def on_init(t):
global t0
t0 = time.time()
global step
step = 0
global df_prices, df_WS
df_prices = pandas.DataFrame(columns=[
'clearing_price', 'clearing_quantity', 'unresponsive_loads',
'slack_t-1'
])
df_WS = pandas.read_csv('glm_generation_' + city + '/' + market_data,
parse_dates=[0],
index_col=[0])
#Find objects
global houses
if flexible_houses == 0:
houses = []
else:
houses = gldimport.find_objects('class=house')[:flexible_houses]
#global df_house_state;
#df_house_state = HHfct.get_settings_houses(houses,interval)
batteries = gldimport.find_objects('class=battery')
global batterylist, EVlist
batterylist, EVlist = gldimport.sort_batteries(batteries)
global df_battery_state
df_battery_state = Bfct.get_settings_batteries(batterylist, interval)
global df_EV_state
df_EV_state = EVfct.get_settings_EVs(EVlist, interval)
pvs = gldimport.find_objects('class=solar')
global pvlist, pvinvlist
pvlist, pvinvlist = gldimport.sort_pvs(pvs)
global df_PV_state
df_PV_state = PVfct.get_settings(pvlist, interval)
global df_prices, df_WS
df_prices = pandas.DataFrame(columns=[
'clearing_price', 'clearing_quantity', 'unresponsive_loads',
'slack_t-1'
])
df_WS = pandas.read_csv('glm_generation_' + city + '/' + market_data,
parse_dates=[0],
index_col=[0])
print('Initialize finished after ' + str(time.time() - t0))
return True
def on_init(t):
global t0
t0 = time.time()
global step
step = 0
batteries = gldimport.find_objects('class=battery')
global batterylist, EVlist
batterylist, EVlist = gldimport.sort_batteries(batteries)
global df_EV_state
if EV_data == 'None':
df_EV_state = EVfct.get_settings_EVs_rnd(EVlist, interval)
else:
df_EV_state = EVfct.get_settings_EVs(EVlist, interval)
print('Initialize finished after ' + str(time.time() - t0))
return True
def on_init(t):
global t0;
t0 = time.time()
global step;
step = 0
#Instead of mysql
global df_buy_bids, df_supply_bids, df_awarded_bids;
df_buy_bids = pandas.DataFrame(columns=['timestamp','appliance_name','bid_price','bid_quantity'])
df_supply_bids = pandas.DataFrame(columns=['timestamp','appliance_name','bid_price','bid_quantity'])
df_awarded_bids = pandas.DataFrame(columns=['timestamp','appliance_name','bid_price','bid_quantity','S_D'])
#Find objects
global houses;
if flexible_houses == 0:
houses = []
else:
houses = gldimport.find_objects('class=house')[:flexible_houses]
#global df_house_state;
#df_house_state = HHfct.get_settings_houses(houses,interval)
batteries = gldimport.find_objects('class=battery')
global batterylist, EVlist;
batterylist, EVlist = gldimport.sort_batteries(batteries)
global df_battery_state;
df_battery_state = Bfct.get_settings_batteries(batterylist,interval)
global df_EV_state;
df_EV_state = EVfct.get_settings_EVs(EVlist,interval)
global df_prices, df_WS;
df_prices = pandas.DataFrame(columns=['clearing_price','clearing_quantity','unresponsive_loads','slack_t-1'])
df_WS = pandas.read_csv('glm_generation_'+city+'/'+market_data,parse_dates=[0],index_col=[0])
#df_WS = pandas.DataFrame(index=pandas.to_datetime(df_WS.index.astype(str)),columns=df_WS.columns,data=df_WS.values)
print('Initialize finished after '+str(time.time()-t0))
return True
def on_init(t):
global t0;
t0 = time.time()
global step;
step = 0
#Instead of mysql
global df_buy_bids, df_supply_bids, df_awarded_bids;
df_buy_bids = pandas.DataFrame(columns=['timestamp','appliance_name','bid_price','bid_quantity'])
df_supply_bids = pandas.DataFrame(columns=['timestamp','appliance_name','bid_price','bid_quantity'])
df_awarded_bids = pandas.DataFrame(columns=['timestamp','appliance_name','bid_price','bid_quantity','S_D'])
#Find objects
global houses;
if flexible_houses == 0:
houses = []
else:
houses = gldimport.find_objects('class=house')[:flexible_houses]
#global df_house_state;
#df_house_state = HHfct.get_settings_houses(houses,interval)
batteries = gldimport.find_objects('class=battery')
global batterylist, EVlist;
batterylist, EVlist = gldimport.sort_batteries(batteries)
global df_battery_state;
df_battery_state = Bfct.get_settings_batteries(batterylist,interval)
global df_EV_state;
if EV_data == 'None':
df_EV_state = EVfct.get_settings_EVs_rnd(EVlist,interval)
else:
df_EV_state = EVfct.get_settings_EVs(EVlist,interval)
pvs = gldimport.find_objects('class=solar')
global pvlist, pvinvlist;
pvlist, pvinvlist = gldimport.sort_pvs(pvs)
global df_PV_state;
df_PV_state = PVfct.get_settings(pvlist,interval)
if load_forecast == 'perfect':
global df_PV_forecast;
try:
df_PV_all = pandas.read_csv('glm_generation_'+city+'/perfect_PV_forecast_'+month+'_all.csv',parse_dates=True,skiprows=8)
df_PV_all['# timestamp'] = df_PV_all['# timestamp'].str.replace(r' UTC$', '')
df_PV_all['# timestamp'] = pandas.to_datetime(df_PV_all['# timestamp'])
df_PV_all.set_index('# timestamp',inplace=True)
df_PV_forecast = pandas.DataFrame(index=df_PV_all.index,columns=['PV_infeed'],data=df_PV_all[df_PV_state['inverter_name']].sum(axis=1))
df_PV_forecast.to_csv('glm_generation_'+city+'/perfect_PV_forecast_'+month+'.csv')
except:
print('No perfect PV forecast found, use myopic PV forecast')
df_PV_forecast = None
global df_prices, df_WS;
df_prices = pandas.DataFrame(columns=['clearing_price','clearing_quantity','unresponsive_loads','slack_t-1'])
df_WS = pandas.read_csv('glm_generation_'+city+'/'+market_data,parse_dates=[-1],index_col=[0])
df_WS = pandas.DataFrame(index=pandas.to_datetime(df_WS.index.astype(str)),columns=df_WS.columns,data=df_WS.values.astype(float))
#Align weekdays of Pecan Street Data and WS: For yearly data only (TESS, not powernet)
# year_sim = parser.parse(gridlabd.get_global('clock')).replace(tzinfo=None).year
# first_weekday = pandas.Timestamp(year_sim,1,1).weekday()
# first_weekday_WS = df_WS.index[0].weekday()
# while not first_weekday_WS == first_weekday:
# df_WS = df_WS.iloc[1:]
# first_weekday_WS = df_WS.index[0].weekday()
# df_WS =df_WS.iloc[:(365*24*12)] #Should not be hardcoded
# df_WS.index = pandas.date_range(pandas.Timestamp(year_sim,1,1,0),pandas.Timestamp(year_sim,12,31,23,55),freq='5min')
print('Initialize finished after '+str(time.time()-t0))
return True
def on_init(t):
global t0;
t0 = time.time()
global step;
step = 0
#Documentation
global df_buy_bids, df_supply_bids, df_awarded_bids;
df_buy_bids = pandas.DataFrame(columns=['timestamp','appliance_name','bid_price','bid_quantity'])
df_supply_bids = pandas.DataFrame(columns=['timestamp','appliance_name','bid_price','bid_quantity'])
df_awarded_bids = pandas.DataFrame(columns=['timestamp','appliance_name','bid_price','bid_quantity','S_D'])
#Customers
customers = gldimport.find_objects('class=house')
#Find houses with flexible HVAC (0 in MVP)
global houses;
if flexible_houses == 0:
houses = []
else:
houses = gldimport.find_objects('class=house')
global df_house_state;
df_house_state = HHfct.get_settings_houses(houses,interval)
#Batteries and EVs
batteries = gldimport.find_objects('class=battery')
global batterylist, EVlist;
batterylist, EVlist = gldimport.sort_batteries(batteries)
global df_battery_state;
df_battery_state = Bfct.get_settings_batteries(batterylist,interval)
global df_EV_state;
if (EV_data == 'None') or (len(EVlist) == 0):
df_EV_state = EVfct.get_settings_EVs_rnd(EVlist,interval)
else:
df_EV_state = EVfct.get_settings_EVs(EVlist,interval)
#PVs
pvs = gldimport.find_objects('class=solar')
global pvlist, pvinvlist;
pvlist, pvinvlist = gldimport.sort_pvs(pvs)
global df_PV_state;
df_PV_state = PVfct.get_settings(pvlist,interval)
if load_forecast == 'perfect':
global df_PV_forecast;
try:
df_PV_all = pandas.read_csv(input_folder+'/perfect_PV_forecast_'+dt_sim_time.month+'_all.csv',parse_dates=True,skiprows=8)
df_PV_all['# timestamp'] = df_PV_all['# timestamp'].str.replace(r' UTC$', '')
df_PV_all['# timestamp'] = pandas.to_datetime(df_PV_all['# timestamp'])
df_PV_all.set_index('# timestamp',inplace=True)
df_PV_forecast = pandas.DataFrame(index=df_PV_all.index,columns=['PV_infeed'],data=df_PV_all[df_PV_state['inverter_name']].sum(axis=1))
df_PV_forecast.to_csv(input_folder+'/perfect_PV_forecast_'+dt_sim_time.month+'.csv')
except:
print('No perfect PV forecast found, use myopic PV forecast')
df_PV_forecast = None
#Tokens
global df_tokens;
df_tokens = pandas.DataFrame(columns=['clearing_price','clearing_quantity','partial','alpha','unresponsive_loads','system_mode','slack_t-1'])
global df_controlroom;
df_controlroom = pandas.read_csv(input_folder + '/' + control_room_data,index_col=[0],parse_dates=True) # This includes the control room ts of coincident peak forecast
global df_WS;
df_WS = pandas.read_csv(input_folder + '/' + market_data,index_col=[0],parse_dates=True) # This includes the control room ts of coincident peak forecast
print('Initialize finished after '+str(time.time()-t0))
return True