Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control

Hahn, Jannik; Schoeffauer, Richard; Wunder, Gerhard; Stursberg, Olaf

doi:10.1109/tcns.2021.3124260

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…As mentioned, AoI is especially well suited if the network is part of a closed control loop. In [10], the authors show how forecasting AoI values can improve the performance of model-predictive controllers with a common control goal. For cellular networked control systems, [11] compares the AoI metric with a Value-of-Information metric which considers the expected information content of an update.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Exact Numerical Age-of-Information Evaluation in Multi-Agent Systems

Schoeffauer¹,

Wunder²

2022

Preprint

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We present an algorithm for the numerical evaluation of the state-space distribution of an Age-of-Information network. Given enough computational resources, the evaluation can be performed to an arbitrary high precision. An Age-of-Information network is described by a vector of natural numbers, that track how outdated status information from various agents is. Our algorithm yields the means to determine any moment of the corresponding stochastic process. This can be extremely valuable for cases in which the network consists of controllers that communicate with one another, as it potentially allows for less conservative control behavior. It also enables the comparison of different policies regarding their performance (minimizing the average Age-of-Information) to a much more accurate degree than was possible before. This is illustrated using the conventional MaxWeight policy and the optimal policy. We also validate and compare the algorithm with Monte-Carlo-Simulations.

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Section: Introductionmentioning

confidence: 99%

An Algorithm for Exact Numerical Age-of-Information Evaluation in Multi-Agent Systems

Schoeffauer¹,

Wunder²

2022

Preprint

Self Cite

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Research on multi-time scale cooperative optimal scheduling of source, load, and storage in microgrid with flexible resources

Zhu,

Wen,

Lei

et al. 2024

J. Phys.: Conf. Ser.

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The large-scale integration of wind power and photovoltaics (PV) has increased the uncertainty in power system operation, posing challenges to energy balance. To enhance the flexibility and renewable energy integration capability of the new power system, a multi-timescale coordinated optimization method for microgrid generation-load-storage considering flexibility resource regulation potential is proposed. Based on the regulation potential of demand-side flexibility resources, their multi-timescale characteristics are utilized for operation and regulation. Simulation results confirm that the model can enhance the integration capability of renewable energy, smooth the load curve, and reduce system operating costs.

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Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control

Cited by 2 publications

References 32 publications

An Algorithm for Exact Numerical Age-of-Information Evaluation in Multi-Agent Systems

An Algorithm for Exact Numerical Age-of-Information Evaluation in Multi-Agent Systems

Research on multi-time scale cooperative optimal scheduling of source, load, and storage in microgrid with flexible resources

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