data.datasets.hydrogen_etrago.power_to_h2

Complexity

Total Complexity

8

Size/Duplication

Total Lines	242
Duplicated Lines	19.42 %

Importance

Changes

0

# -*- coding: utf-8 -*-
"""
Module containing the definition of the AC grid to H2 links

In this module the functions used to define and insert into the database
the links between H2 and AC buses are to be found.
These links are modelling:
  * Electrolysis (carrier name: 'power_to_H2'): technology to produce H2
    from AC
  * Fuel cells (carrier name: 'H2_to_power'): techonology to produce
    power from H2
  * Waste_heat usage (carrier name: 'PtH2_waste_heat'): Components to use 
    waste heat as by-product from electrolysis
  * Oxygen usage (carrier name: 'PtH2_O2'): Components to use 
    oxygen as by-product from elctrolysis
    
 
"""
import pandas as pd
import math
import geopandas as gpd
from itertools import count
from sqlalchemy import text
from shapely.geometry import MultiLineString, LineString, Point
from shapely.wkb import dumps
from egon.data import db, config
from egon.data.datasets.scenario_parameters import get_sector_parameters
from pathlib import Path
import numpy as np
from shapely.strtree import STRtree



def insert_power_to_h2_to_power():
    """
    Insert electrolysis and fuel cells capacities into the database.
    For electrolysis potential waste_heat- and oxygen-utilisation is 
    implemented if district_heating-/oxygen-demand is nearby electrolysis
    location

    The potentials for power-to-H2 in electrolysis and H2-to-power in
    fuel cells are created between each HVMV Substaion (or each AC_BUS related 
    to setting SUBSTATION) and closest H2-Bus (H2 and H2_saltcaverns) inside 
    buffer-range of 30km. 
    For oxygen-usage all WWTP within MV-district and buffer-range of 10km 
    is connected to relevant HVMV Substation
    For heat-usage closest central-heat-bus inner an dynamic buffer is connected 
    to relevant HVMV-Substation.
    
    All links are extendable. 

    This function inserts data into the database and has no return.

    Parameters
    ----------
    scn_name : str
        Name of the scenario

    Returns
    -------
    None

    """
    scenarios = config.settings()["egon-data"]["--scenarios"]  
    
    # General Constant Parameters  
    DATA_CRS = 4326  # default CRS
    METRIC_CRS = 32632  # demanded CRS
 
    ELEC_COST = 60  # [EUR/MWh]
    O2_PRESSURE_ELZ = 13  # [bar]
    FACTOR_AERATION_EC = 0.6  # [%] aeration EC from total capacity of WWTP (PE)
    FACTOR_O2_EC = 0.8  # [%] Oxygen EC from total aeration EC
    O2_PRESSURE_MIN = 2  # [bar]
    MOLAR_MASS_O2 = 0.0319988  # [kg/mol]
    PIPELINE_DIAMETER_RANGE = [0.10, 0.15, 0.20, 0.25, 0.30, 0.40, 0.50]  # [m]
    TEMPERATURE = 15 + 273.15  # [Kelvin] degree + 273.15
    UNIVERSAL_GAS_CONSTANT = 8.3145  # [J/(mol·K)]
  
    # Power to O2 (Wastewater Treatment Plants)
    WWTP_SEC = {
        "c5": 29.6,
        "c4": 31.3,
        "c3": 39.8,
        "c2": 42.1,
    }  # [kWh/year] Specific Energy Consumption
    
    H2 = "h2"
    WWTP = "wwtp"
    AC = "ac"
    H2GRID = "h2_grid"
    ACZONE_HVMV = "ac_zone_hvmv"
    ACZONE_EHV = "ac_zone_ehv"
    ACSUB_HVMV = "ac_sub_hvmv"
    ACSUB_EHV = "ac_sub_ehv"
    HEAT_BUS = "heat_point"
    HEAT_LOAD = "heat_load"
    HEAT_TIMESERIES = "heat_timeseries" 
    H2_BUSES_CH4 = 'h2_buses_ch4' 
    AC_LOAD = 'ac_load'
    HEAT_AREA = 'heat_area'
    
    #buffer_range
    buffer_heat_factor= 625  # [m/MW_th] 625/3125 for worstcase/bestcase-Szeanrio
    max_buffer_heat= 5000 #[m] 5000/30000 for worstcase/bestcase-Szenario 
    Buffer = {
        "O2": 5000,  # [m] 
        "H2_HVMV": 5000, 
        "H2_EHV": 20000,
        "HVMV": 10000,
        "EHV": 20000,
        "HEAT": 5000,
    }  
    
    # connet to PostgreSQL database (to localhost)
    engine = db.engine()
    
    data_config = config.datasets()
    sources = data_config["PtH2_waste_heat_O2"]["sources"]
    targets = data_config["PtH2_waste_heat_O2"]["targets"]
    
    for SCENARIO_NAME in scenarios:
        
        if SCENARIO_NAME not in ["eGon100RE", "eGon2035"]:
            continue
        
        scn_params_gas = get_sector_parameters("gas", SCENARIO_NAME)
        scn_params_elec = get_sector_parameters("electricity", SCENARIO_NAME)
        
        AC_TRANS = scn_params_elec["capital_cost"]["transformer_220_110"]  # [EUR/MW/YEAR]
        AC_COST_CABLE = scn_params_elec["capital_cost"]["ac_hv_cable"]   #[EUR/MW/km/YEAR]
        ELZ_CAPEX_SYSTEM = scn_params_gas["capital_cost"]["power_to_H2_system"]   # [EUR/MW/YEAR]
        ELZ_CAPEX_STACK = scn_params_gas["capital_cost"]["power_to_H2_stack"]  # [EUR/MW/YEAR]
        ELZ_LIFETIME_Y = scn_params_gas["lifetime"]["power_to_H2_system"]  # [Year] 
        if SCENARIO_NAME == 'eGon2035':
            ELZ_OPEX = scn_params_gas["capital_cost"]["power_to_H2_OPEX"]# [EUR/MW/YEAR]
        else:
            ELZ_OPEX = 0  # [EUR/MW/YEAR] , for eGon100RE OPEX are already included in SYSTEM and STACK costs
        H2_COST_PIPELINE = scn_params_gas["capital_cost"]["H2_pipeline"]  #[EUR/MW/km/YEAR] 
        ELZ_EFF = scn_params_gas["efficiency"]["power_to_H2"] 
        
        HEAT_COST_EXCHANGER = scn_params_gas["capital_cost"]["Heat_exchanger"]  # [EUR/MW/YEAR]
        HEAT_COST_PIPELINE = scn_params_gas["capital_cost"]["Heat_pipeline"] # [EUR/MW/YEAR]  
      
        O2_PIPELINE_COSTS = scn_params_gas["O2_capital_cost"]   #[EUR/km/YEAR]
        O2_COST_EQUIPMENT = scn_params_gas["capital_cost"]["O2_components"]  #[EUR/MW/YEAR]
         
        FUEL_CELL_COST = scn_params_gas["capital_cost"]["H2_to_power"]   #[EUR/MW/YEAR]
        FUEL_CELL_EFF = scn_params_gas["efficiency"]["H2_to_power"] 
        FUEL_CELL_LIFETIME = scn_params_gas["lifetime"]["H2_to_power"]
                               
        
        
        def export_o2_buses_to_db(df):
            max_bus_id = db.next_etrago_id("bus")
            next_bus_id = count(start=max_bus_id, step=1)
            schema = targets['buses']['schema']
            table_name = targets['buses']['table']

            db.execute_sql(
                        f"DELETE FROM {schema}.{table_name} WHERE carrier = 'O2' AND scn_name='{SCENARIO_NAME}'"
                )
            df = df.copy(deep=True)
            result = []
            for _, row in df.iterrows():
                bus_id = next(next_bus_id)
                result.append(
                    {
                        "scn_name": SCENARIO_NAME,
                        "bus_id": bus_id,
                        "v_nom": "110",
                        "type": row["KA_ID"],
                        "carrier": "O2",
                        "x": row["Koord_Kläranlage_rw"],
                        "y": row["Koord_Kläranlage_hw"],
                        "geom": dumps(
                            Point(
                                row["Koord_Kläranlage_rw"], row["Koord_Kläranlage_hw"]
                            ),
                            srid=DATA_CRS,
                        ),
                        "country": "DE",
                    }
                )
            result_df = pd.DataFrame(result)
            result_df.to_sql(table_name, engine, schema=schema, if_exists="append", index=False)
        
        
        wwtp_spec = pd.read_csv(Path(".")/"WWTP_spec.csv")
        export_o2_buses_to_db(wwtp_spec)  # Call the function with the dataframe
        
        # dictionary of SQL queries
        queries = {
            WWTP: f"""
                    SELECT bus_id AS id, geom, type AS ka_id
                    FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]}
                    WHERE carrier in ('O2') AND scn_name = '{SCENARIO_NAME}'
                    """,
            H2: f"""
                    SELECT bus_id AS id, geom 
                    FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]}
                    WHERE carrier in ('H2_grid', 'H2')
                    AND scn_name = '{SCENARIO_NAME}'
                    AND country = 'DE'
                    """,
            H2GRID: f"""
                    SELECT link_id, geom, bus0, bus1
                    FROM {sources["links"]["schema"]}.{sources["links"]["table"]}
                    WHERE carrier in ('H2_grid') AND scn_name  = '{SCENARIO_NAME}'
                    """,
            AC: f"""
                    SELECT bus_id AS id, geom
                    FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]}
                    WHERE carrier in ('AC')
                    AND scn_name = '{SCENARIO_NAME}'
                    AND v_nom = '110'
                    """,
            ACSUB_HVMV: f"""
                    SELECT bus_id AS id, point AS geom
                    FROM {sources["hvmv_substation"]["schema"]}.{sources["hvmv_substation"]["table"]}
                    """,
            ACSUB_EHV:f"""
                    SELECT bus_id AS id, point AS geom
                    FROM {sources["ehv_substation"]["schema"]}.{sources["ehv_substation"]["table"]}
                    """,
            ACZONE_HVMV: f"""
                    SELECT bus_id AS id, ST_Transform(geom, 4326) as geom
                    FROM {sources["mv_districts"]["schema"]}.{sources["mv_districts"]["table"]}
                    """,
            ACZONE_EHV: f"""
                    SELECT bus_id AS id, ST_Transform(geom, 4326) as geom
                    FROM {sources["ehv_voronoi"]["schema"]}.{sources["ehv_voronoi"]["table"]}
                    """,
            HEAT_BUS: f"""
        			SELECT bus_id AS id, geom
        			FROM {sources["buses"]["schema"]}.{sources["buses"]["table"]}
        			WHERE carrier in ('central_heat')
                    AND scn_name = '{SCENARIO_NAME}'
                    AND country = 'DE'
                    """,
        }