As the demand for electricity continues to grow and new generating assets are taking longer to come online, managing energy usage and peak load is more important than ever before. Historically, energy usage data was after-the-fact reporting allowing businesses and utilities to make adjustments based on past data. With many new technological developments, real-time energy analytics allows decision makers to act instantaneously, saving money and helping to support the reliability of the electric grid. This article explores energy analytics, how they work, and their growing popularity in the energy sector.
What Are Real-Time Energy Analytics?
Real-time energy analytics is a system that allows users to continually collect, process, and analyze energy consumption data. These systems often consist of smart meters, IoT technologies, AI energy monitoring software, and older communications protocols that work in conjunction to effectively translate data to a centralized system. Because the modern electrical system has many moving parts and various technologies, implementing effective real-time analytics has become quite a challenge for businesses and grid operators alike. Nonetheless, insights into real-time consumption data are becoming increasingly important as RTO and ISO operators attempt to balance energy supply with demand. Let’s explore some of the details required to properly engineer these systems.
Understanding Real-Time Energy Monitoring
Real-time energy monitoring is a vital component of the energy market, allowing businesses to become more energy efficient, while also helping grid operators effectively dispatch generation. This process typically contains the following components:
Data Collection
During this phase, data is collected from an array of devices in its original format. A smart meter at a manufacturing facility might collect 15-minute interval data detailing the total kW and kWh being consumed by the plant. Upstream, a Remote Terminal Unit (RTU) could be communicating with a generating asset, such as a solar farm, feeding real-time electricity generation data to a grid operator.
Data Normalization
Because the data being collected is in various formats based on the collection device, it needs to be translated into a common format. During this step, the data is normalized into a single format so that it can be analyzed at large. In the downstream markets, electricity data is typically reported in kilowatts (kW) or kilowatt hours (kWh), while upstream data is usually represented in megawatts (MW). These datasets need to be reported in a single unit type in order to generate accurate reporting. Extract, load, and translate (ETL) tools can be used to normalize large data sets. Many utilities and data analytics providers have automated algorithms to translate data in real-time.
Data Warehousing
Next, the data needs to be stored in a central database or data warehouse. Depending on the software being used for the data analytics tool, these databases can be different. .NET developers often use relational databases such as PostgreSQL to process and store large amounts of information. Some other common types of databases include MySQL, MariaDB, Oracle, MSSQL, SQLite, MongoDB, Redis, Cassandra, Elasticsearch, Firebase, and DynamoDB. No matter what type of database is used, they all serve the same purpose of warehousing the data so it can be analyzed and reported on.
Data Analytics
Finally, after the data has been normalized and stored in a backend database, users can generate reports to analyze the data and make decisions. Energy software providers will typically use a different frontend framework that is connected to the database through an API. The software’s frontend is developed to display dynamic dashboards that can instantaneously report on the data for fast decision-making. A commercial customer might want to view their peak energy demand in time intervals matching utility billing tariffs. These types of reports would allow them to make decisions about energy efficiency investments to reduce energy expenses. Grid operators, on the other hand, might elect to view 5-minute interval consumption data in order to effectively plan for generation dispatch.
Here is what a schematic diagram might look like for commercial data analysis:
A properly engineered energy data analytics program can measure and report on the following datasets:
Energy Consumption
Real-time data analytics allows for the tracking of business energy consumption so businesses can identify peak load usage and make adjustments to lower costs. This also can help to identify energy waste and bad power factor, so that plant operators can make adjustments to operations or implement energy-saving measures.
Demand Load Forecasting
Accurate demand load forecasting allows businesses to predict peak demand during critical periods that affect energy capacity costs. They can utilize this data to participate in demand response planning and to look for alternative generation resources that can help offset the coming spikes in energy capacity prices.
Grid Performance
Utilities and RTO/ISO operators can utilize data analytics to monitor voltage and frequency levels, power quality, and outages. This allows them to identify weaknesses in the transmission network and to make proactive maintenance decisions to avoid outage events.
Asset Monitoring
Tracking the performance of individual assets or motors allows operators to prevent failures before they occur. Furthermore, real-time analysis sheds insight into inefficient assets that can be maintained or replaced to reduce operating costs.
The Benefits Of Real-Time Energy Analytics
There are many advantages of implementing a real-time data monitoring system for both businesses and utilities alike.
For Utilities & Grid Operators
- Improve grid reliability and transition to smart-grid dynamics
- Enhance demand response programs
- Integrate renewable energy generation seamlessly into the power system
- Dispatch battery resources to combat real-time spikes in consumer demand
- Lower costs through fault-detection and predictive maintenance
- Lessen the need for real-time energy market dispatch through accurate forecasting in the day-ahead markets
- Accurately price FTR contracts by forecasting nodal and zonal pricing
For Commercial & Industrial Customers
- Lower energy costs through accurate efficiency planning
- Ensure accuracy in billing without having to conduct retro-active utility bill audits
- Optimize energy usage during lower-cost, off-peak periods
- Support sustainability goals through accurate renewable energy production data
- Enhance decarbonization efforts through accurate reporting
- Reduce maintenance costs through preventative actions
Challenges Of Implementing Real-Time Data Analytics
Despite the many benefits of having a tool to report on energy consumption in real time, implementing these systems properly can be quite challenging. Here’s why:
Data Complexity
Collecting and processing large amounts of data can be challenging for any organization. The need for competent data scientists and software engineers is almost a must to take on this task. Today, artificial intelligence is playing a larger role in data collection; however, it requires a human-in-the-loop approach so that the system can be engineered properly to your needs.
Integrating With Legacy Systems
While the energy sector has experienced many technological advancements, most systems are still operating on dated legacy systems. Finding ways to collect and integrate data from analog systems can be challenging, to say the least, if not impossible.
Cybersecurity Risks
Today, the electricity grid still operates on analog protocol as a security measure. In fact, electric generators are required to communicate with RTOs and ISOs via Skada and RTU protocols. Introducing newer technologies into the system poses a security risk challenge that many grid operators have yet to solve.
Implementation Costs
Lastly, purchasing energy data analytics tools, or designing your own system, can be quite costly. It’s important to do a cost-benefit analysis to evaluate if a return on investment is possible. The payback periods could be too long for smaller energy users.
Want Help Analyzing Energy Data For Better Outcomes?
Having an effective data reporting tool can not only make your business more efficient, it can drastically improve your bottom line. But, implementing these systems can be confusing and challenging. At Diversegy, our team of energy experts has over 100 years of combined experience analyzing energy consumption data to help our clients make better decisions. Whether you are a large organization ready to invest in energy technology or simply looking for better data to make informed decisions, we can help. Contact our team today to learn more about how you can utilize our years of energy experience.
