Power-simulation

Simulation of battery for Greener Power Solutions with a modbus server. A software engineering project for Rijksuniversiteit Groningen.

Prerequisites

The software requires Python 3.5

To use this project you need to clone the repo:

git clone https://github.com/SjoerdHilhorst/power-simulation.git

Install the required python packages in requirements.txt with pip

pip install -r requirements.txt

Usage

Setting up a simulation

1. Navigate to config/env.py
2. Input the device information, and simulation parameters
3. Update the fields dictionary to match your own battery setup

• reg_type to the type of modbus registry
• address to the address in the modbus server
• encode to the type of encoding used for the field (see encoding for more details)
• init (optional) to fill a register with a initial value

Changing the input of a field

The simulation has predefined relations for every relational field.

Field	Relation
active_power_converter	active_power_in - active_power_out
reactive_power_converter	reactive_power_in - reactive_power_out
soc	previous SoC + [(active_power_converter) / (Some configurable max battery capacity, say 330 kWh)] * 3600.
voltage_l1_l2_in	Gaussian distribution centered around 400, deviation 3
voltage_l2_l3_in	Gaussian distribution centered around 400, deviation 3
voltage_l3_l1_in	Gaussian distribution centered around 400, deviation 3
current_l1_in	active_power_in / (sqrt(3) * voltage_l1_l2_in * power_factor_in)
current_l2_in	active_power_in / (sqrt(3) * voltage_l2_l3_in * power_factor_in)
current_l3_in	active_power_in / (sqrt(3) * voltage_l3_l1_in * power_factor_in)
frequency_in	Gaussian distribution centered around 50, deviation 0.01
voltage_l1_l2_out	Gaussian distribution centered around 400, deviation 3
voltage_l2_l3_out	Gaussian distribution centered around 400, deviation 3
voltage_l3_l1_out	Gaussian distribution centered around 400, deviation 3
current_l1_out	active_power_out / (sqrt(3) * voltage_l1_l2_out * power_factor_out)
current_l2_out	active_power_out / (sqrt(3) * voltage_l2_l3_out * power_factor_out)
current_l3_out	active_power_out / (sqrt(3) * voltage_l3_l1_out * power_factor_out)
frequency_out	Gaussian distribution centered around 50, deviation 0.01

Active power in, active power out, reactive power in, and reactive power out are fields from input and have to be provided by the user. For every depended field (see table), it also is possible change the relations.

The user can define or redefine the input in the following 2 ways:

CSV

Suppose we want current_l2_out from historic_battery_data.csv instead of the predefined relation.

1. in config/env.py update from_csv with the line current_l2_out: 'historic_battery_data' dictionary. Note that the key, in this case current_l2_out refers to a column in the csv table and not the simulation field.
2. in simulations/simulation.py add a method which overrides the method in the simulation_super class:

def get_current_l2_out(self):
  current_l2_out = self.csv_reader.get_from_csv('current_l2_out')
  return current_l2_out

Note that the method name get_current_l2_out has to be the same as in the simulation_super class

Mathematical function

Suppose now we want active_power_converter to be (active_power_in - active_power_out) * 0.1) instead of the predefined relation.

1. in simulations/simulation.py add a method which overrides the method in the simulation_super class:

def get_activer_power_converter(self):
  apc = self.battery.get_value(self.fields['active_power_in']) - self.battery.get_value(self.fields['active_power_out'])
  return apc * 0.1

Note that the method name get_active_power_converter has to be the same as in the simulation_super class

Adding a new field

Suppose now we want to add a new field custom to our simulation

1. in config/env.py update fields with your new field. If we want the field to be constant we can make use of the init to provide a constant value.
2. To provide a relation we can add a new method to simulations/simulations like for example the sine of the time elapsed:

    def get_custom(self):
        return sin(self.time_elapsed)

3. Add the method to the update_custom function method, so that custom field will get updated every iteration

    def update_custom(self):
        self.battery.set_value(self.fields['custom'], self.get_custom())

Graph

To enable/disable the graph and/or add new fields to the plot, update graph in config/env.py

Database

To enable/disable the Database, update database in config/env.py. Note that enabling the database will hinder performance of the program.

Now you're all setup, run

python3 main.py

Authors

• Mariya
• Victor
• Chris
• Sjoerd

Acknowledgements

Huge thanks to Jasper Clarijs from Greener Power Solutions for setting us up with the project and answering questions and Alex Tutea our TA For guiding us through the software engineering course.