TimeSmart: Timeliness of Information in Smart Grids Networks logo

TimeSmart: Timeliness of Information in Smart Grids Networks

Duration: Oct 2022 — Mar 2025

(GA: 101063721)

TimeSmart project will investigate the applicability of the novel Age of Information metric in smart grid networks. While the metric has become a valuable tool for measuring the system’s performance, its practical value and impact in the real-time system are left unanswered. This project seeks to remedy that by applying the metric to a system in which the timing of collected data, currently measured through jitter or latency, profoundly impacts management and control. The AoI offers a new perspective on how the system should collect and process information, as such decisions are also based on the context of processed information(their semantic nature). In turn, the new approach can offer an innovative way of improving the efficiency of renewable electrical energy supply and electrical loads by taking advantage of the available edge infrastructure. This project aims to adopt the AoI metric in smart grid networks to improve the energy transmission efficiency, achievable through more timely collected information, to save energy.

The proposed research will be conducted by dr. Jernej Hribar and supervised by prof. Mihael Mohorčič, and dr. Carolina Fortuna from Institut Jožef Stefan. The applicant will also undertake a non-academic placement at ComSensus under the supervision of dr. Andrej Čampa.

Age of Information

The Age of Information (AoI) concept has recently been proposed in Information sciences, and researchers have already demonstrated its value as a tool to measure system’s performance. The AoI measures the time passed since a source of information, e.g., sensor, has generated information, e.g., took an observation, in the form of a status update. Each status update contains a timestamp revealing when the update was generated. These status updates are then collected at the sink, e.g., a monitor, where the timeliness of information is measured. For illustration, Figure 1 shows the basic system model as considered in the AoI literature and Figure 2 depicts the sample path of the AoI process at the sink.

Figure 1 - System Model Illustration.
Figure 1 - System Model Illustration.

It was also shown that the AoI has a profound impact on the design and control of communication networks. For example, even when the system can be described with a simple queuing model the network architecture has to be altered to minimize the AoI of collected information. The AoI should not be confused with latency or jitter, the traditional metrics used in communication networks. The former measures only the time necessary to transfer information from the source to the sink, while the latter refers to small intermittent delays during data transfers. Both are thus only measures of the quality of connection and not telling anything about the transferred information. AoI on the other hand is also connected to the context of processed information (their semantic nature) and has already revolutionized how the information is collected and processed in a real-time system.

Figure 2 - Age of Information Sample Process.
Figure 2 - Age of Information Sample Process.

Smart grids are a prime example of real-time systems that can benefit from the application of the AoI metric. By applying AoI to smart grids andleveraging the available edge infrastructure, it will be possible to improve the control of renewable electric power supply and electric loads. Any improvement in the efficiency of energy transmission that can be achieved through timely collected information can save a huge amount of energy in the European Union. The proposed project will help the European Union achieve its energy efficiency target of at least 32.5% by 2030. In addition, the AoI metric can play a crucial role in other smart grid scenarios, such as demand side management optimization, self-healing, etc.

Objectives of the Project

The main objective of the TimeSmart is to develop a novel way of monitoring and controlling smart grid networks. New insights, provided by the AoI metric, will be utilised to develop sustainable and energy-efficient control of smart grid resources. Develop criteria that quantifies the timelines of information Evaluate and simulate the effects of timeliness Design of a novel architecture to improve the timeliness of information Implement a fully operational AI-powered management system to demonstrate the gains of employing the AoI metric in smart grid networks




Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning
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15th International Conference on Agents and Artificial Intelligence, 22-24 Feb. 2023, Lisbon, Portugal