Economic Analysis of Distributed Battery Storage
This economic analysis of distributed battery storage examined the impact of falling battery prices on the utility business model. It was commissioned for a major utility in California under the auspices of the University of California, San Diego's School of Global Policy and Strategy.
The analysis examines the economic incentives for installing battery storage systems faced by Commercial and Industrial (C&I) customers of the utility. In California, it is common practice for utilities to charge higher electricity prices during peak times of the day in this customer segment. By installing battery storage systems, customers can shave their peak demand (i.e. smooth out their load curve) and thereby significantly reduce their electricity bills. The analysis simulated the peak-load shaving potential from installing batteries for a representative sample of the utility's C&I customers. The resulting battery-optimized load profiles were then used to calculate the customers' reduced electricity bills, which were juxtaposed with the installation and maintenance costs of the battery systems in a simple NPV model under different battery price reduction scenarios.
In addition to this static analysis of the economic viability of installing battery storage for the utility's C&I customers described above, the study also included a dynamic component, looking at the so-called "utility death spiral". As regulated entities, electric utilities in California earn a guaranteed rate of return based on their long-term infrastructure investments in the grid. Rate schedules in turn, including those with peak-surcharge components (which we call demand charges), are based on these guaranteed rates of return. If the utility looses a substantial fragment of revenue from demand charges because its customers install batteries, it has to increase these charges in order to keep earning the same rate of return. Higher demand charges however will make the installation of battery storage systems economical for an even larger fragment of customers. The theorized resulting vicious cycle is called the utility death spiral.
The study was focused on the C&I segment because demand charges account for a large fraction of these customers' electricity bills, creating much stronger incentives for them to adopt battery systems when compared to residential customers for the time being. Furthermore, a significant portion of the utility’s revenue comes from C&I customers, and nearly half of its C&I revenue comes from demand charges. The study sought to quantify the portion of the utility’s C&I revenue that would be at risk (i.e. exposed) due to customers obtaining battery systems to shave their peak load and reduce demand charges. In doing so, the team also examined the sensitivities of the model's most important assumptions. It was then asked to explore how changes in battery technology and prices might affect the utility's business strategy and policy.
Through sensitivity analysis the study finds that its results are highly sensitive to the cost of battery systems, suggesting the need for the utility to track closely how these costs are changing in the marketplace. For every US$10/kwh drop in battery prices, the analysis estimated a total revenue loss of about US$5 million. Many other factors—such as O&M costs and the discount rate—were found to be largely insignificant to the core analysis results.
The analysis shows that the utility may experience a significant revenue shift due to increased storage adoption. The exposed revenue would increase drastically if battery prices outperform expectations. Helping regulators understand how these lost revenues will affect the sustainability of current tariff structures is of utmost importance, as is the need for utilities to explore alternative revenue models in recovering the fixed costs of the power grid.