Stacked Value

Combine Distributed Energy Resources and Goals to Dramatically Raise ROI

By Eric Hoevenaars, Enbala Power Networks

There is no question that energy storage is transforming our power markets. When organizations like the Breakthrough Energy Coalition-backed by world leaders that include Bill Gates, Mark Zuckerberg, Richard Branson and Jeff Bezos-declare energy storage as an investment priority, the once somewhat nerdy topic has become downright sexy. Storage is hot.

But when it comes to achieving an ROI from storage investments, it could be a lot hotter, mostly because many energy storage purchasers today are using only a fraction of its capacity-and for only a small percentage of the available years. In addition, many target only one use with their energy storage systems, even though batteries can provide multiple uses and value streams.

In the Rocky Mountain Institute’s (RMI) report titled “The Economics of Battery Energy Storage,” researchers note that most cost-benefit analyses of energy storage devices focus on a single purpose, and usually one of three intents: demand charge reduction, backup power or increasing solar self-consumption.

RMI points out that business models which plan only to use batteries for a minority of the time represent a lost opportunity and leave significant value sitting untouched on the energy table. For example, RMI findings say that an energy storage system dispatched solely for demand charge reduction is utilized for only 5 to 50 percent of its useful life, whereas dispatching batteries for a primary application and then re-dispatching them to provide multiple, stacked services make the economics of storage much more favorable and create much greater cumulative value-to customers, utilities and the grid.

In fact, bundling grid applications to stack up multiple values can potentially deliver a total value that exceeds the energy storage cost, especially in combination with a holistic approach that combines battery storage with other distributed energy resources (DERs) such as controllable loads. Constraint-based load control captures the energy storage that inherently exists at commercial and industrial (C&I) customer sites in the form of HVAC systems, water storage tanks and more. By combining behind the meter battery storage with onsite load control, value streams increase dramatically and can cut battery project payback times in half through benefits like smoothing grid fluctuations and providing the foundation for demand response (DR), ancillary services and dispatchable power programs.

1 + 1 = a LOT more than 2

Enbala recently quantified the economics of storage alone vs. storage plus load for a large energy service provider (ESP). The ESP wanted to control battery energy storage on C&I accounts so that it could engage in a power purchase agreement (PPA) with a West Coast utility. The primary goal was to provide capacity to the regional independent system operator (CAISO) using behind-the-meter storage systems. The utility would retain the rights to the value from such capacity supplied by these assets.

As long as the network could maintain its commitment level to the ISO, however, additional services and associated revenue streams could be stacked on, with the ESP retaining the rights to this additional value. Using conservative assumptions, the project demonstrated that the ESP could provide the reliable capacity needed by CAISO while also stacking on greater value by offering C&I customers peak demand management capabilities.

Following is a look at the study that shows how using multiple DERs to achieve multiple goals is the key to gaining maximum value from storage and other DER investments.

Pinpointing profit

The two types of DERs evaluated in this project were battery energy storage systems and loads under control by Symphony, Enbala’s distributed energy resource management system (DERMS). The primary value stream examined was the aforementioned PPA between the ESP and a California utility that was contracting with CAISO to provide 5 MW of capacity with a four-hour duration during the four highest cost hours in the day-ahead market. By purchasing this capacity, the utility gained the value of the capacity resource, and the ESP was paid an assumed PPA price of $20/kW/month.

Meanwhile, the ESP retained rights to the energy and ancillary services that could be provided by the storage systems and loads at C&I customer sites. In this project, demand-charge management surfaced as a win-win opportunity for the ESP and its C&I customers because they faced some of the highest demand ratchets in the country-sometimes as high as $36/kW/month.

The Payoff in the Trade-Off

In looking at the two value streams, researchers recognized the trade-off in using a battery energy storage system for multiple goals, such as DR capacity plus demand charge reduction. The trade-off doesn’t necessarily, however, lower the capacity to be gained from the battery investment. In fact, it raises it.

Suppose a 1 MW battery is installed at a site to be used for DR. The logical assumption is that a full MW of capacity could be used. For example, when the asset is called by the utility or ISO, 100 percent of the inverter capacity could be drawn for the duration of the four-hour event. If part of that battery capacity is also being used for demand management, however, the use of the stored energy must be optimized between the DR and demand-management goals. That’s where the extra value comes in.

The study calculated this tradeoff for several types of premises and examined four-hour battery storage systems with varying sizes depending on the on-site demand profile. Table 1 give a look at the various facility types used in the calculations and the assumed battery size installed behind-the-meter:

Looking at the large office building example in Table 1, consider the potential value a battery alone could deliver. With a 360 kW/1,440 kWh energy storage system, a large office could average an 83 kW reduction in monthly peak demand (equal to 23 percent of the inverter capacity). By leveraging a DERMS platform, this same system could stack value streams to provide an additional 331 kW of capacity (92 percent of the inverter capacity) in a DR event.

In going one step further and adding load control to the battery asset, the total value created by each battery storage system is an even higher percentage of the inverter capacity. Table 2 shows that by combining storage with load the storage systems’ effective inverter capacity increases by 11 percent for demand reduction, plus an additional 3 percent for DR capacity.

Interpreting the Numbers

These results demonstrate two fundamental conclusions:

1. By stacking value streams with intelligent co-optimization, a battery system at an average facility can reduce on-site demand by 30 percent of its inverter capacity while still achieving a 92 percent response to a four-hour system capacity event.

2. By aggregating and optimizing the behind-the-meter battery with onsite load control, those value streams increase to 47 percent for demand reduction with a 95 percent capacity response, an improvement of 17 percent demand reduction plus 3 percent for capacity.

All in, these benefits translate into a real impact on the bottom line. The research project calculated that by leveraging a DERMS to stack the values of storage plus load, the payback for a battery project will be cut in half-down from 15 years to 7.5. Similarly, the project internal rate of return (IRR) approaches 10 percent for a project that otherwise would have no return on investment.

TABLE 1: Facility Types and Battery Sizes


TABLE 2: Average Capacity and Demand Reduction (as a percentage of inverter capacity)

A DERMS platform can also deliver additional electricity bill savings by shifting energy use from on-peak to off-peak periods. In the service territory studied, the potential savings through time-of-use (TOU) rate optimization are generally smaller than for demand charge reduction, but when storage and load-shedding weren’t needed for demand charge reduction or capacity, there still would be value for C&I customers, although it wasn’t factored into the calculations for this particular project.

In addition, the value of ancillary services or deferral of distribution-system upgrades were not calculated for this project, but both of these value streams might come into play over the next few years. Depending on the location of the assets, it might be possible to manage peak at a substation or feeder to defer new distribution system investment. After all, upgrades often are driven by a small number of peak hours, so they frequently can be avoided by leveraging the flexibility of DERs.

Making it work

Gaining the ability to optimize multiple revenue streams requires specific functionality within the optimization solution. First, a system that can provide hierarchical optimization is needed. The system must be able to calculate benefits for players at different levels of the grid-from the end-use customers like C&I accounts or a college campus to a feeder, a substation, an ISO or a utility’s system. The hierarchy can be configured to match the topology of a solution. For example, a coordinating node could be deployed at a substation to manage a feeder that is communicating with DERs on that feeder. Another coordinating node could be deployed within a pricing zone to manage all the substations within that pricing zone.

FIGURE 1: Aggregating DERs and Stacking Value Streams Improves Economics

To make hierarchical optimization pay off, the software also should have a system that allows for formulation of resource cost curves or price signals at each DER. In other words, each resource and operation of that resource should be represented as a dynamic cost. This allows each resource owner to specify parameters to be taken into account in the cost profile of the resource. A simple transactional approach can then be used to compare the price that a utility or system operator is willing to pay and the cost of the resource to provide a service at any given time. These calculations need to take place in real time.

In addition, the solution needs coordinated control and intelligent optimization. Without these critical features, a network of batteries will fail to realize its full value and can introduce new risks because, typically, battery controllers do not have any awareness into the other processes occurring at the customer’s premises. This lack of awareness can unintentionally impact the DR baseline while operating for other purposes, such as reducing the site’s monthly demand charge. It also can result in power export during periods of low consumption or unintentional shifting of the site peak to another time of day, rather than reducing the peak.

Given these capabilities, an optimization platform can provide enormous value for power providers and their customers. On the utility side, it can help defer capital investments, balance demand with capacity or provide renewable firming, regulation service and more. Many of these benefits carry over to the system operator, as well.

For C&I customers, peak-demand management means energy-bill savings. Depending on the type of business and utility rates, a demand charge can add as much as 50 percent to a C&I customer’s electric bill, according to Navigant Research. Lowering peak demand can translate into a sizeable payoff.

All of these value streams are impressive individually and even more compelling when they can be stacked together.

Eric Hoevenaars is a senior systems analyst for Enbala Power Networks, an industry leader in distributed energy resources management systems. He has spent more than six years modelling advanced energy systems with renewable and energy storage technologies and holds a master of science degree in mechanical engineering with a focus on power systems modelling from the University of Victoria. His experience has included techno-economic optimization for system sizing and customer targeting, which he used to develop detailed models of economic opportunities. Eric has also been a member of the Institute for Integrated Energy Systems (IESVic) and the Sustainable Systems Design Laboratory.

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