As energy storage becomes increasingly essential to make the power grid reliable, accurate modeling is an essential element in making decisions and understanding your return on investment.
Using the exact same battery technology, you could be spending up to 58% more on total cost of ownership, purely due to differences in operating conditions, as explained below. Our modeling tools help you identify these cost drivers and plan your battery projects more efficiently and cost effectively.
Gain valuable insights into a practical, integrated approach to advanced energy storage modeling, along with a case study, to help you better plan, forecast, and deploy storage in today's dynamic landscape.
Evaluating energy storage solutions solely on upfront cost can lead to long-term performance issues and increased risk of failing capacity tests, ultimately leading to a decrease in ROI. Understanding your real-world operating conditions and market drivers is key to battery selection.
Here’s what advanced energy storage modeling can help you answer:
To answer these questions, we explored an analytical modeling framework built on three layers.
Utilizing two purpose-built modeling tools - and the right data assumptions - offers the flexibility and accuracy required to make data-driven planning and siting decisions.
In this scenario, we looked at a lithium-ion phosphate battery in various locations with a 20-year project life. Using a 10 MW, four-hour battery in ERCOT North, we modeled three operating strategies:
What we found are significant differences in capital and operational expenses across the modes of operation.
As shown above, we saw a:
We sized batteries for all three operating modes to ensure the battery’s State of Health (SoH) remained above the end-of-life threshold of 65%. The four-hour, two-cycle battery needed the highest Beginning of Life (BoL) Capacity because it has the highest degradation rate due to performing a high number of cycles.
As seen in the chart below, there was a 13% difference in State of Health (SoH) between the highest and lowest degradation rates.
To maintain 40 MWh capacity throughout the project life, the four-hour, two-cycle battery needed the highest BoL Capacity because it has the highest degradation rate from performing a high number of cycles.
The four-hour, one-cycle shallow battery needed the least BoL capacity, since it has less degradation because it is operated within a narrow SoC band.
The BoL capacity difference between the largest and smallest battery size is 50%, as shown above.
The four-hour, one-cycle shallow battery has the highest round-trip efficiency due to:
The four-hour, two-cycle deep battery has a higher round-trip efficiency than the four-hour, one-cycle deep battery, driven by:
As shown above, there was a 2.5% round-trip efficiency gap between the best- and worst-performing battery models.
The biggest takeaway is that how you operate your battery can matter more than which battery you purchase.
Utilities using Integrated Resource/Systems Planning (IRP/ISP) tools can now test different technologies, alternative manufacturers, and varying durations and terms. This enables them to refine parameters and optimize operations as well as ensure that energy targets are being met.
Developers can prospect multiple markets and locations to create defensible documentation of bankable valuations. This enables them to refine their projects based on the most profitable modes of operation.
Energy storage is a fast-moving space, requiring more advanced analytics than ever.
By simulating Storlytics’ alternative battery operations parameters through the EnCompass power planning model and incorporating Horizons’ Advisory Outlook, you can:
Want to explore this modeling approach in more depth and check a second case study where we compared a non-lithium battery technology with a lithium LFP battery? Listen to the full, on-demand webinar.