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stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study

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JOURNAL OF PROCESS control 2016年 43卷 24-37页

作者： Parisio, Alessandra Rikos, Evangelos Glielmo, Luigi Univ Manchester Sch Elect & Elect Engn Manchester Lancs England Ctr Renewable Energy Sources & Saving CRES Dept PVs & Syst Athens Greece Univ Sannio Dept Engn Benevento Italy

Microgrids are subsystems of the distribution grid which comprises generation capacities, storage devices and flexible loads, operating as a single controllable system either connected or isolated from the utility grid. In this work, microgrid management system is developed in a stochastic framework. It is seen as a constraint-based system that employs forecasts and stochastic techniques to manage microgrid operations. Uncertainties due to fluctuating demand and generation from-renewable energy sources are taken into account and a two-stage stochastic programming approach is applied to efficiently optimize microgrid operations while satisfying a time-varying request and operation constraints. At the first stage, before the realizations of the random variables are known, a decision on the microgrid operations has to be made. At the second stage, after random variables outcomes become known, correction actions must be taken, which have a cost. The proposed approach aims at minimizing the expected cost of correction actions. Mathematically, the stochastic optimization problem is stated as a mixed-integer linear programming problem, which is solved in an efficient way by using commercial solvers. The stochastic problem is incorporated in a model predictive control scheme to further compensate the uncertainty through the feedback mechanism. A case study of a microgrid is employed to assess the performance of the on-line optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid: experimental results show the feasibility and the effectiveness of the proposed approach. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： stochastic model predictive control Two stage stochastic programming Microgrids Optimization Mixed integer linear programming

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stochastic model-predictive control for Lane Change Decision of Automated Driving Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第6期67卷 4771-4782页

作者： Suh, Jongsang Chae, Heungseok Yi, Kyongsu Univ Calif Berkeley Mech Engn Berkeley CA 94720 USA Seoul Natl Univ Seoul 151744 South Korea

This paper describes lane change motion planning with a combination of probabilistic and deterministic prediction for automated driving under complex driving circumstances. The autonomous lane change should arrive safely at the destination. The subject vehicle needs to perceive and predict the behaviors of other vehicles with sensors. From the information of other vehicles, a collision probability is defined using a reachable set of uncertainty propagation. In addition, the lane change risk is monitored using predicted time-to-collision and safety distance to guarantee safety in lane change behavior. A safe driving envelope is defined as constraints based on the combinatorial prediction (probabilistic and deterministic) of the behavior of surrounding vehicles. To obtain the desired steering angle and longitudinal acceleration to maintain the automated driving vehicle under constraints, a stochastic model-predictive control problem is formulated. The proposed model has been evaluated by performing lane change simulations in MATLAB/Simulink, while considering the effect of combination prediction. Also, the proposed algorithm has been implemented on a test vehicle. The simulation and test results show that the proposed algorithm can handle complicated lane change scenarios, while guaranteeing safety.

关键词： Automated driving control lane change decision real-time implementation stochastic model predictive control vehicle test

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Design of stochastic model predictive control for Wind Energy Conversion System 14

Design of Stochastic Model Predictive Control for Wind Energ...

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14th International Workshop on Complex Systems and Networks (IWCSN)

作者： Shi, Yuntao Xiang, Xiang Zhang, Yuan Sun, Dehui North China Univ Technol Key Lab Beijing Field Bus Technol & Automat Beijing 100144 Peoples R China

ISBN: (纸本)9781538618905

This paper investigates an approach for Wind Energy Conversion System (WECS) with model uncertainties and disturbances based on scenario tree stochastic model predictive control (SMPC). First, in order to describe the randomness of the system, the WECS's Markov jump model is formulated with the help of the statistical data of wind. Next, the uncertain stochastic model predictive control problem is transformed into deterministic model predictive control (MPC) problem through enumerating the model jump scenarios. Finally, to meet the real-time performance of the WECS, the method of off-line calculating and online calling the controller is adopted to improve the operation speed of SMPC. Simulation results show that the proposed SMPC method has good control performance.

关键词： wind energy conversion system stochastic model predictive control Markov jump model scenario tree operation speed

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stochastic model predictive control for Wind Turbines With Doubly Fed Induction Generators

Stochastic Model Predictive Control for Wind Turbines With D...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Kou, Peng Liang, Deliang Gao, Lin Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian Peoples R China

ISBN: (纸本)9781509041688

A key issue in the control of double fed induction generator (DFIG) wind turbine systems is to achieve an expected performance in the presence of stochastic wind speeds. To address this issue, this paper establishes a stochastic model predictive control (SMPC) scheme for the DFIG wind turbine system. The salient feature of this scheme is that it explicitly takes into account the uncertainties in wind speed forecasts. Using wind speed predictive distributions, the forecasted wind speeds are modeled as Gaussian disturbances. using probabilistic constraints, the uncertainties in these disturbances are incorporated into the SMPC problem formulation. By converting the probabilistic constraints into deterministic constraints, the formulated stochastic programming problem is recast as a convex quadratic optimization problem, which can be solved very efficiently. In this way, the computed control actions handle the uncertainties associated with the wind speed forecasts, thus ensuring the optimal operation of DFIG. Simulation resuIts validate the effectiveness of the proposed scheme.

关键词： wind energy double fed induction generator stochastic model predictive control probabilistic constraints stochastic programming

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Scenario-based stochastic model predictive control forWind Energy Conversion System 28

Scenario-based Stochastic Model Predictive Control forWind E...

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28th Chinese control and Decision Conference

作者： Zhou, Yang Shi, Yuntao North China Univ Technol Coll Elect & Control Engn Beijing 100144 Peoples R China Key Lab Field Bus & Automat Beijing Beijing 100144 Peoples R China

ISBN: (纸本)9781467397148

This paper develops an approach for wind energy conversion system (WECS) control based on stochastic model predictive control (SMPC). The structure combines the Markov chain that illustrates the WECS behavior, a stochastic optimization method based on scenario, and quadratic programming. By using quadratic programming, SMPC can generally handle larger dimension of state model than stochastic dynamic programming for changes of WECS behavior. The simulation results demonstrate the effectiveness of the optimal control of WECS by scenario - based stochastic model predictive control algorithm.

关键词： Wind Energy Conversion System Markov Chain Scenarios Tree stochastic model predictive control

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stochastic Nonlinear model predictive control with Active model Discrimination: A Closed-Loop Fault Diagnosis Application

Stochastic Nonlinear Model Predictive Control with Active Mo...

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20th World Congress of the International-Federation-of-Automatic-control (IFAC)

作者： Heirung, Tor Aksel N. Mesbah, Ali Univ Calif Berkeley Dept Chem & Biomol Engn Berkeley CA 94720 USA

This paper addresses the problem of model predictive control with multiple models for nonlinear systems subject to stochastic disturbances. The multiple models can represent various operating conditions such as system faults or failures, or arise from model structure uncertainty. The paper presents a stochastic nonlinear model predictive control (SNMPC) approach with endogenous learning for active discrimination between the competing models based on closed loop system observations. The system learning is endogenized through explicit inclusion of a model discrimination measure into the stochastic optimal control problem, which facilitates probabilistic discrimination between the predictions of multiple models. The control approach uses a Bayesian estimation algorithm for recursive estimation of the probabilities that represent the degree to which each model predicts the online system observations. The performance of the proposed SNMPC approach with active model discrimination is demonstrated for closed-loop fault diagnosis. (C) 2017, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： stochastic model predictive control model structure discrimination Closed-loop active fault diagnosis

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stochastic model predictive control for Optimal Economic Operation of a Residential DC Microgrid

Stochastic Model Predictive Control for Optimal Economic Ope...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Gulin, Marko Matusko, Jadranko Vasak, Mario Univ Zagreb Fac Elect Engn & Comp Lab Renewable Energy Syst Zagreb 41000 Croatia

ISBN: (纸本)9781479978007

In this paper we present power flow optimization of a residential DC microgrid that consists of photovoltaic array, batteries stack and fuel cells stack with electrolyser, and is connected to the grid via bidirectional power converter. The optimization problem aims to minimize microgrid operating costs and is formulated using a linear program that takes into account the storages charge and discharge efficiency. To account for power predictions uncertainty, optimization problem is defined in a stochastic framework by using chance constraints. Since we assume that the error in realization of power predictions will he compensated by utility grid, chance constraints are defined for power exchange between the microgrid and the utility grid. Finally, we investigate a stochastic model predictive control for the closed-loop power management in the microgrid. Performance verification of the proposed approach is performed on simulations for two-month period.

关键词： Microgrid stochastic model predictive control Linear Program Power Flow Optimization Chance Constraints

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stochastic model predictive control of Nonlinear Continuous-Time Systems using the Unscented Transformation

Stochastic Model Predictive Control of Nonlinear Continuous-...

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European control Conference (ECC)

作者： Voelz, Andreas Graichen, Knut Univ Ulm Inst Measurement Control & Microtechnol Ulm Germany

ISBN: (纸本)9783952426937

The paper presents a stochastic model predictive control (MPC) scheme for nonlinear continuous-time stochastic systems with chance constraints. The approach uses the unscented transformation to predict the mean and covariance of the nonlinear continuous-time dynamics. Special emphasis is put on integrating the resulting unscented MPC formulation within a real-time gradient algorithm by exploiting the structure of the optimality conditions. The effectiveness of the approach is demonstrated for an automotive emergency braking scenario with collision avoidance.

关键词： continuous time systems gradient methods modelling nonlinear systems optimisation predictive control stochastic systems MPC scheme gradient algorithm nonlinear continuous-time system optimality condition structure stochastic model predictive control unscented transformation Approximation methods Cost function Covariance matrices Nonlinear systems Real-time systems stochastic processes Uncertainty

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A stochastic model predictive control to heterogeneous rail freight car fleet sizing problem

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2015年 82卷 162-198页

作者： Mienkovic, Milos S. Bojovic, Nebojsa J. Svadlenka, Libor Melichar, Viastimil Univ Belgrade Div Management Railway Rolling Stock & Tract Fac Transport & Traff Engn Belgrade 11000 Serbia Univ Pardubice Jan Perner Transport Fac Dept Transport Management Mkt & Logist Pardubice 53210 Czech Republic

In this paper a rolling horizon approach is applied for simultaneous optimization of the rail freight car fleet size and allocation problem. Developed dynamic model of loaded and empty rail freight car flows explicitly treats state, control and station capacity constraints in presence of various freight car types under the partial substitutability among them. Demands and traveling times are considered as random variables. Proposed approach is applied to a set of test cases and it shown to be successful, ultimately providing a new managerial tool for more effective and efficient planning and analyzing rail freight car fleets. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Railway Freight cars Fleet size Allocation stochastic model predictive control ARIMA-Kalman EOQ Random newsboy

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Scenario-based stochastic model predictive control for Wind Energy Conversion System

Scenario-based Stochastic Model Predictive Control for Wind ...

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第28届中国控制与决策会议

作者： Yang Zhou Yuntao Shi College of Electrical and Control Engineering North China University of Technology The Key Lab of Field Bus and Automation of Beijing

ISBN: (纸本)9781467397155

This paper develops an approach for wind energy conversion system(WECS) control based on stochastic model predictive control(SMPC).The structure combines the Markov chain that illustrates the WECS behavior,a stochastic optimization method based on scenario,and quadratic *** using quadratic programming,SMPC can generally handle larger dimension of state model than stochastic dynamic programming for changes of WECS *** simulation results demonstrate the effectiveness of the optimal control of WECS by scenario-based stochastic model predictive control algorithm.

关键词： Wind Energy Conversion System Markov Chain Scenarios Tree stochastic model predictive control

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