How best to predict how much money solar-plus-storage will save
Companies seeking to invest in solar energy solutions supported by energy storage find it difficult to project the savings storage will bring. Energy storage vendors are increasingly producing analysis tools to help provide the necessary calculations, but these are of varying reliability. Amongst the variables that can cause difficulties are hard-to-predict factors such as weather patterns and changing load patterns. Modelling weather is difficult, and load variability is also subject to constant fluctuations. Faced with these challenges, investors can ask for performance guarantees that provide compensation if the system doesn't meet the expected performance levels.
Increasingly, companies shopping for solar energy solutions now consider supporting these installations with energy storage. But it remains a challenge to run the numbers and prove how much money a solar-plus-storage project will actually save.

To meet the need for quick and credible calculations, several energy storage vendors, integrators, and software providers have introduced energy storage analysis tools. All these tools claim—in various terms—to deliver accurate projections of the financial value of solar-plus-storage projects. But here’s the rub: What does accuracy mean in each case? Can companies rely on the results of these analyses as a reliable projection of future returns, or do they serve as an indication of possible value?

Often, it’s the latter, because some tools ignore important but hard-to-predict factors, such as future weather and changing load profiles. Their projections are based on best-case scenarios under ideal conditions or with predictable patterns. Yet if a company is going to decide whether to accept or reject a solar-plus-storage proposal based on the analysis provided by one of these tools, the software needs to be able to model the complexity of the real world. And due to that complexity, these tools require the guiding hand of an experienced energy storage professional.

Let’s unpack these two issues:

Accounting for hard-to-predict factors

Weather variability. The challenge of solar PV intermittency is not a problem of the changing position of the sun over a solar array. That impact is rather easy to calculate. It’s the cloud cover over the array that defies easy prediction, and that wreaks havoc on energy bills when facilities incur heavy demand charges because they need to draw power from the grid.

Appropriately sized energy storage can offset such intermittency, but it is impossible to size a system accurately without taking weather into account. A storage system that is too small won’t always meet the need, and demand charges will occur. A system that is too large will be financially infeasible.

Modeling weather variability is possible, but it requires analysis of stochastic (random probability), rather than deterministic (fixed probability) processes. Historical weather data is not enough, especially in an era of climate change. Deriving the appropriate stochastic model requires the collection of years of performance data from actual energy storage installations. This is why many of the newer software providers, as well as integrators that don’t own their storage equipment, agree not to talk about the weather.

Load variability. Most commercial and industrial facilities experience seasonal, daily and even minute by minute fluctuations in their loads. One can make competent assumptions about seasonal changes. Like the weather factor, unpredictable load variability is a reality that is very convenient to ignore.

Load variability is also a stochastic process, with many factors that can be accurately modeled only with advanced algorithms, derived from copious empirical data and industry experience.

The human component

Mathematical models are never perfect. Neither are people. But together, first-rate analysts and sophisticated algorithms can deliver the best attainable predictive accuracy. While algorithms excel in rapid, multifactor analysis, it takes an experienced human to decide which factors to analyze. Self-service online software tools—some of which are now tantalizingly free—can’t adequately accommodate the idiosyncrasies of a commercial solar-plus-storage project.

Experienced energy storage engineers are uniquely qualified, both to employ advanced energy storage analysis tools and to interpret the results. Ideally, these engineers will also be the ones operating the system, using the same software to refine the charge and discharge parameters and identify opportunities for additional savings when unforeseen circumstances arise.

Performance guarantees: the litmus test of storage models

The typical solar-plus-storage buyer would be hard-pressed to formally compare energy storage analysis capabilities based on proprietary algorithms and claims of engineering competence. There’s no easy apples-to-apples comparison in this wide-ranging field. But there is a simple way to assess the legitimacy of an energy storage provider’s claims: Ask for a performance guarantee. Providers that are confident in their projections can stand behind them with a guarantee of compensation if the energy storage system does not meet specified performance projections under actual loads. When providers do offer performance guarantees, their compensation claims should be corroborated with an evaluation of their financial stability.

As solar-plus-storage installations proliferate, at both the distributed and the utility scales, we will all have much more real-world data about energy storage performance and cost savings. It will be interesting to see how companies leverage that data to make more profitable energy investment decisions.

Sean Kiernan is Green Charge’s Vice President and General Manager for Solar and Commercial Energy Storage.