Case Studies

Measured examples of storage used for efficiency optimization

These case studies describe common storage outcomes without exaggeration: peak reduction strategies, better use of on-site renewables, and clear resilience planning for critical loads. Each example emphasizes the decisions that matter most: data quality, constraints, operating mode, and verification after commissioning.

How to read these examples

Storage results depend on load shape, equipment limits, tariff rules, and site operations. The goal of these write-ups is to show a repeatable method: define a baseline, set a dispatch target, and validate outcomes using metered kW and kWh. If you want an estimate for your site, share your interval data and tariff summary through the contact form.

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Baseline: Define a reference period and confirm meter quality.
Dispatch target: Pick the few intervals that matter most.
Verification: Compare kW peaks and shifted kWh post-commissioning.

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Case studies library

The scenarios below are based on typical projects we support: a site with demand charges, a solar-heavy facility trying to reduce exports, and an operation that needs predictable backup behavior. Each case highlights the constraint that shaped the system most, because constraints are what turn a generic battery into an optimized storage asset.

If you are evaluating storage as part of an ad campaign or vendor comparison, use these examples to align expectations. The primary question is not what the battery can do in theory; it is what it can do within your interconnection limit, inverter ratings, reserve requirements, and operating schedule.

Commercial facility: peak shaving with predictable dispatch windows

Objective: reduce the highest demand intervals without changing production schedules.

Peak management

Key constraint

The site had short, repeatable peaks tied to HVAC staging and afternoon equipment ramp-up. The battery inverter rating, not energy capacity, was the limiting factor for clipping these spikes.

Approach

We mapped the top demand intervals across multiple weeks, then defined a dispatch schedule with guardrails: reserve margin for unexpected peaks, a maximum discharge duration to avoid depleting early, and a recharge rule that avoids shifting load into the same peak window.

Verification plan

Post-commissioning, the primary checks were: (1) comparison of peak kW to the baseline period, (2) confirmation that discharge happened only during targeted windows, and (3) review of inverter clipping events to ensure settings stayed within thermal limits.

Metrics: peak kW, discharge duration, recharge timing

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Solar-heavy site: increase self-consumption while limiting grid export

Objective: capture midday surplus and reduce evening imports, without stressing interconnection limits.

Renewable capture

Key constraint

Export was constrained by interconnection rules. The control system needed to respond to fast PV changes, keeping net export below a defined threshold while still prioritizing charge when surplus appeared.

Approach

We defined a two-layer strategy: (1) a real-time export limiter to respect the threshold, and (2) a daily schedule that reserves capacity for the expected midday window. The schedule avoided overfilling early in the day so the battery could absorb late peaks in PV production.

Verification plan

We validated net export against meter readings, then reviewed charge timing and state-of-charge profiles. A key check was confirming that the export limiter did not cause rapid oscillations that could reduce equipment life or create power quality concerns.

Metrics: exported kWh, imported kWh, ramp response

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Industrial operation: load smoothing to reduce short spikes

Objective: keep demand within a stable band during process ramps and equipment starts.

Load smoothing

Key constraint

The most disruptive events were brief spikes and ramps, sometimes measured in minutes. The system needed fast response without overreacting to meter noise or transient readings.

Approach

We built a smoothing controller around a site-specific demand ceiling and a ramp-rate limiter. The controller was tuned using a staged approach: test with conservative setpoints, observe response, then tighten the band while monitoring temperature and cycle counts.

Verification plan

The primary review was statistical: distribution of demand spikes, number of excursions above the target band, and the relationship between smoothing events and battery cycling. We also validated that smoothing did not create new peaks during recharge.

Metrics: spike count, ramp rate, recharge impact

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Critical loads: resilience mode with defined reserve behavior

Objective: maintain specified equipment during outages while preserving battery health.

Resilience

Key constraint

Backup expectations were higher than the system could support if it served all loads. The success factor was load prioritization: define what must remain online, what can be delayed, and how the site should behave during restoration and restart.

Approach

We documented the critical load list, defined reserve state-of-charge, and configured an operating mode that keeps a buffer for outages while still allowing limited optimization during normal operation. The design included explicit thresholds for when optimization stops and reserve protection begins.

Verification plan

Commissioning tests included simulated outage transitions (where permitted), verification of islanding behavior, and confirmation that the battery respected reserve settings. Post-testing, we reviewed event logs and confirmed that restart sequences matched the agreed load priority plan.

Metrics: reserve SOC, transition time, runtime assumptions

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What stays consistent across successful projects

Regardless of size, effective storage projects share a few habits. First, they define the operational objective in plain terms. “Reduce peak demand” must become a specific peak window and a target ceiling. “Use more solar” must become a charge strategy that respects export limits and preserves capacity for the right hours. Without that specificity, control settings drift and results become hard to verify.

Second, they match controls to constraints. Inverter kW, round-trip efficiency, reserve requirements, and thermal limits are not administrative details; they shape what dispatch is possible. Finally, they measure outcomes with the same meter and interval resolution used to define the baseline. This keeps comparisons fair and prevents over-crediting improvements that are really seasonal or operational changes.

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Data quality

Interval data and clear tariff rules are the foundation. If data is missing or resolution is inconsistent, optimization becomes guesswork.

Control clarity

Simple rules that operators understand often outperform complex automation that no one can validate or safely maintain.

Safety first

Dispatch strategies must respect equipment limits, interconnection rules, and safe operating envelopes, especially during abnormal conditions.

Verification

Compare like-for-like periods and confirm dispatch occurred at the intended times. Verification is what turns “claims” into confidence.