Battery technology risk
Technology risk for energy storage projects is managed best by energy storage developers. There are also aspects of design and planning risk which should appropriately be allocated to us. We hold the necessary information and capability to ensure appropriate engineering and design, construction, commissioning, and operations & maintenance of our technologies. The biggest risk is the quality of the performance data that informs financial modeling, such as credible data about performance on the timescale of the loan’s tenor and for the remaining life of the project. This post will concern itself solely with revenue-impacting construction phase risk. Operational phase and cost-impacting risks will be discussed in other posts.
For energy storage to be a credible participant in the energy sector, salient information needs to be shared by technology developers with financial engineers. Financial models based on useless data may put a project on the map, but do not assure a successful operational phase capable of repaying loans and providing a return on equity invested. As has been observed from time to time in the solar photovoltaic industry, poor planning can lead to projects that do not generate sufficient revenue. Metrics that speak to revenue- and cost-impacting performance need to be a focus for our industry. Most demonstrations provide information on how and if a technology works; notable energy storage demonstrations report how well, for how long, and at what cost.
Battery performance and revenue-generating potential
Understanding the reliability and predictability of performance of a battery system is key in modeling project finance cash flows. For convenience, I like to think about battery system performance over the tenor of the loan and for the remaining lifetime of the project. This highlights the importance of cover ratios for the lenders as distinct from cash available to fund operations & maintenance and provide the returns an equity investor might expect.
For a battery system, revenues are generated from energy deliveries (in kWh or MWh) and/or from sinking and sourcing power (in kW or MW). In order to model energy revenues, it is important to understand how much energy will be deliverable by the battery system. To model power revenues, as in the provision of ancillary services such as regulation, we need to understand the operable timescales of the battery system. In both cases, responsiveness, duration of service, and notification capability affect the value of the energy or power service provided. Looking at a battery system (or any energy storage), through the usual lens of nameplate capacity, power vs energy maps, or Ragone plots does not provide sufficient information for financial modeling. Rather, they speak to design-side considerations at best and are, at worst, aspirational. Energy storage is generally tailored to its application but failing that, its revenue-generation potential needs to be tailored to the use case of the project. The data provided to the financial engineers and the resultant financial models must reflect these circumstances.
The most important metric a technology developer can provide is an understanding of power vs. efficiency. As a practical matter, let’s limit the chart to a four-dimensional matrix:
Efficiency: (X-Axis) Measured from 0% to 100% of nameplate energy delivered
Power: (Y-Axis) Measured from 0% to 100% of nameplate power
Temperature: Each curve plotted on the chart is associated with a temperature in Celsius
Time: Each curve can actually be a family of related curves at Temperature T and time (0 = commissioning and at end-of-life).
This map allows derivation of energy revenues that can be expected from a battery system and of capacity availability on the power axis to model power revenues. An understanding of potential environmental risks relating to any technology limitations is immediately apparent by comparing families of curves at different temperatures and allowing the design of mitigation strategies (heating, cooling) during the planning stage.
I have yet to see a chart of this type shared in our industry, so I asked my partner Rob Ferber to assist me in visualizing the results for a Lithium mixed oxide battery cells as an example. The results are fascinating and underscore the importance of excellent battery system design.