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Output feedback distributed economic model predictive control for parallel system in process networks

IMA JOURNAL OF MATHEMATICAL control AND INFORMATION 2024年第3期41卷 564-589页

作者： Zhang, Shuzhan Li, Jia Zhao, Dongya Spurgeon, Sarah K. China Univ Petr East China Coll New Energy Qingdao 266580 Peoples R China UCL Dept Elect & Elect Engn Torrington Pl London WC1E 7JE England

This paper proposes a distributed economic model predictive control algorithm for parallel systems which uses only output feedback. Such parallel systems are a fundamental system architecture frequently encountered in process networks where, in many cases, the state of the plant is not measurable. economic performance is a key consideration in the operation of such industrial plants and it is of interest to develop theoretically rigorous approaches to tackle what is a practically very relevant scenario. The competitive couplings and competitive constraints inherent in parallel systems are explicitly addressed in the proposed controller design framework. Three measures are considered for optimization of such parallel systems including an economic stage cost function, energy efficiency and tracking accuracy. An economic cost function is optimized by the resulting distributed controller which can realize global control performance while also reducing the computational time for large-scale parallel systems. Stability of the system is formally proved using the principles of dissipativity. Finally, the effectiveness of the proposed theoretical approach is verified by both numerical simulation and experimentation to demonstrate practical relevance.

关键词： parallel system architectures distributed economic model predictive control output feedback

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distributed economic model predictive control for a Wind-Photovoltaic-Battery Microgrid Power System

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第2期11卷 1089-1099页

作者： Jia, Yubin Dong, Zhao Yang Sun, Changyin Chen, Guo Southeast Univ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2033 Australia

This paper proposed a dual-mode distributed economic model predictive control (EMPC) for a nonlinear wind-photovoltaic-battery microgrid power system (WPB-MPS). The communications between each subsystem are taken into consideration and the economic objectives that are established based on the aspects that: satisfy the total demand;make full use of the power generated by the WPB-MPS;optimize the battery's state of charge;and reduce the fluctuation of power exchange with the grid. The subsystem-based EMPCs work iteratively and cooperatively to solve the economic objective functions, which reflect economies of the system and not necessary to drive the system to a certain steady state as hierarchical control. A dual-mode Lyapunov-based EMPC has been formulated to guarantee the stability of the system with disturbance, and under certain conditions, the closed-loop system state can be driven and hold in a safe region. Simulations demonstrate the advantages and efficiency of the proposed method.

关键词： economics Batteries Optimization Power system stability Photovoltaic systems Mathematical model Wind-photovoltaic-battery microgrid power system distributed economic model predictive control nonlinear system economic cost function dual-mode stability

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PV/Hydrogen DC microgrid control using distributed economic model predictive control

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RENEWABLE ENERGY 2024年 222卷

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

The integration of hydrogen energy into a photovoltaic-dominated microgrid is now becoming a promising approach to improve the photoconversion efficiency and enhance the operating reliability. However, the energy management and power regulation of the Photovoltaic/Hydrogen DC microgrid face challenges due to the intermittency of photovoltaic (PV) power generation and the randomness of load. In this paper, a distributed economic model predictive control (DEMPC) scheme is developed for a PV/Hydrogen DC microgrid, which integrates the energy management, economic optimization, and power regulation into one optimal control framework. Based on the developed distributed converter-based mathematical model, three local controllers are designed to achieve the economic targets of PV subsystem, alkaline electrolyzer subsystem, and proton exchange membrane fuel cell subsystem respectively, which cooperate with each other through the communication network to realize the power supply-demand balance, DC bus voltage stability, and economic optimization. A mixed integer nonlinear programming algorithm utilizing the finite converter switching states is embedded into the DEMPC to solve the non-convex local optimization problems efficiently. The effectiveness and superiority of the proposed DEMPC scheme are verified by simulations under varying irradiance and load conditions, indicating that the DEMPC can achieve a comparable overall dynamic economic performance with a significantly reduced computation burden and power oscillation, compared to the centralized economic model predictive control (CEMPC).

关键词： distributed economic model predictive control Solar energy Hydrogen generation Fuel cell

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A distributed economic model predictive control-based FPPT scheme for large-scale solar farm

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SOLAR ENERGY 2024年 278卷

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

Increasing solar-photovoltaic-power penetration necessitates the implementation of flexible power point tracking (FPPT) in solar farms. However, coordinating multiple solar photovoltaic (PV) power generation systems (SPVPGSs) poses a significant challenge due to their inherent intermittency and varying dynamic characteristics, complicating FPPT implementation. To overcome this challenge, this paper proposes a distributed economic model predictive control (DEMPC) scheme to achieve FPPT, while simultaneously enhancing overall economic performance in solar farms. By integrating solar farm control and local control into one optimal control framework, this scheme eliminates the need for power allocation, PV voltage reference calculation, and pulse width modulation. Leveraging the SPVPGS model and soft power constraint, DEMPC controllers are designed to achieve the economic targets of solar farms, which cooperate through a communication network to realize FPPT and economic optimization. Additionally, the strong nonlinearity of SPVPGS causes a non-convex mixed integer nonlinear programming (MINLP) problem, solved by a MINLP algorithm using finite converter switching states. Simulations under step-changed irradiance and power reference, as well as urgent maintenance conditions, demonstrate that the DEMPC-based FPPT scheme significantly outperforms the traditional hierarchical model predictive control-based FPPT scheme, presenting both superior dynamic response and enhanced economic performance in FPPT implementation.

关键词： Solar photovoltaic power generation Solar farm Flexible power point tracking distributed economic model predictive control

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Intrusion-Detector-Dependent distributed economic model predictive control for Load Frequency Regulation With PEVs Under Cyber Attacks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2021年第9期68卷 3857-3868页

作者： Hu, Zhijian Liu, Shichao Luo, Wensheng Wu, Ligang Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China Carleton Univ Dept Elect Ottawa ON K1S 5B6 Canada Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China

With the participation of a significant number of plug-in electric vehicles (PEVs), it is really challenging to achieve economic-effective in load frequency control (LFC) while sustaining satisfiable system performance. To tackle this challenge, a new distributed economic model predictive control (DEMPC) strategy is proposed for the LFC with the large-scale PEV participation. In the light of the vulnerability of LFC to false data injection (FDI) attacks, a model-based chi(2) intrusion detection unit is integrated with the proposed DEMPC. This model-based intrusion detection unit can not only monitor the FDI attacks, but also generate a model-based state prediction for the DEMPC once the data is identified as compromised. Then, an event-triggering mechanism is presented to reduce the computation and communication burdens of each area controller. Simulation studies of a four-area power system are conducted and the results validate the effectiveness of the proposed intrusion detection unit and event-triggering conditions for the DEMPC.

关键词： Plug-in electric vehicles load frequency control distributed economic model predictive control false data injection attacks chi(2) intrusion detection event-triggering mechanism

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distributed economic model predictive control of Standalone Wind/Solar/Battery Power System

Distributed Economic Model Predictive Control of Standalone ...

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第32届中国过程控制会议(CPCC2021)

作者： Xuan Wang Xiaobing Kong Xiangjie Liu School of Control and Computer Engineering North China Electric Power University

Regarding the large-scale,geographically dispersed standalone wind/solar/battery power generation system,this paper constituted a distributed economic model predictive control(DEMPC).In this DEMPC,the real-time economic optimization can be achieved by solving the cost function which represents the priority of solar power,attenuating fluctuations of battery power and avoiding the frequent charge/discharge cycle in *** satisfaction of load demand is expressed in terms of coupling constraint and have to be satisfied at all *** results demonstrate the advantages and efficiency of the proposed method.

关键词： distributed economic model predictive control standalone wind/solar/battery system economic cost function

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distributed economic model predictive control for cooperative supply chain management using customer forecast information

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IFAC JOURNAL OF SYSTEMS AND control 2021年 15卷

作者： Koehler, Philipp N. Mueller, Matthias A. Pannek, Juergen Allgoewer, Frank Univ Stuttgart Inst Syst Theory & Automat Control Pfaffenwaldring 9 D-70569 Stuttgart Germany Leibniz Univ Hannover Inst Automat Control D-30167 Hannover Germany BIBA Bremer Inst Prod & Logist GmbH D-28359 Bremen Germany Univ Bremen Dept Prod Engn D-28359 Bremen Germany

Efficient operation of supply chain systems by means of distributed model predictive control is studied in this work. The main focus is on the exploitation and sharing of predictive information on delivery and demand throughout the supply chain. Based on the availability of customer demand predictions, which are assumed to be reliable to some extent, two distributed model predictive control algorithms for supply chain operation are proposed, analyzed and investigated in numerical simulations. The mechanisms employed for information exchange throughout the supply chain differ in both approaches. The first approach establishes and implements the exchange of semi-accurate predictions, which explicitly requires predicted trajectories to only vary slightly from one time step to the next. In the second approach, information exchange is rather indirect by means of terminal constraints in the local MPC formulations, explicitly relying on the stock and flow nature of the overall system. The two approaches considerably differ in terms of system setup, requirements and corresponding results, and hence provide a flexible framework for leveraging predictive information in supply chain system management. As such, they form a basis for further investigations towards the ultimate goal of quantifying the value of predictive information in supply chain systems. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Decentralized and distributed control Complex logistic systems modeling and decision making in complex systems Multiagent systems distributed economic model predictive control

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distributed economic model predictive control for Operational Safety of Nonlinear Processes

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AICHE JOURNAL 2017年第8期63卷 3404-3418页

作者： Albalawi, Fahad Durand, Helen Christofides, Panagiotis D. Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA

Achieving operational safety of chemical processes while operating them in an economically-optimal manner is a matter of great importance. Our recent work integrated process safety with process control by incorporating safety-based constraints within model predictive control (MPC) design;however, the safety-based MPC was developed with a centralized architecture, with the result that computation time limitations within a sampling period may reduce the effectiveness of such a controller design for promoting process safety. To address this potential practical limitation of the safety-based control design, in this work, we propose the integration of a distributed model predictive control architecture with Lyapunov-based economic model predictive control (LEMPC) formulated with safety-based constraints. We consider both iterative and sequential distributed control architectures, and the partitioning of inputs between the various optimization problems in the distributed structure based on their impact on process operational safety. Moreover, sufficient conditions that ensure feasibility and closed-loop stability of the iterative and sequential safety distributed LEMPC designs are given. A comparison between the proposed safety distributed EMPC controllers and the safety centralized EMPC is demonstrated via a chemical process example. (C) 2017 American Institute of Chemical Engineers

关键词： distributed economic model predictive control process safety computation time

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Dissipativity based distributed economic model predictive control for residential microgrids with renewable energy generation and battery energy storage

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RENEWABLE ENERGY 2017年第0期100卷 18-34页

作者： Zhang, Xinan Bao, Jie Wang, Ruigang Zheng, Chaoxu Skyllas-Kazacos, Maria Univ New South Wales Sch Chem Engn Sydney NSW 2052 Australia

The combination of renewable energy generation and battery energy storage has been widely recognized as a promising solution to the problems associated with variability of renewable energy in residential microgrid. However, due to the low renewable feed-in tariffs in many countries, microgrid users are generally not motivated to install expensive battery systems if they can only be used to satisfy the objective of grid operator. From this perspective, a microgrid power market that encourages users to install batteries for energy-trading will be helpful for the deployment of batteries. For such circumstances, this paper introduces a user-driven microgrid power market. The possible pricing schemes are discussed and an illustrative price controller is presented. The potential destabilizing effect of the collective trading behavior of users is analyzed. A novel dissipativity based distributed economic model prediction control approach is proposed to allow microgrid users to optimize their own benefits while ensuring the performance and stability of the residential microgrid. A simulation study with photovoltaic energy generation and Vanadium Redox batteries is presented to illustrate the efficacy of the proposed method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Residential microgrid distributed economic model predictive control Dissipativity distributed battery energy storage Power market

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Wind farm control using distributed economic MPC scheme under the influence of wake effect

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ENERGY 2024年 309卷

作者： Wang, Wenwen Kong, Xiaobing Li, Gangqiang Liu, Xiangjie Ma, Lele Liu, Wenting Lee, Kwang Y. North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China CRRC Shandong Wind Power Co Ltd Jinan 250000 Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

As wind farms (WFs) expand in scale, there is a growing need for active power control to track the reference power benchmark issued by the grid dispatch center and also the imperative to reduce the fatigue load on key components of each wind turbine (WT). The presence of the wake effect causes a decrease in power generation for downstream WTs and an increase in the fatigue load. Consequently, the suppression of the wake effect has emerged as a critical control objective for WFs. For tackling the challenge, this article designs a hierarchical WF control framework. The upper-level controller employs a sequential convex programming (SCP) approach to maximize the WF's captured wind energy function and determine the optimal induction factors for the WTs. The lower-layer controller uses a distributed economic model predictive control (DEMPC) scheme to control the WT locally to achieve reference power tracking while reducing the fatigue load on each WT. Finally, the effectiveness of the designed algorithm was verified by conducting the simulation on a wind farm containing nine WTs.

关键词： Wind farm Wake effect Reference power tracking Fatigue load distributed economic model predictive control

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