Slimmie Documentation

Slimmie is an Energy Management System (EMS) that coordinates solar panels, home batteries, charging stations, and heat pumps. It offers two primary optimization modes:

1. Self-consumption Optimization: Maximizes the use of locally generated energy.
2. Energy Cost Optimization: Minimizes costs for users with dynamic energy contracts by analyzing hourly energy prices to determine the most cost-effective strategy.

The Groups feature allows you to organize devices to match your electrical installation (similar to a single-line diagram). This is critical for correct system operation and overload protection. There are two configuration modes available in the Groups tab of the commissioning interface:

1. Simple Groups (Default): Recommended for most installations. Slimmie automatically uses the first energy meter configured as Netverbinding (Grid connection) as the main meter and places all devices into a single group. This mimics a setup with one energy meter and one distribution board.
2. Advanced Groups: Used for complex installations, such as those requiring multiple energy meters, sub-metering, or separate overcurrent monitoring for specific groups (e.g., a charging plaza).

The Overview screen displays the current state of the energy system. Energy flows between components are represented by moving colored dots, where the direction of movement indicates the flow of energy. Power is displayed in kW next to each component.

Commonly visible components include:

• Netaansluiting (Grid connection): Consumption from or injection into the electricity grid.
• Zonne-energie (Solar energy): Current power from solar panels.
• Batterij (Battery): Charging/discharging power and state of charge.
• Laadpaal (Charging station): Current charging power.
• HVAC: Power of linked heat pumps, boilers, or other HVAC devices.
• Woning (Home): Current home consumption.

Slimmie uses machine learning to predict grid power (net power) for the next 12 to 36 hours. The predictions are based on historical data, the specific time, the day of the week, and solar irradiation from weather data.

Slimmie predicts grid power rather than separate PV or consumption power because:

1. Grid power is the primary parameter for billing and control algorithms.
2. Grid power is typically measured directly by the energy meter, providing more accurate data than calculated base-load consumption or potentially unread PV inverters.

When using the 'Minimize Costs' control mode, Slimmie determines battery behavior based on the lowest possible total energy costs. The algorithm considers four primary factors:

1. Energy Prices: Both purchase and sale prices for the known period.
2. Predicted Grid Power: Uses predictions to estimate expected grid power with a built-in margin for uncertainty.
3. Battery Efficiency: Accounts for energy loss during charging and discharging (roundtrip efficiency).
4. Minimum Price Difference: Only triggers actions when the price difference between cheap and expensive periods is large enough to avoid unprofitable cycles and unnecessary wear.

Requirement: Cost optimization requires a dynamic energy contract.

Slimmie calculates energy costs based on variable costs and reimbursements per kWh for electricity taken from or delivered to the grid. Fixed annual costs (e.g., supplier fees, data management, capacity tariffs) are excluded as they do not affect the per-kWh price.

Energy costs consist of four components:

1. Energy Component: The amount per kWh charged by the supplier (Fixed, Variable, Dynamic, Flex, or Advanced).
2. Network Costs: Distribution and transport tariffs from the grid operator (e.g., Fluvius in Flanders).
3. Levies (Heffingen): Government taxes such as energy contributions and electricity excise duties.
4. VAT (BTW): 6% VAT is applied to all components except the Flemish Energy Fund levy. VAT is ignored for professional customers as it is recoverable.

Slimmie uses machine learning to predict net grid power for the next 12 to 36 hours. These predictions are based on historical data, the specific time and day of the week, and solar irradiation from weather data.

Predicting net grid power (rather than just PV production or consumption) is preferred because:

1. It is the primary parameter for billing and control algorithms.
2. It relies on direct measurements from the electricity meter, which are more accurate than calculated values or data from inverters that may not be fully read.

The Overview screen visualizes the current state of the energy system. Energy flow is indicated by moving colored dots on lines connecting system components. The direction of movement shows the flow of energy, and the current power is displayed in kW next to each component.

Commonly visible components include:

• Netaansluiting (Grid Connection): Consumption from or injection to the grid.
• Zonne-energie (Solar Energy): Current power from solar panels.
• Batterij (Battery): Charge/discharge power and state of charge.
• Laadpaal (Charging Station): Current charging power.
• HVAC: Power of connected heat pumps, boilers, or other HVAC devices.
• Woning (Home): Current home consumption.

Slimmie uses machine learning to predict net power for the near future (12 to 36 hours ahead) based on available weather and price data. The predictions are derived from historical data relative to a specific time, day of the week, and solar irradiation from weather data.

Slimmie focuses on predicting net power (the power at the electricity grid meter) rather than just PV production or consumption because:

1. Net power is the primary parameter for billing and control algorithms.
2. Grid meter values are typically measured directly, making them more accurate than calculated base-load consumption or PV values from non-integrated inverters.

Tapping a component in the Overview opens a widget containing detailed status and control information. A widget consists of:

1. Title and Control Mode: Displays the device type and current control mode (e.g., Cost Optimization, No Control, Peak Shaving Only, or Self-Consumption Optimization).
2. Analyze Data (Gegevens analyseren): A shortcut to the historical data for that device in the Analyseren tab.
3. Status Description: Explains what Slimmie is currently doing with the device or the strategy being applied.
4. Most Limiting Factor: Indicates the primary factor currently determining or limiting the device's control.
5. Current Status: Displays the current state or power (e.g., battery charging at 2.0 kW).
6. Additional Information: Device-specific data. For batteries, this includes State of Charge (SoC %), stored energy (kWh), and total capacity (kWh).