A Look at Two Types of Battery Installations: Front of the Meter and Behind the Meter

Where you install a battery system – and which side of the electricity meter it sits on – fundamentally changes how it operates and who benefits from it.

There are two primary configurations for battery installations: front of the meter and behind the meter. Understanding the difference between these two approaches is crucial for anyone working in renewable energy, grid operations, or facility management.

Front of the meter batteries act as standalone grid assets, typically co-located with renewable energy generators like solar farms. They charge and discharge based on wholesale market prices, helping utilities flatten demand curves and provide grid stability services.

Behind the meter batteries, on the other hand, are installed on the customer side – at data centers, factories, and other large energy consumers. From the grid’s perspective, these batteries are invisible, appearing simply as changes in the facility’s energy consumption. They enable energy arbitrage and peak shaving, allowing facilities to dramatically reduce their electricity costs.

Both configurations require sophisticated software to optimize charging and discharging decisions, whether that’s making bids into energy markets or predicting facility usage patterns.

Check out the video clip below from our free course “Introduction to Software for Battery Systems” to learn more from instructor Peter Gruenbaum. Better yet, enroll for free right now to learn more about the critical role software plays in making these systems economically viable.

Transcript below.

 There are two types of battery installations, which are called in front of the meter and behind the meter.

In front of the meter batteries are often located near energy generators, especially wind and solar farms. Behind the meter batteries are typically located near energy consumers, such as data centers, factories, and so on. 

Here’s a diagram of what a front of the meter system looks like. In this case, we have a wind farm co-located with several batteries. These are connected to the grid. The wind farm sends energy into the grid, and the batteries either send energy to the grid or take energy from the grid. Also on the grid is a factory. There’s a meter between the factory and the grid that measures how much electricity the factory uses. From the grid’s perspective, the batteries are in front of the meter, since they’re on the same side of the meter as the rest of the grid.

In a front of the meter system, the battery acts as both a generator and a load, where a load means that it uses electricity. This is a relatively new concept for energy markets, so often you have to register the batteries with the energy market as two separate units – one that’s a generator and one that’s a load. Software is running that makes bids into the energy market. 

If the software makes an offer to buy energy and that offer is awarded, then the battery charges from the grid. If the software makes an offer to sell energy and that offer is awarded, then the battery discharges into the grid. You’re going to need sophisticated software in order to generate these bids.

Now let’s look at a diagram that shows what a behind the meter system looks like. Here the battery system is placed next to a factory that uses electricity. The meter that measures electricity has the grid on one side and the factory and battery system on the other side. So from the grid’s perspective, the batteries are behind the meter.

In a behind the meter system, the utility doesn’t even need to know that the batteries exist. In the previous diagram, from the grid’s perspective, when the battery system discharges, it’s as if the factory is using less energy. When the battery system charges, it’s as if the factory is using more energy.

This allows the system to perform energy arbitrage, charging when the price is low, and discharging when it’s high, saving the factory significant money. In addition, peak shaving prevents power spikes, which also saves the factory money. This means you need sophisticated software to optimize the battery system. It needs to predict when the factory is expected to use the most energy and incorporate time of use pricing to charge and discharge at the optimal times.