on March 15th, 2021

As energy demand continues to increase in parallel with the growth of the world population, our energy systems need to evolve to be more flexible, sustainable, and distributed.

It was only a month ago that extreme weather events highlighted the fragility of the Texas electrical grid. As Texans’ demand for electricity soared, utility operators revealed just how poorly prepared they were for the cold weather.

The Texas electricity grid failed in spectacular fashion, and there are chances it will happen again. Such scenarios are likely to grow in frequency and intensity worldwide as the impacts of climate change continues to challenge our current  infrastructure.

Essential to goals of sustainability and reliability, the electricity sector is undergoing a transformation which will change the way utilities and customers interact. The introduction of smart grids represents an opportunity to move the energy industry into a new era of efficiency.

What is a smart grid?

The term smart grid (SG) is used to describe the integration of information and digital communication technologies with power grid systems. This enables bi-directional communication and power flow that can enhance security, reliability, and efficiency of the power system.

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Connectivity lies at the core of this modern grid infrastructure, which is provided by the Internet of Things (IoT). However, constant connectivity and communication introduces a massive data volume that demands techniques far superior to conventional methods to allow for proper analysis and decision-making.

Whilst an IoT-integrated SG system can provide efficient energy transmission, lower management costs, better security and integration of renewable energy, big data analysis and machine learning techniques are essential to realizing these benefits.

The role of big data

The interconnected nature of the smart grid means constantly generating ever-increasing quantities of data that requires collection, storage, and management. To utilize the power of the IoT, a robust solution is required to fully employ the power of the smart system.

A typical distribution utility deals with thousands of terabytes (TB) of new data every year. This data comes from various sources including smart meters, field measurement devices, remote terminal units, smart plugs, and programmable thermostats.

Therefore, employing effective big data analytics plays a critical role not only for the efficient operation of future electric grids but also for the development of proper business models for the key stakeholders.

Even though electric utilities have realized that deployment of big data analytics is a must and not a choice, implementation of big data analytics in utility framework is lagging.

Implementing machine learning

Machine learning is a data analysis technique in which computers are taught to make decisions based on experience. With the increase in the amount of big data, machine learning has become a crucial technique to solve problems.

The steady evolution of computational methods, specifically in data management and analysis has enabled several machine learning techniques to be implemented in smart grid applications. It fits in as the final piece of the smart grid system which is driven by data collection, analysis, and decision making.

Machine learning techniques provide an efficient way to analyze, and then make appropriate decisions to run the grid; and thus enables the smart grid to function as it is intended to. Machine learning functionalities include:

  • Predictions of consumption price
  • Power generation
  • Future optimum scheduling
  • Fault detection and
  • Detection of network intruders

Enhancing security with machine learning

Due to the complex nature of smart grid technology, there are manifold opportunities for attackers to disrupt the system. Therefore, a comprehensive defense strategy is required to cover all sorts of threats and vulnerabilities a smart grid can encounter.

Machine learning techniques have been proved to be an especially useful tool to prevent a denial-of-service attack. Machine learning can take detect attacks, take proactive actions to address known issues and notify security administrators.

The migration of the electric sector towards smart grids demands the continuous development of machine learning techniques since their implementation can harmoniously integrate all the components used. This then grants reliability in smart electric systems as well as guaranteeing quality, efficiency and continuity.

As we move forward in a world where infrastructure catastrophes like those witnessed in Texas become the norm, our leaders and governments must guard against this fragility with a more considered approach to increasingly flexible and sustainable smarter energy grids.

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