Calculation Basis
How Voltary calculates PV, battery, and tariff scenarios.
Voltary calculates PV, battery, and tariff scenarios from time-series data. Consumption, PV generation, battery behavior, electricity prices, and economic assumptions are simulated over an analysis horizon.
The results help compare options consistently: Which PV size fits the load profile? How does a battery change grid import, self-consumption, and economics? How do tariff assumptions, feed-in revenue, price development, and battery costs interact?
Voltary shows scenario results under the selected settings and assumptions. They help classify how different configurations may perform in comparison. The focus is a consistent comparison of several options under the same rules.
Which Questions Voltary Answers
Voltary looks at technical and economic questions together. Whether a PV system or battery is sized well depends on technical properties, generation and consumption, tariffs and costs, and especially the user’s objective.
- How much of the electricity consumption can be covered by PV and battery storage?
- How does a battery change grid import, feed-in, and self-consumption?
- Which battery or PV size is economically attractive under the selected assumptions?
- How strongly do electricity prices, feed-in revenue, investment costs, maintenance, and price development affect the result?
- Which configuration better fits a goal such as self-sufficiency, total profit, or return?
Voltary calculates how much electricity is used directly, stored, discharged from the battery, imported from the grid, or fed in. These flows produce technical metrics such as self-consumption and self-sufficiency as well as economic metrics such as savings, payback, ROI, profit, NPV, and IRR.
Which Data Is Used
The calculation combines user inputs, uploaded data, product assumptions, and external data sources. The exact inputs depend on the selected workflow: existing PV system, battery retrofit, new PV system, or tariff comparison.
User data and uploads
- consumption data or a generated load profile
- existing PV generation, grid import, feed-in, or measured data where available
- information about location, household, consumption, and existing system
PV and location data
- planned or existing PV capacity
- orientation, tilt, and areas of planned roof sections
- location-based yield profiles for new PV scenarios based on the European Commission’s PVGIS service
Battery and cost assumptions
- nominal capacity, usable capacity, and minimum reserve
- charge and discharge power as limits for short-term flexibility
- charge and discharge efficiencies for storage losses
- self-discharge and standby or auxiliary demand where relevant
- battery aging, guaranteed cycles, and lifetime assumptions
- investment costs, maintenance costs, and recurring cost components
- stored manufacturer or product data for selected battery models when a concrete model is evaluated
Tariff and market data
- electricity price, feed-in tariff, and fixed tariff components
- daily updated day-ahead spot market data for dynamic tariffs, currently based on Bundesnetzagentur | SMARD.de
- regional or country-specific cost and rule components, for example feed-in rules such as Germany’s Solarspitzengesetz
Economic assumptions
- analysis horizon
- inflation or price development
- discount rate for financial metrics such as NPV
- ongoing costs and recurring fixed components
Depending on the selected path, Voltary works either with uploaded measured data or with a generated profile. For new PV systems, location- and orientation-based yield profiles are derived. This treats a planned system as a time-resolved profile rather than only as an annual generation value.
Why Voltary Uses Time-Resolved Simulation
PV and battery systems need a time-based view. Two households can have the same annual consumption and still get very different results if consumption, PV generation, and electricity prices are distributed differently over time.
A battery depends especially strongly on this timing. Its value emerges when energy is available for charging at one point and useful demand for discharge occurs later. Dynamic tariffs also act through concrete price windows and the timing of consumption and flexibility.
The simulation therefore runs in fixed time intervals. Depending on the data basis and selected setting, Voltary works at 15- or 60-minute resolution. Load, PV generation, battery operation, grid import, feed-in, and tariff prices are evaluated over time before they are summarized into monthly, annual, or financial metrics.
This time-series logic is central to the comparison. It shows whether a battery actually reduces residual load peaks, whether PV generation occurs at the right time of day, and whether price differences are large enough to change a scenario economically.
How the Calculation Works in Principle
The detailed calculation differs by workflow, but follows a consistent basic logic.
First, consumption, PV generation, prices, and technical parameters are brought into a shared time grid. Time axes, units, missing values, and the selected resolution are prepared so each interval can be evaluated consistently.
Next, the PV profile is determined. For existing systems, Voltary uses measured or uploaded data. For planned systems, location-, orientation-, and area-based yield profiles are generated, including cases with multiple roof surfaces or orientations.
Voltary then simulates battery operation for each interval. Capacity, charge and discharge power, efficiencies, minimum reserve, usable capacity, and the selected tariff and cost assumptions are considered.
Based on this, grid import, feed-in, self-consumption, and battery use are valued with the selected tariff, market, and cost assumptions.
Finally, the time series are combined into technical and economic metrics, making the scenarios under review comparable.
Important Assumptions and Controls
Every simulation is based on assumptions. Many can be adjusted by the user; others apply as documented product defaults when more specific inputs are missing.
Analysis horizon
Voltary usually evaluates long-term scenarios over multiple years. This allows price development, aging, maintenance costs, and cumulative cash flows to be included.
Time resolution
Depending on the data basis and selected setting, the simulation uses 15- or 60-minute intervals. Finer resolution can represent short-term load, PV, and price patterns more precisely, provided the input data supports it.
Efficiencies
Charging and discharging create losses. Voltary therefore values stored electricity with the corresponding charge and discharge losses.
Battery limits
Capacity, charge power, discharge power, and minimum reserve limit how flexibly a battery can react in a given interval. The right size depends on whether capacity, power, costs, and usage patterns fit the objective.
Battery aging
Usable battery capacity can decline over time. This affects long-term results and is explained separately.
PV degradation
PV generation can decline slightly over the analysis horizon. This makes a multi-year view more realistic than simply repeating the first year.
Price development
Electricity prices, feed-in revenue, and costs can be projected over time. Small changes in these assumptions can have significant long-term effects.
Maintenance and fixed costs
Recurring costs and fixed tariff components can be included in the economics. They matter because many relevant costs are not tied directly to one kilowatt-hour.
These values are model assumptions and can vary by workflow, user input, or product default. Results should therefore always be read together with the underlying assumptions.
Battery Aging
Voltary accounts for the fact that battery storage can lose usable capacity over time. The model distinguishes between two types of aging:
- calendar aging, which occurs over time even without active cycling
- cycle aging, which results from charging and discharging
Simplified:
Capacity loss = calendar component + cycle component
Available battery capacity = nominal capacity x (1 - capacity loss)
Calendar aging mainly depends on elapsed time and the typical state of charge. Cycle aging mainly depends on how strongly and how often the battery is used. Deeper cycles stress the battery more than shallow charge and discharge movements.
Advanced settings can adjust sensitivities for calendar and cycle aging. Higher calendar sensitivity evaluates time-dependent aging more conservatively. Higher cycle sensitivity evaluates use and deeper cycles more conservatively.
The aging assumptions are a model approximation. Manufacturer warranties, cell models, and temperature-dependent lifetime analyses remain separate evaluation bases.
What the Metrics Mean
Self-consumption
Self-consumption describes which share of PV generation is used in the building or through the battery. High self-consumption can be economically attractive when self-used electricity is worth more than exported electricity.
Self-sufficiency
Self-sufficiency describes which share of electricity consumption is covered by own generation and storage. High self-sufficiency reduces grid import; the best economics can still be achieved by a different configuration depending on costs, tariff, and objective.
ROI
ROI relates average annual benefit to the investment. It is useful for simple comparisons and should be read together with cash flow, payback, profit, and financial metrics.
Payback period
The payback period shows after how many years cumulative nominal net cash flows reach the initial investment. It is intuitive, but sensitive to assumptions about price development, costs, and use.
Profit
Profit looks at the net effect over the analysis horizon after investments and ongoing assumptions. It can prefer different scenarios than ROI, because a larger investment can generate more absolute value even with a lower relative return.
NPV and IRR
NPV discounts future cash flows to today’s value. IRR is the internal rate at which the discounted cash-flow series equals zero. Both are helpful for financial comparisons, while remaining model-dependent.
Each metric answers a different question. One configuration can be strong on self-sufficiency but weaker economically. Another can have a good ROI but produce less absolute savings. Voltary therefore shows several metrics side by side.
How Results Should Be Interpreted
The most important results are comparison values. When multiple scenarios are calculated with the same assumptions, the comparison shows which configuration performs better under those conditions.
Absolute euro amounts are modeled expected values under the selected assumptions. This matters especially for long analysis horizons, because weather, consumption, prices, regulation, and real system operation remain uncertain over years.
- Scenario differences are often more meaningful than isolated result values.
- Sensitivities help make decisions more robust.
- Results should be viewed together with the assumptions.
- An economically plausible configuration should remain understandable under more cautious assumptions.
The calculation provides a structured decision basis that should be compared with offers, technical planning data, and individual circumstances. Installation planning and financial advice remain separate review steps.
Model Limits
Voltary provides scenario and comparison calculations. Real results can differ because weather, consumption behavior, electricity prices, regulation, system availability, and installation details can change over years.
The simulation covers the most important energy flows, technical limits, and economic assumptions. Manufacturer-specific control logic, thermal cell models, and individual warranty conditions are detail questions that should be reviewed separately.
Economic results also need context. Taxes, financing, subsidies, and individual contract details must be reviewed separately.
The model limits reflect deliberate product choices. Voltary prioritizes a comprehensible, consistent, and comparable simulation. This makes results easier to understand; individual real systems can still behave differently in operation.
The calculation basis is updated when model assumptions, data sources, or product functionality change.