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electric_market_game.py
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'''
Tony Shu
Electricity Market
'''
import logging
#Comment line below to display optimization outputs ie. Runtime, Variable count, etc.
'''-------------------------------------'''
logging.basicConfig(level=logging.ERROR)
'''-------------------------------------'''
import pypsa
import pypsa.opf
from pypsa.opt import (l_constraint, l_objective, LExpression, LConstraint,
patch_optsolver_free_model_before_solving,
patch_optsolver_record_memusage_before_solving,
empty_network, free_pyomo_initializers)
from pypsa.opt import (l_constraint, l_objective, LExpression, LConstraint,
patch_optsolver_free_model_before_solving,
patch_optsolver_record_memusage_before_solving,
empty_network, free_pyomo_initializers)
from pypsa.descriptors import (get_switchable_as_dense, get_switchable_as_iter,allocate_series_dataframes, zsum, Dict)
pypsa.pf.logger.setLevel(logging.ERROR) #Disable extra output from optimization
import numpy as np
import pandas as pd #Only v.23 works as .24 removes sort in append
import os
import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
import random
import re
import time
import copy
import sys
import datetime
from six.moves import map, zip, range, reduce
from six import itervalues, iteritems
import six
from pyomo.environ import (ConcreteModel, Var, Objective,
NonNegativeReals, Constraint, Reals,
Suffix, Expression, Binary, SolverFactory)
'Plotting Libraries'
from bokeh.plotting import figure, output_file, show
from bokeh.models import ColumnDataSource, Plot, LinearAxis, Grid
from bokeh.models.glyphs import Step
from bokeh.io import curdoc, show
'Math Libraries'
import statsmodels.tsa.stattools as ts
import statsmodels.api as sm
from statsmodels.tsa.arima_model import ARIMA
from sklearn.preprocessing import normalize
'''
GLOBAL VARIABLES
'''
OPTIMIZATION_SOLVER = 'cbc'
CARTOGRAPHY_DATA = 'cartopy_data_dir\\'
cartopy.config['data_dir'] = CARTOGRAPHY_DATA
#INDIA_DATA_DIR = 'india_data\\'
INDIA_DATA_DIR = 'india_data_cases\\'
INDIA_NETWORK_SCENARIO = 'L,H,8,1275'
SIM_OUTPUT_DIR = 'sim_output\\'
TABLEAU_OUTPUT_DIR = 'tableau_output\\'
DEMAND_RESPONSE_BIDS = 'aggregator_data\\Generated_Data\\med-night-bids.csv'
DEFAULT_NO_CHARGING = False
BIDS_FOLDER = 'aggregator_data\\Generated_Data\\'
SCENARIO_NAME = 'med-night'
ALL_SCENARIOS_OUTPUT_FOLDER = 'output_scenarios\\'
V2G_BIDS = 'aggregator_data\\V2G_bids\\v2g_bids_med_lowered.csv'
STARTING_TIME_BLOCK = 0
#DEMAND_RESPONSE_BIDS = 'india_data_cases\\Generated_EV_Data\\Generated_EV_bids_data.csv'
#take a copy of the components pandas.DataFrame
#create a pandas.DataFrame with the properties of the new component attributes.
#the format should be the same as pypsa/component_attrs/*.csv
override_components = pypsa.components.components.copy()
override_component_attrs = Dict({k : v.copy() for k,v in pypsa.components.component_attrs.items()})
MIN_SYS_RESERVE_ENERGY_MW = 250.0
CVOLL = 9000.0
CRESERVE = 1000.0
CDRCURTAIL = 600.0
CURRENT_TIME_INDEX = 0
CURRENT_HOUR_INDEX = 0 #Current hour of the day
GENERATOR_CLUSTER_SIZE = 10 #Capture 25%, 50%, 75%, or 100% of fleet on or off
GENERATOR_OUTAGE = {}
GENERATOR_CLUSTER_OUTAGE = []
DAILY_FIXED_DR = None
#MIN_SYS_RESERVE_ENERGY_MW = 0.0
'''
-----------------
'''
'''
Utility Functions
'''
def MW_to_GW(mw_pow):
return mw_pow/1000.0
def GW_to_MW(gw_pow):
return gw_pow*1000.0
def write_output(filename,string, output_dir = None):
if output_dir is None:
with open(SIM_OUTPUT_DIR + filename,'w') as outfile:
outfile.write(string)
else:
with open(output_dir + filename, 'w') as outfile:
outfile.write(string)
def write_tableau_output(output_dir,rtm_networks,dam_uc_networks,dam_ed_networks, generator_data, cluster_generator_data,
dam_dr_data, outage_data, dam_vre_available, dam_curtailment, dam_unmet_demand, dam_unmet_reserve,
rtm_unmet_demand, rtm_unmet_reserve, rtm_vre_available, rtm_curtailment, rtm_dr_curtailment, dam_reserve_data,
time_block, scenario_name, write_type):
'''Write the outputs for Tableau Visualization'''
global CURRENT_TIME_INDEX
'Demand Response Data'
if write_type == 'w':
dr_data_str = 'Zone,Attribute,Value,Timeblock,Scenario\n'
rtm_dr_data_str = 'Zone,Attribute,Value,Timeblock,Scenario\n'
else:
dr_data_str = ''
rtm_dr_data_str = ''
#DAM DR
day = 0
for dr_data in dam_dr_data:
for (zone, snapshot) in dr_data.keys():
dr_data_str = dr_data_str + str(zone) + ',t' + str(snapshot + 1 + (24 * day)) + ',' + str(dr_data[(zone,snapshot)]) + ',t' + str(time_block) + ',' + scenario_name + '\n'
day = day + 1
#RTM DR
hour = 0
for dr_data in rtm_dr_curtailment:
if dr_data is not None:
for (zone, snapshot) in dr_data.keys():
rtm_dr_data_str = rtm_dr_data_str + str(zone) + ',t' + str(snapshot + 1 + (4 * hour)) + ',' + str(dr_data[(zone,snapshot)]) + ',t' + str(time_block) + ',' + scenario_name + '\n'
hour = hour + 1
with open(output_dir + 'tab_dam_demand_response_load.csv',write_type) as outfile:
outfile.write(dr_data_str)
with open(output_dir + 'tab_rtm_demand_response_curtailment.csv',write_type) as outfile:
outfile.write(rtm_dr_data_str)
'''---'''
'DAM Data'
if write_type == 'w':
dam_gen_p_str = 'Resource,Zone,Attribute,Value,Timeblock,Scenario\n'
dam_storage_p_str = 'Resource,Zone,Attribute,Value,Timeblock,Scenario\n'
dam_storage_soc_str = 'Resource,Zone,Attribute,Value,Timeblock,Scenario\n'
dam_lmp_str = 'Zone,Attribute,Value,Timeblock,Scenario\n'
dam_load_str = 'Zone,Attribute,Value,Timeblock,Scenario\n'
dam_reserve_str = 'Resource,Attribute,Value,Timeblock,Scenario\n'
else:
dam_gen_p_str = ''
dam_storage_p_str = ''
dam_storage_soc_str = ''
dam_lmp_str = ''
dam_load_str = ''
dam_reserve_str = ''
#UC
total_t = 0
for scuc_network in dam_uc_networks:
cur_day = int(total_t / 24.0)
dam_gens = scuc_network.generators_t
for (name,gen_power) in dam_gens.p.iteritems():
t = total_t + 1
gen_id = int(re.split('([\d]+)',name)[1])
try:
resource_name = str(generator_data[gen_id]['Resource'])
except:
resource_name = str(cluster_generator_data[gen_id]['Resource'])
'VRE Generation Output is Different'
if resource_name == 'Solar' or resource_name == 'Wind':
#Printing total VRE available
for avail_pow in dam_vre_available[cur_day][name]:
dam_gen_p_str = dam_gen_p_str + ( resource_name + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(avail_pow) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
#Printing total curtail from each VRE Gen
t = total_t + 1
for curtail_pow in dam_curtailment[cur_day][name]:
dam_gen_p_str = dam_gen_p_str + ('Curtailment' + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(np.abs(curtail_pow)) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
else:
for pow in gen_power:
#Clustering causes new generator IDs
try:
dam_gen_p_str = dam_gen_p_str + ( resource_name + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(np.abs(pow)) + ',t' + str(time_block) + ',' + scenario_name + '\n')
except:
dam_gen_p_str = dam_gen_p_str + (resource_name + ',' + str(cluster_generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(np.abs(pow)) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
dam_storage_units = scuc_network.storage_units_t
for (name, stor_power) in dam_storage_units.p.iteritems():
t = total_t + 1
for pow in stor_power:
gen_id = int(re.split('([\d]+)',name)[1])
dam_storage_p_str = dam_storage_p_str + (str(generator_data[gen_id]['Resource']) + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(pow) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
for (name, stor_soc) in dam_storage_units.state_of_charge.iteritems():
t = total_t + 1
for soc in stor_soc:
gen_id = int(re.split('([\d]+)',name)[1])
dam_storage_soc_str = dam_storage_soc_str + (str(generator_data[gen_id]['Resource']) + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(np.abs(soc)) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
dam_loads = scuc_network.loads_t
zone = 0
for (name,zone_loads) in dam_loads.p.iteritems():
t = total_t + 1
zone = zone + 1
for load in zone_loads:
dam_load_str = dam_load_str + str(zone) + ',t' + str(t) + ',' + str(load) + ',t' + str(time_block) + ',' + scenario_name + '\n'
t = t + 1
total_t = total_t + 24
#ED
total_t = 0
for scuc_price_network in dam_ed_networks:
dam_buses = scuc_price_network.buses_t
for (name,bus_price) in dam_buses.marginal_price.iteritems():
t = total_t + 1
for price in bus_price:
dam_lmp_str = dam_lmp_str + str(name) + ',t' + str(t) + ',' + str(price) + ',t' + str(time_block) + ',' + scenario_name + '\n'
t = t + 1
total_t = total_t + 24
#Unmet Variables
day = 0
for unmet_demand_data in dam_unmet_demand:
for (zone, snapshot) in unmet_demand_data.keys():
dam_gen_p_str = dam_gen_p_str + ( 'Unmet Demand' + ',' + str(zone)
+ ',t' + str(snapshot + 1 + (24 * day)) + ',' + str(unmet_demand_data[(zone,snapshot)])
+ ',t' + str(time_block) + ',' + scenario_name + '\n')
day = day + 1
#DAM Reserves
day = 0
for reserve_data in dam_reserve_data:
for (unit_id, snapshot) in reserve_data.keys():
gen_id = int(re.split('([\d]+)',unit_id)[1])
try:
resource_name = str(generator_data[gen_id]['Resource'])
except:
resource_name = str(cluster_generator_data[gen_id]['Resource'])
dam_reserve_str = dam_reserve_str + (resource_name + ',t' + str(snapshot + 1 + (24 * day)) + ',' + str(reserve_data[(unit_id,snapshot)])
+ ',t' + str(time_block) + ',' + scenario_name + '\n')
day = day + 1
with open(output_dir + 'tab_dam_generator_power.csv',write_type) as outfile:
outfile.write(dam_gen_p_str)
with open(output_dir + 'tab_dam_prices.csv',write_type) as outfile:
outfile.write(dam_lmp_str)
with open(output_dir + 'tab_dam_storage_unit_power.csv',write_type) as outfile:
outfile.write(dam_storage_p_str)
with open(output_dir + 'tab_dam_storage_unit_soc.csv',write_type) as outfile:
outfile.write(dam_storage_soc_str)
with open(output_dir + 'tab_dam_loads.csv',write_type) as outfile:
outfile.write(dam_load_str)
with open(output_dir + 'tab_dam_reserves.csv',write_type) as outfile:
outfile.write(dam_reserve_str)
'''---'''
'RTM Data'
if write_type == 'w':
rtm_gen_p_str = 'Resource,Zone,Attribute,Value,Timeblock,Scenario\n'
rtm_price_str = 'Zone,Attribute,Value,Timeblock,Scenario\n'
rtm_load_str = 'Zone,Attribute,Value,Timeblock,Scenario\n'
rtm_storage_p_str = 'Resource,Zone,Attribute,Value,Timeblock,Scenario\n'
rtm_storage_soc_str = 'Resource,Zone,Attribute,Value,Timeblock,Scenario\n'
else:
rtm_gen_p_str = ''
rtm_price_str = ''
rtm_load_str = ''
rtm_storage_p_str = ''
rtm_storage_soc_str = ''
total_t = 0
for rtm_network in rtm_networks:
cur_hour = int(total_t / 4.0)
#Power
rtm_gens = rtm_network.generators_t
for (name, gen_power) in rtm_gens.p.iteritems():
t = total_t + 1
for pow in gen_power:
gen_id = int(re.split('([\d]+)',name)[1])
'V2G resources should have V2G in the name'
if 'V2G' in name:
rtm_gen_p_str = rtm_gen_p_str + re.split('V2G ([a-zA-Z\s]+) ([\d]+)',name)[1] + ',1,t' + str(t) + ',' + str(np.abs(pow)) + ',t' + str(time_block) + ',' + scenario_name + '\n'
else:
try:
resource_name = str(generator_data[gen_id]['Resource']) #.replace('Old','').replace('New','')
'Printing the curtailment and available VRE of Solar and Wind'
#if False: #resource_name == 'Solar' or resource_name == 'Wind':
if resource_name == 'Solar' or resource_name == 'Wind':
#DO NOTHING WILL PRINT VRE IN DIFFERENT SET
'''
#Printing total VRE available
for avail_pow in rtm_vre_available[cur_hour][name]:
rtm_gen_p_str = rtm_gen_p_str + ( resource_name + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(avail_pow) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
#Printing total curtail from each VRE Gen
t = total_t + 1
for curtail_pow in rtm_curtailment[cur_hour][name]:
rtm_gen_p_str = rtm_gen_p_str + ('Curtailment' + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(np.abs(curtail_pow)) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
'''
else:
rtm_gen_p_str = rtm_gen_p_str + resource_name + ',' + str(generator_data[gen_id]['zone']) + ',t' + str(t) + ',' + str(np.abs(pow)) + ',t' + str(time_block) + ',' + scenario_name + '\n'
except:
try:
# Clustered Generators
resource_name = str(cluster_generator_data[gen_id]['Resource']) #.replace('Old','').replace('New','')
rtm_gen_p_str = rtm_gen_p_str + resource_name + ',' + str(cluster_generator_data[gen_id]['zone']) + ',t' + str(t) + ',' + str(np.abs(pow)) + ',t' + str(time_block) + ',' + scenario_name + '\n'
except:
# V2G Outputs as their IDS should not be in the generator dictionary
rtm_gen_p_str = rtm_gen_p_str + 'V2G,1,t' + str(t) + ',' + str(np.abs(pow)) + ',t' + str(time_block) + ',' + scenario_name + '\n'
t = t + 1
#VRE Power
rtm_gens = rtm_network.generators_t
for (name, gen_power) in rtm_gens.p.iteritems():
t = total_t + 1
#for pow in gen_power:
gen_id = int(re.split('([\d]+)',name)[1])
try:
resource_name = str(generator_data[gen_id]['Resource']) #.replace('Old','').replace('New','')
'Printing the curtailment and available VRE of Solar and Wind'
if resource_name == 'Solar' or resource_name == 'Wind':
#Printing total VRE available
for avail_pow in rtm_vre_available[cur_hour][name]:
#print(avail_pow)
rtm_gen_p_str = rtm_gen_p_str + ( resource_name + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(avail_pow) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
#Printing total curtail from each VRE Gen
t = total_t + 1
for curtail_pow in rtm_curtailment[cur_hour][name]:
rtm_gen_p_str = rtm_gen_p_str + ('Curtailment' + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(np.abs(curtail_pow)) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
except:
'other resources'
#Storage
rtm_storage_units = rtm_network.storage_units_t
for (name, stor_power) in rtm_storage_units.p.iteritems():
t = total_t + 1
for pow in stor_power:
gen_id = int(re.split('([\d]+)',name)[1])
rtm_storage_p_str = rtm_storage_p_str + (str(generator_data[gen_id]['Resource']) + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(pow) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
for (name, stor_soc) in rtm_storage_units.state_of_charge.iteritems():
t = total_t + 1
for soc in stor_soc:
gen_id = int(re.split('([\d]+)',name)[1])
rtm_storage_soc_str = rtm_storage_soc_str + (str(generator_data[gen_id]['Resource']) + ',' + str(generator_data[gen_id]['zone'])
+ ',t' + str(t) + ',' + str(np.abs(soc)) + ',t' + str(time_block) + ',' + scenario_name + '\n')
t = t + 1
#Prices
rtm_buses= rtm_network.buses_t
for (name, bus_price) in rtm_buses.marginal_price.iteritems():
t = total_t + 1
for price in bus_price:
rtm_price_str = rtm_price_str + str(name) + ',t' + str(t) + ',' + str(price) + ',t' + str(time_block) + ',' + scenario_name + '\n'
t = t + 1
rtm_loads = rtm_network.loads_t
zone = 0
for (name,zone_loads) in rtm_loads.p.iteritems():
t = total_t + 1
zone = zone + 1
for load in zone_loads:
rtm_load_str = rtm_load_str + str(zone) + ',t' + str(t) + ',' + str(load) + ',t' + str(time_block) + ',' + scenario_name + '\n'
t = t + 1
total_t = total_t + 4
#Unmet Variables
day = 0
hour = 0
for unmet_demand_data in rtm_unmet_demand:
for (zone, snapshot) in unmet_demand_data.keys():
rtm_gen_p_str = rtm_gen_p_str + ('Unmet Demand' + ',' + str(zone)
+ ',t' + str(snapshot + 1 + (4 * hour)) + ',' + str(unmet_demand_data[(zone,snapshot)])
+ ',t' + str(time_block) + ',' + scenario_name + '\n')
hour = hour + 1
with open(output_dir + 'tab_rtm_generator_power.csv',write_type) as outfile:
outfile.write(rtm_gen_p_str)
with open(output_dir + 'tab_rtm_prices.csv',write_type) as outfile:
outfile.write(rtm_price_str)
with open(output_dir + 'tab_rtm_loads.csv',write_type) as outfile:
outfile.write(rtm_load_str)
with open(output_dir + 'tab_rtm_storage_unit_power.csv',write_type) as outfile:
outfile.write(rtm_storage_p_str)
with open(output_dir + 'tab_rtm_storage_unit_soc.csv',write_type) as outfile:
outfile.write(rtm_storage_soc_str)
'''---'''
'Outage Data'
if write_type == 'w':
outage_generator_str = 'Generator_ID,Resource,Zone,Attribute,Value,Timeblock,Scenario\n'
else:
outage_generator_str = ''
total_t = 0
for day in range(len(outage_data)):
for (gen_id,current_snapshot_outage,outage_left) in outage_data[day]:
t = 24 * day
for out_ctr in range(outage_left):
outage_generator_str = (outage_generator_str + str(gen_id) + ',' + str(cluster_generator_data[gen_id]['Resource']) +
',' + str(cluster_generator_data[gen_id]['zone']) + ',t' + str(t + out_ctr) +
',' + str(np.abs(current_snapshot_outage)) + ',t' + str(time_block) + ',' + scenario_name + '\n')
with open(output_dir + 'tab_gen_outage.csv',write_type) as outfile:
outfile.write(outage_generator_str)
'''
-----------------
'''
'''
Data Import Functions
'''
def read_india_generators():
'R_ID,zone,voltage_level,Resource,RENEW,THERM,DISP,NDISP,STOR,DR,HEAT,NACC,HYDRO,VRE,'
'Commit,Min_Share,Max_Share,Existing_Cap_MW,New_Build,Cap_size,Max_Cap_MW,Min_Cap_MW,'
'Min_Share_percent,Max_Share_percent,Inv_cost_per_MWyr,Inv_cost_per_Mwhyr,Fixed_OM_cost_per_MWyr,'
'Var_OM_cost_per_MWh,Externality_cost_MWh,Start_cost,Start_fuel_MMBTU_per_start,Heat_rate_MMBTU_per_MWh,'
'Fuel,Min_power,Self_disch,Eff_up,Eff_down,Ratio_power_to_energy,Max_DSM_delay,Ramp_Up_percentage,'
'Ramp_Dn_percentage,Up_time,Down_time,NACC_Eff,NACC_Peak_to_Base,Reg_Up,Reg_Dn,Rsv_Up,Rsv_Dn,Reg_Cost,'
'Rsv_Cost,Fixed_OM_cost_per_MWhyr,Var_OM_cost_per_MWh_in,Hydro_level'
#df = pd.read_csv(INDIA_DATA_DIR + 'Generators_data_New_Scenarios.csv')
print('Reading Generator Data: ' + INDIA_DATA_DIR + 'Generators_data.csv')
df = pd.read_csv(INDIA_DATA_DIR + 'Generators_data.csv')
generators = {}
cluster_generators = {}
#cluster_resources = ['CCGT Old', 'CCGT New', 'Coal Old', 'Coal new', 'Nuclear Old', 'Nuclear New', 'Biomass']
cluster_resources = ['CCGT Old', 'CCGT New' , 'Coal Old', 'Coal new', 'Nuclear Old', 'Nuclear New', 'Biomass']
#cluster_resources = ['CCGT New', 'Coal New']
fuels = read_india_fuels()
for (index, gen_data) in df.iterrows():
'Convert KW to MW'
generators[gen_data['R_ID']] = gen_data
#Ivan is in GW
generators[gen_data['R_ID']]['Existing_Cap_MW'] = float(generators[gen_data['R_ID']]['Existing_Cap_MW']) * 1000.0
#Power to Energy Ratio
#if generators[gen_data['R_ID']]['Resource'] == 'Hydro Reservoir':
# generators[gen_data['R_ID']]['Existing_Cap_MW'] = generators[gen_data['R_ID']]['Existing_Cap_MW'] / generators[gen_data['R_ID']]['Ratio_power_to_energy']
#if generators[gen_data['R_ID']]['Resource'] == 'Pumped Hydro Storage':
# generators[gen_data['R_ID']]['Existing_Cap_MW'] = generators[gen_data['R_ID']]['Existing_Cap_MW'] / generators[gen_data['R_ID']]['Ratio_power_to_energy']
#if generators[gen_data['R_ID']]['Resource'] == 'Batteries':
# generators[gen_data['R_ID']]['Existing_Cap_MW'] = generators[gen_data['R_ID']]['Existing_Cap_MW'] / generators[gen_data['R_ID']]['Ratio_power_to_energy']
#Ivan is in GW
generators[gen_data['R_ID']]['Var_OM_cost_per_MWh'] = float(generators[gen_data['R_ID']]['Var_OM_cost_per_MWh']) * 1000.0
generators[gen_data['R_ID']]['Heat_rate_MMBTU_per_MWh'] = float(generators[gen_data['R_ID']]['Heat_rate_MMBTU_per_MWh']) * 1000.0
generators[gen_data['R_ID']]['Start_cost'] = float(generators[gen_data['R_ID']]['Start_cost']) * 1000.0
'Convert to Float'
generators[gen_data['R_ID']]['Eff_up'] = float(generators[gen_data['R_ID']]['Eff_up'])
generators[gen_data['R_ID']]['Eff_down'] = float(generators[gen_data['R_ID']]['Eff_down'])
generators[gen_data['R_ID']]['Hydro_level'] = float(generators[gen_data['R_ID']]['Hydro_level'])
generators[gen_data['R_ID']]['Ramp_Up_percentage'] = float(generators[gen_data['R_ID']]['Ramp_Up_percentage'])
generators[gen_data['R_ID']]['Ramp_Dn_percentage'] = float(generators[gen_data['R_ID']]['Ramp_Dn_percentage'])
generators[gen_data['R_ID']]['Self_disch'] = float(generators[gen_data['R_ID']]['Self_disch'])
'Convert to Int'
if generators[gen_data['R_ID']]['Up_time'] > 24:
generators[gen_data['R_ID']]['Up_time'] = 24
else:
generators[gen_data['R_ID']]['Up_time'] = int(generators[gen_data['R_ID']]['Up_time'])
if generators[gen_data['R_ID']]['Down_time'] > 24:
generators[gen_data['R_ID']]['Down_time'] = 24
else:
generators[gen_data['R_ID']]['Down_time'] = int(generators[gen_data['R_ID']]['Up_time'])
'Startup costs for generators'
generators[gen_data['R_ID']]['Start_cost'] = generators[gen_data['R_ID']]['Start_cost'] * generators[gen_data['R_ID']]['Existing_Cap_MW']
'Calculate Marginal Cost'
## VOM[$/MWh] + FuelCost[$/MWh]
generators[gen_data['R_ID']]['Marginal_Cost'] = (generators[gen_data['R_ID']]['Var_OM_cost_per_MWh'] +
generators[gen_data['R_ID']]['Heat_rate_MMBTU_per_MWh'] * fuels[generators[gen_data['R_ID']]['Fuel']]['Cost_per_MMBtu'])
'Calculate Cluster Size'
generators[gen_data['R_ID']]['Num_Generators'] = int(generators[gen_data['R_ID']]['Existing_Cap_MW'] / (generators[gen_data['R_ID']]['Cap_size'] * 1000.0 ) )
if generators[gen_data['R_ID']]['Num_Generators'] == 0:
generators[gen_data['R_ID']]['Num_Generators'] = 1
'Divide into equal cluster sizes'
if generators[gen_data['R_ID']]['Num_Generators'] >= GENERATOR_CLUSTER_SIZE and generators[gen_data['R_ID']]['Resource'] in cluster_resources and GENERATOR_CLUSTER_SIZE != 0:
new_nameplate_capacity = generators[gen_data['R_ID']]['Existing_Cap_MW'] / float(GENERATOR_CLUSTER_SIZE)
new_num_generators = int(generators[gen_data['R_ID']]['Num_Generators'] / float(GENERATOR_CLUSTER_SIZE))
for cluster_num in range(GENERATOR_CLUSTER_SIZE):
new_gen_id = int(str(gen_data['R_ID']) + str(cluster_num))
cluster_generators[new_gen_id] = generators[gen_data['R_ID']].copy()
cluster_generators[new_gen_id]['Num_Generators'] = new_num_generators
'Set New Startup Cost'
cluster_generators[new_gen_id]['Start_cost'] = cluster_generators[new_gen_id]['Start_cost'] / GENERATOR_CLUSTER_SIZE
#SYMMETRY PROBLEMS
cluster_generators[new_gen_id]['Heat_rate_MMBTU_per_MWh'] = float(cluster_generators[new_gen_id]['Heat_rate_MMBTU_per_MWh']) + float(cluster_num) * 55.0
cluster_generators[new_gen_id]['Marginal_Cost'] = (cluster_generators[new_gen_id]['Var_OM_cost_per_MWh'] +
cluster_generators[new_gen_id]['Heat_rate_MMBTU_per_MWh'] * fuels[cluster_generators[new_gen_id]['Fuel']]['Cost_per_MMBtu'])
cluster_generators[new_gen_id]['Existing_Cap_MW'] = new_nameplate_capacity + (10.0 * float(cluster_num))
else:
#DANGER IF R_ID somehow ends up duplicating with new gen id?
cluster_generators[gen_data['R_ID']] = generators[gen_data['R_ID']].copy()
#print(str(generators[gen_data['R_ID']]['Start_cost']) + generators[gen_data['R_ID']]['Resource'])
#generators is for previous runs before we do the clustering
#cluster_generators is used in the simulation with our generator clusters
#both kept for backwards compatability
return generators, cluster_generators
def read_india_fuels():
'fuel_indices, Fuel, Cost_per_MMBtu, CO2_content_tons_perMMBtu'
df = pd.read_csv(INDIA_DATA_DIR + 'Fuels_data.csv')
fuels = {}
for (index, fuel_data) in df.iterrows():
fuels[fuel_data['Fuel']] = fuel_data
fuels[fuel_data['Fuel']]['Cost_per_MMBtu'] = float(fuels[fuel_data['Fuel']]['Cost_per_MMBtu'])
fuels[fuel_data['Fuel']]['CO2_content_tons_perMMBtu'] = float(fuels[fuel_data['Fuel']]['CO2_content_tons_perMMBtu'])
return fuels
def read_india_network():
'Names,Share of zonal demand,Network_zones,VRE_Share,DistrZones,CO_2_Max_ton_MWh,'
'InZoneLossFact_Int,InZoneLossFact_W,InZoneLossFact_I,InZoneLossFact_N,VRE_Share,'
'Share_in_MV,DistrLossFact_LV_Net_Quad,DistrLossFact_MV_Net_Linear,DistrLossFact_LV_Total_Linear,'
'Predicted Average Loss at Peak,Assumed Distr. Headroom,Distr_Max_Inject,Distr_Max_Withdraw,'
'Distr_Inject_Max_Reinforcement_MW,Distr_Withdraw_Max_Reinforcement_MW,Distr_MV_Reinforcement_Cost_per_MW_yr,'
'Distr_LV_Reinforcement_Cost_per_MW_yr,DistrMarginFact_LV_Linear,DistrMarginFact_LV_Quad,'
'DistrMarginFact_MV_Linear,DistrMargin_MV_Max,DistrMargin_MV_DiscountFact,Network_lines,'
'Link_names,z1,z2,z3,z4,z5,Line_Loss_Percentage,Line_Max_Flow_MW,Initial_by_2015,Line_Max_Reinforcement_MW,'
'Line_Reinforcement_Cost_per_MW_yr,Line_Voltage_kV,Line_Resistance_ohms,Line_X_ohms,'
'Line_R_ohms,Thetha_max,Peak_Withdrawal_Hours,Peak_Injection_Hours'
df = pd.read_csv(INDIA_DATA_DIR + 'Network.csv')
links = {}
zones = ['1','2','3','4','5']
for (index, network_data) in df.iterrows():
## Locate bus0 and bus1
bus0 = ''
bus1 = ''
if int(network_data['z1']) < 0:
bus0 = 'z1'
elif int(network_data['z2']) < 0:
bus0 = 'z2'
elif int(network_data['z3']) < 0:
bus0 = 'z3'
elif int(network_data['z4']) < 0:
bus0 = 'z4'
elif int(network_data['z5']) < 0:
bus0 = 'z5'
if int(network_data['z1']) > 0:
bus1 = 'z1'
elif int(network_data['z2']) > 0:
bus1 = 'z2'
elif int(network_data['z3']) > 0:
bus1 = 'z3'
elif int(network_data['z4']) > 0:
bus1 = 'z4'
elif int(network_data['z5']) > 0:
bus1 = 'z5'
links[bus0.replace('z',''),bus1.replace('z','')] = network_data
#Ivan GW conversion
links[bus0.replace('z',''),bus1.replace('z','')]['Line_Max_Flow_MW'] = float(network_data['Line_Max_Flow_MW']) * 1000.0
return links, zones
def read_india_loads():
'Voll,Demand_segment,Cost_of_demand_curtailment_perMW,'
'Max_demand_curtailment,Subperiods,Hours_per_period,Sub_Weights,'
'Time_index,Load_MW_z1,Load_MW_z2,Load_MW_z3,Load_MW_z4,Load_MW_z5'
df = pd.read_csv(INDIA_DATA_DIR + 'Load_data.csv')
loads = {}
#Unit Conversion from GW to MW
for (index, load_data) in df.iterrows():
loads[load_data['Time_index']] = load_data
loads[load_data['Time_index']]['Load_MW_z1'] = float(load_data['Load_MW_z1']) * 1000.0
loads[load_data['Time_index']]['Load_MW_z2'] = float(load_data['Load_MW_z2']) * 1000.0
loads[load_data['Time_index']]['Load_MW_z3'] = float(load_data['Load_MW_z3']) * 1000.0
loads[load_data['Time_index']]['Load_MW_z4'] = float(load_data['Load_MW_z4']) * 1000.0
loads[load_data['Time_index']]['Load_MW_z5'] = float(load_data['Load_MW_z5']) * 1000.0
aggregated_loads = []
not_aggregated_loads = {'1':[],'2':[],'3':[],'4':[],'5':[]}
for hour in loads.keys():
aggregated_loads.append(loads[hour]['Load_MW_z1'] +
loads[hour]['Load_MW_z2'] +
loads[hour]['Load_MW_z3'] +
loads[hour]['Load_MW_z4'] +
loads[hour]['Load_MW_z5'])
not_aggregated_loads['1'].append(loads[hour]['Load_MW_z1'])
not_aggregated_loads['2'].append(loads[hour]['Load_MW_z2'])
not_aggregated_loads['3'].append(loads[hour]['Load_MW_z3'])
not_aggregated_loads['4'].append(loads[hour]['Load_MW_z4'])
not_aggregated_loads['5'].append(loads[hour]['Load_MW_z5'])
return loads, aggregated_loads, not_aggregated_loads
def read_india_gen_variability():
'Time_index,Solar/z1,Wind/z1,Biomass/z1,Mini Hydro/z1,'
'Pumped Hydro Storage/z1,Hydro Reservoir/z1,Hydro Run of River/z1,...'
#df = pd.read_csv(INDIA_DATA_DIR + 'Real_Generators_variability.csv')
df = pd.read_csv(INDIA_DATA_DIR + 'Generators_variability.csv')
variability = []
for (index, var_data) in df.iterrows():
variability.append(var_data)
return variability
def read_EV_demand_response_bids():
'Bid_ID,Bidder_ID,zone,Day,start_time,end_time,demand_min_total,'
'demand_max_total,demand_min_timesteps,demand_max_timesteps'
df = pd.read_csv(DEMAND_RESPONSE_BIDS)
#df = pd.read_csv(INDIA_DATA_DIR + 'NO_DR_GENERATED_EV_bids_data.csv')
bids = {}
for (index, bid_data) in df.iterrows():
min_k_t = bid_data['demand_min_timesteps'].split(';')
max_k_t = bid_data['demand_max_timesteps'].split(';')
if not len(min_k_t) == len(max_k_t):
print('EV Bids Format invalid. k_t lens do not equal')
return None
'Correct bid k format'
for i in range(len(min_k_t)):
min_k_t[i] = float(min_k_t[i].replace(']','').replace('[','').replace(' ','').replace('\n','').replace('\'',''))
max_k_t[i] = float(max_k_t[i].replace(']','').replace('[','').replace(' ','').replace('\n','').replace('\'',''))
bid_data['demand_min_timesteps'] = min_k_t
bid_data['demand_max_timesteps'] = max_k_t
'Each Bid Data will contain the charging range for the full 24 hours'
specified_range = range(int(bid_data['start_time']), int(bid_data['end_time']) + 1)
specified_index = 0
charging_hours_max = []
charging_hours_min = []
for hour in range(0,24):
# Will be 0 min and max if not in specified range
if hour in specified_range:
charging_hours_min.append(min_k_t[specified_index])
charging_hours_max.append(max_k_t[specified_index])
specified_index = specified_index + 1
else:
charging_hours_min.append(0.0)
charging_hours_max.append(0.0)
bid_data['charging_hours_min'] = charging_hours_min
bid_data['charging_hours_max'] = charging_hours_max
bid_data['demand_min_total'] = float(bid_data['demand_min_total'])
bid_data['demand_max_total'] = float(bid_data['demand_max_total'])
bid_data['Day'] = int(bid_data['Day'])
if bid_data['Day'] not in bids.keys():
bids[bid_data['Day']] = []
bids[bid_data['Day']].append(bid_data)
if len(bids.keys()) == 0:
return None
return bids
def read_resource_perturbations():
'Resource,Standard Deviation'
try:
df = pd.read_csv(INDIA_DATA_DIR + 'Resource_perturbations.csv')
except:
#print('No Resource_perturbations.csv found in INDIA_DATA_DIR')
return {}
resource_perturbations = {}
for (index, data) in df.iterrows():
resource_perturbations[data['Resource']] = {}
resource_perturbations[data['Resource']]['Standard Deviation'] = float(data['Standard Deviation'])
resource_perturbations[data['Resource']]['Forced Outage Rate'] = float(data['Forced Outage Rate'])
return resource_perturbations
def read_v2g_bids():
'Bid_ID,Bidder_ID,zone,Day,hour,capacity,cost'
df = pd.read_csv(V2G_BIDS)
bids = {}
for (index, bid_data) in df.iterrows():
#NEED TO BE CAREFUL READING Bidder_ID CANT HAVE NUMBERS
bid_data["capacity"] = float(bid_data["capacity"])
bid_data["cost"] = float(bid_data["cost"])
if bid_data['Day'] not in bids.keys():
bids[bid_data['Day']] = []
bids[bid_data['Day']].append(bid_data)
return bids
'''
-----------------
'''
'''
Model Components
'''
def demand_perturbations(loads,is_aggregated, time_index, end_time_index, perturbation_level):
'''
Returns perturbated loads of 15 minute time steps
Inputs:
loads - read_india_loads()
is_aggregated - True/False of whether or not to consider zones
time_index - int of starting time_index
end_time_index - int of end time_index
perturbation_level - level of std_devs
Outputs:
perturbated_15min_loads - randomly perturbated loads with 4 time steps
σ = std(loads[time_index : end_time_index + 1])
θ = avg(loads[time_index : end_time_index + 1])
'''
if is_aggregated:
## Generate perturbated loads for aggregated zone
demand_mean = np.average( np.array(loads[time_index: (end_time_index + 1)]))
demand_std = np.std( np.array(loads[time_index: (end_time_index + 1)]))
demand_std = 0 #NO DEMAND PERTURBATIONS
perturbated_15min_loads = np.random.normal(loc=demand_mean,scale=perturbation_level*demand_std,size=4)
for load_num in range(4):
if perturbated_15min_loads[load_num] < 0.0:
perturbated_15min_loads[load_num] = perturbated_15min_loads[load_num] * -1
else:
'''
## Check Co-Integration between zones
for i in ['1','2','3','4','5']:
for k in ['1','2','3','4','5']:
if not i == k:
ols_summary, rsquared, t_values, cadf, corr_coef, residuals, beta_param = dicky_fuller_coint(loads[i][0:25],loads[k][0:25])
'''
## Generate perturbated loads for each zone
perturbated_15min_loads = {}
for zone in ['1','2','3','4','5']:
demand_mean = np.average( np.array(loads[zone][time_index: (end_time_index + 1)]))
demand_std = np.std( np.array(loads[zone][time_index: (end_time_index + 1)]))
demand_std = 0 #NO DEMAND PERTURBATIONS
perturbated_15min_loads[zone] = np.random.normal(loc=demand_mean,scale=perturbation_level*demand_std,size=4)
for load_num in range(4):
if perturbated_15min_loads[zone][load_num] < 0.0:
perturbated_15min_loads[zone][load_num] = perturbated_15min_loads[zone][load_num] * -1
return perturbated_15min_loads
def variability_perturbations(resource,variability, snapshots, perturbation_level, generator_id, generators):
'''
Returns a series of perturbations around the variability
Inputs:
variability - float
snapshots - list
perturbation_level - float
'''
global GENERATOR_OUTAGE
global GENERATOR_CLUSTER_OUTAGE
if resource == None:
resource = 'Default'
# So no peterbations that cause odd solar generation
if variability == 0:
return np.array([0] * len(snapshots))
resource_perturbations = read_resource_perturbations()
length_of_outage = 24
mu = variability
try:
sigma = resource_perturbations[resource]['Standard Deviation']
forced_outrage_rate = float(resource_perturbations[resource]['Forced Outage Rate']) / length_of_outage
except:
sigma = 0
forced_outrage_rate = 0.0
'Forced Outage Randomization'
random_outage = random.random()
#Clustered
'Check current outages'
current_total_outage = 0
restarted_generators = []
for i in range(len(GENERATOR_CLUSTER_OUTAGE)):
(cluster_id, num_out, timeleft) = GENERATOR_CLUSTER_OUTAGE[i]
'Check how many generators are currently out in cluster'
if cluster_id == generator_id:
current_total_outage = current_total_outage + num_out
GENERATOR_CLUSTER_OUTAGE[i] = (cluster_id, num_out, timeleft - 1)
if ( timeleft - 1 ) == 0:
restarted_generators.append(i)
for to_remove in reversed(restarted_generators):
del GENERATOR_CLUSTER_OUTAGE[to_remove]
'If there exists active generators test outages'
current_snapshot_outage = 0 #How many generators in the cluster are out
if current_total_outage < generators[generator_id]['Num_Generators']:
for i in range(generators[generator_id]['Num_Generators'] - current_total_outage):
random_outage = random.random()
if random_outage < forced_outrage_rate and (current_snapshot_outage + current_total_outage) < generators[generator_id]['Num_Generators']:
current_snapshot_outage = current_snapshot_outage + 1
if current_snapshot_outage > 0:
outage_data = (generator_id,current_snapshot_outage,24)
GENERATOR_CLUSTER_OUTAGE.append( outage_data )
'Calculate current RTM Outage'
cur_gen_total_outage = 0
for (cluster_id, num_out, timeleft) in GENERATOR_CLUSTER_OUTAGE:
if cluster_id == generator_id:
cur_gen_total_outage = cur_gen_total_outage + num_out
# Return RTM Outage variability
if cur_gen_total_outage > 0:
if cur_gen_total_outage > generators[generator_id]['Num_Generators']:
return np.array([0] * len(snapshots))
else:
return np.array([variability - (variability / generators[generator_id]['Num_Generators']) * cur_gen_total_outage ] * len(snapshots))
#Binary
'''
if generator_id in GENERATOR_OUTAGE.keys():
'Outage has already occured'
GENERATOR_OUTAGE[generator_id] = GENERATOR_OUTAGE[generator_id] - 1
if GENERATOR_OUTAGE[generator_id] == 0:
del GENERATOR_OUTAGE[generator_id]
return np.array([0] * len(snapshots))
elif random_outage < forced_outrage_rate:
'Forced Outage Occurs'
GENERATOR_OUTAGE[generator_id] = length_of_outage
GENERATOR_OUTAGE[generator_id] = GENERATOR_OUTAGE[generator_id] - 1
return np.array([0] * len(snapshots))
'''
size = len(snapshots)
perturbated_rtm_vars = np.random.normal(mu,sigma,size)
'Remove negative and above 1 perturbations'
for i in range(len(perturbated_rtm_vars)):
if perturbated_rtm_vars[i] > 1.0:
perturbated_rtm_vars[i] = 1.0
if perturbated_rtm_vars[i] < 0.0:
perturbated_rtm_vars[i] = np.abs(perturbated_rtm_vars[i])
return perturbated_rtm_vars
def Real_Time_Generator_Outage_Variability(resource,variability, snapshots, perturbation_level, generator_id, generators, cur_hour, cur_day):
'''
Generator Outage - Average .75
No Outages - .775
Outage - .65
'''
if resource == None:
resource = 'Default'
# So no peterbations that cause odd solar generation
if variability == 0:
return np.array([0] * len(snapshots))
outage_day = 1
if resource == 'Coal Old' or resource == 'Coal new':
if outage_day == cur_day:
return np.array([.75] * len(snapshots))
else:
return np.array([.75] * len(snapshots))
return np.array([variability] * len(snapshots))
def calculate_curtailment(network):
'''
Calculates the curtailment of renewable energy sources
Resource = network.generator.carrier
'''
curtailment = {}
total_available = {}
solar_curtailment = {}
resources = ['Solar','Wind']
'Calculate Curtailment for each resource type'
for resource in resources:
cur_generators = network.generators.carrier[network.generators.carrier == resource].index
available_res = [0.0] * len(network.snapshots)
used_res = [0.0] * len(network.snapshots)
for gen_name in list(cur_generators):
gen_variability = network.generators_t.p_max_pu[gen_name]
gen_output = network.generators_t.p[gen_name]
gen_capacity = network.generators.p_nom_max[gen_name]
if gen_capacity == float("inf"): #Unit Commitment takes different parameters
gen_capacity = network.generators.p_nom[gen_name]
total_available[gen_name] = []
curtailment[gen_name] = []