EOS/docs/akkudoktoreos/resource.md

% SPDX-License-Identifier: Apache-2.0
(resource-page)=

# Resources (Device Simulations)

## Concepts

The simulations for resources are leaning on general concepts of the [S2 standard].

### Control Types

The control of resources and such what a resource simulation will simulate follows three
basic control principles:

- Operation Mode Based Control (OMBC)
- Fill Rate Based Control (FRBC)
- Demand Driven Based Control (DDBC)

Although these control principles differ enough to separate them into three distinct control types,
there are some common aspects that make them similar:

- Operation Modes
- Transitions and
- Timers.

The objective for a control type is under which circumstances what things can be adjusted, and what
the constraints are for these adjustments. The three control types model a virtual, abstract resource
for simulation.

The abstract resource ignores all details of pyhsical device that are not relevant to energy
management. In addition, physical devices have an enormous variety in parameters, sensors, control
strategies, concerns, safeguards, and so on. It would be practically impossible to develop a
simulation that can
understand all the parameters of all the physical devices on the market. By making the resource more
abstract, its concepts can be translated to all sorts of physical devices, even though internally
they function very differently. As a consequence, it not always possible to make a 100% accurate
description of all the behaviors and constraints in these abstractions. But the abstractions used
in the control types are quite powerful, and should allow you to come pretty close.

The control types basically define how the simulated resource can be described. The user in the end
selects the proper desciption of a physical device using the configuration options provided for
resource simulations. The configuration sets how the simulated resource functions, what it can do and
what kind of constraints it has.

### Resource Simulation

Based on the description of this virtual resource, the resource simulation can make predictions of
what the physical device will do in certain situations, and when it is allowed to execute
instructions generated by the optimization as part of the energy management plan evaluation.

### Resource Status

Once the physical device has changed it's behavior, the resource simulation should be informed
to make the simulation change it's state accordingly.

The actual state of a pyhsical device may be reported to the resource simulation by the
**PUT** `/v1/resource/status` API endpoint.

## Battery

There is a wealth of possible battery operation modes:

<!-- pyml disable line-length -->
| Mode                      | Purpose / Behavior                                                                      | Typical Trigger / Context                                        |
| ------------------------- | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
| **IDLE**                  | Battery neither charges nor discharges (SOC stable).                                    | No active control objective or power imbalance below thresholds. |
| **SELF_CONSUMPTION**      | Charge from PV surplus and discharge to cover local load.                               | PV generation > load (charge) or load > PV (discharge).          |
| **NON_EXPORT**   | Charge from on-site or local surplus with the goal of minimizing or preventing energy export to the external grid. Discharging to the grid is not allowed. | Export limit reached and SOC < SOC_max.                          |
| **PEAK_SHAVING**          | Discharge to keep grid import below a target threshold.                                 | Predicted or measured site load exceeds peak limit.              |
| **GRID_SUPPORT_EXPORT**   | Discharge energy to grid for revenue (V2G, wholesale market, flexibility service).      | Market or signal permits profitable export.                      |
| **GRID_SUPPORT_IMPORT**   | Charge from grid to absorb surplus or provide up-regulation service.                    | Low-price or grid-support signal detected.                       |
| **FREQUENCY_REGULATION**  | Rapid charge/discharge response to grid frequency deviations.                           | Active participation in frequency control.                       |
| **RAMP_RATE_CONTROL**     | Smooth site-level power ramp rates by buffering fluctuations.                           | Sudden PV/load change exceeding ramp limit.                      |
| **RESERVE_BACKUP**        | Maintain SOC ≥ reserve threshold to ensure backup capacity.                             | Resilience mode active, grid operational.                        |
| **OUTAGE_SUPPLY**         | Islanded operation: power local loads using stored energy (and PV if available).        | Grid failure detected.                                           |
| **FORCED_CHARGE**         | Manual or external control command to charge (e.g., pre-event, maintenance). No discharge.  | Operator or optimizer command. |
| **FORCED_DISCHARGE**      | Manual or external control command to discharge. No charge.                             | Operator or optimizer command.                                   |
| **FAULT**                 | Battery unavailable due to fault, safety, or protection state.                          | Fault detected (thermal, voltage, comms, etc.).                  |
<!-- pyml enable line-length -->

The optimization algorithm, the device simulation and the configuration properties only support the
most important of these modes.

### Battery Simulation

The battery simulation assumes an idealized battery model. Under this model, the battery can be
operated in three discrete operation modes with fill rate based control (FRBC):

| **Operation Mode ID**    | **Description**                                                       |
| ------------------------ | --------------------------------------------------------------------- |
| **SELF_CONSUMPTION**     | Charge from local surplus and discharge to cover local load.          |
| **NON_EXPORT**           | Charge from local surplus and do not discharge.                       |
| **FORCED_CHARGE**        | Charge.                                                               |

The **operation mode factor** (0.0–1.0) specifies the normalized power rate relative to the
battery's nominal maximum charge or discharge power. A value of 1.0 corresponds to full-rate
charging or discharging, while 0.0 indicates no power transfer. Intermediate values scale the power
proportionally.

The **fill level** (0.0–1.0) specifies the normalized fill level relative to the
battery's nominal maximum charge. A value of 1.0 corresponds to full while 0.0 indicates empty.
Intermediate values scale the fill level proportionally.

### Battery Configuration

### Battery Stati

To keep the battery simulation in synchonization with the actual stati of the battery the following
resource stati may be reported to EOS by the **PUT** `/v1/resource/status` API endpoint.

#### Battery FRBCActuatorStatus

The operation mode the battery is currently operated.

```json
{
  "type": "FRBCActuatorStatus",
  "active_operation_mode_id": "GRID_SUPPORT_IMPORT",
  "operation_mode_factor": "0.375",
  "previous_operation_mode_id": "SELF_CONSUMPTION",
  "transistion_timestamp": "20250725T12:00:12"
}
```

#### Battery FRBCStorageStatus

The current battery state of charge (SoC).

```json
{
  "type": "FRBCStorageStatus",
  "present_fill_level": "0.88"
}
```

#### Battery PowerMeasurement

The current power that the battery is charged or discharged with \[W\].

```json
{
  "type": "PowerMeasurement",
  "measurement_timestamp": "20250725T12:00:12",
  "values": [
    {
      "commodity_quantity": "ELECTRIC.POWER.L1",
      "value": "887.5"
    },
    {
      "commodity_quantity": "ELECTRIC.POWER.L2",
      "value": "905.5"
    },
    {
      "commodity_quantity": "ELECTRIC.POWER.L2",
      "value": "1100.7"
    },
  ]
}
```

For symmetric (or unknown) power distribution:

```json
{
  "type": "PowerMeasurement",
  "measurement_timestamp": "20250725T12:00:12",
  "values": [
    {
      "commodity_quantity": "ELECTRIC.POWER.3_PHASE_SYM",
      "value": "1000"
    }
  ]
}
```

## Electric Vehicle

The electric vehicle is basically a battery with a reduced set of operation modes.

### Electric Vehicle Instructions

The electric vehicle control instructions assume an idealized EV battery model. Under this model,
the EV battery can be operated in two operation modes:

| **Operation Mode ID** | **Description**                                                         |
| --------------------- | ----------------------------------------------------------------------- |
| **IDLE**              | Battery neither charges nor discharges; holds its state of charge.      |
| **FORCED_CHARGE**     | Charge at a specified power rate up to the allowable maximum.           |

The **operation mode factor** (0.0–1.0) specifies the normalized power rate relative to the
battery's nominal maximum charge power. A value of 1.0 corresponds to full-rate charging, while 0.0
indicates no power transfer. Intermediate values scale the power proportionally.

## Home Appliance

The optimization algorithm supports one start of the home appliance within the optimization
horizon.

### Home Appliance Simulation

### GENETIC0 Home Appliance Configuration

Home appliance to run within the optimization horizon.

```json
{
  "dishwasher1": {
    "device_id": "dishwasher1",
    "consumption_wh": 2000,
    "duration_h": 3
  }
}
```

Home appliance to run within a time window of 5 hours starting at 8:00 every day and another time
window of 3 hours starting at 15:00 every day. See
[Time Window Sequence Configuration](configtimewindow-page) for more information.

```json
{
  "dishwasher1": {
    "device_id": "dishwasher1",
    "consumption_wh": 2000,
    "duration_h": 3,
    "time_windows": {
      "windows": [
        {
          "start_time": "08:00",
          "duration": "5 hours"
        },
        {
          "start_time": "15:00",
          "duration": "3 hours"
        }
      ]
    }
  }
}
```

:::{admonition} Note
:class: note
The optimization algorithm always restricts to one start within the optimization horizon per
energy management run.
:::

### GENETIC Home Appliance Configuration

Home appliance to run once, unconstrained, within the optimization horizon:

```json
{
  "dishwasher1": {
    "device_id": "dishwasher1",
    "consumption_wh": 2000,
    "duration_h": 3,
    "num_cycles": 1
  }
}
```

Home appliance to run within a time window of 5 hours starting at 8:00. Each window's `value`
field carries the 0-based cycle index it applies to -- here there is only one cycle (index `0`),
so `num_cycles` does not need to be set explicitly; it is derived from the distinct cycle indices
found in `cycle_time_windows`. See
[ime Window Sequence Configuration](configimewindow-page) for more information.

```json
{
  "dishwasher1": {
    "device_id": "dishwasher1",
    "consumption_wh": 2000,
    "duration_h": 3,
    "cycle_time_windows": {
      "windows": [
        {
          "start_time": "08:00",
          "duration": "5 hours",
          "value": 0
        }
      ]
    }
  }
}
```

Home appliance that must run **twice** per day, each run confined to its own time window --
cycle 0 in the morning (07:00, 5 hours), cycle 1 in the evening (17:00, 5 hours) -- with at least
1 hour idle between the two runs:

```json
{
  "washing_machine1": {
    "device_id": "washing_machine1",
    "consumption_wh": 2000,
    "duration_h": 2,
    "min_cycle_gap_h": 1,
    "cycle_time_windows": {
      "windows": [
        {
          "start_time": "07:00",
          "duration": "5 hours",
          "value": 0
        },
        {
          "start_time": "17:00",
          "duration": "5 hours",
          "value": 1
        }
      ]
    }
  }
}
```

`min_cycle_gap_h` enforces a minimum idle time between the end of one cycle and the start of the
next; it defaults to `0`, which permits back-to-back runs. To let both cycles run anywhere in a
shared window instead of separate morning/evening slots, give both windows the same `start_time`
and `duration` but keep their distinct `value` (cycle index) -- the optimizer still places each
cycle independently within that shared window.

How many cycles remain to be scheduled on a given energy management run is read at runtime from
the measurement store, under the key configured in `cycles_completed_measurement_key` (defaults
to `{device_id}.cycles_completed`). This lets the optimizer skip cycles the appliance has already
completed earlier the same day.

:::{admonition} Note
:class: note
Unlike GENETIC0, the GENETIC algorithm can plan multiple cycles -- and therefore multiple starts
-- of the same appliance within a single energy management run, whenever `num_cycles` (or the
number of distinct cycle indices in `cycle_time_windows`) is greater than 1.
:::

### Home Appliance Instructions

The home appliance instructions assume an idealized home appliance model. Under this model,
the home appliance can be operated in two operation modes:

| **Operation Mode ID** | **Description**                                                         |
|-----------------------|-------------------------------------------------------------------------|
| **RUN**               | The home appliance is started and runs until the end of it's power      |
|                       | sequence.                                                               |
| **OFF**               | The home appliance does not run.                                        |

The **operation mode factor** (0.0–1.0) is ignored.