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Integrating scenario-based stochastic-model predictive control and load forecasting for energy management of grid-connected hybrid energy storage systems

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第91期48卷 35624-35638页

作者： Abdelghany, Muhammad Bakr Al -Durra, Ahmed Zeineldin, Hatem Gao, Fei Khalifa Univ Sci & Technol Dept Elect & Comp Engn Sas Al Nakhl Campus Abu Dhabi U Arab Emirates Minia Univ Fac Engn Comp & Syst Engn Dept Al Minya Egypt Cairo Univ Elect Power Engn Dept Giza Egypt Univ Technol Belfort Montbeliard UTBM Sch Energy & Comp Sci F-90010 Belfort France

In the context of renewable energy systems, microgrids (MG) are a solution to enhance the reliability of power systems. In the last few years, there has been a growing use of energy storage systems (ESSs), such as hydrogen and battery storage systems, because of their environmentally-friendly nature as power converter devices. However, their short lifespan represents a major challenge to their commercialization on a large scale. To address this issue, the control strategy proposed in this paper includes cost functions that consider the degradation of both hydrogen devices and batteries. Moreover, the proposed controller uses scenarios to reflect the stochastic nature of renewable energy resources (RESs) and load demand. The objective of this paper is to integrate a stochastic model predictive control (SMPC) strategy for an economical/environmental MG coupled with hydrogen and battery ESSs, which interacts with the main grid and external consumers. The system's participation in the electricity market is also managed. Numerical analyses are conducted using RESs profiles, and spot prices of solar panels and wind farms in Abu Dhabi, UAE, to demonstrate the effectiveness of the proposed controller in the presence of *** on the results, the developed control has been proven to effectively manage the integrated system by meeting overall constraints and energy demands, while also reducing the operational cost of hydrogen devices and extending battery lifetime. (c) 2023 The Author(s). Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://***/ licenses/by/4.0/).

关键词： Energy management systems Characterization of uncertainties stochastic model predictive control Hybrid energy storage systems Lifetime characteristics

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Efficient and Simple Gaussian Process Supported stochastic model predictive control for Bioreactors using HILO-MPC

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IFAC-PapersOnLine 2022年第7期55卷 922-927页

作者： Bruno Morabito Johannes Pohlodek Lena Kranert Sebastián Espinel-Ríos Rolf Findeisen Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

model-based control of biotechnological processes is, in general, challenging. Often the processes are complex, nonlinear, and uncertain. Hence modeling tends to be complex and is often inaccurate. For this reason, non-model-based control strategies developed via fask, bench-scale, or pilot plant experiments are often applied in the biotechnology industry. model-based control and optimization techniques can increase processes’ performance and automation level, thereby decreasing costs and guaranteeing the desired specifications. These rely on a model of the process to make predictions and optimize the inputs to the plant. To improve the quality of the models, it is often helpful to use combined first principle and data-driven models together in a hybrid modeling approach which increases the model prediction capabilities. The residual uncertainty of the hybrid model should be taken into account in the control level to satisfy the process specifications and constraints. This paper proposes to use a stochastic model predictive control scheme that exploits a hybrid, Gaussian processes-based model. We outline the effectiveness of the stochastic model-based approach in combination with a suitable Kalman filter for state estimation considering an example biotechnological process. Furthermore, we underline that appropriate tools exist that allow the simple application of such methods even for the novice user. To do so, we use an open-source Python package — HILO-MPC, which allows the simple yet efficient formulation and solution of machine learning-supported optimal control and estimation problems.

关键词： predictive control toolbox machine learning Gaussian process uncertain process biotechnology stochastic model predictive control HILO-MPC

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Probabilistic dynamic-controlled latent variable model for pattern-space modelling and pattern-based stochastic model predictive control

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IFAC-PapersOnLine 2022年第7期55卷 786-791页

作者： Niannian Zheng Yuri A.W. Shardt Xiaoli Luan Fei Liu Department of Automation Jiangnan University 214122 Wuxi China

Industrial processes are measured and controlled using high-dimensional process variables, but its overall operation is usually characterised by low-dimensional patterns. The changes in the pattern are dominated by three features: free motion, controlled motion, and uncertainty. In this paper, all three features are taken into consideration to propose a new probabilistic dynamic-controlled latent variable (PDCLV) model structure using a dynamic Bayesian network for process modelling in the pattern space. To this end, the linear dynamic system characterised by control inputs is introduced, and the expectation maximisation algorithm is specially designed for learning the PDCLV model. Benefitting from the dynamic causality between control inputs and the explicit modelling of the pattern, a method for pattern-based stochastic model predictive control (SMPC) is implemented successfully to realise process optimisation. A case study on an industrial boiler combustion process demonstrates the benefits of the proposed PDCLV structure for pattern-space modelling and pattern-based SMPC.

关键词： Dynamic controlled latent variable pattern space modelling dynamic Bayesian network linear dynamic system with control inputs expectation maximisation stochastic model predictive control

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Recurrent Neural Network-Based Joint Chance Constrained stochastic model predictive control*

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IFAC-PapersOnLine 2022年第7期55卷 780-785页

作者： Shu-Bo Yang Zukui Li Department of Chemical and Materials Engineering University of Alberta Edmonton Canada

A novel recurrent neural network (RNN)-based approach is proposed in this work to handle joint chance-constrained stochastic model predictive control (SMPC) problem. In the proposed approach, the joint chance constraint (JCC) is first reformulated as a quantile-based inequality to reduce the complexity in approximation. Then, the quantile function (QF) in the quantile-based inequality is replaced by the empirical QF using sample average approximation (SAA). Afterwards, the empirical QF is approximated via an RNN-based surrogate model, which is embedded into the SMPC problem formulation to predict quantile values at different sampling instants. By employing the RNN-based approximation, the resulting deterministic optimization problem is finally solved through a nonlinear optimization solver. The proposed approach is applied to a hydrodesulphurisation process to demonstrate its efficiency in handling the SMPC problem.

关键词： Recurrent neural network stochastic model predictive control stochastic optimal control Joint chance constraint Sample average approximation

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An Adaptive stochastic model predictive control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario

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IEEE ACCESS 2020年 8卷 13887-13897页

作者： Pu, Zesong Jiao, Xiaohong Yang, Chao Fang, Shengnan Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

Vehicle-following operation is a typical scenario in the future intelligent transportation environment. Keeping a safe distance is the most important goal in the vehicle-following scenario. For a plug-in hybrid electric bus (PHEB) running in a specific urban route, the challenge will become how to realize the optimal power split in hybrid powertrain under the premise of maintaining driving safety. Considering the above issues, this paper proposes a stochastic model predictive control (SMPC) strategy for PHEBs during vehicle-following scenario. Firstly, Markov chain-based stochastic driving model is built using real-world bus driving condition data, which is applied to predict future demand torque over a finite receding horizon. And then, a sequential quadratic programming (SQP) algorithm is adopted to solve the rolling optimization problem. Meanwhile, brake specific fuel consumption and electric motor efficiency are fitted offline to match the SMPC strategy. Furthermore, a piecewise function is given to adjust the adaptive factor that balancing fuel economy and vehicle-following in the pre-set cost function. Finally, to verify the control performance of the proposed strategy, a nonlinear model predictive control strategy with dynamic programming optimization (DP-MPC) and a rule-based (RB) strategy are employed for comparison study. Results indicate that the proposed strategy is effectiveness to the given driving condition with excellent fuel economy and vehicle-following performance. Under the driving condition of Chongqing 303 bus line in China and China typical, the fuel consumption is reduced by 20.58 & x0025;and 37.89 & x0025;compared with RB strategy, respectively. It is closer to the fuel consumption reduction of 16.77 & x0025;and 13.11 optimized by DP-MPC. Driving safety during vehicle-following also be demonstrated in the driving condition of Chongqing 303 bus line and China typical.

关键词： Plug-in hybrid electric bus energy management vehicle-following stochastic model predictive control sequential quadratic programming optimization

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Charging management of plug-in electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第25期14卷 6179-6188页

作者： Rahmani-Andebili, Mehdi Bonamente, Massimiliano Miller, James A. SUNY Buffalo State Engn Technol Dept Buffalo NY 14222 USA Univ Alabama Dept Phys & Astron Huntsville AL 35899 USA

The charging management of plug-in electric vehicles (PEVs) in San Francisco considering the effect of drag force on the vehicles, the real driving routes of vehicles, the social aspects of drivers' behaviour, the type of PEVs and the PEV penetration level is presented in this study. In this study, the drivers' responsiveness probability, to provide vehicle-to-grid service at the parking lot, is modelled with respect to the value of the incentive, drivers' social class and the real driving routes in San Francisco. Herein, the Monte Carlo Markov Chain is applied to estimate the hourly probability distribution function of the state of charge (SOC) of the PEV fleet in the day. The main data set applied in this study includes the real longitude and latitude of driving routes of vehicles in San Francisco, recorded in every four-minute interval of the day. In this study, a stochastic model predictive control is applied in the optimisation problem to address the variability and uncertainty issues of PEVs' SOC and renewables' power. Herein, quantum-inspired simulated annealing algorithm is applied as the optimisation technique. It is demonstrated that the type of PEVs, the PEV penetration level and even the social class of drivers can affect the problem results.

关键词： stochastic processes simulated annealing battery powered vehicles Markov processes probability predictive control Monte Carlo methods electric vehicle charging vehicle-to-grid probability distribution function PEV fleet driving routes San Francisco stochastic model predictive control PEV penetration level charging management plug-in electric vehicles Monte Carlo Markov chain drag force vehicle-to-grid service PEV SOC quantum-inspired simulated annealing optimisation technique

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Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation

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APPLIED ENERGY 2022年 312卷 118773-118773页

作者： Song, Dongran Li, Ziqun Wang, Lei Jin, Fangjun Huang, Chaoneng Xia, E. Rizk-Allah, Rizk M. Yang, Jian Su, Mei Joo, Young Hoon Cent South Univ Sch Automat Changsha Hunan Peoples R China Chongqing Univ Sch Automat Chongqing Peoples R China Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Kunsan Natl Univ Sch IT Informat & Control Engn Kunsan South Korea

Wind direction is random and time-varying, which is arduous to be accurately predicted. The yaw control based on the predicted wind direction is limited by the accuracy of the wind direction prediction, which leads to narrow improvement in the energy capture efficiency of the wind turbine (WT). For this issue, a stochastic model predictive Yaw control (SMPYC) strategy based on Intelligent Scenarios Generation (ISG) is proposed. Herein, in view of the uncertainty of wind direction prediction, the ISG method is proposed to generate scenarios that characterize it, then the yaw action optimized through the proposed scenario-based SMPYC is performed to improve the energy capture efficiency of WTs. Specifically, ISG creates an optimization problem from scenarios generation in each control period, and the co-evolution bonobo optimizer is improved to solve the optimal scenarios in real time for this high-dimensional multimodal problem. The proposed SMPYC based on ISG is tested using historical wind direction data, and its effectiveness and advantages under different accuracy of wind direction prediction are validated by the test results. The proposed SMPYC reduces the yaw time ratio by 0.35%-1.58% and improves the energy capture efficiency by 0.26%-0.43% in comparison with the baseline MPYC. For a 5 MW WT, the gained energy production could reach 1.14-1.88 x 10(5) kWh in a year, which corresponds to an additional annual profit of 68,000-110,000 yuan. Consequently, the proposed method is promising to enhance the energy capture efficiency and has important application value for reducing the cost of wind power.

关键词： Wind turbine Yaw Energy capture efficiency stochastic model predictive control Intelligent scenarios generation Co-evolutionary bonobo optimizer

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stochastic model predictive braking control for heavy-duty commercial vehicles during uncertain brake pressure and road profile conditions

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control Theory and Technology 2022年第2期20卷 248-262页

作者： Ryota Nakahara Kazuma Sekiguchi Kenichiro Nonaka Masahiro Takasugi Hiroki Hasebe Kenichi Matsubara Graduate School of Integrative Science and Engineering Tokyo City UniversityTamazutsumiSetagaya-kuTokyo 1588557Japan Nabtesco Automotive Co. Ltd.HirakawachoChiyoda-kuTokyo 1020093Japan

When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking *** minimize these uncertainties,we propose a combined longitudinal and lateral controller method based on stochastic model predictive control(SMPC)that is achieved via Chebyshev–Cantelli *** our method,SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements,including chance *** accomplish this,SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control *** main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty *** specifically,the use of Chebyshev–Cantelli inequality suppresses road profile influences,which have characteristics that are different from the Gaussian distribution,thereby improving both braking robustness and control performance against statistical ***,since the Kalman filtering(KF)algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints,the SMPC is reformulated as a KF embedded deterministic ***,the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.

关键词： Heavy-duty commercial vehicle Brake system stochastic model predictive control Road profile

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An alternative optimal strategy for stochastic model predictive control of a residential battery energy management system with solar photovoltaic

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APPLIED ENERGY 2021年 283卷 116289-116289页

作者： Van der Meer, Dennis Wang, Guang Chao Munkhammar, Joakim Uppsala Univ Dept Civil & Ind Engn Uppsala Sweden Univ Calif San Diego Mech & Aerosp Engn San Diego CA 92103 USA

Scenario-based stochastic model predictive control traditionally considers the optimal strategy to be the expectation of the optimal strategies across all scenarios. However, while the stochastic problem involving uncertainties can be substantiated by a large number of scenarios, the expectation of the respective optimal control strategies derived from all scenarios as the optimal control strategy to the problem is challenging to justify. We therefore propose a different approach in which we artfully have the optimization program find the common optimal strategy across all scenarios for the first prediction step at each sample time, which, if it exists, yields the true optimal strategy with greater confidence. We demonstrate the efficacy of the proposed formulation through a case study of a research villa in Boras, Sweden, that is equipped with a battery and a photovoltaic system. We compute a covariance matrix that contains time-dependent information of the data and use it to generate autocorrelated scenarios from the probabilistic forecasts that serve as the uncertain input to the energy management system. We justify the credibility of the optimal solution derived from the proposed formulation with compelling reasoning and quantitative results such as improved self-consumption of photovoltaic power.

关键词： stochastic model predictive control Scenario based Multivariate forecasting Quantile regression Gradient boosting

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An improved stochastic model predictive control operation strategy of integrated energy system based on a single-layer multi-timescale framework

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ENERGY 2021年 235卷 121320-121320页

作者： Wei, Shangshang Gao, Xianhua Zhang, Yi Li, Yiguo Shen, Jiong Li, Zuyi Southeast Univ Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China Southeast Univ Dept Energy & Environm Nanjing 210096 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China IIT Dept Elect & Comp Engn Chicago IL 60616 USA

Economy, robustness and computational efficiency are of paramount metrics for an operation strategy of an integrated energy system (IES). To achieve the trade-off of the three metrics, a multi-layer framework is extensively exploited in existing operation strategies. This work, however, proposes a single-layer multi-timescale framework which can coordinate different operation performances associated with various timescales simultaneously. Based on the framework, an improved stochastic model predictive control (SMPC) operation strategy is further developed by embedding the proposed framework into its prediction horizon. To solve the multi-timescale optimization of the improved SMPC, the constraints and cost function are presented in the multi-timescale form, and the supplied and demands are forecast by the least square support vector machine. A simulator of an IES is thereafter constructed to mimic real system and used to evaluate the performance of the proposed strategy by operation cost, accumulative error and computation time with respect to economy, robustness and computational efficiency, respectively. Finally, the improved SMPC strategy is compared with a traditional single-layer and a hierarchical strategy by a case study. The results show that the improved strategy has the best tradeoff performance aforementioned. The multi-timescale framework can be also integrated into other operation strategies. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Operation strategy stochastic model predictive control Multi-timescale Integrated energy system Operation performance

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