Optimal Partitioning for the Decentralized Thermal Control of Buildings

Chandan, Vikas; Alleyne, Andrew G.

doi:10.1109/tcst.2012.2219308

Cited by 27 publications

(16 citation statements)

References 28 publications

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“…The challenge of coordinated control and the relative merits of centralised and decentralised formulations have been well established [227]. Objectives can be balanced through an appropriate selection of weights [228] or prioritised frameworks [54], while higher-level coordination layers and game theoretic approaches [152] can allow for interaction between otherwise isolated controllers to ensure all actors are satisfied in a manner that can be considered fair. Depending on user-preference, two options are explored here.…”

Section: The Overall Sems Control Architecturementioning

confidence: 99%

“…As previously discussed, the control architecture is founded on a layer of standard controllers, which in the event of a loss of communication, can operate the various assets in an isolated manner. Aside from this however, measures must be included to prevent individual faults (sensor, actuator or model faults) from propagating throughout the system [228]. Once again, the digital twin can play a role in the required analysis for this.…”

Section: Additional Challenges For the Semsmentioning

confidence: 99%

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Smart energy systems for sustainable smart cities: Current developments, trends and future directions

et al. 2019

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Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

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Section: The Overall Sems Control Architecturementioning

confidence: 99%

Section: Additional Challenges For the Semsmentioning

confidence: 99%

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

et al. 2019

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“…Koerber and King () use MPC to control the torque and collective pitch of a wind turbine. Chandan and Alleyne () use centralized and decentralized MPC to regulate the temperature of a 9‐zone 3‐story square building and an 11‐zone circular building. They compare decentralized and centralized control strategies.…”

Section: Classical Controlmentioning

confidence: 99%

Recent advances in control algorithms for smart structures and machines

Soto

Adeli

2017

Expert Systems

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This paper presents a state‐of‐the‐art review of recent control algorithms used primarily for vibration control of smart structures and machines. Control algorithms can be divided into open loop, feedback, feedforward, and combined feedback and feedforward control. Solutions proposed can be subdivided into (a) classical control, (b) optimal control, (c) robust control, (d) intelligent control, and (e) self‐sustaining solutions and energy harvesting used in centralized and decentralized configurations. The most recent research on novel autonomous control systems intersects distributed control theory, agent‐based modeling, bioinspired computational learning, and optimization algorithms.

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“…Regarding the former component, the system partitioning problem has gotten increasing importance in the automatic control community as systems become larger and more challenging, and as the requirements and desired closed-loop performance become more strict. Many partitioning proposals focus on specific dynamical systems, e.g., in thermal control [9], control of electric power distribution systems [10,11], or on a particular control strategy, e.g., in decomposition structure of distributed predictive controllers [12,13]. Regarding the second component for distributed controllers, this paper discusses the distributed optimization based on dynamic games, which have become a quite useful tool in the design of distributed controllers as it has been presented in [14,15].…”

Section: Introductionmentioning

confidence: 99%

Time-varying partitioning for predictive control design: Density-games approach

Barreiro-Gómez

Ocampo‐Martínez

Quijano

2019

Journal of Process Control

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The design of distributed optimization-based controllers for large-scale systems (LSSs) implies every time new challenges. The fact that LSSs are generally located throughout large geographical areas makes difficult the recollection of measurements and their transmission. In this regard, the communication network that is required for a centralized control approach might have high associated economic costs. Furthermore, the computation of a large amount of data implies a high computational burden to manage, process and use them in order to make decisions over the system operation. A plausible solution to mitigate the aforementioned issues associated with the control of LSSs consists in dividing this type of systems into smaller subsystems able to be handled by independent local controllers. This paper studies two fundamental components of the design of distributed optimization-based controllers for LSSs, i.e., the system partitioning and distributed optimization algorithms. The design of distributed model predictive control (DMPC) strategies with a system partitioning and by using density-dependent population games (DDPG) is presented.

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Optimal Partitioning for the Decentralized Thermal Control of Buildings

Cited by 27 publications

References 28 publications

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

Recent advances in control algorithms for smart structures and machines

Time-varying partitioning for predictive control design: Density-games approach

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