive control model predictive control distributed model predictive control nonlinear model predictive control

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