Quick answer
Interval-based simulation of battery charging and discharging to evaluate moving electricity between times. It accounts for load, generation, tariffs, losses and operating constraints, comparing the resulting bills against an explicit baseline.
Answer: Battery time-shift modeling simulates charging at one time and discharging at another to evaluate energy use and financial value. A useful model aligns load, solar generation, tariff periods and battery operating limits at a suitable time step. It calculates charging costs or foregone export credits as well as avoided purchases, losses and constraints. A peak-minus-off-peak price spread alone is not a savings forecast.
Define the comparison before optimizing dispatch
For an existing PV installation, compare PV without storage against the same PV with storage to isolate incremental battery value. Comparing solar-plus-storage against no solar can mix PV savings with battery savings.
Specify the accounting boundary: energy entering and leaving storage at the meter, AC system connection or DC battery terminals. Use matching efficiencies and avoid counting conversion losses twice. Record the tariff version, load data, equipment model, control strategy and simulation revision.
Charging electricity has a cost
For a simple grid-charging cycle at the same AC energy boundary:
Net energy value = discharged AC energy × avoided import price − charged AC energy × charging price.
With matched starting and ending storage states and an assumed round-trip efficiency, charged energy equals discharged energy divided by that efficiency. This simplified calculation excludes demand charges, fixed charges, auxiliaries outside the declared boundary, wear, maintenance and capital costs.
Hypothetical example: deliver 9 kWh AC at a $0.35/kWh avoided price. Assume 90% AC-to-AC round-trip efficiency and a $0.12/kWh charging price. The system needs 9 ÷ 0.90 = 10 kWh of AC charging. Value is 9 × $0.35 − 10 × $0.12 = $1.95 for that cycle. Multiplying the price spread by 9 kWh and then multiplying by efficiency does not represent the two energy flows correctly.
For solar charging, use the foregone export credit for the marginal PV energy that would otherwise have been exported. In the same hypothetical cycle, a $0.05/kWh export credit gives $3.15 − $0.50 = $2.65 of incremental energy value. If that solar energy would instead serve load directly, the comparison changes. “Solar charging is free” is not an adequate financial assumption.
At a flat $0.14/kWh grid import price, that 10-in/9-out cycle costs $1.40 and avoids $1.26, giving −$0.14 before other costs. Flat import pricing can still leave a separate self-consumption opportunity when export compensation differs; it does not automatically make grid-charging arbitrage profitable.
Model the calendar, not one repeated day
| Input | Modeling check |
|---|---|
| Load and PV | Align timestamps, units and interval resolution |
| Tariff | Use actual seasonal, weekday, weekend and applicable holiday rules |
| Export compensation | Represent actual credits and export restrictions |
| Equipment | Enforce charging/discharging power, usable energy and operating limits |
| Reserve | Model energy withheld for the stated backup objective |
| Capacity and wear | Use justified degradation, replacement and cycling assumptions |
| Controller | Distinguish perfect foresight, forecast-driven operation and fixed schedules |
A 365-cycle multiplication assumes a comparable cycle on every day. Test whether the load, charging opportunity, rate calendar and reserve actually permit that. No universal $0.15/kWh threshold establishes a worthwhile installation, and increasing all prices at the same percentage does not establish the future tariff structure.
Dispatch objectives can conflict
SAM’s behind-the-meter documentation distinguishes peak shaving, manual schedules, retail-rate dispatch and self-consumption. It also separates charging permissions and state-of-charge constraints. Its perfect-look-ahead options use knowledge of future modeled inputs; do not present that result as guaranteed real controller performance. These are SAM-specific modeling choices, not evidence that SurgePV supports identical functions. SAM behind-the-meter dispatch.
Energy arbitrage measures energy-price value. Peak shaving must instead reflect the actual demand-billing interval and rules. A short demand spike may disappear in an hourly average. Match the model resolution and time-series targets to the analysis; SAM documents matching requirements for its power-target inputs. SAM battery time-step documentation.
Grid-service dispatch and backup reserve can consume energy needed for another objective. Model one feasible schedule and bill outcome rather than adding several unconstrained maximum-value estimates.
Report what the simulation actually demonstrates
Show baseline and modeled bills, charging energy, delivered energy, ending state, losses, demand outcome and constraint violations. Verify energy conservation and investigate schedules that exceed equipment limits or rely on unsupported grid charging/export.
Compare additional capacity incrementally; a second battery’s value depends on unmet opportunities, not a universal percentage of the first. Present sensitivities to tariffs, forecast errors, usable energy and costs. Financial payback requires the complete incremental cash flow; an energy-value calculation alone is not project bankability or an engineering approval.
See battery storage for equipment and backup distinctions and TOU rate modeling for tariff context. To evaluate available SurgePV outputs, book a demo with your interval data and comparison criteria.
Frequently asked questions
Does round-trip efficiency multiply the price spread?
Not in a correctly defined two-flow calculation. Value the discharged energy and charge the cost of the larger input energy separately.
Is stored solar energy free?
Its incremental opportunity cost depends on what the same energy would otherwise do, including export or direct load service.
Can hourly data model every demand charge?
Not necessarily. Use the tariff’s actual billing interval and an appropriate data/model resolution.
Does an optimized simulation guarantee realized savings?
No. Actual conditions, forecasts, controls, tariffs and equipment operation can differ from the modeled scenario.
Related Glossary Terms
About the Contributors
CEO & Co-Founder · SurgePV
Keyur Rakholiya is identified by SurgePV as its CEO and a company co-founder. His SurgePV author page lists only role information that can be tied to the public profile below; credentials, project totals, testing claims, media appearances, and speaking engagements are not asserted without retained evidence.
Editorial contributor · SurgePV
Rainer Neumann is credited as an editorial contributor on SurgePV content. This profile does not assert engineering credentials, project totals, software-testing experience, education, speaking engagements, or media citations because independent verification evidence is not retained in the publication record.
