Energy System Simulation and Optimization

This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.

Getting Involved

The discussion takes place in the forum. Please report bugs in the Issue Tracker. We welcome code contributions and bug fixes via Pull Requests.

Installation

Good installation guide: https://meintechblog.de/2024/09/05/andreas-schmitz-joerg-installiert-mein-energieoptimierungssystem/

The project requires Python 3.8 or newer.

Quick Start Guide

On Linux (Ubuntu/Debian):

sudo apt install make

On MacOS (requires Homebrew):

brew install make

Next, adjust config.example.py and rename it to config.py. The server can then be started with make run. A full overview of the main shortcuts is given by make help.

Detailed Instructions

All necessary dependencies can be installed via pip. Clone the repository and install the required packages with:

git clone https://github.com/Akkudoktor-EOS/EOS
cd EOS

Next, create a virtual environment. This serves to store the Python dependencies, which we will install later using pip:

virtualenv .venv

Finally, install the Python dependencies for EOS:

.venv/bin/pip install -r requirements.txt

To always use the Python version from the virtual environment, you should activate it before working in EOS:

source .venv/bin/activate

(for Bash users, the default under Linux) or

. .venv/bin/activate

(if using zsh, primarily for MacOS users).

If pip install fails to install the mariadb dependency, the following commands may help:

• Debian/Ubuntu: sudo apt-get install -y libmariadb-dev
• MacOS/Homebrew: brew install mariadb-connector-c

Followed by a renewed pip install -r requirements.txt.

Usage

Adjust config.example.py and rename it to config.py. To use the system, run flask_server.py, which starts the server:

./flask_server.py

Classes and Functionalities

This project uses various classes to simulate and optimize the components of an energy system. Each class represents a specific aspect of the system, as described below:

• PVAkku: Simulates a battery storage system, including capacity, state of charge, and now charge and discharge losses.

• PVForecast: Provides forecast data for photovoltaic generation, based on weather data and historical generation data.

• Load: Models the load requirements of a household or business, enabling the prediction of future energy demand.

• HeatPump: Simulates a heat pump, including its energy consumption and efficiency under various operating conditions.

• Strompreis: Provides information on electricity prices, enabling optimization of energy consumption and generation based on tariff information.

• EMS: The Energy Management System (EMS) coordinates the interaction between the various components, performs optimization, and simulates the operation of the entire energy system.

These classes work together to enable a detailed simulation and optimization of the energy system. For each class, specific parameters and settings can be adjusted to test different scenarios and strategies.

Customization and Extension

Each class is designed to be easily customized and extended to integrate additional functions or improvements. For example, new methods can be added for more accurate modeling of PV system or battery behavior. Developers are invited to modify and extend the system according to their needs.

Input for the Flask Server (as of 30.07.2024)

Describes the structure and data types of the JSON object sent to the Flask server, with a forecast period of 48 hours.

JSON Object Fields

strompreis_euro_pro_wh

• Description: An array of floats representing the electricity price in euros per watt-hour for different time intervals.
• Type: Array
• Element Type: Float
• Length: 48

gesamtlast

• Description: An array of floats representing the total load (consumption) in watts for different time intervals.
• Type: Array
• Element Type: Float
• Length: 48

pv_forecast

• Description: An array of floats representing the forecasted photovoltaic output in watts for different time intervals.
• Type: Array
• Element Type: Float
• Length: 48

temperature_forecast

• Description: An array of floats representing the temperature forecast in degrees Celsius for different time intervals.
• Type: Array
• Element Type: Float
• Length: 48

pv_soc

• Description: An integer representing the state of charge of the PV battery at the start of the current hour (not the current state).
• Type: Integer

pv_akku_cap

• Description: An integer representing the capacity of the photovoltaic battery in watt-hours.
• Type: Integer

einspeiseverguetung_euro_pro_wh

• Description: A float representing the feed-in compensation in euros per watt-hour.
• Type: Float

eauto_min_soc

• Description: An integer representing the minimum state of charge (SOC) of the electric vehicle in percentage.
• Type: Integer

eauto_cap

• Description: An integer representing the capacity of the electric vehicle battery in watt-hours.
• Type: Integer

eauto_charge_efficiency

• Description: A float representing the charging efficiency of the electric vehicle.
• Type: Float

eauto_charge_power

• Description: An integer representing the charging power of the electric vehicle in watts.
• Type: Integer

eauto_soc

• Description: An integer representing the current state of charge (SOC) of the electric vehicle in percentage.
• Type: Integer

start_solution

• Description: Can be null or contain a previous solution (if available).
• Type: null or object

haushaltsgeraet_wh

• Description: An integer representing the energy consumption of a household device in watt-hours.
• Type: Integer

haushaltsgeraet_dauer

• Description: An integer representing the usage duration of a household device in hours.
• Type: Integer

JSON Output Description

This document describes the structure and data types of the JSON output returned by the Flask server, with a forecast period of 48 hours.

JSON Output Fields (as of 30.7.2024)

Explanation of the Output Fields

1. discharge_hours_bin

An array that indicates for each hour of the forecast period (in this example, 48 hours) whether energy is discharged from the battery or not. The values are either 0 (no discharge) or 1 (discharge).

2. eauto_obj

This object contains information related to the electric vehicle and its charging and discharging behavior:

• charge_array: Indicates for each hour whether the EV is charging (0 for no charging, 1 for charging).
• discharge_array: Indicates for each hour whether the EV is discharging (0 for no discharging, 1 for discharging).
• entlade_effizienz: The discharge efficiency as a float.
• kapazitaet_wh: The capacity of the EV’s battery in watt-hours.
• lade_effizienz: The charging efficiency as a float.
• max_ladeleistung_w: The maximum charging power of the EV in watts.
• soc_wh: The state of charge of the battery in watt-hours at the start of the simulation.
• start_soc_prozent: The state of charge of the battery in percentage at the start of the simulation.

3. result

This object contains the results of the simulation and provides insights into various parameters over the entire forecast period:

• E-Auto_SoC_pro_Stunde: The state of charge of the EV for each hour.
• Eigenverbrauch_Wh_pro_Stunde: The self-consumption of the system in watt-hours per hour.
• Einnahmen_Euro_pro_Stunde: The revenue from grid feed-in or other sources in euros per hour.
• Gesamt_Verluste: The total losses in watt-hours over the entire period.
• Gesamtbilanz_Euro: The total balance of revenues minus costs in euros.
• Gesamteinnahmen_Euro: The total revenues in euros.
• Gesamtkosten_Euro: The total costs in euros.
• Haushaltsgeraet_wh_pro_stunde: The energy consumption of a household appliance in watt-hours per hour.
• Kosten_Euro_pro_Stunde: The costs in euros per hour.
• Netzbezug_Wh_pro_Stunde: The grid energy drawn in watt-hours per hour.
• Netzeinspeisung_Wh_pro_Stunde: The energy fed into the grid in watt-hours per hour.
• Verluste_Pro_Stunde: The losses in watt-hours per hour.
• akku_soc_pro_stunde: The state of charge of the battery (not the EV) in percentage per hour.