Importing a new time series

This notebook shows how to add a time series which will determine the grid price at every time step, overwriting the constant value in the base model. We change the cost function, similar to the subsidy example.

from pyehub.energy_hub.ehub_model import EHubModel
from pyehub.energy_hub.utils import constraint
from pyehub.outputter import pretty_print

Extend the EHubModel class and alter the constraint which calculates the operating cost. + calculate_operating_cost(self) is a constraint in the EHubModel class which calculates the operating cost using a constant grid price in the input file. + We also include Feed In Tariff income generated by exporting energy from export_streams. + Specify the grid_price time series. + For every converter which has ‘Grid’ as input, we calculate the operating cost using the grid_price time series. For other input streams, we find the total energy imported across all time steps and multiply it by a constant FUEL_PRICE of that input stream in the usual way. + Impose the operating cost constraint (i.e. operating_cost = total_fuel_bill - total_export_income).

class MyModel(EHubModel):
    """
    This is a subclass of the original EHubModel.

    We create a subclass so that we can modify the superclass (or parent
    class) without modifying the superclass's source code.
    """

    @constraint()
    def calc_operating_cost(self):
        """
        Calculate a new total cost that uses a price per time step.

        Instead of using a fixed price for electricity from the grid, we
        instead use a time series that contains the price per time step.
        """
        # calculate the export income
        total_export_income = 0

        for stream in self.export_streams:
            # If the price is time series
            if self.FEED_IN_TARIFFS[stream] in self.TIME_SERIES:
                for t in self.time:
                    price = self.TIME_SERIES[self.FEED_IN_TARIFFS[stream]][t]
                    energy_exported = self.energy_exported[t][stream]
                    total_export_income += price * energy_exported

            else:
                total_energy_exported = sum(
                    self.energy_exported[time][stream] for time in self.time
                )
                price = self.FEED_IN_TARIFFS[stream]
                total_export_income += price * total_energy_exported

        # Here we add the specific time series called `grid_price`
        grid_price = {
            0: 0.13,
            1: 1.13,
            2: 2.13,
            3: 0.13,
            4: 0.13,
            5: 0.13,
            6: 0.13,
            7: 0.13,
            8: 0.13,
            9: 0.13,
            10: 0.13,
        }
        total_fuel_bill = 0
        for tech in self._data.converters:

            if "Grid" in tech.inputs:
                total_fuel_bill += sum(
                    grid_price[t] * self.energy_imported[t]["Grid"] for t in self.time
                )

            # Default to the fixed price for non `Grid` converters
            for inp in tech.inputs:
                if (inp != "Grid") and (inp in self.import_streams):
                    total_fuel_bill += self.FUEL_PRICES[inp] * sum(
                        self.energy_imported[t][inp] for t in self.time
                    )

        return self.operating_cost == total_fuel_bill - total_export_income

excel_file = "test_file_all_constraints_work.xlsx"  # name of the excel file. [This must be in the current directory]
my_model = MyModel(
    excel=excel_file
)  # instantiate our model. Nothing is solved at this point.
results = my_model.solve()  # solve the model and get back our results
pretty_print(results)  # print the results to the console