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FCR Market

FCRmarket

Source code in markets\FCR_market.py 
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class FCRmarket:
    def __init__(self, market_config, battery_config, day, db):
        """
        Initializes the FCRmarket class with the given configurations and database connection.

        Args:
            market_config (dict): Configuration for the FCR market.
            battery_config (dict): Configuration for the battery, including energy and power limits.
            day (str): The day for which the market is being initialized.
            db (object): Database connection object for retrieving market data.
        """
        raw_data_path = "marketdata"
        market_path = "FCR"
        folder_path = os.path.join(raw_data_path, market_path)
        self.prices = self.get_fcr_prices(day, folder_path, db)
        self.market_config = market_config
        self.battery_config = battery_config
        self.set_marketable_power(battery_config, market_config)
        _, self.revenue = self.calculate_fcr_revenue(
            self.prices, market_config, battery_config
        )

    def get_fcr_prices(self, day, folder_path=None, db=None):
        """
        Retrieves FCR prices from a local file for the given day.

        Args:
            file_path (str): Path to the file containing FCR prices.
            day (str): The day for which FCR prices are retrieved.

        Returns:
            pd.Series: FCR prices for the specified day.
        """
        file_path = os.path.join(folder_path, f"FCR_{day}.csv")
        try:
            fcr_data = pd.read_csv(file_path, index_col=0, parse_dates=True)
            fcr_prices_ger = fcr_data["GERMANY_SETTLEMENTCAPACITY_PRICE_[EUR/MW]"]
        except FileNotFoundError:
            print(f"File {file_path} not found.")
            print("Trying to download the data from the database instead...")
            try:
                fcr_data = db.get_fcr_prices(day)
                fcr_prices_ger = fcr_data["GERMANY_SETTLEMENTCAPACITY_PRICE_[EUR/MW]"]
                fcr_prices_ger.index = pd.to_datetime(fcr_prices_ger.index)
                os.makedirs(folder_path, exist_ok=True)
                fcr_prices_ger.to_csv(file_path)
            except:
                raise Exception(f"Failed to get fcr data for {day}.")

        return fcr_prices_ger

    def get_fcr_prices_from_db(self, day, db):
        """
        Retrieves FCR prices from the database for the given day.

        Args:
            day (str): The day for which FCR prices are retrieved.
            db (object): Database connection object.

        Returns:
            pd.Series: FCR prices for the specified day.
        """
        # read xlsx file as downloaded from website
        fcr_data = db.get_fcr_prices(day)
        # fcr_data = all_fcr_data[all_fcr_data['TENDER_NUMBER']==1]
        fcr_prices_ger = fcr_data["GERMANY_SETTLEMENTCAPACITY_PRICE_[EUR/MW]"]

        # convert day to datetime
        day = pd.to_datetime(day)
        # convert index to datetime
        fcr_prices_ger.index = pd.to_datetime(fcr_prices_ger.index)

        return fcr_prices_ger

    def calculate_fcr_revenue(self, fcr_prices, market_config, battery_config):
        """
        Calculates the revenue for FCR based on prices, market configuration, and battery configuration.

        Args:
            fcr_prices (pd.Series): FCR prices for the day.
            market_config (dict): Market configuration with power share.
            battery_config (dict): Battery configuration with power and energy limits.

        Returns:
            pd.Series: Revenue for each 4-hour block of the day.
        """
        # Calculate the revenue for FCR based on the prices
        fcr_revenue = fcr_prices * self.marketable_power  # in € per 4 hour slot

        daily_revenue = fcr_revenue.sum()  # total revenue for the day

        block_revenue = pd.Series(index=range(6), data=0.0)
        for i in range(6):
            block_revenue[i] = fcr_revenue[i]

        return daily_revenue, block_revenue

    def set_marketable_power(self, battery_config, market_config):
        """
        Sets the marketable power and SOC boundaries based on battery and market configurations.

        Args:
            battery_config (dict): Battery configuration with power and energy limits.
            market_config (dict): Market configuration with power share.

        Notes:
            - Marketable power is calculated based on PQ-regulations formula 3.9.
            - SOC boundaries are calculated based on PQ-regulations formulas 3.7 and 3.8.
        """
        # What we can can max from aFRR regulations and power splitting
        self.marketable_power = (
            battery_config["power"] * market_config["power_share"] / 1.25
        )  # based on PQ-regulations formula 3.9
        self.reserved_power = battery_config["power"] * market_config["power_share"]

        self.lower_soc_boundary = (
            0.25 * self.marketable_power / battery_config["energy"]
        )  # based on PQ-regulations formula 3.8
        self.upper_soc_boundary = (
            1 - 0.25 * self.marketable_power / battery_config["energy"]
        )  # based on PQ-regulations formula 3.7

__init__(market_config, battery_config, day, db)

Initializes the FCRmarket class with the given configurations and database connection.

Parameters:

Name Type Description Default
market_config dict

Configuration for the FCR market.

 required
battery_config dict

Configuration for the battery, including energy and power limits.

 required
day str

The day for which the market is being initialized.

 required
db object

Database connection object for retrieving market data.

 required
Source code in markets\FCR_market.py 
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def __init__(self, market_config, battery_config, day, db):
    """
    Initializes the FCRmarket class with the given configurations and database connection.

    Args:
        market_config (dict): Configuration for the FCR market.
        battery_config (dict): Configuration for the battery, including energy and power limits.
        day (str): The day for which the market is being initialized.
        db (object): Database connection object for retrieving market data.
    """
    raw_data_path = "marketdata"
    market_path = "FCR"
    folder_path = os.path.join(raw_data_path, market_path)
    self.prices = self.get_fcr_prices(day, folder_path, db)
    self.market_config = market_config
    self.battery_config = battery_config
    self.set_marketable_power(battery_config, market_config)
    _, self.revenue = self.calculate_fcr_revenue(
        self.prices, market_config, battery_config
    )

calculate_fcr_revenue(fcr_prices, market_config, battery_config)

Calculates the revenue for FCR based on prices, market configuration, and battery configuration.

Parameters:

Name Type Description Default
fcr_prices Series

FCR prices for the day.

 required
market_config dict

Market configuration with power share.

 required
battery_config dict

Battery configuration with power and energy limits.

 required

Returns:

Type Description

pd.Series: Revenue for each 4-hour block of the day.
Source code in markets\FCR_market.py 
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def calculate_fcr_revenue(self, fcr_prices, market_config, battery_config):
    """
    Calculates the revenue for FCR based on prices, market configuration, and battery configuration.

    Args:
        fcr_prices (pd.Series): FCR prices for the day.
        market_config (dict): Market configuration with power share.
        battery_config (dict): Battery configuration with power and energy limits.

    Returns:
        pd.Series: Revenue for each 4-hour block of the day.
    """
    # Calculate the revenue for FCR based on the prices
    fcr_revenue = fcr_prices * self.marketable_power  # in € per 4 hour slot

    daily_revenue = fcr_revenue.sum()  # total revenue for the day

    block_revenue = pd.Series(index=range(6), data=0.0)
    for i in range(6):
        block_revenue[i] = fcr_revenue[i]

    return daily_revenue, block_revenue

get_fcr_prices(day, folder_path=None, db=None)

Retrieves FCR prices from a local file for the given day.

Parameters:

Name Type Description Default
file_path str

Path to the file containing FCR prices.

 required
day str

The day for which FCR prices are retrieved.

 required

Returns:

Type Description

pd.Series: FCR prices for the specified day.
Source code in markets\FCR_market.py 
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def get_fcr_prices(self, day, folder_path=None, db=None):
    """
    Retrieves FCR prices from a local file for the given day.

    Args:
        file_path (str): Path to the file containing FCR prices.
        day (str): The day for which FCR prices are retrieved.

    Returns:
        pd.Series: FCR prices for the specified day.
    """
    file_path = os.path.join(folder_path, f"FCR_{day}.csv")
    try:
        fcr_data = pd.read_csv(file_path, index_col=0, parse_dates=True)
        fcr_prices_ger = fcr_data["GERMANY_SETTLEMENTCAPACITY_PRICE_[EUR/MW]"]
    except FileNotFoundError:
        print(f"File {file_path} not found.")
        print("Trying to download the data from the database instead...")
        try:
            fcr_data = db.get_fcr_prices(day)
            fcr_prices_ger = fcr_data["GERMANY_SETTLEMENTCAPACITY_PRICE_[EUR/MW]"]
            fcr_prices_ger.index = pd.to_datetime(fcr_prices_ger.index)
            os.makedirs(folder_path, exist_ok=True)
            fcr_prices_ger.to_csv(file_path)
        except:
            raise Exception(f"Failed to get fcr data for {day}.")

    return fcr_prices_ger

get_fcr_prices_from_db(day, db)

Retrieves FCR prices from the database for the given day.

Parameters:

Name Type Description Default
day str

The day for which FCR prices are retrieved.

 required
db object

Database connection object.

 required

Returns:

Type Description

pd.Series: FCR prices for the specified day.
Source code in markets\FCR_market.py 
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def get_fcr_prices_from_db(self, day, db):
    """
    Retrieves FCR prices from the database for the given day.

    Args:
        day (str): The day for which FCR prices are retrieved.
        db (object): Database connection object.

    Returns:
        pd.Series: FCR prices for the specified day.
    """
    # read xlsx file as downloaded from website
    fcr_data = db.get_fcr_prices(day)
    # fcr_data = all_fcr_data[all_fcr_data['TENDER_NUMBER']==1]
    fcr_prices_ger = fcr_data["GERMANY_SETTLEMENTCAPACITY_PRICE_[EUR/MW]"]

    # convert day to datetime
    day = pd.to_datetime(day)
    # convert index to datetime
    fcr_prices_ger.index = pd.to_datetime(fcr_prices_ger.index)

    return fcr_prices_ger

set_marketable_power(battery_config, market_config)

Sets the marketable power and SOC boundaries based on battery and market configurations.

Parameters:

Name Type Description Default
battery_config dict

Battery configuration with power and energy limits.

 required
market_config dict

Market configuration with power share.

 required
Notes
  • Marketable power is calculated based on PQ-regulations formula 3.9.
  • SOC boundaries are calculated based on PQ-regulations formulas 3.7 and 3.8.
Source code in markets\FCR_market.py 
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def set_marketable_power(self, battery_config, market_config):
    """
    Sets the marketable power and SOC boundaries based on battery and market configurations.

    Args:
        battery_config (dict): Battery configuration with power and energy limits.
        market_config (dict): Market configuration with power share.

    Notes:
        - Marketable power is calculated based on PQ-regulations formula 3.9.
        - SOC boundaries are calculated based on PQ-regulations formulas 3.7 and 3.8.
    """
    # What we can can max from aFRR regulations and power splitting
    self.marketable_power = (
        battery_config["power"] * market_config["power_share"] / 1.25
    )  # based on PQ-regulations formula 3.9
    self.reserved_power = battery_config["power"] * market_config["power_share"]

    self.lower_soc_boundary = (
        0.25 * self.marketable_power / battery_config["energy"]
    )  # based on PQ-regulations formula 3.8
    self.upper_soc_boundary = (
        1 - 0.25 * self.marketable_power / battery_config["energy"]
    )  # based on PQ-regulations formula 3.7