1 What are Reduxi AI strategies

Reduxi AI strategies are a set of intelligent, self-learning control algorithms embedded in the Reduxi EMS (Energy Management System). They forecast, optimise and autonomously control energy flows across connected assets such as batteries, PV, EV chargers and heat pumps.

The primary objective is profit optimisation.

1.1 Core definitions

• Forecast is a prediction of future energy flows, including load, PV production, EV usage and grid exchange. It is derived from historical measurements and external inputs such as weather conditions, time of day and usage patterns.
• The objective is to optimise when to consume, store or export energy. For example, the battery charges during periods of low prices or high PV production and discharges during high-price intervals. Electricity market prices (SPOT, ToU) are a key input to this process.
• The optimisation runs in rolling 15-minute intervals. It continuously updates decisions based on the latest system state and forecasts.
• The AI strategy controls devices indirectly: it sends setpoints to the controller, which manages all included devices in real time.
• The system continuously learns from new data and updates its models and optimisation behaviour accordingly.

2 Initial configuration of AI strategies

2.1 Adding AI strategy

The AI strategy (optimisation mode) is added in the Energy Manager strategy. Click the + icon and select AI Energy Manager.

Adding the AI Energy Manager strategy

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Screenshot of the Reduxi Configurator page Strategy / Energy manager with a modal dialog Add on top (rest of the page greyed out). Page: left menu Devices (+), Strategies (minus) with entry Energy manager (selected) and + Add strategy, Utility Rate Plan, Settings (+); top right green Connected; tabs Optimization modes, Included devices, Last measurements, Analyze, Archive, Details; table Name, Event description, Export limit, Goal, Import limit, Advanced with row Default mode: Event description /, Export limit 0 kW, Goal Self-consumption, Import limit 13,6 kW; at the right a + button and a Save button. Dialog Add: entry AI energy manager * with text Use AI agent to optimize battery, HVAC, EVSE and PV. and button Create; entry Schedule with text Scheduled entries define when settings are in effect. and button Create; link Show more; note * premium feature; button Close. Two red arrows: one points to the Create button of AI energy manager, one points to the + button on the page at the right.

Configure the strategy parameters and save. More about the configuration is in the device-specific sections (see ⁠Point 5⁠).

2.2 AI strategy conditions checklist

To enable an AI strategy, the following conditions must be met:

• A strategy is set with an AI optimisation mode. Only one AI strategy can be active at a time.
• A price list is configured.
• Geolocation is set.
• Region, country, timezone and currency are defined.
• The controller is online and connected to the Reduxi cloud.
• The devices have the relevant parameters configured, for example battery SOC, capacity and power limits.
• The location has at least two weeks of historical data (PV, load, grid, etc.) so that reliable forecasts can be generated. See Point 2.3 for the behaviour without sufficient history.
• A valid AI licence is active.
• After all parameters are set, wait at least 15 minutes for the AI strategy to appear in the cloud app and start controlling devices.

Forecast displayed to the right of the Forecast line

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Screenshot of a Reduxi Cloud power chart on a dark background, x axis starting 1. Apr, with a vertical dashed line labelled Forecast at about 16:15; the part right of the line is shown in faded colours. Top icons: power pylon, sun, battery, battery with plug. Buttons: two arrow toggles, 1 min, 15 min (selected), 1 hour, Power (selected), Energy. Upper chart: left axis Consumption and production from -240 kW to 320 kW, right axis 0% to 105% (label SoC), x axis 03:00 to 21:00. Light blue area: a block of about 170 kW from 06:45 to 08:00, a negative spike to about -170 kW around 03:00, negative blocks to about -170 kW around 12:15 and between 13:00 and 14:00, and a faded block of about 170 kW between 19:30 and 20:15. Yellow area above zero up to about 40 kW between 08:00 and 16:00. Green area close to zero. Blue line on the right scale: about 75% until 04:00, 88% from 04:00 to 07:00, falls to about 15% by 08:00, slowly rises to about 40% by 13:00, jumps to about 75% at 14:00, stays 75% until 18:00, falls to about 15% by 22:00. Lower chart Electricity pricing: axis Price [c/kWh] from 0.05 to 0.30; red step line Total (Buy) with peaks of about 0.26 around 07:00 and 0.24 around 19:00; green step line Total (Sell) lower, about 0.10 to 0.21; legend Total (Buy), Total (Sell).

2.3 Advanced notes and options

• AI optimisation can run alongside default and schedule modes. The system can then behave differently at different times of day based on your settings. A typical example is different settings or limits during the day and at night.

AI optimisation next to default and schedule modes

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Screenshot of the Reduxi Configurator page Strategy / Energy manager, tab Optimization modes (other tabs: Included devices, Last measurements, Analyze, Archive, Details); top right green Connected. Table columns: Name, Event description, Export limit, Goal, Import limit, Advanced. Row 1: Default mode; Event description /; Export limit 8,8 kW; Goal Self-consumpti(on) (dropdown, text cut); Import limit 5 kW; edit icon. Row 2: day; Event description 06:00 - 22:00, Monday, Tuesday, Wednesday, Thursday, Friday, January, February, March, April, May, June, July, August, September, October, November, December; Export limit 8.8 kW; Goal Self-consumpti(on); Import limit 18 kW; edit and delete icons. Row 3: AI energy manager * (other cells empty); edit and delete icons.

• Negative price optimisation does not need separate configuration. The AI optimisation mode already handles it when PV is configured accordingly.
• Set grid limits so that the system can reach the desired goal. For example, if the grid limit is 10 kW but the normal consumption is 100 kW, the optimisation target cannot be met. The user must define the limitation accordingly.
• If the AI optimisation cannot be achieved because of restrictive grid limits, the system automatically falls back to the configured default optimisation mode (e.g. self-consumption) as defined in the UI.
• Reduxi can control devices only to the extent that they follow control signals correctly. If a device does not respond as expected, verify its behaviour with the manual control modes. For example, some PV power plants cannot be curtailed, some batteries do not follow the requested setpoint, and in hybrid inverter systems PV and battery control are often coupled. Reduxi adapts to these constraints, but the limitations remain.
• Reduxi AI strategies coordinate all included devices towards a shared optimisation objective. You define which device categories are included (for example battery storage) and which are excluded (for example HVAC systems). We recommend including all categories, because this brings the highest value when everything works towards a common goal.
• Included devices are actively managed by the AI and participate in the optimisation. Excluded devices operate independently under their own control logic and are not affected by the strategy.

Behaviour with limited historical data (first ~2 weeks)

• When there is not enough data for forecasting, no PV, load or grid forecasts are available.
• If both import and export are allowed, the AI performs energy arbitrage.
• If either import or export is restricted, the AI switches to self-consumption mode.
• After enough historical data is available, the system switches to regular AI mode automatically.

2.4 Update rates

• The AI strategy is recalculated every 15 minutes. At the same interval, the updated strategy is deployed to the controller, using the latest system state, weather data and inputs.
• Once deployed to the controller, the strategy is executed locally in real time. It reacts immediately to measurements and grid conditions.
• If a strategy is reconfigured, activation and the initial forecast calculation take up to 15 minutes.

3 Energy consumption and production forecasting

Accurate forecasting of energy consumption and production is essential for the performance of AI strategies.

The Reduxi forecasting model uses geolocation data, historical behaviour and weather inputs (mainly solar irradiation and temperature). It predicts future energy flows for the next day and beyond.

The forecast is provided in 15-minute intervals. You can view it in the power chart, in the 15-minute view.

3.1 Photovoltaic (solar) power plant production forecast

PV production depends on historical behaviour, time of day and year, and weather input, mainly irradiation and temperature.

To simplify the configuration, Reduxi does not require the installed power, surface area or orientation of the PV power plant. These characteristics are inferred automatically from historical PV production.

3.2 Consumption (load) forecast

The consumption forecast also uses historical data and weather inputs.

In addition, the model accounts for usage patterns such as time of day (for example lower consumption at night) and day of week (for example weekdays and weekends).

Consumption forecasting is the most complex part. It must account for less predictable behaviour within short timeframes, for example starting large machines in industrial environments or starting an EV charging.

At the moment, all loads are forecast together, including EV charging and HVAC. This will be improved in future versions.

4 Battery power forecast

Battery power is the part of the system that can be actively controlled.

Its forecast is derived from optimisation goals such as peak shaving and cost optimisation. More details are in the following sections.

4.1 Grid baseline forecast

The grid-level forecast of consumption and production is essential for effective system control. It defines the expected energy exchange with the grid. All AI optimisation is built around it.

The grid forecast is important because:

• Deviations from the expected baseline can lead to costs.
• Consumption and production must stay within grid constraints, such as import and export limits or fuse limits.

The baseline forecast enables the AI to:

• reduce grid import and use the battery instead
• shift consumption to manage peaks and avoid exceeding limits

For services such as flexibility or balancing, the baseline forecast is also the reference point, because:

• it enables flexibility services as a deviation from the baseline
• it determines potential penalties or rewards

5 Description of AI strategies per category

AI strategies can control all device categories to achieve a common objective. The following sections describe the functionality of each category.

5.1 AI strategy for battery (BESS)

Battery Energy Storage Systems (BESS) maximise savings and earnings in the following modes:

• Energy arbitrage. Charge the battery when energy prices are low. Use or sell the stored energy when prices are higher.
• Charge from grid, discharge to load. If discharge to the grid is not allowed, the battery discharges to cover the consumption. The battery charges from the grid in the low-price intervals and is used later when prices are higher. This shifts consumption and reduces costs.
• Charge from PV, discharge to grid. If charging from the grid is not allowed, the battery charges from PV. The energy can later be sold at higher prices. This increases the value of solar production compared to direct feed-in.

In all three modes, the Reduxi BESS AI module calculates the optimal time windows for charging and discharging. It considers:

• Energy buying price, including all specified fees, network charges and other components. The price must be defined in advance in the cloud app.
• Energy selling price, including all specified fees, network charges and other components.
• PV production, consumption and grid forecast.
• Grid and battery power limits.
• Battery charge and discharge losses, and battery degradation cost.

Examples:

• The energy price is low at 12:00 and high at 20:00. The battery charges around 12:00 and discharges around 20:00.
• It considers grid and battery power limits. If the grid limit is low and the required charging energy cannot be reached in a shorter time, the charging period is extended, for example from 11:00 to 15:00.
• It considers the PV production forecast and charges the battery from PV instead of from the grid, or from a combination of both.
• If the price difference is too small to cover the charge and discharge losses and the battery degradation cost, the battery does not perform the charge/discharge cycle.

5.1.1 BESS AI configuration

The screenshot shows the AI BESS configuration.

AI BESS configuration

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Screenshot of the Reduxi Configurator page Strategy / Energy manager, tab Optimization modes (other tabs: Included devices, Last measurements, Analyze, Archive, Details); top right green Connected. Table columns: Name, Event description, Export limit, Goal, Import limit, Advanced. Row 1: Default mode; Event description /; Export limit 8,8 kW; Goal Self-consumpti(on) (dropdown, text cut); Import limit 5 kW; edit icon. Row 2: day; Event description 06:00 - 22:00, Monday, Tuesday, Wednesday, Thursday, Friday, January, February, March, April, May, June, July, August, September, October, November, December; Export limit 8.8 kW; Goal Self-consumpti(on); Import limit 18 kW; edit and delete icons. Row 3: AI energy manager * (other cells empty); edit and delete icons.

• Arbitrage or grid-restricted mode. The operating mode is determined by the grid charge and discharge restrictions. With the UI switches, you configure the system for pure arbitrage or for optimisation that respects grid import and export constraints.
• Min savings/earnings per kWh. To account for battery degradation, define the minimum savings or earnings that a cycle must generate. If the expected value is below this threshold, the battery is not used. This protects battery lifetime. A typical recommended value is 0.02 EUR/kWh. It is estimated with this calculation: cost of the battery / total energy over battery lifetime = 50,000 EUR / (10,000 cycles × 250 kWh) = 0.02 EUR/kWh.
• Capacity reserved to cover load. The forecast is never ideal. A percentage of the battery capacity can be reserved to cover local consumption and prevent exceeding grid limits.
• Capacity reserved for surplus (PV) production. Similarly, a percentage of the battery capacity can be reserved for PV surplus production, in case the PV produces more than planned.
• The optimiser also considers the energy loss during charging and discharging. The loss of the complete charge and discharge cycle is estimated at 8%.

5.1.2 BESS AI SOC range excursions

AI optimisation remains in control at all times, even when the battery SOC moves outside the default preferred AI SOC range. If the system determines that charging from the grid is not possible and the current SOC is lower than MIN AI SOC, the AI optimisation operates within the MIN and MAX SOC limits defined in the strategy, instead of the AI SOC limits.

Below the low SOC limit for AI

If increased load consumption would cause the export limit to be crossed (requiring peak shaving) and the SOC drops below the low AI SOC limit, the system starts charging automatically. There are two scenarios:

1. SOC significantly below the MIN AI SOC value: the system calculates the charging needs over a longer time horizon. It identifies the most cost-effective charging windows based on current and forecast spot prices.
2. SOC has only just crossed the MIN AI SOC value: the system starts charging during the next 15-minute interval. This is the more common case, because the AI continuously aims to stay within the default AI SOC range and reacts as soon as it detects that the limit has been crossed.

Above the high SOC limit for AI

If the SOC exceeds the MAX AI SOC limit, the excess energy is stored until an optimal time frame arises, either for self-consumption or for selling to the grid.

Special case: grid-only charging with insufficient PV

If the system is allowed to charge the battery only from the grid and PV production is lower than the site load, the battery is not charged. It remains in standby and is used only for peak shaving, until the SOC reaches the MIN SOC value defined for the battery.

Illustration of all segments of operation:

All segments of operation

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Diagram of battery state of charge (SOC) ranges as one horizontal bar from 0% (left) to 100% (right), on a white background. Segments from left to right: grey segment, label above: Protected reserve, 0-10% · below MIN SOC; light red segment, label: Low SOC excursion, 10-20% · below MIN AI SOC; large light green segment, label inside: Default AI SOC range, 20-95% · normal AI optimization; small orange segment at the right end, label: High SOC excursion, 95-100% · above MAX AI SOC. Dotted lines connect the labels Protected reserve, Low SOC excursion and High SOC excursion to their segments. Scale labels under the bar: 0% and 100%.

5.2 AI strategy for PV

The PV AI strategy works best in combination with a battery. PV production is then stored in the battery and reused in periods of high energy prices.

If no battery is involved, the PV AI strategy still maximises the profit:

• When the energy price is positive, the PV production covers the load and any surplus is exported to the grid up to the export limit.
• When the sell price of energy is negative but the buy price is positive, the system works as self-consumption with zero export. The PV production covers the load but does not export to the grid.
• When both the buy and the sell price of energy are negative, the mode is set to max-consumption. PV production is stopped, because it would otherwise result in losses.

5.3 AI strategy for EV charging (EVSE)

The AI strategy for EV charging is the most advanced strategy in the Reduxi AI strategy portfolio.

See the full description in this article: EVSE AI Strategy for Smart EV Charging

5.4 AI strategy for heat pump control (HVAC)

The HVAC AI strategy reduces energy costs without compromising comfort. Buildings have high thermal inertia. Heating and cooling can be shifted in time with only a small impact on indoor conditions.

When controlling heat pumps, Reduxi uses the operating modes the device supports, such as OFF, ECO, NORMAL and BOOST. The heat pump then operates within its designed control logic. This maintains optimal efficiency and protects the inverter. Operation remains safe and does not negatively impact equipment lifetime.

The heat pump savings are achieved in two ways:

a. Price-based optimisation

Energy prices vary throughout the day. Reduxi increases heat pump operation when prices are low and reduces it when prices are high.

For example, if electricity is more expensive between 17:00 and 20:00, the system can pre-heat the building slightly before this period. During the expensive hours, it reduces consumption and maintains comfort through the thermal inertia of the building.

This simulated example shows how the heat pump increases energy use during low-price intervals and reduces consumption during high-price intervals. The dotted grey line represents the energy price.

Price-based optimisation: the dotted grey line is the energy price

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Chart of a simulated heat pump strategy over one day, 5 Feb 2026 03:00 to 6 Feb 2026 00:00 (x axis Time). Left axis: Price [EUR/MWh] from 0 to 120; right axis: Temperature [C] from -1 to 1. Legend: Buy Price (dotted grey, shown active), Scaled Price (COP), Low Threshold, High Threshold, Historic Mean, Temperature, COP (these are faded), BOOST1, BOOST2, ECO1, ECO2. Dotted grey line Buy Price: about 80 to 90 EUR/MWh in the early hours, drops to about 0 at about 09:30 and stays near 0 until about 15:30, then rises to about 115 around 17:30 and declines to about 60 to 90 until midnight. Background areas: light red BOOST1 from about 09:30 to 15:30; light green ECO1 from about 16:30 to 19:30.

b. Weather-based optimisation

Heat pumps operate more efficiently at higher outdoor temperatures. This is defined by the Coefficient of Performance (COP). Reduxi uses weather forecasts to shift heating to periods with more favourable conditions. This improves efficiency and lowers costs, again without affecting comfort.

This simulated example shows how the heat pump reduces energy use during the night and increases power during warmer daytime hours to improve efficiency. The dotted orange line represents the temperature forecast.

Weather-based optimisation: the dotted orange line is the temperature forecast

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Chart of a simulated heat pump strategy over one day, 5 Feb 2026 03:00 to 6 Feb 2026 00:00 (x axis Time). Left axis: Price [EUR/MWh] from 99 to 101; right axis: Temperature [C] from 0 to 10. Legend: Buy Price, Scaled Price (COP), Low Threshold, High Threshold, Historic Mean, Temperature (dotted orange), COP, BOOST1 (red), BOOST2, ECO1 (light green), ECO2 (green). One dotted orange step line, Temperature: rises from about 0 C at 00:00 to a maximum of about 10 C around 12:00 to 14:00, then falls back to about 0 C at midnight. Background areas: light green ECO1 from 00:00 to about 04:00 and from about 22:00 to 24:00; light red BOOST1 from about 11:00 to 15:00. No price lines are visible.

6 Examples of AI strategies in practice

Key information for the AI strategy is visualised in the Charts view. Use the power chart in 15-minute resolution. The view includes:

Upper graph

• Historical power measurements: yellow = PV production, green = site consumption, blue = battery power, orange line = grid exchange
• Forecast from the current time until the end of the day

Bottom graph

• Red = energy purchase price, including all defined fees
• Green = energy selling price, including all defined fees
• Prices must be preconfigured in the cloud web application

Power chart with forecast (upper graph) and prices (bottom graph)

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Screenshot of a Reduxi Cloud power chart on a dark background, date 02. 03. 2026 ~ 02. 03. 2026 with arrows to change the day. Top icons: power pylon, sun, battery, battery with plug. Buttons: two small arrow toggles, 1 min, 15 min (selected), 1 hour, Power (selected), Energy. Upper chart: left axis Consumption and production from -240 kW to 320 kW, right axis from 0% to 100%, x axis 2. Mar 00:00 to 22:00. Yellow area (above zero, about 40 to 120 kW between 08:00 and 15:00), light blue area (peaks of about 170 to 210 kW around 07:00 and 17:30 to 19:30, and negative values down to about -160 kW between 08:00 and 13:00), green area close to zero, and a blue line on the right scale that falls from about 75% to 15% between 07:00 and 08:00, rises to 100% around 13:00 and falls to 15% around 20:00. Red letters mark areas: A at the first blue peak around 07:00, B in the yellow area around 10:00, C in the yellow area around 13:00, D at the evening blue peaks around 18:00. Lower chart titled Electricity pricing: axis Price [EUR/kWh] from -0.10 to 0.40; red step line Total (Buy) and green step line Total (Sell); both are about 0.10 and 0.05 at night, peak around 07:30 (about 0.20 and 0.15), fall to about 0.03 and -0.01 between 08:30 and 14:30, and peak at about 0.34 and 0.29 around 18:00.

6.1 Example: a typical example with a battery and PV

Battery and PV, segments A to E

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Screenshot of a Reduxi Cloud power chart on a dark background, date 04. 03. 2026 ~ 04. 03. 2026, with a vertical dashed line labelled Forecast at about 12:30 (the area right of it is shown in faded colours). Top icons: power pylon, sun, battery, battery with plug. Buttons: two arrow toggles, 1 min, 15 min (selected), 1 hour, Power (selected), Energy. Upper chart: left axis Consumption and production from -600 kW to 800 kW, right axis 0% to 105%, x axis 4. Mar 00:00 to 22:00. Light blue area at about -230 kW from 00:00 to 06:30, a blue block of about 500 kW between 06:30 and 07:30 with smaller blue peaks around 08:00 and 09:15; yellow area above zero growing to about 200 kW between 08:30 and 12:30 and fading to zero by 17:00 in the forecast part; light blue at about -400 kW between 11:30 and 12:30 and faded blue below zero between 12:30 and 18:00; large green band between about -200 kW and zero over the whole day; evening faded blue block of about 480 kW between 17:30 and 19:00 with small peaks around 20:00 to 22:00. Blue line on the right scale: rises from about 30% to 60% by 06:30, falls to about 15% by 08:00, stays low until 12:30, rises to about 90% around 17:30, then falls to about 15% by 19:30. Red letters: A under the light blue area around 02:00, B at the blue block around 07:00, C in the green band around 10:00, D in the faded green band around 14:00, E above the evening blue block around 18:00. Lower chart Electricity pricing: axis Price [c/kWh] from 0.00 to 0.40; red step line Total (Buy) about 0.12 until 06:00, about 0.14 from 06:00 to 22:00, about 0.12 afterwards; green step line Total (Sell) about 0.14 at night, peak about 0.33 around 07:00 (red letter B marks it), down to about 0.01 around 12:00, peak about 0.35 around 18:00, about 0.12 at 23:00.

• A – charging the battery during the night at low prices while keeping the grid limit at 240 kW
• B – discharging the battery to the grid at high prices
• C – using solar production to cover consumption at the location
• D – charging the battery from forecast solar production during a low-price period (forecast)
• E – discharging the battery to the grid at high prices (forecast)

6.2 Example: utility scale PV (dedicated location to PV production)

Utility scale PV, segments A to D

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Screenshot of a Reduxi Cloud power chart on a dark background, date 02. 03. 2026 ~ 02. 03. 2026 with arrows to change the day. Top icons: power pylon, sun, battery, battery with plug. Buttons: two small arrow toggles, 1 min, 15 min (selected), 1 hour, Power (selected), Energy. Upper chart: left axis Consumption and production from -240 kW to 320 kW, right axis from 0% to 100%, x axis 2. Mar 00:00 to 22:00. Yellow area (above zero, about 40 to 120 kW between 08:00 and 15:00), light blue area (peaks of about 170 to 210 kW around 07:00 and 17:30 to 19:30, and negative values down to about -160 kW between 08:00 and 13:00), green area close to zero, and a blue line on the right scale that falls from about 75% to 15% between 07:00 and 08:00, rises to 100% around 13:00 and falls to 15% around 20:00. Red letters mark areas: A at the first blue peak around 07:00, B in the yellow area around 10:00, C in the yellow area around 13:00, D at the evening blue peaks around 18:00. Lower chart titled Electricity pricing: axis Price [EUR/kWh] from -0.10 to 0.40; red step line Total (Buy) and green step line Total (Sell); both are about 0.10 and 0.05 at night, peak around 07:30 (about 0.20 and 0.15), fall to about 0.03 and -0.01 between 08:30 and 14:30, and peak at about 0.34 and 0.29 around 18:00.

• A – discharging the battery to the grid at high prices
• B – low-price period. Charging the battery purely from solar, with 0 kW at the grid, which means no import and no export
• C – after the battery is full, exporting PV energy to the grid
• D – discharging the battery to the grid during the three high-price intervals to maximise earnings

6.3 Example: household location with zero feed-in

Household with zero feed-in, segments A to D

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Screenshot of a Reduxi Cloud power chart on a dark background, date 14. 02. 2026 ~ 14. 02. 2026. Top icons: power pylon, battery, sun, battery with plug. Buttons: two arrow toggles, 1 min, 15 min (selected), 1 hour, Power (selected), Energy. Upper chart: left axis Consumption and production from -7.5 kW to 3 kW, right axis 0% to 105%, x axis 14. Feb 00:00 to 22:00. Green area below zero (about -3 kW most of the day, deeper to about -4 kW around 12:00); light blue area: a block of about 2.4 kW from 08:30 to 10:00 above zero, a block down to about -6 kW from 06:00 to 07:00, a block down to about -6.5 kW from 13:00 to 15:00, and blocks of about 2 kW above zero from 16:00 to 21:00; yellow area above zero with a small hump up to about 1 kW between 12:00 and 16:00. Blue line on the right scale: about 30% at 00:00, 80% around 06:30, 45% around 11:00, 100% at 15:00 to 16:00, falls to about 20% after 21:00. Red letters: A under the block at 06:00 to 07:00, B at the block at 08:30 to 10:00, C in the green area around 13:00, D in the green area around 19:00. Lower chart Electricity pricing: axis Price [c/kWh] from 0.75 to 2.50; red step line Total (Buy) about 1.80 at night, peaks about 2.30 around 09:00 and 2.38 around 18:00; green step line Total (Sell) about 1.12 at night, peaks about 1.60 around 09:00 and 1.68 around 18:00; legend Total (Buy), Total (Sell).

• A – charging the battery from the grid while keeping the grid limit at 6 kW
• B – discharging the battery to reduce grid import during high-price periods
• C – charging the battery from the grid and solar, with a maximum grid exchange of 6 kW, to store energy for the expensive evening hours
• D – discharging the battery during the expensive evening hours. No export to the grid is allowed due to the feed-in limit

6.4 Example: utility PV

Utility PV, segments A to D

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Screenshot of a Reduxi Cloud power chart on a dark background, date 28. 02. 2026 ~ 28. 02. 2026. Top icons: power pylon, battery, sun, battery with plug. Buttons: two arrow toggles, 1 min, 15 min (selected), 1 hour, Power (selected), Energy. Upper chart: left axis Consumption and production from -600 kW to 1.5 MW, right axis 0% to 105%, x axis 28. Feb 00:00 to 22:00. Yellow area (above zero) forms a bell shape from about 07:00 to 17:00 with a maximum of about 500 kW around 12:30; orange spiky line inside it between 08:00 and 11:00. Light blue area goes down to about -450 kW between 11:00 and 15:00 with spikes down from 08:30 to 11:00, and a tall block of about 900 kW between 17:30 and 19:00. Green area near zero. Blue line on the right scale: about 15% until 07:00, rises to 100% at 14:00 to 16:30, falls to 15% around 19:30. Red letters: A on the left part of the yellow area (about 10:00), B in its centre (about 12:30), C on its right part (about 15:30), D above the evening blue block (about 18:00). Lower chart Electricity pricing: axis Price [c/kWh] from -0.03 to 0.15; red step line Total (Buy) and green step line Total (Sell), both about 0.07 to 0.09 in the morning, falling to 0.00 between 09:30 and 15:30, rising to about 0.12 and 0.11 around 18:00, then about 0.06 at 23:00.

• A – intervals in which PV energy is either exported or stored in the battery. This depends on the cost in 15-minute intervals
• B – low-price period, purely charging the battery
• C – after the battery is fully charged, exporting PV production to the grid
• D – exporting the stored energy during high-price periods

6.5 Example: consumption forecast of a production location with 7:00–15:00 working time

Load forecast and solar production forecast

Text description of this image

Screenshot of a Reduxi Cloud power chart in faded forecast colours on a dark background, date 05. 03. 2026 ~ 05. 03. 2026. Top icons: power pylon, sun, battery, battery with plug. Buttons: two arrow toggles, 1 min, 15 min (selected), 1 hour, Power (selected), Energy. Upper chart: left axis Consumption and production from -120 kW to 90 kW, right axis 0% to 105% (label SoC), x axis 5. Mar 00:00 to 22:00. Olive-yellow area above zero in a bell shape from 06:00 to 18:00 with a maximum of about 70 kW around 11:00. Dark green area below zero: about -10 kW at night, falling to about -90 to -100 kW between 07:30 and 15:00 and returning to about -10 kW after 18:00; a thin line inside it between about -20 kW and -80 kW from 07:30 to 17:00. Lower chart Electricity pricing: axis Price [c/kWh] from 0.00 to 0.30; red step line Total (Buy) about 0.14 at night, peak about 0.24 around 08:00, near 0.00 between 11:00 and 14:30, peak about 0.24 around 16:30, about 0.22 to 0.15 in the evening; green step line Total (Sell) about 0.10 at night, peak about 0.18 around 08:00, near 0.00 around midday, peak about 0.18 around 17:00; legend Total (Buy), Total (Sell). No letters or highlights.

The chart shows a typical load forecast of a company with working hours from 7:00 to 15:00. It also shows a typical solar production forecast. The two forecasts are used to plan energy use accordingly.

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