Smart grid analytics can make a long-lasting difference to how the future power sectors would run. Like I said in the first volume of this series, matching supply with demand as a result of better knowledge from analytics would help energy suppliers and grid operators make the best decisions.
Analytics make sense from statistics and data collected from various sources, and smart grid analytics goes beyond analyzing this data. I would not call them the same thing because a lot of analyses make up analytics.
Analytics give more focus to a particular event, to know why it occurred and what to expect later on as a result.
As smart grids involve a more frequent measurement of rates and power usage and newer energy generation technologies like renewable energy is integrated, data is gathered from time to time. This measurement is not done manually but via smart meters and other data collection sources, thereby helping utilities and their customers manage their bills and rates of power usage. To interpret all these forms of data that come into the grid, smart grid analytics works across a broad scope, as illustrated in the table below.
|Smart Grid Analytics Scope||Overview||Aspects|
|Operations analytics||This involves the functions that manage the operational aspects of the smart grid, that is, how the entire system is run.||
|Signal analytics||Signal analytics makes the most of the state of the signals that are gotten from the sensors on the advanced metering infrastructure.||
|State analytics||This involves various analyses and interpretations of the state of the grid, even geographically.||
|Enterprise analytics||This analyzes the business expectations and economic values of the entire grid management system.|
|Customer use analytics||This analytics makes use of the operational data of customers, their demand, and their reactions.||
|Event analytics||In the case of unprecedented events, as well as planned ones, analytics also makes room for organizing energy schedules.||
Opportunities for Smart Grid Analytics
According to a report on the market analysis, trends, and forecasts on smart grid analytics, the dynamics affecting how much smart grid analytics can do, shift from time to time. These factors, asides from macroeconomic factors and internal market forces, include:
1. Customer acceptance and engagement
More electric power consumers have come to accept the smart grids deployed in projects by private firms and public institutions, though not so many have engaged the setups. More of those who engaged in trials have been typical volunteers, according to a report after a survey.
In the same study, 55% of the funding from smart grid projects in Europe was from the EU and other government agencies, while 45% came from private investments. 87% of smart grid projects in Europe have received funding despite the business case uncertainty that has restricted many private firms from investing in the ongoing projects. Meanwhile, only 65 out of 112 customers responded to the survey.
2. Regulatory policies
Regulations change from time to time, and recently, policies supporting the growth of renewable energy and smart grids have been put in place. Notably, the European Union has played a significant role in making smart grids effective by policies like the strategic research agenda road mapped for 2007-2035
3. Innovative structures
The structures available are key factors to how much smart grid analytics would thrive. The more innovative systems are implemented, the more innovations like this collaboration between technological advancements and the power grid would improve.
Thus, there are opportunities for smart grid analytics to thrive when these factors are in favour and all work towards achieving the set goals.
Other contributing factors.
Investments have been made in many projects involving smart grids all around the world. These investments have happened over time because grids enabled with smart technologies ensure reliable, secure, and efficient electricity management, meeting the objectives of any power grid.
While these attractive features are of great importance, I cannot stop at them without mentioning that the only way to sustain that power quality management and proper communication, planning, and management of data derived intelligently by smart meters is by the use of smart grid analytics.
The constant development of smart grids has provided a platform for smart grid analytics. Coupled with the fact that smart grids utilize wired and wireless communication infrastructure, information systems, demand response management system, SCADA (Supervisory Control and Data Acquisition), GIS (Geographical Information System), CIS (Customer Information System), and advanced metering infrastructure. Each platform in a smart grid provides an opportunity for smart grid analytics. This is because of the complexity that comes with data from decentralized energy systems.
More so, these issues of data management are the ones that give smart grid analytics a chance. For example, various standards (like models) have been put in place because of differences between networks and devices in terms of bandwidth constraints, energy constraints, continuous and non-continuous data, and the likes. Also, the need to manage massive data and ensure data privacy has led to more and more opportunities for smart grid analytics.
Future of Smart Grid Analytics
I strongly believe that we can achieve a lot with smart grid analytics. Experts expect the market to grow at a CAGR of 25% between 2019 and 2024, given the increase in IoT and big data analytics. In addition, new information and communication technologies keep penetrating the grid, which is enough to make smart grid analytics grow stronger globally.
Speaking of global locations, it is projected that the Asia-Pacific region is likely to witness the fastest growth, particularly in the two countries that dominate the region – India and China. This growth is predicted because the energy consumption in India since 2000 has doubled, and there is potential for more. Interestingly, the current market for smart grid analytics is also really competitive in Europe and worldwide, and this is a driver for growth.
IoT is one technology that is likely to take the grids to another level, despite the predicted challenges of cyberattacks, impersonation, the need for more IT infrastructure, data tampering, security, and privacy issues. While these challenges are already being combated using cybersecurity techniques and blockchain technology as a secured and distributed database, more researchers and developers are making better ways to handle them. As a result, the future of smart grid analytics would include IoT, more than it is now, in how smart grids operate. However, the existing IT infrastructure has led to the discovery and application of certain technologies used for smart grid analytics.
Best Technologies for Smart Grid Analytics
Smart grid analytics has evolved over the years and consists of various techniques involving the integration of data from electric power sources, analysis, processing, and visualization.
There are generally four kinds of analytics. In order of complexity, they are
- predictive, and
There is also cognitive analytics though it is a recent advancement that combines many functions. Of the four, the smart grid operators prioritize predictive analytics to find out what could happen at any point in time.
Nevertheless, the best technologies that have also evolved for smart grid analytics include Business Intelligence and Data Analysis (BI&DA), or big data analytics. The two terms were introduced to companies around 2009-2010 and have been unified as they work together to form the most relevant technology for smart grid analytics.
1. Business Intelligence (BI) and Data Analysis
Business intelligence is a broad term that comprises several activities aimed at helping companies make better use of the data at their disposal. Also, data analytics is used to make conclusions from raw data by computational analysis. Business intelligence and data analytics fuse together to maximize the benefits of the smart grid by applying business-centric methodologies to get useful information from the smart grid. BI makes use of tools and software to mine data, process it, and make meaning out of it using tools like spreadsheets, OLAP (Online Analytical Processing), data mining tools, and data reporting and visualization software. In the end, the data that has been collected is evaluated, optimized, and re-evaluated.
BI and DA also perform some processes to manage and create centralized data from various sources continuously. Some of these processes that occur before any form of analysis include pattern mining (that is, identifying patterns and similar arrangements), classification, the association of rule mining, clustering, making of regressions, and detection of outliers.
2. Other technologies that form the framework – Databases like Apache Hadoop, MapReduce, SQL
Asides from the techniques and technologies used to analyze data, a proper database is necessary for smart grid analytics. The currently available databases form software frameworks that are open source and they spread offline data across clusters and nodes for easy processing. They have improved with time, so new ones have more specific functions than the previous ones. For example, though Apache Hadoop is a popular and basic database, there is a need to use MapReduce because of background indexing. More so, for the sake of ad-hoc querying issues, SQL has been introduced.
The NoSQL database allows the newer data management technologies that are easier to scale and perform optimally than its counterparts. Some of the NoSQL open source database types include Cassandra, Elastic Search, MongoDB, and Hbase. Hbase and Cassandra are each column stores based on the concept of BigTable. However, Hbase bases also on Apache Hadoop, while Cassandra bases on DynamoDB.
The Important Role of Big Data in Smart Grid Analytics
Big data refers to the massive amount of data that an institution, unit, or system has to manage to utilize efficiently. The amount of data chunked into the smart grid through the smart meters, weather updates, social media, programmable thermostat, traffic updates, remote terminal units, and so on can be overwhelming and difficult to evaluate using the existing traditional methods. Of course, big data goes beyond the size, but the data format is usually diversified in the power grid.
Big data analytics is of utmost importance in the smart grid as relevant information needs to be extracted from available data.
In a day, there can be 30GB of PMU data and 120GB of smart meter measurements, not to mention 16GB of weather data from satellites, radars, and weather forecast models, and up to 2.7GB of vegetation and topography data.
They all come in at a high velocity and give updates in intervals of about 1-15 minutes. With the variety and complexities involved, big data plays a significant role.
Some ways in which big data analytics play a role in smart grid analytics include:
- power generation analysis
- load management with demand
- performance analysis of energy consumption
- forecasting and scheduling of loads
- evaluation of economic effects and constraints.
Big data analytics plays its role in the smart grid and motivates all that has to do with smart grid analytics. Hive Power provides a SaaS platform that can provide smart grid analytics services, and can as well manage flexibility. This platform is called the Flexibility Orchestrator. It helps energy suppliers and grid operators optimize smart grids and gain the most of them. It also enables stakeholders in the renewable energy industry to improve their operations.