## About Our Service

ES-Analytics is a web-based information service that helps electric utilities determine when, where, and how to best deploy, or encourage their customers to deploy, energy storage on their networks.

For example, a utility might consider deploying energy storage to mitigate voltage issues caused by large numbers of solar PV systems connected to a single distribution line. There are multiple options for deploying storage in this situation: a single large storage system in the substation, a few distributed community energy storage units, or an incentive program to encourage solar PV system owners to deploy behind the meter energy storage.

By estimating the costs of using energy storage in application examples representing each of these options, ES-Analytics can help the utility determine which approach is likely to be most cost effective. The graph to the left shows how this data might be represented on the site.

When a utility has made the decision to invest in an energy storage solution, ES-Analytics can provide guidance on how to properly size the system to meet application requirements without paying for more capacity than is required. As utilities increasingly deploy multi-MW energy storage systems, fine tuning the size of the systems can save hundreds of thousands or millions of dollars.

ES-Analytics employs a sophisticated optimization algorithm to determine the amount of capacity and power an energy storage system must start with to meet application requirements over the full lifetime of the system in the field. This optimization process is necessary for battery based energy storage systems because cycling the systems causes battery capacity and power to fade.

The amount of energy a battery can store, and the amount of power it can deliver, both fade as the battery is cycled over time. If this battery fade did not occur, it would be fairly straightforward to determine how large a battery system needs to be (i.e. what energy storage capacity it should have) in order to meet the requirements of a given application. Unfortunately, battery capacity and power do fade, and they do so at different rates for different types of batteries; even different types of lithium ion batteries can have significantly different rates of capacity and power fade.

The diagram to the right shows how two different batteries might have to be sized to meet the energy requirement of an application. The amount of energy the application requires during a single discharge is shown in orange. In this example, the batteries are designed to last roughly 15 years in the application. In other words, the batteries must start out with enough capacity that they can still meet the application energy requirement after 15 years of capacity fade.

The green line represents the capacity of Battery 1, which starts out much higher than the application energy requirement, and falls over time. After 15 years, the expected capacity of Battery 1 equals the application energy requirement. Battery 2, shown in blue, follows the same trend. However, the capacity of Battery 2 falls at a much slower rate than Battery 1, so Battery 2’s starting capacity can be much lower. Clearly, the best battery for this application depends not just on the $/kWh cost of the batteries, but how much capacity fade they will experience over their lifetimes in the application.

For a given battery technology, the rates of capacity and power fade depend heavily on how the battery system is used. In other words, a given battery system’s rates of capacity and power fade depend on how frequently it is cycled, how much energy is discharged each cycle relative to the total capacity of the battery system, the temperature at which the battery system operates, the power level during discharge, and so on. Changing just one of these factors can greatly impact the rate of capacity fade of the battery system.

The figure to the left represents this concept. In this case, Battery 1, shown in green, is evaluated for three different applications. Each application requires the same total amount of energy to be discharged in a single cycle, but each requires a different number of cycles per year. Application 1 requires only occasional cycling, Application 2 requires a moderate level of cycling, and Application 3 requires constant cycling. As shown in the figure, the battery must start out with much more capacity if it is used in Application 3 than if it is used in Application 1. A similar dynamic could occur if the three applications instead differed by the temperature at which they operate, the power levels they must deliver, or other factors.

This complexity turns battery system sizing into an optimization problem: the system must have enough capacity to meet a particular application’s requirements for the full system lifetime, and no more. Purchasing a system with too little capacity risks early system failure, while purchasing more capacity than is necessary wastes money. ES-Analytics has developed an optimization model to determine how a battery system should be sized to minimize its costs given a set of application requirements.

Thanks to the complex relationship between energy storage system capabilities, application requirements, and energy storage system size (and therefore cost), it becomes very difficult to maintain a general understanding of how much it costs to deploy energy storage in specific types of grid-tied applications. This is particularly true because, while battery technology is generally improving and battery costs are generally falling, those improvements and cost reductions happen at different rates for different types of batteries. This is true even for different flavors of lithium ion batteries.

To help utilities keep a handle on these costs and cost trends, ES-Analytics aims to track the costs of using energy storage in a variety of example applications. These well-defined application examples represent a fairly comprehensive overview of all the ways grid-tied energy storage can support a utility or its customers. Tracking the cost of using storage in the examples helps utilities determine when costs are getting low enough for a particular type of application to take a closer look. The applications currently covered on the site are shown in the graph below.

The cost of using energy storage in a particular application will depend on the energy storage technology, and specific product, used. Therefore ES-Analytics uses its optimization model to estimate the costs of multiple energy storage products in each application, to determine which products offer the most economical solutions. ES-Analytics tracks the performance of battery based energy storage products as a way to accelerate this process. Basic information is currently available on the website for the products shown in the graph below.

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