Transactive Control in Smart Cities

Annaswamy, Anuradha M.; Guan, Yue; Tseng, Hung-Wei; Hao, Zhou; Phan, Thao; Yanakiev, Diana

doi:10.1109/jproc.2018.2790841

Cited by 37 publications

(25 citation statements)

References 27 publications

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“…Further, both charging stations and EVs are acting as coupling agents considering their effect on the operation of power systems (charging demand) and transportation network (traffic congestion). Reducing traffic congestion in transportation network is considered as one of the main goals of smart cities [9]. Based on 2018 Global EV Outlook of International Energy Agency (IEA), there is a 50% expansion of total number of EVs as compared with 2016.…”

Section: From the To Interdependent Smart City Infrastructures To Thementioning

confidence: 99%

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Distributed Holistic Framework for Smart City Infrastructures: Tale of Interdependent Electrified Transportation Network and Power Grid

2019

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Plug-in Electric Vehicles (PEVs) play a pivotal role in transportation electrification. The flexible nature of PEVs' charging demand can be utilized for reducing charging cost as well as optimizing the operating cost of power and transportation networks. Utilizing charging flexibility of geographically spread PEVs requires design and implementation of efficient optimization algorithms. There is a synergy between electro mobility (e-Mobility) infrastructures (including charging stations) and PEVs. In this paper, we introduce a holistic framework to model interdependent nature of power systems and electrified transportation networks, enhance the operational performance of these systems as a network-of-networks, and explain the required information exchange via coupling agents (e.g., PEVs and charging stations). We develop a holistic framework that enables distributed coordination of interdependent networks through the IoT lens. To this end, we propose to use a fully distributed consensus+innovations approach. This iterative algorithm achieves a distributed solution of the decision making for each agent through local computations and limited communication with other neighboring agents that are influential in that specific decision. For instance, the optimal routing decision of a PEV involves a different set of agents as compared with the optimal charging strategy of the same PEV. The exogenous information from an external network/agent can affect internal operation of the other agents. For instance, having some information about traffic congestion at the transportation networks changes the decision of PEVs to charge their battery at another location. Our approach constitutes solving an iterative problem, which utilizes communication at the smart city layer, as a network of infrastructures, including power grid and electrified transportation network, that enables fully distributed coordination of agents. Fully distributed decision making enables scalability of the solution and plug-and-play capability, as well as increasing the information privacy of PEVs by only requiring limited local information exchange with neighboring agents. We investigate a detailed application of our framework for interdependent power systems and electrified transportation networks. To this end, we first identify the functionalities, constraints, objectives, and decision variables of each network. Then, we investigate the interdependent interactions among these networks and their corresponding agents. INDEX TERMS Consensus+innovations, distributed processing, smart city, interdependent infrastructures, electrified transportation networks, smart mobility, power systems, electric vehicles, cooperative charging, optimality conditions.

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Section: From the To Interdependent Smart City Infrastructures To Thementioning

confidence: 99%

“…where δ k and η k denote positive tuning parameters. F is the feasible space spanned by equations (9) and (10). The projection operators enforce feasibility of updated variables.…”

Section: A Problem Formulationmentioning

confidence: 99%

Distributed Holistic Framework for Smart City Infrastructures: Tale of Interdependent Electrified Transportation Network and Power Grid

2019

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“…•transactive control-based road pricing [4] (http://aaclab.mit .edu/smart-city.php) •real-time signal control [19], [20] (http://gbicautomation.eee.ntu .edu.sg/cpisrg/urban/urban_ index.html) •platooning of heavy-duty vehicles [7], [8] (http://www.kth .se/ees/forskning/strategiskaforskningsomraden/intelligentatransportsystem/smart-mobilitylab) •veh icle control [5] (ht t p:// n e w s s t a n d . c l e m s o n .…”

Section: Research Activitiesmentioning

confidence: 99%

Technical Committee on Smart Cities [Technical Activities]

2019

IEEE Control Syst.

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“…FOUNDATION 2: TRANSACTIVE CONTROL A concept that is eliciting significant attention of late is Transactive Control [1], [13], [15], a feedback control strategy enabled through economic contracts. A typical transactive controller consists of an incentive signal sent to the consumer from the infrastructure and a feedback signal received from the consumer, and together the goal is to ensure that the underlying resources are optimally utilized.…”

Section: ) Global Optimization Using Local and Distributed Controlmentioning

confidence: 99%

“…Given that in a smart infrastructure, the consumer plays an active role and can carry out decisions that impact the infrastructure dynamics, the question that arises is about the actual signal that the consumer responds to. Defined broadly as a mechanism through which system-and component-level decisions are made through economic contracts negotiated between the components of the system, in conjunction with or in lieu of traditional controls [1], transactive control is being explored in depth in the context of a smart grid infrastructure. Some of the basic features and tools that have been examined under this heading are discussed in this paper.…”

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confidence: 99%

Foundations of Infrastructure-CPS

Annaswamy

Hussain

Chakrabortty

et al. 2016

2016 American Control Conference (ACC)

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Abstract-Infrastructures have been around as long as urban centers, supporting a society's needs for its planning, operation, and safety. As we move deeper into the 21st century, these infrastructures are becoming smart -they monitor themselves, communicate, and most importantly self-govern, which we denote as Infrastructure CPS. Cyber-physical systems are now becoming increasingly prevalent and possibly even mainstream. With the basics of CPS in place, such as stability, robustness, and reliability properties at a systems level, and hybrid, switched, and eventtriggered properties at a network level, we believe that the time is right to go to the next step, Infrastructure CPS, which forms the focus of the proposed tutorial. We discuss three different foundations, (i) Human Empowerment, (ii) Transactive Control, and (iii) Resilience. This will be followed by two examples, one on the nexus between power and communication infrastructure, and the other between natural gas and electricity, both of which have been investigated extensively of late, and are emerging to be apt illustrations of Infrastructure CPS.

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Transactive Control in Smart Cities

Cited by 37 publications

References 27 publications

Distributed Holistic Framework for Smart City Infrastructures: Tale of Interdependent Electrified Transportation Network and Power Grid

Distributed Holistic Framework for Smart City Infrastructures: Tale of Interdependent Electrified Transportation Network and Power Grid

Technical Committee on Smart Cities [Technical Activities]

Foundations of Infrastructure-CPS

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