Event-triggered online energy flow control strategy for regional integrated energy system using Lyapunov optimization

Wang, Guofeng; Yang, Xiaodong; Cai, Wenhui; Zhang, Youbing

doi:10.1016/j.ijepes.2020.106451

Cited by 17 publications

(7 citation statements)

References 32 publications

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“…From Fig 6 and Fig 7 we can fully infer the AoI at both schedulers and the controller since the AoI at the first scheduler is identically zero, meanwhile the AoI at the controller is the sum of the AoI at the second scheduler and the relative AoI between the second scheduler and the controller. That is, ∆ k [1] = 0 since d[1] = 0, ∆ k [2] given in Fig 6,and [2]. Furthermore, we observe that when the second scheduler and the controller have the same AoI, or if the decrease in dVoI is not large enough, then the optimal policy for the scheduler is to not transmit.…”

Section: Sensor Scheduler 2 Controllermentioning

confidence: 89%

“…The two network schedulers are responsible of deciding δ i k [2], δ i k [3] for system i. Moreover, the communication network is shared among all N loop.…”

Section: Shared Communication Networkmentioning

confidence: 99%

“…In the definition of the estimators in (12) we assumed u i 0:k−1 to be available to all decision makers. However, from the previous equality we can conclude that in order for a scheduler j > 1 to compute its value of information it needs to know its age of information, the relative age of information with respect to its successor, and its position in the decision chain H[i] defined in (2). That is, it is function of locally available information.…”

Section: B Distributed Value Of Informationmentioning

confidence: 99%

“…In such domain, a plethora of different agents and their respective feedback loops are constrained to communicate through a shared network. The application domains are numerous and include for example robotics [1], smart energy grids [2], autonomous driving [3], and smart cities [4]. From a theoretical perspective the common denominator of the aforementioned applications is the presence of multiple feedback loops closed over a shared communication network.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Value of Information in Feedback Control over Multi-hop Networks

Abara¹,

Hirche²

2021

Preprint

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Recent works in the domain of networked control systems have demonstrated that the joint design of medium access control strategies and control strategies for the closed-loop system is beneficial. However, several metrics introduced so far fail in either appropriately representing the network requirements or in capturing how valuable the data is. In this paper we propose a distributed value of information (dVoI) metric for the joint design of control and schedulers for medium access in a multi-loop system and multi-hop network. We start by providing conditions under certainty equivalent controller is optimal. Then we reformulate the joint control and communication problem as a Bellman-like equation. The corresponding dynamic programming problem is solved in a distributed fashion by the proposed VoI-based scheduling policies for the multi-loop multi-hop networked control system, which outperforms the well-known time-triggered periodic sampling policies. Additionally we show that the dVoI-based scheduling policies are independent of each other, both loop-wise and hop-wise. At last, we illustrate the results with a numerical example.

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Section: Sensor Scheduler 2 Controllermentioning

confidence: 89%

“…The two network schedulers are responsible of deciding δ i k [2], δ i k [3] for system i. Moreover, the communication network is shared among all N loop.…”

Section: Shared Communication Networkmentioning

confidence: 99%

Section: B Distributed Value Of Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed Value of Information in Feedback Control over Multi-hop Networks

Abara¹,

Hirche²

2021

Preprint

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“…The energy storage unit is connected to the DC microgrid as an auxiliary power supply to absorb or release redundant electric energy to maintain the normal operation of the system. However, frequent charge and discharge mode switching will seriously affect the life of batteries and converters [38][39][40][41].…”

Section: System Architecture Of Photovoltaic-storage DC Microgridmentioning

confidence: 99%

Fuzzy piecewise coordinated control and stability analysis of the photovoltaic‐storage direct current microgrid

Xie

Zhang

et al. 2022

IET Renewable Power Gen

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In order to ensure the safe and reliable operation of photovoltaic-storage DC microgrid in islanded mode, this paper proposes a piecewise coordinated control strategy based on fuzzy logic to independently judge the working mode of microgrid. Firstly, according to the operation characteristics of microgrid, the device-level control of each unit (photovoltaic, energy storage and load) is designed successively. Then, according to the requirement of supply and demand balance of microgrid, fuzzy logic is introduced to realize the independent division of threshold nodes on the basis of piecewise control, so as to ensure the fast switching of working mode and stable operation of the system in various complex environments, that is system-level control. And the method of impedance analysis is used to analyze the stability of microgrid under different working modes. Finally, the effectiveness and feasibility of the proposed coordination control strategy are verified by MATLAB/Simulink simulation results.

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