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# -*- coding: utf-8 -*- |
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""" |
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SPDX-FileCopyrightText: Patrik Schönfeldt |
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SPDX-FileCopyrightText: DLR e.V. |
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SPDX-License-Identifier: MIT |
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""" |
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import logging |
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import warnings |
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from collections import namedtuple |
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from datetime import datetime |
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from pathlib import Path |
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import pandas as pd |
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import pytz |
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from cost_data import discounted_average_price |
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from cost_data import energy_prices |
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from cost_data import investment_costs |
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from create_timeseries import reshape_unevenly |
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from matplotlib import pyplot as plt |
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from oemof.network import graph |
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from oemof.tools import debugging |
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from oemof.tools import logger |
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from oemof.tools.economics import annuity |
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from shared import prepare_input_data |
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from oemof.solph import Bus |
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from oemof.solph import EnergySystem |
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from oemof.solph import Flow |
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from oemof.solph import Investment |
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from oemof.solph import Model |
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from oemof.solph import Results |
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from oemof.solph import components as cmp |
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warnings.filterwarnings( |
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"ignore", category=debugging.ExperimentalFeatureWarning |
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) |
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logger.define_logging() |
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def calculate_annuity(value): |
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return value / 20 |
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def calculate_fix_cost(value): |
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return value / 20 |
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def prepare_technical_data(minutes): |
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data = namedtuple("data", "even uneven") |
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df = prepare_input_data().resample(f"{minutes} min").mean() |
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df["ev charge (kW)"] = 0 |
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df_un = reshape_unevenly(df) |
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return data(even=df, uneven=df_un) |
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def prepare_cost_data(): |
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pass |
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def solve_model(data, year=2025, es=None, n=20, r=0.05): |
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if es is None: |
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es = EnergySystem(timeindex=data.index) |
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var_cost = discounted_average_price(energy_prices(), r, n, year) |
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invest_cost = investment_costs().loc[year] |
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# Create Investment objects from cost data |
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investments = {} |
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for key in ["gas boiler", "heat pump", "battery", "pv"]: |
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try: |
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epc = annuity( |
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invest_cost[(key, "specific_costs [Eur/kW]")], n, r |
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) |
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except KeyError: |
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epc = annuity( |
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invest_cost[(key, "specific_costs [Eur/kWh]")], n, r |
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) |
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fix_cost = calculate_fix_cost(invest_cost[(key, "fixed_costs [Eur]")]) |
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investments[key] = Investment(ep_costs=epc, fixed_costs=fix_cost) |
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# Buses |
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bus_el = Bus(label="electricity") |
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bus_heat = Bus(label="heat") |
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bus_gas = Bus(label="gas") |
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es.add(bus_el, bus_heat, bus_gas) |
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# Sources |
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es.add( |
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cmp.Source( |
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label="PV", |
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outputs={ |
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bus_el: Flow( |
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fix=data["PV (kW/kWp)"], |
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nominal_capacity=investments["pv"], |
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) |
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}, |
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) |
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) |
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es.add( |
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cmp.Source( |
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label="Shortage_heat", outputs={bus_heat: Flow(variable_costs=99)} |
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) |
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) |
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es.add( |
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cmp.Source( |
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label="Grid import", |
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outputs={ |
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bus_el: Flow( |
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variable_costs=var_cost["electricity_prices [Eur/kWh]"] |
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) |
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}, |
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) |
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) |
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es.add( |
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cmp.Source( |
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label="Gas import", |
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outputs={ |
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bus_el: Flow(variable_costs=var_cost["gas_prices [Eur/kWh]"]) |
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}, |
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) |
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) |
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# Battery |
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es.add( |
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cmp.GenericStorage( |
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label="Battery", |
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inputs={bus_el: Flow()}, |
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outputs={bus_el: Flow()}, |
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nominal_capacity=investments["battery"], # kWh |
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min_storage_level=0.0, |
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max_storage_level=1.0, |
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balanced=True, |
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loss_rate=0.001, # 0.1%/h |
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inflow_conversion_factor=0.95, # Lade-Wirkungsgrad |
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outflow_conversion_factor=0.95, # Entlade-Wirkungsgrad |
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) |
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) |
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# Sinks |
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es.add(cmp.Sink(label="Excess_el", inputs={bus_el: Flow()})) |
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es.add(cmp.Sink(label="Excess_heat", inputs={bus_heat: Flow()})) |
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es.add( |
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cmp.Sink( |
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label="Heat demand", |
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inputs={ |
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bus_heat: Flow( |
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fix=data["heat demand (kW)"], nominal_capacity=5.0 |
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) |
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}, |
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) |
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) |
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es.add( |
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cmp.Sink( |
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label="Electricity demand", |
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inputs={ |
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bus_el: Flow( |
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fix=data["electricity demand (kW)"], nominal_capacity=1.0 |
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) |
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}, |
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) |
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) |
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es.add( |
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cmp.Sink( |
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label="Electric Vehicle", |
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inputs={ |
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bus_el: Flow(fix=data["ev charge (kW)"], nominal_capacity=1.0) |
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}, |
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) |
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) |
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es.add( |
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cmp.Sink( |
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label="Grid Feed-in", |
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inputs={ |
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bus_el: Flow( |
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variable_costs=-var_cost["pv_feed_in [Eur/kWh]"] / 1000 |
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) |
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}, |
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) |
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) |
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# Heat Pump |
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es.add( |
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cmp.Converter( |
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label="Heat pump", |
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inputs={bus_el: Flow()}, |
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outputs={ |
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bus_heat: Flow(nominal_capacity=investments["heat pump"]) |
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}, |
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conversion_factors={bus_heat: data["cop"]}, |
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) |
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) |
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# Gas Boiler |
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es.add( |
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cmp.Converter( |
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label="Gas Boiler", |
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inputs={bus_gas: Flow()}, |
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outputs={ |
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bus_heat: Flow(nominal_capacity=investments["gas boiler"]) |
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}, |
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conversion_factors={bus_heat: data["cop"]}, |
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) |
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) |
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graph.create_nx_graph(es, filename=Path(Path.home(), "test_graph.graphml")) |
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# Create Model and solve it |
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logging.info("Creating Model...") |
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m = Model(es) |
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logging.info("Solving Model...") |
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m.solve(solver="cbc", solve_kwargs={"tee": False}) |
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# Create Results |
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return Results(m) |
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def process_results(results): |
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flow = results["flow"] |
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year = flow.index[0].year |
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end_time = pytz.utc.localize( |
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datetime.strptime(f"{year + 1}-01-01 00:00", "%Y-%m-%d %H:%M") |
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) |
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intervals = pd.Series( |
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flow.index.diff().seconds / 3600, index=flow.index |
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).shift(-1) |
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intervals.iloc[-1] = (end_time - flow.index[-2]).seconds / 3600 - 1 |
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# print(flow.mul(intervals, axis=0).sum()) |
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soc = results["storage_content"] |
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soc.name = "Battery SOC [kWh]" |
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investments = results["invest"].rename( |
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columns={ |
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c: c[0].label |
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for c in results["invest"].columns |
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if isinstance(c, tuple) |
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}, |
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) |
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print(investments) |
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def compare_results(even, uneven): |
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flow_e = even["flow"] |
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flow_u = uneven["flow"] |
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# print(flow_e) |
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# print(flow_u) |
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# |
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# # interval_hours = df.groupby(buckets).size().sort_index() |
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# # interval_hours.name = 'interval_hours' |
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# |
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# |
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# print("Energy Balance") |
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# print(flow.sum()) |
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# print("") |
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# print("Investment") |
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# print(investments.squeeze()) |
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# investments.squeeze().plot(kind="bar") |
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# |
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# day = 186 # day of the year |
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# n = 2 # number of days to plot |
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# flow = flow[day * 24 * 6 : day * 24 * 6 + n * 24 * 6] |
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# soc = soc[day * 24 * 6 : day * 24 * 6 + 48 * 6] |
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# |
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# supply = flow[[c for c in flow.columns if c[1].label == "electricity"]] |
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# supply = supply.droplevel(1, axis=1) |
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# supply.rename(columns={c: c.label for c in supply.columns}, inplace=True) |
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# demand = flow[[c for c in flow.columns if c[0].label == "electricity"]] |
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# demand = demand.droplevel(0, axis=1) |
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# demand.rename(columns={c: c.label for c in demand.columns}, inplace=True) |
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if __name__ == "__main__": |
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my_data = prepare_technical_data(10) |
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start = datetime.now() |
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results_even = solve_model(my_data.even) |
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time_even = datetime.now() - start |
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start = datetime.now() |
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results_uneven = solve_model(my_data.uneven) |
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time_uneven = datetime.now() - start |
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process_results(results_even) |
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process_results(results_uneven) |
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compare_results(results_even, results_uneven) |
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print("*** Times ****") |
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print("even", time_even) |
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print("uneven", time_uneven) |
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oemof /
oemof-solph
