Still Not Modeling Your Energy Storage Projects? It Could Be Costing You Millions

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.

Why Better Energy Storage Modeling Matters

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:

• What’s the most cost-effective battery design for a given use case?
• How does a battery’s technology, method of operation, and location affect battery life?
• How can you determine the ideal technology and mode of operation for a given power market or utility?
• How do different operating profiles impact the lifetime of your specific energy storage technology, and in turn, its revenue?

To answer these questions, we explored an analytical modeling framework built on three layers. 

1. Yes Energy’s EnCompass: Simulates batteries with a breadth of operating conditions, including cycles per day and depth of discharge.
2. Horizons Energy Advisory: Provides forecasting parameters to quantify the operating value of batteries in different physical locations, technologies, and modes of operation.
3. Storlytics Energy Storage: Estimates the rate of degradation, losses, auxiliary load, and capital costs needed for different battery technologies and modes of operation.

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.

Case Study: Storage Design Tradeoffs in ERCOT

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:

1. One Cycle Per Day – Unlimited Deep Discharge
2. One Cycle Per Day – Shallow Discharge (20% to 80% State of Charge, or SoC)
3. Two Cycles Per Day – Unlimited Deep Discharge

Cost of Ownership

What we found are significant differences in capital and operational expenses across the modes of operation.

As shown above, we saw a: 

• 58% difference in the overall cost of ownership across battery models.
• 56% gap in CapEx between the highest and lowest-cost systems.
• 71% variation in operational costs across battery configurations. This significant difference in overall cost of ownership between different scenarios is primarily driven by the following technical parameters.

Degradation 

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.

Beginning of Life Capacity

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.

Project Round-Trip Efficiency Comparison

The four-hour, one-cycle shallow battery has the highest round-trip efficiency due to:

• Minimal idle time
• Narrow state of charge operating range
• Lower charge/discharge rate operation
• Lower auxiliary load from lower BoL capacity.

The four-hour, two-cycle deep battery has a higher round-trip efficiency than the four-hour, one-cycle deep battery, driven by:

•     Less idle time
•     Larger BoL capacity, enabling a lower charge/discharge rate
•     Reduced internal and auxiliary losses.

As shown above, there was a 2.5% round-trip efficiency gap between the best- and worst-performing battery models.

Other Key Findings

• The shallow discharge battery, despite generating the least revenue, delivered the highest net profit over the project life due to lower CapEx, reduced auxiliary loads, and lower degradation.
• The two-cycle/day deep discharge battery offers higher energy throughput and potential revenue (market dependent), but it comes with increased capital and operational costs.
• Differences in round-trip efficiency, operating loss, and thermal load led to meaningful cost divergence, even with identical nameplate specs.

The biggest takeaway is that how you operate your battery can matter more than which battery you purchase.

For Utilities: Smarter IRPs and System Planning

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.

For Developers: More Confident Energy Storage Siting and Financing

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.

The Bigger Picture: A Smarter Way to Model

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:

• Capture greater value from your storage assets,
• Reduce uncertainty in development and planning, and
• Make smarter, more profitable investment decisions.

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.