Smart grids fuse energy development with technological advancements. Using sensors, IoT, and other computing devices, there is a provision for two-way communication between consumers and utility providers in a smart grid. As an artificially intelligent system, a huge amount of data comes from various sources, e.g. smart meters. All the unstructured data gathered from these sources can only be valuable with smart grid analytics.
Smart grid analytics are systematic computational analyses of the data produced in the grids. With these analytics, one can get a more precise interpretation, communication, and identification of data trends or meaningful patterns from the data that comes in. Thus, it is essential to improve grid operations and predict the next course of action.
A Brief History
From the 1990s, attempts at electronic metering, control and monitoring evolved into smart grids. From automated meter readings in the 1980s to Advanced Metering Infrastructures in the 1990s, attempts have been made to go beyond measuring power usage to maximizing the information.
The concepts of analytics can be traced back to the 19th century with Frederick Winslow Taylor’s time management exercises and Henry Ford’s measurements of assembly lines’ speeds. It would interest you to know that predictive analytics (which is now of high importance in smart grids) started in the 1940s. However, it did not attract any attention until the 1960s, when decision support systems became popular. By 2005, businesses applied analytics to make iterative explorations on past activities and make decisions to plan the future.
Applying analytics to smart grid data is what birthed smart grid analytics. The problem of big data (as Roger Magoulas called it in 2005) has always existed as long as the internet. Around early 2012, big data in smart grid systems initiated collaboration between smart grid integration companies and data analytics start-ups.
As grids became smarter, grid data analytics also developed, using available technologies such as machine learning techniques. Computing techniques like statistics, machine learning (under artificial intelligence), and data analytics are now being applied in various facets, and the power sector is not left out. As we will see, smart grid analytics gives relevant information that helps set the course of upcoming activities for the effective distribution of power.
Three Things You Should Know About The Current Trends In Smart Grid Analytics
For one, the smart grids analytics market in Europe was projected in 2019 to grow at the compounded rate of 11-12.7% by 2025. This growth is based on how advanced grid technologies are embraced on a broader scale. However, I have observed these trends;
1. There is currently rapid growth in investment in smart grids projects and, subsequently, smart grids analytics.
Many countries in the European Union have invested in smart grids projects and are recording successes. Of the projects, up to 59% are demonstration projects, 32% are for deployment, while 9% are research and development projects. A significant highlight is the smart meter roll-out in Italy that takes up to 71% of these projects aforementioned.
Smart meters are installed in all of these projects, and to get relevant information from the data, smart grids analytics have to be employed. These projects result from increased interest and initiatives channelled to the ongoing energy transformation and sustainability goals.
2. Smart grid analytics work with real-time data even with the increased speed and variety of requirements.
This easy adaptation is because they are entirely computerized and are built on the blocks of advanced technologies. Smart grid analytics can now generate information from high-speed data of various forms needed for the grids’ operation and prior knowledge of what to put in as resources.
3. Digital technologies and cloud computing would continue to improve and allow for more data computation.
Digital data, which highest storage used to be terabytes, is now accessible on larger scales like exabytes and zettabytes. Manual methods and previous ways of analyzing this data are becoming redundant. Also, with the inclusion of renewable energy in the conventional grids, the adaptation of intelligent systems is increasing, and the need for grid data analytics will follow this trend.
Challenges Of Smart Grid Analytics
Despite the enormous advantages and improved technologies, there are still a few challenges. Some include:
- Cost implications – the initial costs of setting up smart grids make many grid operators sceptical about using smart grid analytics. For the grid as well, it usually includes the costs of sensors and other components in making it effective. The analytics themselves are part of what makes the smart grid a modern electric system. However, it is worth the investment to foster a low-carbon economy and a greener world.
- Security concerns – the fact that smart grids allow for two-way communication is a concern as the data is prone to cyber-attacks. Despite this, cybersecurity has continued to improve and is developing better solutions using codes and encrypted data.
- Customer demand – the demand needed for effective use of smart grid analytics is higher than what exists now. Not enough grid operators have adopted analytics, and the low-scale usage is not optimal. More large-scale energy supplying and distributing firms need to embrace the new technologies at this time.
I must re-emphasize that smart grid analytics is crucial to improving smart grids’ efficiency, reliability, and sustainability. And Hive Power provides a SaaS platform with intelligent grid analysis and a flexibility management solution called the Flexibility Orchestrator to help the renewable energy industry key players improve their activities and offer services more desirably.
The use of smart grid analytics benefits both the consumers and the suppliers of power because it improves energy management, allows for more efficient power transmission, and lowers the cost of operation and management of energy. In addition, with the use of analytics, demand would match supply more because of improved decision making.