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Control and Optimisation of Power Grids Using Smart Meter Data: A Review

journal contribution
posted on 2024-11-03, 09:05 authored by Zhiyi Chen, Ali Moradi AmaniAli Moradi Amani, Xinghuo YuXinghuo Yu, Mahdi JaliliMahdi Jalili
This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay.

Funding

Dynamic Deep Learning for Electricity Demand Forecasting

Australian Research Council

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History

Related Materials

  1. 1.
    DOI - Is published in 10.3390/s23042118
  2. 2.
    ISSN - Is published in 14248220

Journal

Sensors

Volume

23

Number

2118

Issue

4

Start page

1

End page

26

Total pages

26

Publisher

MDPI AG

Place published

Switzerland

Language

English

Copyright

Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Former Identifier

2006121783

Esploro creation date

2023-05-10

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