Consumption Standards for Smart Energy Distribution

The demand for smart energy grids is constantly on a rise. Energy consumers from big to small are relying on energy distribution based on their needs. “One size fits all” is no longer applicable. But how an energy distributor would distinguish between the amounts of energy for a school and a factory to perform in the most efficient yet cost saving way? The distributor needs to rely on consumption standards for different building types

One of our clients, a Belgian company from the Deloitte’s 50 technology fast-growing list, approached us with an ambitious goal to extend its global reach with new locations in the USA, Canada, and France to serve a 1.5-million user base. As a part of the solutionwe built to innovate their IWMS platform, we validated the concept of smart energy distribution on a real-life example. For this, we were required to define an energy consumption standard for a facility type of our choice. We chose local schools as a playground for our experiment.

We collected data about consumption patterns among local schools

As a starting point, we gathered measurements for five ordinary schools located in one district to ensure similar utility services, weather and geo-conditions. We modeled the hierarchy of utility meters in our energy management system. Then we entered measurements of consumed energy within the time span between 2012 and 2014.

After obtaining measurements from the utility meters, it was critical to analyze the data on the right basis.

When we look at the data from the utility meter, we see only bold numbers. Even if records are high, we don’t know the cause of the elevated energy usage. To understand consumption patterns, we need to rely on more comprehensive calculations. We should use normalized degree days’ data, consider working hours, and on-premise people during facility operation. It is very important to ensure comparison of adequate data to build accurate benchmarks.

                                                                   Yurii Nykon, Delivery Director

We analyzed specific metrics to establish commodity benchmarks

Our energy management system provided us with calculating algorithms for all necessary correlations. We got energy usage per square meter, normalized weather consumption patterns, and peak load periods. These metrics gave much more accurate and comprehensive insight on energy consumption. As an example, you can see how these five different schools used electricity over the time span of the measurement collection.

Figure 1. Analysis of electricity consumption among five examined schools

As it’s seen, the school #30 had much higher consumption rate in comparison to four other schools. This means that school #30 should be addressed for an energy saving strategy. At the same time, other schools had relatively similar consumption rates. These schools are used for the electricity consumption benchmark. The same approach was applied to other commodities to establish benchmarks for each type.

We united benchmarks into one consumption standard for schools

With all the data at hand, we were able to establish a reasonable standard for schools as an examined facility type. We developed a district standard based on four schools except #30 targeted for improvements. The energy management system provides flexibility to enter new correlations to justify higher or lower amount of energy needed to guarantee the standard does not harm normal operation of the building. Energy distribution companies can use these industry standards to:

  • Understand end-user energy demands
  • Optimize pricing strategy based on energy consumption patterns
  • Maximize energy savings to reduce costs and protect the environment
  • Create a strategy for a more efficient use of alternative energy sources

Perspective of a brighter future at lower costs

In a further projection, the standards for different buildings and industries can serve as a basis for governmental institutions to introduce energy policies for entire states.

At the launch moment, the client planned to reach the following results

  • Collect, store, and analyze up to 50,000 data points/sec
  • Receive a 50% drop in short-term peak loads and 15% drop in overall among the user base
  • Reduce electricity bills by 10% after deployment
  • Obtain enough data for demand planning and benchmarking
  • Improve meter infrastructure to provide real-time, integrated view of the grid
  • Predict ROI for energy distribution projects

This energy management solution occupies a higher ground on the market because of the widest user base and an unparalleled amount of data. The energy distribution perspective here is fired up to shine brightly. By getting rid of universal allocation of resources, distributors can get much closer to their customers’ loyalty and compete for lion’s share on the power market.