The Two Pitfalls Most BESS Operators Face

Many asset operators face the same challenge: Battery energy storage systems (BESS) consistently underperform. It’s not unusual for operators to see lower-than-expected availability, reduced energy throughput, and revenue numbers that don’t meet forecasts. 

But the root causes for these outcomes are no mystery; they’re just widely accepted. In fact, most BESS assets fall into the same two traps: balancing strategies that lack precision and state estimates that don’t reflect reality. 

Why do these issues persist across so many sites? And what are the consequences for BESS asset operators? Let’s take a closer look. 

The Two Big Pitfalls

BESS underperformance often comes down to balancing and energy estimates—two areas where conventional control systems consistently fall short. 

1. Relying on ineffective battery balancing strategies

Cell imbalance (i.e., where individual battery cells within a string develop unequal states of charge) is a persistent challenge in BESS operations. But most asset operators lack the visibility to balance in a targeted, effective way.

This is because conventional BMS (Battery Management Systems) and EMS (Energy Management Systems) offer only limited visibility into the balance state of racks or blocks, leaving operators to make balancing decisions based largely on guesswork.

And whichever way they lean, they’re likely to run into trouble: 

• Balancing too often means taking systems offline unnecessarily, resulting in excessive downtime and lost revenue opportunities. 
• Balancing too little leaves imbalances to persist, ultimately reducing available energy and compromising long-term system performance.  

2. Using inaccurate state estimates

Ideally, state estimates help operators plan with confidence to make operational decisions and determine market participation strategies. But native control systems frequently misreport available energy and power estimates.

In fact, SoC (State of Charge), SoE (State of Energy), and SoP (State of Power) are often off the mark by 15%. In extreme cases, the discrepancy can be as high as 90%. 

When estimates are unreliable, operators are forced to choose between conservative dispatch strategies or exposure to financial risks: 

• Underestimates cause operators to under-dispatch and leave profitable energy untapped. 
• Overestimates create risks of set-point deviations, plus the financial penalties that come when systems fail to meet commitments. 

Either way, inaccurate signals compromise asset performance. 

Why are these two pitfalls so widespread?

Ineffective balancing and inaccurate state estimates aren’t caused by shoddy batteries or poor operating practices. They’re the result of fundamental limitations baked into the way conventional BESS control systems operate. This is why:

Battery modeling is hard

It’s an understatement to say that batteries aren’t simple. 

These non-linear, time-varying electrochemical devices change dramatically as they age and also vary significantly depending on the manufacturer and chemical makeup. 

Accordingly, accurately modeling battery behavior is a challenging technical feat that requires both substantial expertise and experience to get right, particularly when dealing with chemistries like LFP (lithium iron phosphate), which confound traditional state estimate methods. 

Accuracy isn’t a priority for OEMs

The truth is, BESS manufacturers compete on price and warranty—not signal quality. 

This means accuracy isn’t always a top priority. 

With little incentive to invest in the advanced modeling and algorithms needed for high-fidelity signals, OEMs often let accuracy fall to the wayside. Down the line, operators are left with state estimates they can’t fully trust. 

Plant controls take BMS signals at face value 

In most cases, EMS and plant control systems accept signals from BESS containers without question. But this can set the stage for a “garbage in, garbage out” phenomenon.

If inaccurate BMS data feeds the system, then project-level control signals can be compromised, risking greater operational errors and eventual lost revenue potential for asset operators. 

What these two pitfalls mean for BESS asset operators

Ineffective balancing and inaccurate state estimates are not just technical grievances that operators can ignore. Actually, they directly impact the two metrics most important to operators: BESS asset availability and revenue opportunities. 

Limited availability 

Without accurate information about the balance state of battery racks or blocks, it’s too easy for asset operators to end up balancing too much or too little based on fixed schedules or arbitrary routines. These balancing “strategies” are more like educated guesswork and end up creating excessive, unnecessary downtime. 

Meanwhile, when energy and power estimates are unreliable, operators can’t strategically dispatch power, often leaving capacity on the table.

Together, blind balancing and low-fidelity estimates shrink asset availability and limit overall system performance. 

Fewer revenue opportunities

Inaccurate energy and power estimates ultimately make their way to the trading table, where they influence market participation decisions—often to costly results. 

Both overestimates and underestimates carry risks:

If operators overcommit based on inflated capacity estimates, they risk financial penalties. But if they play it conservative and limit participation, then they can miss out on profitable dispatch opportunities. 

Either way, the result is lost BESS revenue—not because of battery problems but because of flawed assumptions. 

Avoid the pitfalls most asset operators face with Zitara for BESS

Most BESS asset operators rely on balancing strategies that lack precision and unreliable state estimates that don’t reflect true power and energy availability. The result is lower availability, missed revenue opportunities, and generally underperforming assets. 

Zitara for BESS takes a different approach to avoid these costly missteps. 

Zitara for BESS is a purpose-built software solution that uses advanced battery modeling to enable smarter balancing and more accurate state estimates. 

Deployed on premises via Zitara-managed hardware, its two software modules—Zitara Balance and Zitara Power—seamlessly integrate with existing control systems. Zitara Balance enables targeted, efficient balancing based on real need, while Zitara Power provides highly accurate, predictive energy and power state estimates. 

Together, this duo empowers operators to plan dispatch confidently, optimize asset utilization, and maximize revenue opportunities. 

Want the full technical breakdown? Download our white paper to learn more about the fundamental limitations of conventional BESS control systems and how to maximize asset availability, performance, and revenue with Zitara for BESS. 

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