def dispatch(self, dt_sim_time, p_lem, alpha):
inverter = self.name
if (self.Q_bid > 0.0) and (self.P_bid > p_lem):
gridlabd.set_value(inverter, 'P_Out', str(-self.Q_bid * 1000.))
mode = 1.
elif (self.Q_bid > 0.0) and (self.P_bid == p_lem):
print('This HVAC is marginal; no partial implementation yet: ' +
str(alpha))
gridlabd.set_value(inverter, 'P_Out', str(-self.Q_bid * 1000.))
mode = 1.
else:
gridlabd.set_value(inverter, 'P_Out', str(0.0))
mode = 0.
parameter_string = '(timedate, P_bid, Q_bid, mode, DeltaE)' #DeltaE at beginning of period
value_tuple = (
dt_sim_time,
float(self.P_bid),
float(self.Q_bid),
float(mode),
float(self.DeltaE),
)
myfct.set_values('EV_' + self.ID + '_state_out', parameter_string,
value_tuple)
self.P_bid = -100000.0
self.Q_bid = 0.0
return
def dispatch(self, dt_sim_time, p_lem, alpha):
#import pdb; pdb.set_trace()
inverter = 'Bat_inverter_' + self.name.split('_')[-1]
if (self.Q_buy_bid > 0.0) and (self.P_buy_bid > p_lem):
gridlabd.set_value(inverter, 'P_Out', str(-self.Q_buy_bid * 1000.))
elif (self.Q_buy_bid > 0.0) and (self.P_buy_bid == p_lem):
print('This HVAC is marginal; no partial implementation yet: ' +
str(alpha))
gridlabd.set_value(inverter, 'P_Out', str(-self.Q_buy_bid * 1000.))
elif (self.Q_sell_bid > 0.0) and (self.P_sell_bid < p_lem):
gridlabd.set_value(inverter, 'P_Out', str(self.Q_sell_bid * 1000.))
elif (self.Q_sell_bid > 0.0) and (self.P_sell_bid == p_lem):
print('This HVAC is marginal; no partial implementation yet: ' +
str(alpha))
gridlabd.set_value(inverter, 'P_Out', str(self.Q_sell_bid * 1000.))
else:
gridlabd.set_value(inverter, 'P_Out', str(0.0))
myfct.set_values(
self.name + '_state_out',
'(timedate, p_demand, p_supply, q_demand, q_supply)',
(dt_sim_time, str(self.P_buy_bid), str(
self.P_sell_bid), str(self.Q_buy_bid), str(self.Q_sell_bid)))
self.P_sell_bid = 100000.0
self.P_buy_bid = -100000.0
self.Q_sell_bid = 0.0
self.Q_buy_bid = 0.0
def switch_off(s_awarded,dt_sim_time):
#houses
if 'GLD' in s_awarded['appliance_name']:
gridlabd_functions.set(s_awarded['appliance_name'],'thermostat_control','NONE')
#batteries
elif 'Bat' in s_awarded['appliance_name']:
gridlabd_functions.set('Bat_inverter_'+s_awarded['appliance_name'][8:],'P_Out',0.0)
#EV
elif 'EV' in s_awarded['appliance_name']:
gridlabd_functions.set('EV_inverter_'+s_awarded['appliance_name'][3:],'P_Out',0.0)
else:
print s_awarded['appliance_name']+' is not recognized'
mysql_functions.set_values('system_operations', '(timedate,q,costs,appliance_name)',(dt_sim_time,float(s_awarded['q_bid']),float(s_awarded['p_bid']),s_awarded['appliance_name'],))
return
def shutdown_house(unresp_load,dt_sim_time,houses_off=[],market=None):
if market:
Pmax = market.Pmax
else:
Pmax = 100.0
while True:
house_OFF = np.random.choice(houselist_inflex)
while house_OFF in houses_off:
house_OFF = np.random.choice(houselist_inflex)
house_currHVAC = float(gridlabd_functions.get(house_OFF,'hvac_load')['value'][:-3])
if house_currHVAC > 0.0:
unresp_load -= house_currHVAC
print 'Switch off '+str(house_OFF)+' to '+str(unresp_load)
gridlabd_functions.set(house_OFF,'thermostat_control','NONE')
gridlabd_functions.set(house_OFF,'system_mode','OFF')
houses_off += [house_OFF]
mysql_functions.set_values('system_operations', '(timedate,q,costs,appliance_name)',(dt_sim_time,house_currHVAC,Pmax,'HVAC'+house_OFF[3:],))
break
print 'Ready: '+str(unresp_load)
return unresp_load, houses_off
retail = Mfct.Market()
retail.reset()
retail.Pmin = 0.0
retail.Pmax = 100.0
retail.Pprec = prec
#Get historical parameters from mysql database
df_prices = mysql_functions.get_values('clearing_pq', dt_sim_time-datetime.timedelta(days=1))
mean_p = df_prices['clearing_price'].iloc[-36:].mean() #last hour
var_p = df_prices['clearing_price'].var()
if math.isnan(mean_p):
mean_p = (retail.Pmax - retail.Pmin)/2
#print var_p
if math.isnan(var_p) or var_p == 0.0:
var_p = 0.10
mysql_functions.set_values('market_prices', '(mean_price,var_price,timedate)',(float(mean_p),float(var_p),dt_sim_time-datetime.timedelta(seconds=interval),))
###
#Demand side
###
#HVAC
df_bids_HVAC = HHfct.calc_bids_HVAC(dt_sim_time,df_house_state,retail,mean_p,var_p)
retail = HHfct.submit_bids_HVAC(dt_sim_time,retail,df_bids_HVAC)
print retail.D_active
#Batteries
df_bids_battery = HHfct.calc_bids_battery(dt_sim_time,df_battery_state,retail,mean_p,var_p)
retail = HHfct.submit_bids_battery(dt_sim_time,retail,df_bids_battery)
#PV
def initialize():
mysql_functions.clear_databases()
print('Initialize')
#Start time of simulation is '2017-07-01 12:00:00' EST+5 - get this from GLOBALS or likewise
interval = int(gridlabd_functions.get('retail',
'interval')['value']) #in seconds
#prev_timedate = datetime.datetime(2015, 6, 30, 23, 60 - (interval/60))
#prev_timedate = datetime.datetime(2015, 7, 1, 4, 60 - (interval/60))
dt = parser.parse(os.getenv("clock"))
prev_timedate = dt - timedelta(minutes=interval / 60)
#Get list of house objects in GLM file and assign to global GLD variable "houselist"
#houses = gridlabd_functions.find('class=house')
#houselist = [];
#Read in wholesale market prices
df_prices = pandas.read_csv('ercot_2017.csv', parse_dates=[0])
mysql_functions.set_WSmarket(df_prices)
#Check if downgrading pycurl changes gridlabd_functions
# from sqlalchemy import create_engine
# engine = create_engine("mysql://*****:*****@127.0.0.1/gridlabd")
# con = engine.connect()
# df_prices.to_sql(name='WS_market',con=con,if_exists='append')
# con.close()
for house in houselist:
#houselist.append(name)
#Fills TABLE market_houses (can this be done via GridlabD directly?)
mysql_functions.set_values('market_houses', '(house_name)', (house, ))
#Fills TABLE market_appliances (can this be done via GridlabD directly?)
#HVAC
k = float(gridlabd_functions.get(house, 'k')['value'])
T_min = float(gridlabd_functions.get(house, 'T_min')['value'])
T_max = float(gridlabd_functions.get(house, 'T_max')['value'])
heat_q = float(
gridlabd_functions.get(
house,
'heating_demand')['value'][1:-3]) #heating_demand is in kW
hvac_q = float(
gridlabd_functions.get(
house,
'cooling_demand')['value'][1:-3]) #cooling_demand is in kW
#mysql_functions.set_values('market_HVAC', '(house_name,appliance_name,k,T_min,T_max,P_heat,P_cool)',(house['name'],'HVAC_'+str(houses.index(house)+1),k,T_min,T_max,heat_q,hvac_q,))
mysql_functions.set_values(
'market_HVAC',
'(house_name,appliance_name,k,T_min,T_max,P_heat,P_cool)', (
house,
'HVAC_' + house[4:],
k,
T_min,
T_max,
heat_q,
hvac_q,
))
#Fills TABLE market_appliance_meter
#HVAC
heating_setpoint = float(
gridlabd_functions.get(house, 'heating_setpoint')['value'][1:-5])
cooling_setpoint = float(
gridlabd_functions.get(house, 'cooling_setpoint')['value'][1:-5])
#Set values for previous period, i.e. start - interval
#mysql_functions.set_values('market_HVAC_meter', '(system_mode,heating_setpoint,cooling_setpoint,active,timedate,appliance_id)',('OFF',heating_setpoint,cooling_setpoint,0,prev_timedate,houses.index(house)+1,))
mysql_functions.set_values(
'market_HVAC_meter',
'(system_mode,av_power,heating_setpoint,cooling_setpoint,active,timedate,appliance_id)',
(
'OFF',
0.0,
heating_setpoint,
cooling_setpoint,
0,
prev_timedate,
int(house.split('_')[-1]),
))
#gridlabd_functions.set('houselist',';'.join(houselist))
#batteries = gridlabd_functions.find('class=battery')
for battery in batterylist:
house_name = 'GLD_' + battery[8:]
#Fills TABLE market_appliances
SOC_max = float(
gridlabd_functions.get(battery, 'battery_capacity')['value']
[:-3]) / 1000 #Wh in Gridlabd -> kWh
str_i_max = gridlabd_functions.get(battery,
'I_Max')['value'][:-2].replace(
'-', '+')
i_max = str_i_max.split('+')[1]
u_max = float(gridlabd_functions.get(battery, 'V_Max')['value']
[:-2]) * float(i_max) / 1000 #W -> kW #better inverter?
eff = float(
gridlabd_functions.get(battery, 'base_efficiency')['value'][:-5])
mysql_functions.set_values(
'market_battery',
'(house_name,appliance_name,appliance_id,SOC_max,u_max,eff)', (
house_name,
battery,
int(battery.split('_')[-1]),
SOC_max,
u_max,
eff,
))
#Fills TABLE market_appliance_meter
SOC_0 = float(
gridlabd_functions.get(battery,
'state_of_charge')['value'][:-3]) * SOC_max
mysql_functions.set_values('market_battery_meter',
'(SOC,active,timedate,appliance_id)', (
SOC_0,
0,
prev_timedate,
int(battery.split('_')[-1]),
))
for EV in EVlist:
house_name = 'GLD_' + EV[3:]
#Fills TABLE market_appliances
SOC_max = float(
gridlabd_functions.get(EV, 'battery_capacity')['value']
[:-3]) / 1000 #Wh in Gridlabd -> kWh
str_i_max = gridlabd_functions.get(EV, 'I_Max')['value'][:-2].replace(
'-', '+')
i_max = str_i_max.split('+')[1]
u_max = float(gridlabd_functions.get(EV, 'V_Max')['value']
[:-2]) * float(i_max) / 1000 #W -> kW #better inverter?
eff = float(
gridlabd_functions.get(EV, 'base_efficiency')['value'][:-5])
charging_type = gridlabd_functions.get(EV, 'charging_type')['value']
k = gridlabd_functions.get(EV, 'k')['value']
mysql_functions.set_values(
'market_EV',
'(house_name,appliance_name,appliance_id,SOC_max,u_max,eff,charging_type,k)',
(
house_name,
EV,
int(EV.split('_')[-1]),
SOC_max,
u_max,
eff,
charging_type,
k,
))
#Fills TABLE market_appliance_meter
mysql_functions.set_values(
'market_EV_meter', '(connected,SOC,active,timedate,appliance_id)',
(
0,
0,
0,
prev_timedate,
int(EV.split('_')[-1]),
))
#Set all cars offline/disconnected in the beginning
gridlabd_functions.set(EV, 'generator_status', 'OFFLINE')
df_events = pandas.read_csv('EV_events.csv', index_col=[0])
df_events.to_csv('EV_events_pop.csv')
# PVs = gridlabd_functions.find('class=solar')
for pv in pvlist:
house_name = 'GLD_' + pv[3:]
#Fills TABLE market_appliances
inverter_name = 'PV_inverter_' + pv[3:]
rated_power = float(
gridlabd_functions.get(inverter_name,
'rated_power')['value'][:-3]) / 1000
mysql_functions.set_values(
'market_pv',
'(house_name,appliance_name,inverter_name,appliance_id,rated_power)',
(
house_name,
pv,
inverter_name,
int(pv.split('_')[-1]),
rated_power,
))
#Fills TABLE market_appliance_meter
production = float(
gridlabd_functions.get(inverter_name,
'P_Out')['value'][:-3]) / 1000
mysql_functions.set_values('market_pv_meter',
'(P_Out,timedate,appliance_id)', (
production,
prev_timedate,
int(pv.split('_')[-1]),
))
return dt