Source code for oemof.demandlib.demand

# -*- coding: utf-8 -*-
"""
Created on Fri Jul 24 19:11:38 2015

@author: caro
"""

import numpy as np
import pandas as pd
from oemof.demandlib import energy_buildings as eb
from oemof.tools import helpers


[docs]class electrical_demand(): '''Calculate the electrical demand for a region with different methods. This class calculates the electrical demand for a region. Therefore several different methods can be applied. Parameters ---------- method : {'scale_profile_csv', 'scale_profile_db', 'scale_entsoe', 'calculate_profile'} Method to calculate the demand for your region. Explanation: 'scale_profile_csv': read only profile from csv and scale it with given or calculated demand 'scale_profile_db': read only profile from database and scale it with given or calculated demand 'scale_entsoe': read entsoe profile from database and scale it with given or calculated demand 'calculate_profile: Calculate profile from the standard load profiles of the three demand sectors (households, service, industry) and the corresponding annual electric demand. Depending on which of these values is chosen, additional parameters might be required, as detailed below. Other Parameters ---------------- path : str Required for a `method` value of `'scale_profile_csv'`. Should be the '/path/to/the/csv/file'. filename : str Required for a `method` value of `'scale_profile_csv'`. Should be the name of the file found under `path`. conn : Required for `method` values of `'scale_profile_db'` or `'scale_profile_entsoe'`. annual_elec_demand : int Required for `method` values of `'scale_profile_csv'`, `'scale_profile_db'` or `'scale_profile_entsoe'`. Annual demand of your region. Works so far only with a given value. Calculating the demand from statistic data for the whole region can be an option for further development. ann_el_demand_per_sector : dictionary Required for a `method` value of `'calculate_profile'`. Specification of annual electric demand and the corresponding standard load profile type (selp_type) for every sector. Dictionary is structured as follows. Key defining the sector is followed by value that can be int, float, None or can be omitted, e.g.: .. code-block:: python ann_el_demand_per_sector = { 'h0': int, 'g0': float, 'g1': None, ... 'g6': int, 'i0': int} If ann_el_demand is None, more parameters to calculate the demand are necessary: (works so far only if ann_el_demand for every or no sector is specified) population : int Population of your region. ann_el_demand_per_person : list of dictionaries Specification of the annual electric demand for one household according to the household type (from single to four-person households), e.g.: .. code-block:: python ann_el_demand_per_person = [ {'ann_el_demand': int, 'household_type': {'one', 'two', 'three', 'four'}}, ...] household_structure : list of dictionaries Number of people living in every household type. Specification for your region, e.g.: .. code-block:: python household_structure = [ {household_members': int, 'household_type': {'one', 'two', 'three', 'four'}}, ...] comm_ann_el_demand_state : int Annual electric demand of the service sector of the next bigger region, if not given for your region. comm_number_of_employees_state : int Number of employees in the service sector of the next bigger region. comm_number_of_employees_region : int Number of employees in the service sector of your region. Attributes ---------- annual_demand : int Included in `**kwargs`. Given with initialization or calculated or to be calculated within this class according to selected method. dataframe : pandas dataframe elec_demand : array_like profile : e_slp : Returns ------- Notes ----- References ---------- statistics ... B. Schachler: Bewertung des Einsatzes von Kraft-Wärme-Kopplungsanlagen hinsichtlich der CO2-Emissionen bei wachsendem Anteil Erneuerbarer Energien, Masterarbeit, Technische Universität Berlin, 2014 Examples -------- These are written in doctest format, and should illustrate how to use the function. z. B. Summe bilden von elec_demand und abgleichen mit ann_el_demand ''' def __init__(self, method, **kwargs): self.annual_demand = kwargs.get('annual_elec_demand') if self.annual_demand is None: self.annual_demand = self.calculate_annual_demand_region() self.dataframe = helpers.create_basic_dataframe(kwargs.get('year')) self.decider(method, **kwargs)
[docs] def decider(self, method, **kwargs): ''' ''' if method == 'scale_profile_csv': self.profile = self.read_from_csv(path= kwargs.get('path'), filename= kwargs.get('filename')) self.elec_demand = self.scale_profile() elif method == 'calculate_profile': self.e_slp = self.read_selp().slp # normalize slp timeseries to annual sum of one self.e_slp.drop('date', axis=1, inplace=True) self.e_slp = self.e_slp.div(self.e_slp.sum(axis=0), axis=1) # calculate annual demand for sectors with `None` for key in kwargs['ann_el_demand_per_sector'].keys(): if kwargs['ann_el_demand_per_sector'][key] is None: if key.startswith('g', 0, 1): kwargs['ann_el_demand_per_sector'][key] = ( self.calculate_annual_demand_commerce(**kwargs)) elif key.startswith('h', 0, 1): kwargs['ann_el_demand_per_sector'][key] = ( self.calculate_annual_demand_households(**kwargs)) elif key.startswith('i', 0, 1): kwargs['ann_el_demand_per_sector'][key] = ( self.calculate_annual_demand_industry(**kwargs)) # multiply given annual demand with timeseries self.elec_demand = self.e_slp.multiply(pd.Series( kwargs['ann_el_demand_per_sector']), axis=1).dropna(how='all', axis=1) return self.elec_demand
[docs] def read_from_csv(self, **kwargs): ''' read entire demand timeseries or only profile for further processing from csv ''' self.profile = pd.read_csv(kwargs.get('path') + kwargs.get('filename'), sep=",") return self.profile
[docs] def read_from_db(self): ''' read entire demand timeseries or only profile for further processing from database ''' return
[docs] def read_entsoe(self): return
[docs] def read_selp(self): self.e_slp = eb.bdew_elec_slp(self.dataframe) return self.e_slp
[docs] def scale_profile(self): ''' scale a given profile to a given annual demand, which is the sum of the single profile values ''' self.elec_demand = (self.profile / self.profile.sum() * self.annual_demand) return self.elec_demand
#TODO: implement??
[docs] def calculate_annual_demand_region(self): ''' calculate annual demand from statistic data ''' self.annual_demand = 50 + 50 return self.annual_demand
[docs] def calculate_annual_demand_households(self, **kwargs): hh_ann_el_demand_per_person = ( kwargs.get('number_household_members')['one'] / kwargs.get('household_members_all') * kwargs.get('ann_el_demand_per_person')['one'] + kwargs.get('number_household_members')['two'] / kwargs.get('household_members_all') * kwargs.get('ann_el_demand_per_person')['two'] + kwargs.get('number_household_members')['three'] / kwargs.get('household_members_all') * kwargs.get('ann_el_demand_per_person')['three'] + kwargs.get('number_household_members')['four'] / kwargs.get('household_members_all') * kwargs.get('ann_el_demand_per_person')['four']) return kwargs.get('population') * hh_ann_el_demand_per_person
[docs] def calculate_annual_demand_commerce(self, **kwargs): return (kwargs.get('comm_ann_el_demand_state') / kwargs.get('comm_number_of_employees_state') * kwargs.get('comm_number_of_employees_region'))
[docs] def calculate_annual_demand_industry(self, **kwargs): return (kwargs.get('ind_ann_el_demand_state') / kwargs.get('ind_number_of_employees_state') * kwargs.get('ind_number_of_employees_region'))
[docs]class heat_demand(): # not implemented yet pass