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Co-optimization of energy management and eco-driving considering fuel cell degradation via improved hierarchical model predictive control

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Green Energy and Intelligent Transportation 2024年第6期3卷 1-14页

作者： Caixia Liu Yong Chen Renzong Xu Haijun Ruan Cong Wang Xiaoyu Li School of Mechanical Engineering Hebei University of TechnologyTianjin 300130China School of Mechanical Engineering Guangxi UniversityGuangxi 530004China Institute for Clean Growth&Future Mobility Coventry UniversityUK Tianjin Key Laboratory of New Energy Automobile Power Transmission and Safety Technology School of Mechanical EngineeringHebei University of TechnologyTianjin 300130China School of Vehicle and Mobility Tsinghua UniversityBeijing 100190China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300130China

An advanced eco-driving technology is widely recognized as having enormous potential to reduce the vehicle fuel ***,most research on eco-driving focuses on the stability and safety for vehicle operating while disregarding its comfort and *** meet the requirements for safety and comfort,at the same time,enhance the economic performance of the vehicles,an improved hierarchical model predictive control cooperative optimization strategy is proposed for fuel cell hybrid electric vehicle with car-following ***,the upper-level model predictive controller controls the velocity,inter-vehicle distance and acceleration to guarantee safety and comfort for *** to the velocity information obtained from the upper model predictive controller,the lower-level improved model predictive controller considers the impact of disturbance changes on vehicle economy and aims to minimize the vehicle operating cost considering fuel cell degradation,so as to allocate energy ***,the enhancement of economic performance of proposed strategy is verified with the results of comparative study that 3.09%economic improvement on the premise of assuring safety and comfort of driving.

关键词： Fuel cell hybrid electric vehicle hierarchical model predictive control Car-following Eco-driving

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Regional decentralized coordinated AGC based on hierarchical model predictive control

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 225卷

作者： Zhang, Xiaohui Deng, Changhong Cao, Peng Long, Zhijun Wuhan Univ Sch Elect Engn & Automat Wuchang Dist Bayi Rd 299 Wuhan Hubei Peoples R China

With the increasing penetration of distributed renewable energy sources (DREs), centralized automatic generation control (AGC) strategies are no longer sufficient to meet the demand for frequency control in power systems. In this paper, a decentralized coordinated (DC) control strategy is proposed that integrates DERs into AGC, ensuring frequency stability. Firstly, a comprehensive framework for DC-AGC is established, encompassing virtual equivalent units of DERs, and an analysis of the stability and control performance is conducted. Subsequently, a dynamic clustering and virtual equivalent model for distributed wind turbines is established, wherein the parameters are dynamically adjusted to adapt to the time-varying characteristic. Moreover, a hierarchical model predictive control (H-MPC) is proposed, with a state observer designed in the upper layer to achieve decoupling between two control systems. The simulation results demonstrate that the proposed DC-AGC structure and H-MPC with a state observer effectively exploit the frequency regulation capability of DERs, thereby reducing maximum frequency deviation and transition process time. The controller's robustness is validated when DERs' prediction error range of +/- 5%.

关键词： Distributed renewable energy sources Automatic generation control Decentralized coordinated control hierarchical model predictive control State observer

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Demand response for residential buildings using hierarchical nonlinear model predictive control for plug-and-play

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APPLIED ENERGY 2024年 369卷

作者： Wang, Cuiling Wang, Baolong You, Fengqi Tsinghua Univ Dept Bldg Sci Beijing Key Lab Indoor Air Qual Evaluat & Control Beijing 100084 Peoples R China Cornell Univ Syst Engn Ithaca NY 14853 USA Cornell Univ Robert Frederick Smith Sch Chem & Biomol Engn Ithaca NY 14853 USA Tsinghua Univ Sch Architecture Dept Bldg Sci Beijing 100084 Peoples R China

In the smart grid, residential inverter air conditioners (AC) with significant demand response (DR) potential due to their load flexibility and as the major contributors to peak electricity, need to be grid-responsive to relieve power supply-demand imbalance and ensure thermal comfort. model predictive control (MPC) has strong capabilities for unlocking the flexibility of residential buildings to realize DR by responding to electricity prices. However, the high computational requirements and complex control system integration processes make the application of MPC a significant challenge. A hierarchical nonlinear MPC (HNLMPC) is developed to realize gridresponsive control for residential inverter ACs by responding to real-time electricity price signals. The controller consists of three parts: the upper-level supervisor MPC, the lower-level optimal PID controller, and the signal converter. The indoor air temperature is selected as the optimized setpoint sequence passed from the upper level to the lower level. A nonlinear prediction model is developed considering the dynamic performances of the inverter AC and the coupled thermal response of an air-conditioned room. A test platform is constructed using Simulink and Simscape to access the DR performance of HNLMPC by comparing it with different rule-based control methods, hierarchical linear MPC, and centralized MPC. The control results show that HNLMPC can achieve peak load shifting and peak shaving without sacrificing thermal comfort by adjusting the room temperature to charge and discharge cooling for the building's thermal mass. Additionally, it enables plug-and-play capability for practical applications, reducing the dependency on local computing power and the need for accurate models. Compared to basic rule-based control, HNLMPC reduces peak-hour energy consumption by 31.6% and total electricity costs by 14.3% over the entire cooling season.

关键词： Demand response hierarchical model predictive control Nonlinear model Plug-and-play

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hierarchical model predictive control for Coordinated Electric Railway Traction System Energy Management

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第7期20卷 2715-2727页

作者： Novak, Hrvoje Lesic, Vinko Vasak, Mario Univ Zagreb Lab Renewable Energy Syst Fac Elect Engn & Comp Zagreb 10000 Croatia

The paper presents a railway energy management system based on hierarchical coordination of electric traction substation energy flows and on-route trains energy consumption. The railway system is divided into energy-efficient individual trains energy consumption management as lower level, and the price-efficient electric traction substation energy flows management as higher level. The levels are coordinated through parametric hierarchical model predictive control with the main goal of additionally increasing the energy efficiency and decreasing the operational costs of the overall system. Through interactions with the power grid on the higher level, the system is able to provide ancillary services and respond to various grid requests. At the same time, lower level trains driving profiles are adjusted to attain the minimal cost of system operation with timetables and on-route constraints respected. The developed algorithm is verified on a detailed real case study scenario put together with a railway operator and a trains manufacturer. The presented results show significant cost and energy consumption reductions achieved by simultaneous coordination of several trains supplied from the same traction substation.

关键词： Train traction energy consumption electric traction substation energy flows energy management hierarchical model predictive control

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hierarchical model predictive control of wind farm with energy storage system for frequency regulation during black-start

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 119卷 105893-000页

作者： Liu, Weipeng Liu, Yutian Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control 17923 Tingshi Rd Jinan 250061 Peoples R China

In high wind power penetration power system, using wind farm equipped with energy storage system (WF-ESS) as black-start (BS) source needs to maintain system frequency stability when starting up ancillary machine or picking up load. A hierarchical model predictive control (HMPC) method of WF-ESS for frequency regulation during BS is proposed in this paper, which consists of two control modes: frequency regulation mode and reserve recovery mode. For the first control mode, a PI controller is used to generate reference power for WF-ESS and to recover system frequency to 50 Hz. The proposed HMPC is divided into WF level and wind turbine (WT) level to accurately track the reference power and guarantee secure operation of each WT. Considering WTs' different operation conditions, WF level MPC distributes reference power to WTs according to the proportion of single WT reserve to total WF reserve. The ESS power is utilized as supplementary when WF output power is insufficient. The rotor speed and pitch angle constraints are included in the WT level MPC while providing frequency response. After frequency deviation is eliminated, WF and ESS are coordinated by the second control mode to avoid a second frequency drop meanwhile reserve a certain proportion of WF available power for managing potential frequency disturbances in the future. A case study is conducted to show the effectiveness of the proposed control method under both low and high wind conditions.

关键词： hierarchical model predictive control Frequency regulation Wind farm Energy storage system Black-start

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Two-level hierarchical model predictive control with an optimised cost function for energy management in building microgrids

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APPLIED ENERGY 2021年 285卷 116420-116420页

作者： Yamashita, Daniela Yassuda Vechiu, Ionel Gaubert, Jean-Paul ESTIA Inst Technol 97 Allee Theodore MonodESTIA 2 F-64210 Bidart France Univ Poitiers Lab Informat & Automat Syst LIAS 4 Rue Pierre Brousse Poitiers France

Hybrid energy storage systems have been increasingly envisaged for building microgrids to soften the drawbacks arising from the unpredictability of renewable energy resources and dwelling occupancy. The combination of longand shortterm energy storage systems can enhance the building microgrid capacity of shifting the demand peak toward periods of power generation, increasing the marks of self-consumption rate. However, the design of energy management systems for hybrid energy storage microgrids is more complex than single ones due to a greater number possible solutions. Faced with this issue, this paper proposes a two-level hierarchical model predictive controller (HMPC) enhanced by two data driven modules to improve the performance of building microgrids equipped with hybrid energy storage continuously and automatically. With minimum pre-design steps, the two data-driven algorithms improve the accuracy of Li-ion batteries and hydrogen storage models and determine adequate parameters for the HMPC cost function. Relying predominantly on data measurements, the proposed hierarchical controller determines which energy storage device must be run on a daily basis based on the estimation of the annual self-consumption rate and the annual microgrid operation costs. This real-time analysis decreases microgrid expenditure because it avoids grid penalisation regarding the energy autonomy index and reduces the degradation and maintenance of energy storage devices. Compared to a standard rule based strategy, the proposed controller reduces annual costs up to 5% in residential buildings and 9% in non-residential ones. In contrast, compared to a conventional HMPC the annual expenditure is reduced from 1% to 7% in both types of buildings.

关键词： hierarchical model predictive control Hydrogen storage system Li-ion batteries Data-driven algorithms Economic optimisation

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hierarchical-stochastic model predictive control for a grid-interactive multi-zone residential building with distributed energy resources

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JOURNAL OF BUILDING ENGINEERING 2024年 89卷

作者： Langner, Felix Frahm, Moritz Wang, Weimin Matthes, Joerg Hagenmeyer, Veit Karlsruhe Inst Technol Inst Automat & Appl Informat D-76344 Eggenstein Leopoldshafen Germany Univ North Carolina Charlotte Dept Engn Technol Charlotte NC 28223 USA

Using model predictive control (MPC) is a promising method for enabling grid-interactive efficient buildings. Since MPC relies on a building model and the forecasts of external disturbances to derive optimal inputs, the uncertainties due to forecast errors and model inaccuracies can deteriorate the control performance. Most existing MPC studies for the built environment are deterministic MPC without consideration of uncertainties even though several different methods (e.g., stochastic MPC) are available in the literature to deal with them. Even studies that consider forecast uncertainties often neglect model inaccuracies. Hence, in the present paper, a novel hierarchical-stochastic MPC is proposed considering forecast uncertainties and model inaccuracies, and its performance is compared with deterministic, stochastic, and hierarchical MPC for power management in a residential building with distributed energy resources. The control objective is the cost-optimal scheduling of a heat pump, a battery for energy storage, and a rooftop photovoltaic system. Measurement data is used to identify the building model. The results for one-week simulation in winter show that (1) the deterministic MPC results in an unacceptable level of temperature constraint violations in two out of five rooms;(2) the hierarchical MPC can reduce the temperature constraint violations to an acceptable level at the expense of increased cost;(3) the stochastic MPC achieves the same reduction in temperature constraint violations as the hierarchical MPC but at slightly lower costs;and (4) the new proposed hierarchical-stochastic MPC results in both lower temperature constraint violations and lower financial expenses than the use of stochastic or hierarchical MPC individually.

关键词： hierarchical model predictive control Building energy management Uncertainty analysis model predictive control Energy efficiency

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Nonlinear multivariable hierarchical model predictive control for boiler-turbine system

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ENERGY 2015年第Part1期93卷 309-322页

作者： Kong, Xiaobing Liu, Xiangjie Lee, Kwang Y. North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

Economic optimal control has been a major concern in modern power plant. The HMPC (hierarchical model predictive control) incorporates both the plant-wide economic process optimization and regulatory process control into a hierarchical control structure, in which the model predictive control technology has been an effective tool for solving the higher-layer economic optimization problems. Since the power plants are typically nonlinear multivariable large-scale processes, applications of the HMPC can be computationally extensive and resulting in nonlinear and non-convex optimization problems. Since the power plant dynamics changes with load, fuzzy model representing the local input output relations of the nonlinear power plant system is incorporated to facilitate the convex QP (quadratic program) routine, and thus realize the HMPC. Detailed analysis on power plant steam-boiler generation system has been made to demonstrate the effectiveness of the proposed nonlinear HMPC. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： hierarchical model predictive control Convex optimization Boiler-turbine system Fuzzy model

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hierarchical model predictive control: A Review

Hierarchical Model Predictive Control: A Review

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第26届中国过程控制会议（CPCC2015）

作者： AN Ai-min WANG Ni Liu Fu-chao DU Pai-dong LIANG Chen Lanzhou University of Technology Institute of Electric Power Scientific Research State Grid of Gansu Province

Due to continual variations in the material prices of industrial production, various optimal algorithms and advanced control strategies have been developed to overcome this variability and achieve maximum economic profit. In recent years, the different hierarchical model predictive control(HMPC) methods are proposed and used to optimize the operation, control and management of productive processes in pursuit of the greatest economic benefit at the lowest cost. HMPC is the important strategies for tracking the economic optimum in industrial production processes, and it has been applied in numerous industrial processes. In this paper, the optimal control principle and the basic control structure of HMPC are reviewed based on domestic and foreign research achievements. Various works about HMPC studied by the predecessors are discussed in the paper. The advantages, disadvantages and applications of various HMPC methods are analyzed. Additional research directions and developments in the HMPC field are proposed.

关键词： hierarchical model predictive control setpoint tracking optimal operation structure decomposition subsystems

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A multiobjective-based switching topology for hierarchical model predictive control applied to a hydro-power valley

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IFAC Proceedings Volumes 2013年第20期46卷 534-539页

作者： A. Núñez C. Ocampo-Martinez B. De Schutter F. Valencia J.D. López J. Espinosa Section of Road and Railway Engineering Delft University of Technology Delft The Netherlands Institut de Robòtica i Informàtica Industrial (CSIC-UPC) Technical University of Catalunya Barcelona Spain Delft Center for Systems and Control Delft University of Technology Delft The Netherlands School of Mechatronics Universidad Nacional de Colombia Medellín Colombia

In a hierarchical model predictive control (H-MPC) framework, this paper explores suitable time-variant structures for the hierarchies of different local MPC controllers. The idea is to adapt to different operational conditions by changing the importance of the local controllers. This is done by defining the level of the hierarchy they belong to, and solving within each level the local MPC problem using the information provided by the higher levels at the current time step and the predicted information from the lower levels obtained in the previous time step. As selecting a hierarchy results in a combinatorial optimization problem, it is explicitly solved for a limited number of relevant topologies only and then the switching between topologies is defined with a multiobjective optimizer, so as to decide the best H-MPC scheme according to the expected performance. A comparison with fixed-topology H-MPC controllers is done, showing the effectiveness of the proposed approach for the power control of a hydro-power valley.

关键词： hierarchical model predictive control Switching Topologies Supervisory control Multiobjective Optimization Hydro-Power Valley

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