Residential Energy Storage Economics: Page 5 of 5


As these case studies illustrate, you can use LCOS calculations to quickly compare energy storage solutions for a specific application or to evaluate the impact of changing certain design variables and assumptions. As useful as this may be, it is important to recognize that the LCOS metric has some limitations as a basis of comparison and a project assessment tool.

Cost vs. value. In late 2015, the financial advisory and asset management firm Lazard published a detailed cost comparison of various energy storage technologies in a wide variety of applications on both sides of the meter. “Lazard’s Levelized Cost of Storage Analysis—Version 1.0” (see Resources) not only models LCOS based on current technology prices, but also looks at how LCOS might change in the next 5 years based on projected capital cost decreases. While the authors compare the LCOS for various storage technologies, including lithium-ion batteries, to that of a gas peaker plant as a baseline, they also note that LCOS does not “purport to provide an ‘apples-to-apples’ comparison to conventional or renewable electric generation.”

This is true in part because cost tells only a piece of the story. An interactive PV system, for example, clearly has a lower levelized cost of energy than an energy storage system. However, the energy storage system can keep the customer’s lights on in the event of a power outage, which is very important to some customers. A cost-oriented metric such as LCOS does not capture that value. Similarly, some energy storage systems can support specific loads or applications that others cannot. For example, adding inverter capacity to a sonnenBatterie increases system costs and negatively impacts LCOS, but could improve the value proposition for the customer if the expanded system is able to power a deep-well pump in the event of an outage. Value-based considerations are very important when comparing energy storage systems, which residential applications often deploy as a means of improving service reliability. In many energy storage scenarios, the cheapest solution may not provide the best value.

Stacking revenue and benefits. Even if we exclude factors that are difficult to quantify in dollars and cents—such as reliability or environmental attributes—it is impossible to understand the value proposition for energy storage without considering the revenue side of the equation, which can quickly get complicated. In an ideal use case, an energy storage system provides multiple revenue-generating services via application stacking, as illustrated in Figure 4. Some of these revenue streams, including time-of-use and demand reduction savings, depend entirely on variable load profiles and utility tariffs. To model these revenues, you need access to specialized software, detailed interval meter data and a database of utility rate structures.

Perhaps the most important challenge facing residential energy storage is the need to unlock additional revenue streams, which is as much a policy problem for utility regulators as it is a technology problem for manufacturers and vendors. The authors of RMI’s report on battery economics note that when you use an energy storage system for a single application, such as self-consumption or backup power, that leaves something like 50%–99% of the battery capacity unused over the life of the system. The bad news, of course, is that resource underutilization leaves potential value on the table and increases the LCOS. The good news is that removing the regulatory barriers that prevent application and revenue stacking can tilt the economics in favor of behind-the-meter energy storage.

Green Mountain Power’s pilot program offering Tesla Powerwall batteries to its customers is a good example of how an innovative utility can leverage additional value from residential energy storage systems. According to public filings, the utility estimates that its net present value for a leased Powerwall is roughly $50 per system per month over a 10-year term. (A $37.50 per month customer fee offsets the additional monthly costs associated with the Powerwall deployments.) To create this revenue stream, the utility will discharge the Powerwall batteries during “times of high market prices to help lower its energy costs,” as well as during “times of peak load to reduce significant capacity and transmission expenses.” Green Mountain Power expects that in addition to providing backup power for end users, the Powerwall deployments will “smooth grid impacts caused by a high penetration of solar energy, potentially avoiding more expensive, traditional upgrades.” The company is also deploying 10 additional units as part of a pilot microgrid project, which will “contribute to improving the reliability of the Rutland 46 kW subtransmission network during system contingencies.”

While current business models typically leverage one or two use cases for energy storage, RMI identifies “thirteen fundamental electricity services” that can benefit “three major stakeholder groups” (system operators, utilities and end users). The report also notes that “the further downstream battery-based energy storage systems are located, the more services they can offer to the system at large.” Utilities can even aggregate a network of residential energy storage systems and operate this as a virtual power plant. At the end of the day, grid parity for energy storage is more about leveraging and monetizing these many value streams than it is about achieving a LCOS lower than the retail price of electricity.


Matthias B. Krause / Berkeley, CA / matthiasbkrause [AT]

David Brearley / SolarPro / Ashland, OR /


GTM Research and the Energy Storage Association, “US Energy Storage Monitor: 2015 Year in Review,” March 2016,

GTM Research and the Solar Energy Industries Association (SEIA), “US Solar Market Insight: 2015 Year in Review,” March 2016,

Lazard, “Lazard’s Levelized Cost of Storage Analysis—Version 1.0,” November 2015,

Rocky Mountain Institute, “The Economics of Battery Energy Storage,” October 2015,

Rocky Mountain Institute, “The Economics of Grid Defection,” February 2014,

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