Overcoming Electricity Storage as an Information-Constrained Asset
The Economics of Intelligent Energy Storage, Part 2/8
[ Miss part 1 of this series? Catch up here. ]
Energy storage systems are often described as physical infrastructure, such as batteries, inverters, containers, and racks. When projects underperform financially, the explanation usually points to hardware — chemistry limitations, insufficient capacity, or high upfront cost.
But those explanations miss a more fundamental limitation.
Most energy storage systems are not constrained by hardware but by information.
Specifically, they’re information-constrained assets. Operators make decisions with incomplete visibility in the demand context, asset conditions, degradation risks, and future opportunity costs. Economic outcomes suffer when information is coarse, delayed, or averaged, even when high-performing hardware is used.
To leapfrog energy storage ROI, we must treat energy storage as an economic system rather than just a technical one.
The shift from physical capacity to economic performance
Traditional storage economics ties value to capacity, i.e., how many kilowatt-hours a system can store and discharge. Dispatch decisions follow simple rules, such as charging when prices are low and discharging when prices are high. Meanwhile, most models treat degradation as a fixed lifecycle penalty.
This conventional approach assumes that:
The cost of stored energy is stable.
All units of stored energy are economically equivalent.
Degradation is predictable and uniform.
However, as discussed in part 1 of this series, those assumptions increasingly fail in real-world deployments, where markets are volatile, demand is uneven, and assets age unevenly.
The value of energy varies dramatically depending on when, where, and under what conditions it is delivered. Operators simplify decisions when they lack visibility in these factors. For example, they may use static thresholds, conservative operating windows, and averaged cost models. These strategies reduce risk but also cap upside, resulting in a system that’s physically capable but economically underutilized.
What “information-constrained assets” really means
Energy storage as an information-constrained asset doesn't imply a lack of sensors or data collection capabilities. On the contrary, most modern battery systems already generate large volumes of telemetry. The constraint lies in how business decision-makers and operators can use data insights in real-time to optimize battery behaviors for various objectives.
Most traditional deployments aggregate data or sample it infrequently. They model degradation using static curves rather than real-world behaviors. Instead of measuring an asset’s state of health (SoH), they infer it indirectly. They also fail to couple external signals (e.g., price, demand, weather) tightly to internal states.
In effect, systems operate with a blurred or incomplete picture of reality, leading to decisions based on rigid rules, not real-world circumstances or forecasts derived from contextual information.
Without high-resolution, timely, and actionable information supported by AI-driven, predictive analytics capabilities to deploy insights in real-time, storage systems can't distinguish between high-value and low-value operating conditions. This constraint leads to similar dispatch decisions even when the economic consequences are significantly different.
The same stored energy has different values
Consider two identical battery systems with the same nominal capacity.
In one case, the discharge occurs during a mild demand period, with low prices, minimal urgency, and no operational stress on the asset. In another, the discharge happens during a constrained, high-demand interval. Reliability is critical, and customers are willing to pay a premium sufficient to cover the additional degradation.
Physically, both systems discharge the same amount of energy. Economically, the outcomes are different due to contextual factors like demand intensity, market conditions, asset SoH, opportunity costs of future use, and tolerance of degradation.
Traditional models flatten these distinctions, leading to flawed decisions. On the other hand, information-aware models surface these variables and turn them into actionable insights. Most importantly, operators and decision-makers can use the data to reduce uncertainty, the “culprit” behind many overly conservative decisions.
How uncertainty depresses return
One of the least visible, yet most expensive consequences of operating with limited information is uncertainty. Operators respond conservatively when they’re unsure about actual remaining capacity, true degradation sensitivity, future price or demand conditions, or risk of failure under aggressive operation. As a result, they hold back energy, avoid cycles, and miss monetization opportunities.
While conservative decisions help mitigate uncertainty, they often hurt ROI. Uncertainty becomes an implicit tax on storage economics.
Instead of adding capacity, operators can increase output by reducing uncertainty. But how? We need the ability to improve visibility so that decision-makers can quantify risks, price them, manage them, and perhaps accept them in exchange for higher value.
Beyond operations: Using information and analytics to advance economics
Data insights impact energy storage beyond incremental, operational improvements, like smoother dispatch, better monitoring, and fewer surprises. High-fidelity data and predictive analytics allow operators to change the economics of energy storage deployment:
Differentiate between high-value and low-value dispatch opportunities.
Explicitly price degradation instead of making blanket assumptions.
Align energy delivery with demand urgency rather than static schedules.
Make forward-looking decisions that account for future opportunity costs.
This approach isn’t new. Many industries have adopted data-driven, dynamic pricing. Airlines, logistics companies, and ride-sharing platforms don’t price services based solely on average cost. Instead, they use information to dynamically match supply, demand, and asset strain.
Energy storage has historically lacked the tools to do the same, until now.
Enabling Data-Driven, Dynamic Pricing in Energy Storage
Software-defined battery (SDB) built on the Tanktwo Battery Operating System (TBOS) provides the data infrastructure and analytics required to transition storage economics from static pricing assumptions to data-driven, dynamic valuation.
TBOS continuously captures high-resolution telemetry, including voltage, temperature, state of charge, depth of discharge, and cycle behavior, at the cell and module level. The software also calculates a lifecycle index for assessing asset health and degradation.
When combined with external demand signals, such as market pricing, load forecasts, and environmental conditions, this data allows operators to estimate the true, moment-by-moment economic cost of delivering stored energy, including the incremental degradation induced by each decision.
Rather than assuming a fixed cost per kilowatt-hour, our Battery AI capabilities translate workload-dependent strain into a quantifiable economic input. As a result, pricing and dispatch decisions accurately reflect demand urgency, asset condition, and future opportunity costs.
In practice, operators can justify charging more for energy delivered under punishing conditions, set more aggressive depth-of-discharge (DoD) when the business case exists, or hold capacity in reserve when future value is expected to be higher.
By turning battery systems into information-responsive assets, TBOS makes dynamic energy pricing operationally feasible. Storage operators don't have to choose between conservative asset protection and aggressive value capture. Instead, they can align pricing, dispatch, and degradation in real time to support accurate decision-making.
From information-constrained to information-responsive
When storage systems become information-responsive with the ability to observe, predict, and adapt, the economic model changes:
Degradation is no longer just a penalty; It becomes a priced input.
Dispatch is no longer reactive; It becomes selective.
Decision-makers and operators will no longer treat energy as a homogeneous commodity. It becomes a differentiated product with value that varies with context. This shift to higher ROI and more efficient use of resources doesn’t require new battery chemistry or hardware. Instead, it starts with treating information as first-class economic input.
If limited information suppresses storage value, the next question is: What happens when we have the ability to reduce uncertainty?
