KEY RESULTS During the forecasting period of 2021 to 2028, the worldwide smart grid data analytics market is expected to grow at a CAGR of 10.46%. Factors such as the demand for efficient energy consumption, the shifting trend toward the use of renewable energy, the rising digitalization of electricity infrastructure, and government rules and policies, among others, all contribute to market growth.
Introduction to the Global Smart Grid Data Analytics Market
Smart grid is a new technology that aids in the efficient operation of the system, and smart grid data analytics provide the best solution for the entire system. The market is expected to increase due to rising customer demand for cost-effective power supply and the reduction of energy production costs through the use of smart grid data analytics due to its features.
To optimise the system, utility owners can operate the entire network more efficiently by replacing outdated systems with smart metres, sensors, and automation. The data collected and analysed by all smart metres, sensors, and other automated systems is massive; this data may be analysed and estimated from production and supply. Smart grid data assists utility service providers in analysing the power demand from consumers’ end, which is necessary for the efficient running of the power system.
Smart grid data analytics aids in the collection of useful data generated by all of the devices. It aids in the analysis of data and the forecasting of future loads, such as short-term, medium-term, and long-term forecasting. Load forecasting is the prediction of future load based on previously collected data.
Load forecasting is crucial in power systems since it aids operations because the electricity provided by generating stations must match the demand from the consumer end, and any surplus power generation can result in a significant loss for the utility network. Another application of smart grid data analytics is determining load flow behaviour. We are able to find better solutions for the system by applying analytics.
Grid refers to the interconnection of the entire power system, from the generating station to transmission, substation, local substation, and consumer. We can determine the peak load and the dip, comprehensive load behaviour using smart grid data analytics. We can prevent power cuts by analysing load behaviour, which benefits the transmission industry, particularly load dispatching centres.
The utility industry’s worldwide rollout and deployment of millions of smart metres has resulted in huge data generation in terms of both volume and velocity. The overflow of customer-centric and grid operation-centric data from a variety of sources, including the Advanced Metering Infrastructure (AMI) or smart metres, Supervisory Control and Data Acquisition (SCADA) systems, intelligent/learning sensors like learning thermostats, and other data management systems like Content Management System (CMS), and Outage Management System (OMS) (OMS). Also contributing to this massive data creation are back-office and front-office systems such as Customer Information System (CIS) and Geographic Information System (GIS).
AMI analytics, demand response analytics/advanced analytics for Demand Response Management Systems (DRMS), asset analytics, grid optimization analytics, energy data forecasting/load forecasting, and visualisation tools are the main segments of the smart grid analytics market. Each solution has a varied scope of application. AMI analytics solutions, for example, address AMI deployment, operations, customer billing, revenue protection, and other applications; grid optimization analytics, on the other hand, address grid operations, voltage and VAR (Volt-Ampere Reactive) optimization, outage management, and so on.
The smart grid analytics market has grown at a faster rate in recent years as a result of a number of factors, including the deployment of smart metering infrastructure, which generates a massive amount of data, the limited nature of non-renewable energy sources, and the rising demand for consumer engagement. The development in cloud-based solutions/Software-as-a-Service (SaaS) solutions, increasing demand for system integrators, and a shift in focus on grid operation optimization are all market prospects in the smart grid analytics environment.