Most BESS balancing strategies fall short because they rely on fixed routines or reactive triggers—not real-time data. Without visibility into cell-level imbalance, operators are forced to balance blindly, leading to lower availability, less dispatchable energy, and missed revenue opportunities.
Cell imbalance is a leading cause of BESS underperformance. It occurs when individual battery cells in the same string drift out of sync and develop different States of Charge (SoC). For operators, that leads to lower asset availability, less dispatchable energy, and fewer opportunities to generate revenue.
To prevent or correct imbalance, most operators rely on basic balancing strategies built into conventional control systems. But these approaches aren’t actually suited for real-world BESS operations. They increase downtime, limit performance, and overlook the true state of the system.
Here’s a look at the three most common balancing strategies—and why they fail to meet the demands of modern BESS assets.
3 most used balancing strategies for BESS assets
Most BESS asset operators continue to rely on default balancing strategies—but these strategies depend on guesswork rather than real-time, cell-level insights.
- Scheduled balancing
This method triggers balancing at fixed intervals, such as every night, once a week, or after a predefined runtime.
Why it fails: A schedule doesn’t reflect actual asset conditions. It may trigger battery balancing when no imbalance is present—or skip it when intervention is needed most. Either way, operators are balancing blindly, creating unnecessary downtime and missing opportunities to operate (and generate BESS revenue) at full capacity.
- Symptomatic balancing
In this case, balancing only occurs after an asset displays signs of imbalance, such as reduced capacity, erratic behavior, or unexpected PCS shutdowns.
Why it fails: Acting on symptoms means imbalance has already caused performance degradation. By the time your system alerts you, the damage is done—and it may not be easily or quickly corrected. With this reactive strategy, operators allow degradation to persist, cause downtime, and increase operational risk.
- Round-robin balancing
With the round-robin strategy, racks or blocks are balanced in an untargeted sequence, regardless of their actual balance state.
Why it fails: This approach assumes all racks and blocks require equal attention—but in real-world BESS operations, that’s rarely if ever the case. As a result, the round-robin approach needlessly pulls healthy blocks offline while delaying intervention for those that need it, simultaneously reducing availability and creating avoidable downtime.
Why these strategies fall short
Each of these balancing strategies shares a common limitation: they lack actionable, real-time data.
Without visibility into imbalance at the cell level, operators are left in the dark to guess what to balance and when. In turn, that uncertainty drives poor decisions that undermine asset availability, degrade performance, and limit profitability.
Excessive downtime
Scheduled and round-robin balancing often take racks and blocks offline when they don’t need balancing. And over time, all that extra downtime adds up, reducing system availability and stifling revenue generation.
Reduced available energy
By the time you can see the symptoms of cell imbalance, some energy has already become inaccessible. But symptomatic balancing waits too long to intervene, leaving imbalances unaddressed until they’ve already reduced your assets’ ability to deliver full capacity.
Missed market opportunities
Poorly timed, imprecise balancing can prevent participation in market events.
Whether a rack is taken offline too soon or left online too long, common balancing strategies cause operators to miss peak pricing windows or ancillary service revenue.
The bottom line
Scheduled, symptomatic, and round-robin balancing each apply a one-size-fits-all approach to the highly nuanced problem of cell imbalance. With no real-time visibility into cell-level conditions, these strategies force operators to balance blindly—and ultimately waste uptime, energy, and money.
It’s time for a more precise, data-driven approach to balancing.
Smarter balancing for BESS assets with Zitara Balance
Zitara Balance is an on-premise algorithm that monitors cell-level raw data inputs to produce accurate, real-time balance/imbalance signals at the rack level.
By giving asset operators insight into when and where imbalance is occurring, Zitara Balance empowers you to identify imbalance early so you can balance more strategically and reduce unnecessary, costly downtime.
The result:
- Up to 75% reduction in balancing-related downtime
- Increased total dispatchable energy
- Stronger asset availability, performance, and ROI
Learn more about how to maximize asset availability, performance, and profitability with smarter balancing. Download the white paper.
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