Modular Hierarchical Model Predictive Control for Coordinated and Holistic Energy Management of Buildings

Vašak, Mario; Banjac, Anita; Hure, Nikola; Novak, Hrvoje; Marušić, Danko; Lešić, Vinko

doi:10.1109/tec.2021.3116153

Cited by 29 publications

(4 citation statements)

References 26 publications

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“…In hierarchical strategies, the control is divided into three levels consisting of primary, secondary and tertiary control [19][20][21][22][23]. The primary controller operates locally and has the shortest response time.…”

Section: Hierarchical Control Strategiesmentioning

confidence: 99%

Control Concepts for a Decentralized Battery Management System to Optimize Reliability and Battery Operation

Reindl¹,

Eriksson²,

Niemetz³

et al. 2023

Atlantis Highlights in Engineering

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Battery systems are used in a wide range of safety-relevant applications, such as electric vehicles, unmanned aerial vehicles and home storage systems. Safety, reliability and availability of the battery system therefore play a key role. In addition, the useful service lifetime of the batteries determines the environmental impact and economic efficiency of the overall system. One possible solution is to give batteries a second life in applications with lower requirements in terms of dynamic behavior or capacity. Heterogeneous battery systems consist of batteries with differences in cell technology, age, capacity, and optimal operating range. To meet the safety, reliability, and availability requirements a scalable, Decentralized Battery Management System (DBMS) based on a distributed control system is proposed. Batteries, generators, and loads have Local Control Units (LCUs) consisting of a microcontroller, a measurement unit, and a DC/DC converter with adjustable voltage and current limits. These LCUs are the basis for the communication-based, cooperative system control and enhance the reliability and scalability of the battery system compared to conventional centralized structures. They record and manage the operating parameters and provide the basis for predictive energy management and battery residual value estimation. As a fallback strategy, a droop-based control of the DC/DC converters is used in addition to the communication-based one. Transition conditions between the control modes are defined and the control methods are compared and differentiated. The performance and the resulting benefits of batteries are determined by the control strategies. In this paper, the requirements for the control strategies for different operating modes, including startup, severe fluctuations of the DC power line voltage, and safe shutdown, are analyzed. Keywords:Renewable energy sources • battery management system • second life battery • decentralized control • distributed control • control strategies • droop control • battery optimal operation

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Section: Hierarchical Control Strategiesmentioning

confidence: 99%

Control Concepts for a Decentralized Battery Management System to Optimize Reliability and Battery Operation

Reindl¹,

Eriksson²,

Niemetz³

et al. 2023

Atlantis Highlights in Engineering

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show abstract

“…A Deep Boltzmann Machine (DBM), which can find highlevel trends in data, can be used to predict how people will use renewable energy in the future [18]. It will be able to find patterns in the data that has been collected, which will help predict [19] both long-term behavior and possible problems that could stop a lot of people from using green energy. Deep Boltzmann Machines (DBMs), which are a type of generative deep learning model, have been used in many different areas.…”

Section: Algorithmmentioning

confidence: 99%

The Role of AI in Mitigating Climate Change: Predictive Modelling for Renewable Energy Deployment

Alharbe,

Alluhaibi

2023

IJACSA

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This study looks at how AI algorithms like Random Forest, Support Vector Machines (SVM), and Deep Boltzmann Machine (DBM) can be used for predictive modeling to make it easier to use renewable energy sources while reducing the negative effects of climate change. Predictive models based on Artificial Intelligence show possible ways to get the most out of green energy sources, which could lead to fewer carbon emissions. The results of the preliminary studies show that these AI systems can make accurate predictions about how green energy will be made because they are good at making predictions and generalizing. This feature makes it possible to use resources effectively, which improves the reliability of the grid and encourages more people to use green energy sources. Ultimately, employing these AI programs will serve as powerful tools in combating climate change and fostering a more sustainable and eco-friendly environment.

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“…Te most generalized approaches to the coordination of local control systems are based on a centralized (for a small number of LCS and a small distance between them) or hierarchical (for a large number of LCS or a large distance between them) architecture of the coordination system [56][57][58]. Such systems are widely used, and they provide high efciency when controlling the thermal regime of individual buildings and groups.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Coordination of Temperature Control in Multiarea Premises

2022

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With local control of a large number of objects that mutually influence each other, the problem of coordinating local control systems to achieve the best overall result arises. If the structure of the system (the number of control objects and the parameters of interaction) can change frequently, then the process of setting up/training a centralized coordinator will take an unacceptably large part of the action time and require a significant amount of resources. In this work, the use of decentralized coordination is proposed to solve the problem. As a basic task for research on decentralized coordination control of objects that mutually influence each other, stabilizing the comfort temperatures was set in multizone rooms using movable heaters. Providing individual thermal comfort is an important problem. In particular, there are many multiarea premises with conflicting requirements for the comfort of habitats. This problem can be solved with the help of movable heaters and air conditioners. However, the presence of heat flows between areas with different specified parameters makes it difficult to adjust them. The work aims to improve the quality of thermal control in multiarea premises with a dynamic structure for the location of movable heaters. To achieve this goal, we proposed the concept of Movable Smart Heaters (MSH). A group of Movable Smart Heaters that could influence each other and exchange information forms a dynamic system with a changing structure since switching on/off or moving one MSH to another area changes the mutual influence and connections in the system. The criteria for control quality are defined and evaluated. The proposed coordination algorithms make it possible to optimize the operating modes of the system automatically when its structure and/or settings are changed. Simulation of the system is performed with the use of a worked-out modelling library in Scilab. The results of comparing the MSH system’s efficiency show an increase in comfort while reducing energy consumption.

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Modular Hierarchical Model Predictive Control for Coordinated and Holistic Energy Management of Buildings

Cited by 29 publications

References 26 publications

Control Concepts for a Decentralized Battery Management System to Optimize Reliability and Battery Operation

Control Concepts for a Decentralized Battery Management System to Optimize Reliability and Battery Operation

The Role of AI in Mitigating Climate Change: Predictive Modelling for Renewable Energy Deployment

Decentralized Coordination of Temperature Control in Multiarea Premises

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