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EMS/HEMS.py

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Add HEMS
2024-04-06 17:08:29 -04:00
# NOTE: for two homes
# <editor-fold desc="Import libraries and packages">
import itertools
import numpy as np
import pandas as pd
import pylab as pl
import pyomo.environ as py
# NOTE: model
from matplotlib import pyplot as plt, gridspec
from matplotlib.pyplot import legend
from pyomo.core import value, RangeSet
# data = pd.ExcelFile('input_new_cluster.xlsx') #it will read the excel one time you do not need to read again and gain
# Parse the different tab
Update HEMS.py code
2024-05-31 10:58:57 -04:00
sheet_data = pd.read_excel("input_new_cluster.xlsx", sheet_name='data')
sheet_EV = pd.read_excel("input_new_cluster.xlsx", sheet_name='EV')
sheet_EWH = pd.read_excel("input_new_cluster.xlsx", sheet_name='EWH')
sheet_wateruse = pd.read_excel("input_new_cluster.xlsx", sheet_name='Water_Use')
sheet_watertemp = pd.read_excel("input_new_cluster.xlsx", sheet_name='Water_Temp')
sheet_home = pd.read_excel("input_new_cluster.xlsx", sheet_name='Home')
sheet_ESS = pd.read_excel("input_new_cluster.xlsx", sheet_name='ESS')
sheet_PV = pd.read_excel("input_new_cluster.xlsx", sheet_name='PV')
sheet_ambient = pd.read_excel("input_new_cluster.xlsx", sheet_name='Ambient_Temp')
sheet_load = pd.read_excel("input_new_cluster.xlsx", sheet_name='Base_Load')
Add HEMS
2024-04-06 17:08:29 -04:00
# </editor-fold>
# </editor-fold>
model = py.ConcreteModel()
# <editor-fold desc="Reading the data from Excel file"> # i think it should be outside the loop
# <editor-fold desc="Sets: time intervals">
model.h = py.Set(initialize=sheet_home.home)
model.alpha = py.Param(initialize=39, doc='time of arrival ') # 6:30 = 39
model.beta = py.Param(initialize=89, doc='time of departure ') # 7:00 = 89
model.t_final = py.Param(initialize=96, doc='final time of the day ')
model.second = py.Param(initialize=2, doc='second time of the day ')
model.t = py.Set(initialize=sheet_data.time, ordered=True, doc='time period')
model.EV_Dur = py.Set(initialize=RangeSet(model.alpha.value, model.beta.value),
doc='time interval for EV')
model.EV_Dur1 = py.Set(initialize=RangeSet(model.alpha.value + 1, model.beta.value),
doc='time interval for EV after arrival')
model.t_second = py.Set(initialize=RangeSet(model.second.value, model.t_final.value),
doc='time greater then 1')
model.t_first = py.Param(initialize=1, doc='first time of the day ')
model.ist_interval = py.Set(initialize=RangeSet(model.t_first.value, model.alpha.value - 1),
doc='time period before the arrival time')
model.second_interval = py.Set(initialize=RangeSet(model.beta.value + 1, model.t_final.value),
doc='time interval after departure')
# </editor-fold>
# NOTE:Parameter
# Base load : uncontrollable loads
model.base_load = py.Param(model.h, model.t,
initialize=dict(zip(list(itertools.product(model.h.data(), model.t.data())),
[i for x in sheet_load.columns if "load" in x for i in
sheet_load.loc[:, x].values])), doc="Base Load")
model.PV = py.Param(model.h, model.t, initialize=dict(zip(list(itertools.product(model.h.data(), model.t.data())),
[i for x in sheet_PV.columns if "PV" in x for i in
sheet_PV.loc[:, x].values])), doc="PV Production")
model.max = py.Param(initialize=100, doc='maximum value for selling and buying power')
# <editor-fold desc="Electric Vehicle Parameters">
# todo this is need to be done for single value but tow home has seperate values
model.ChargeRate_EV = py.Param(model.h, initialize=dict(zip(list(itertools.product(model.h.data())),
[i for x in sheet_EV.columns if "charging_rate" in x for i
in
sheet_EV.loc[:, x].values])), doc="charging rate of EV")
model.DischRate_EV = py.Param(model.h, initialize=dict(zip(list(itertools.product(model.h.data())),
[i for x in sheet_EV.columns if "rate_of_discharging" in x
for i in
sheet_EV.loc[:, x].values])), doc="Discharging rate of EV")
model.Ch_Effc_EV = py.Param(model.h, initialize=dict(zip(list(itertools.product(model.h.data())),
[i for x in sheet_EV.columns if "charging_efficiency" in x for
i in
sheet_EV.loc[:, x].values])), doc="charging efficency of EV")
model.DischEffc_EV = py.Param(model.h, initialize=dict(zip(list(itertools.product(model.h.data())),
[i for x in sheet_EV.columns if
"efficiency_of_discharging" in x for i
in sheet_EV.loc[:, x].values])),
doc="Discharging efficency of EV")
model.Cap_EV = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EV.columns if "capacity" in x for i in
sheet_EV.loc[:, x].values])), doc="Capacity of EV")
model.End_percentage_EV = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EV.columns if "end" in x
for i in
sheet_EV.loc[:, x].values])),
doc="Departure energy of EV")
model.In_Percentage_EV = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EV.columns if "initial" in x
for i in
sheet_EV.loc[:, x].values])),
doc="Initial energy of EV")
model.Energy_EV_dep = py.Param(model.h, initialize=dict(
zip(model.h.data(), np.array(model.Cap_EV.values()) * model.End_percentage_EV.values())), # just changeing np>py
doc="Departure temperature of EV")
model.Energy_EV_In = py.Param(model.h, initialize=dict(
zip(model.h.data(), np.array(model.Cap_EV.values()) * model.In_Percentage_EV.values())),
doc="Initial energy of EV")
# </editor-fold>
# <editor-fold desc="Energy Storage System Parameter">
model.ChargeRate_ESS = py.Param(model.h, initialize=dict(zip(list(itertools.product(model.h.data())),
[i for x in sheet_ESS.columns if "charging_rate" in x for i
in
sheet_ESS.loc[:, x].values])), doc="charging rate of ESS")
model.DischRate_ESS = py.Param(model.h, initialize=dict(zip(list(itertools.product(model.h.data())),
[i for x in sheet_ESS.columns if "rate_of_discharging" in x
for
i in
sheet_ESS.loc[:, x].values])),
doc="Discharging rate of ESS")
# model.ChargeRate_ESS = py.Param(initialize=float(sheet_ESS.charging_rate1), doc='Charging rate of ESS ')
# model.DischRate_ESS = py.Param(initialize=float(sheet_ESS.discharging_rate1),
# doc='Discharging rate of ESS ')
model.Cap_ESS = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_ESS.columns if "capacity" in x for i in
sheet_ESS.loc[:, x].values])), doc="Capacity of ESS")
model.End_percentage_ESS = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_ESS.columns if "end" in x
for i in
sheet_ESS.loc[:, x].values])),
doc="Departure energy of ESS")
model.In_Percentage_ESS = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_ESS.columns if
"initial" in x for i in
sheet_ESS.loc[:, x].values])),
doc="Initial energy of ESS")
model.Ch_Effc_ESS = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_ESS.columns if "charging_efficiency" in x
for i in
sheet_ESS.loc[:, x].values])),
doc="charging efficiency of ESS")
model.DischEffc_ESS = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_ESS.columns if
"efficiency_of_dicharging" in x for
i in
sheet_ESS.loc[:, x].values])),
doc="Discharging efficiency of ESS")
model.Energy_ESS_In = py.Param(model.h, initialize=dict(
zip(model.h.data(), np.array(model.Cap_ESS.values()) * model.In_Percentage_ESS.values())),
doc="Initial energy of ESS")
model.End_En_ESS = py.Param(model.h, initialize=dict(
zip(model.h.data(), np.array(model.Cap_ESS.values()) * model.End_percentage_ESS.values())),
doc="Departure energy of ESS")
# </editor-fold>
# <editor-fold desc="Electric Water Heater Parameter">
model.tetta_low = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EWH.columns if "tetta_low" in x for i in
sheet_EWH.loc[:, x].values])),
doc="Lower bound of the water temperature")
model.tetta_up = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EWH.columns if "tetta_up" in x for i in
sheet_EWH.loc[:, x].values])),
doc="Upper bound of the water temperature")
model.tetta_amb_int = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EWH.columns if "tetta_amb_init" in x for i
in
sheet_EWH.loc[:, x].values])),
doc="Initial ambient temperature")
# todo need to use iteration for this below code
model.tetta_amb = py.Param(model.h, model.t,
initialize=dict(zip(list(itertools.product(model.h.data(), model.t.data())),
[i for x in sheet_ambient.columns if "celsius" in x for i in
sheet_ambient.loc[:, x].values])), doc="Outdoor temperature")
model.Q = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EWH.columns if "capacity" in x for i in
sheet_EWH.loc[:, x].values])), doc="Power of the EWH")
model.R = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EWH.columns if "thermal_resistance" in x for i in
sheet_EWH.loc[:, x].values])), doc="Thermal resistance")
model.C = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EWH.columns if "thermal_capacitance" in x for i in
sheet_EWH.loc[:, x].values])), doc="Thermal resistance")
model.M = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EWH.columns if "water_cap" in x for i in
sheet_EWH.loc[:, x].values])), doc="Water Capacity (L)")
model.tetta_EWH_int = py.Param(model.h, initialize=dict(zip((model.h.data()),
[i for x in sheet_EWH.columns if "tetta_wat_init" in x for i
in
sheet_EWH.loc[:, x].values])), doc="Initial temperature")
model.water_use = py.Param(model.h, model.t,
initialize=dict(zip(list(itertools.product(model.h.data(), model.t.data())),
[i for x in sheet_wateruse.columns if "Litre" in x for i in
sheet_wateruse.loc[:, x].values])), doc="Hot Water usage")
# </editor-fold2
print('this is first home')
# <editor-fold desc="Price, Base load, and time duration parameters">
model.Buy_price = py.Param(model.t, initialize=dict(zip(sheet_data.time, sheet_data.Buy_price)),
doc='Buying Price')
model.Sell_price = py.Param(model.t, initialize=dict(zip(sheet_data.time, sheet_data.Sell_price)), doc='Selling Price')
# Time duration: we took 15 mint granularity so in one hour it will be 1/4
model.time_d = py.Param(initialize=(1 / 4), doc='time duration ')
# model.DPT = py.Param(initialize=0.069, doc='Daily power tariff ') # .069
# </editor-fold>
# NOTE:Variable
# <editor-fold desc="buying and selling variables">
model.P_Buy_Grid = py.Var(model.h, model.t, bounds=(0, None))
model.P_Sell_Grid = py.Var(model.h, model.t, bounds=(0, None))
model.S_P_sell = py.Var(model.h, model.t, within=py.Binary)
model.S_P_buy = py.Var(model.h, model.t, within=py.Binary)
model.peak = py.Var(within=py.NonNegativeReals)
# </editor-fold>
# <editor-fold desc="Electric vehicle Variables">
model.P_EV_Charge = py.Var(model.h, model.t, bounds=(0, None))
model.P_EV_Disch = py.Var(model.h, model.t, bounds=(0, None))
model.P_EV_Disch_Home = py.Var(model.h, model.t, bounds=(0, None))
model.P_EV_Disch_Grid = py.Var(model.h, model.t, bounds=(0, None))
model.S_EV_Charge = py.Var(model.h, model.t, within=py.Binary)
model.S_EV_Disch = py.Var(model.h, model.t, within=py.Binary)
model.Energy_EV = py.Var(model.h, model.t, bounds=(0, None)) # SOC of the EV
# </editor-fold>
# <editor-fold desc="Energy storage system Variables">
model.Energy_ESS = py.Var(model.h, model.t, bounds=(0, None)) # SOC of the ESS
model.S_ESS_Charge = py.Var(model.h, model.t, within=py.Binary)
model.P_ESS_Disch = py.Var(model.h, model.t, bounds=(0, None))
model.P_ESS_Charge = py.Var(model.h, model.t, bounds=(0, None))
model.P_ESS_Charge_Grid = py.Var(model.h, model.t, bounds=(0, None), doc='ESS charging from the Grid')
model.P_ESS_Disch_Home = py.Var(model.h, model.t, bounds=(0, None))
model.P_ESS_Disch_Grid = py.Var(model.h, model.t, bounds=(0, None))
model.S_ESS_Disch = py.Var(model.h, model.t, within=py.Binary)
# </editor-fold>
# <editor-fold desc="PV variables">
model.PV_Home = py.Var(model.h, model.t, bounds=(0, None))
model.PV_Grid = py.Var(model.h, model.t, bounds=(0, None))
model.PV_Battery = py.Var(model.h, model.t, bounds=(0, None))
# </editor-fold>
# <editor-fold desc="EWH Variables">
model.tetta_EWH_wat = py.Var(model.h, model.t)
model.S_EWH = py.Var(model.h, model.t, within=py.Binary)
model.P_EWH = py.Var(model.h, model.t, doc='power of EWH')
# </editor-fold>
# NOTE:Constraints
# <editor-fold desc="Buying and Selling Power">
def Power_buy(model, h, i):
return model.P_Buy_Grid[h, i] == model.base_load[h, i] + model.Q[h] * model.S_EWH[h, i] + model.P_EV_Charge[h, i] - \
model.P_EV_Disch_Home[h, i] + model.P_ESS_Charge[h, i] - \
model.P_ESS_Disch_Home[h, i] - model.PV_Home[h, i]- model.PV_Battery[h, i]
model.Const_1 = py.Constraint(model.h, model.t, rule=Power_buy, doc='Power buy from the Grid')
def Power_buy2(model, h, i):
return model.P_Buy_Grid[h, i] <= model.max * model.S_P_buy[h, i]
model.Const_1a = py.Constraint(model.h, model.t, rule=Power_buy2,
doc='removing the nonlinearity in the objective fucntion')
def Power_sell1(model, h, i):
return model.P_Sell_Grid[h, i] == model.P_EV_Disch_Grid[h, i] + model.PV_Grid[h, i] + model.P_ESS_Disch_Grid[h, i]
model.Const_2 = py.Constraint(model.h, model.t, rule=Power_sell1, doc='Power sell to the Grid')
def Power_sell2(model, h, i):
return model.P_Sell_Grid[h, i] <= model.max * model.S_P_sell[h, i]
model.Const_2a = py.Constraint(model.h, model.t, rule=Power_sell2, doc='Power sell to the Grid')
def Status_Power(model, h, i):
return model.S_P_buy[h, i] + model.S_P_sell[h, i] <= 1
model.Const_3 = py.Constraint(model.h, model.t, rule=Status_Power,
doc='Buying and selling power will not occur at same time')
# </editor-fold>
# <editor-fold desc="flexibility">
# model.P_max = py.Var(model.h, model.t, bounds=(0, None), doc=" Updated maximum demand of house")
# model.P_flex = py.Var(model.h, model.t, bounds=(0, None), doc=" Flexibility available in one house")
# model.P_flex_all = py.Var(model.h, model.t, bounds=(0, None), doc=" Sum of the Flexibility in all")
# model.S_device = py.Var(model.h, model.t, within=py.Binary, doc=" Status of the devices") # but i not using this
# model.flex_index = py.Param(doc='flexibility index ')
#
# #Todo need to verify these equation with omer:
#
# #
# def P_h_flex(model, h, i):
# return model.P_flex_all[i] == sum(
# model.base_load[h, i] * model.S_device[h, i] - model.base_load[h, i] * model.S_device[h, i] for h in model.h
# for i in model.t)
#
#
# model.Const_4a = py.Constraint(model.h, model.t, rule=P_h_flex, doc='sum of the flexibility')
#
#
# def P_flexibility(model, h, i):
# return model.P_flex_all[i] == sum([model.P_flex[h, i] for h in model.h for i in model.t])
#
#
# model.Const_4b = py.Constraint(model.h, model.t, rule=P_flexibility, doc='sum of the flexibility')
#
#
# def Flexibility_index(model, h, i):
# return model.flex_index[h, i] == model.P_flex[h, i] / model.P_flex_all[i]
#
#
# model.Const_4c = py.Constraint(model.h, model.t, rule=Flexibility_index, doc='sum of the flexibility')
#
#
# # this equation make the two way communication
# def P_maximum(model, h, i):
# return model.P_max[h, i] == model.P_max[h, i - 1] - model.P_flex[h, i]
#
#
# model.Const_4d = py.Constraint(model.h, model.t, rule=P_maximum, doc='updated maximum power')
# </editor-fold>
# <editor-fold desc="Electric vehicle Constraints">
def Power_EV_Charge_limit1(model, h, i):
return model.P_EV_Charge[h, i] <= model.ChargeRate_EV[h] * model.S_EV_Charge[h, i]
model.Const_EV_1 = py.Constraint(model.h, model.t, rule=Power_EV_Charge_limit1,
doc='Charging power of EV (Upper limit)')
def Power_EV_Charge_limit2(model, h, i):
return model.P_EV_Charge[h, i] >= 0.2 * (model.ChargeRate_EV[h] * model.S_EV_Charge[h, i])
model.Const_EV_2 = py.Constraint(model.h, model.t, rule=Power_EV_Charge_limit2,
doc='Charging power of EV (lower limit)')
def Power_EV_Disch_limit1(model, h, i):
return model.P_EV_Disch[h, i] <= (model.DischRate_EV[h] * model.S_EV_Disch[h, i])
model.Const_EV_3 = py.Constraint(model.h, model.t, rule=Power_EV_Disch_limit1,
doc='Discharging power of EV (Upper limit)')
def Power_EV_Disch_limit2(model, h, i):
return model.P_EV_Disch[h, i] >= 0.2 * (model.DischRate_EV[h] * model.S_EV_Disch[h, i])
model.Const_EV_4 = py.Constraint(model.h, model.t, rule=Power_EV_Disch_limit2,
doc='Discharging power of EV (Lower limit)')
def Status_EV(model, h, i):
return model.S_EV_Disch[h, i] + model.S_EV_Charge[h, i] <= 1
model.Const_EV_5 = py.Constraint(model.h, model.t, rule=Status_EV,
doc='Charging and discharging will not occur at same time')
def Power_EV_Disch(model, h, i):
return model.P_EV_Disch[h, i] == (
(model.P_EV_Disch_Home[h, i] + model.P_EV_Disch_Grid[h, i]) / model.DischEffc_EV[h])
model.Const_EV_6 = py.Constraint(model.h, model.t, rule=Power_EV_Disch, doc='Discharging power of EV to '
'Home and Grid')
def SoC_EV1(model, h, i):
return model.Energy_EV[h, i] == model.Energy_EV_In[h]
# + (
# model.P_ESS_Charge[h, i] * model.Ch_Effc_ESS[h] - model.P_ESS_Disch[h, i]) * model.time_d
model.Const_EV_7a = py.Constraint(model.h, [model.alpha.value], rule=SoC_EV1, doc='SoC of the EV at arrival')
def SoC_EV2(model, h, i):
return model.Energy_EV[h, i] == model.Energy_EV[h, i - 1] + (
model.P_EV_Charge[h, i] * model.Ch_Effc_EV[h] - model.P_EV_Disch[h, i]) * model.time_d
model.Const_EV_7b = py.Constraint(model.h, model.EV_Dur1, rule=SoC_EV2, doc='SoC of the EV after arrival time')
# one equation of the SoC
# def SoC_EV(model, i):
# return model.Energy_EV[i] == model.Energy_EV_In + (model.P_EV_Charge[i] * model.Ch_Effc_EV - model.P_EV_Disch[i]) * model.time_d
#
#
# model.Const_EV_7 = py.Constraint(model.EV_Dur, rule=SoC_EV, doc='SoC of the EV')
def EV_availability1(model, h, i):
return model.Energy_EV[h, i] == 0
model.Const_EV_8a = py.Constraint(model.h, model.ist_interval, rule=EV_availability1,
doc='SOC available before arrival time')
def EV_availability2(model, h, i):
return model.Energy_EV[h, i] == 0
model.Const_EV_8b = py.Constraint(model.h, model.second_interval, rule=EV_availability2,
doc='SOC available after departure time')
def EV_status_available1(model, h, i):
return model.S_EV_Disch[h, i] + model.S_EV_Charge[h, i] == 0
model.Const_EV_9a = py.Constraint(model.h, model.ist_interval, rule=EV_status_available1,
doc='EV availability before arrival time')
def EV_status_available2(model, h, i):
return model.S_EV_Disch[h, i] + model.S_EV_Charge[h, i] == 0
model.Const_EV_9b = py.Constraint(model.h, model.second_interval, rule=EV_status_available2,
doc='EV availability after arrival time')
def EV_SoC_limit1(model, h, i):
return model.Energy_EV[h, i] >= 0.1 * model.Cap_EV[h]
model.Const_EV_10 = py.Constraint(model.h, model.EV_Dur, rule=EV_SoC_limit1, doc='Minimum SoC of EV')
def EV_SoC_limit2(model, h, i):
return model.Energy_EV[h, i] <= model.Cap_EV[h]
model.Const_EV_11 = py.Constraint(model.h, model.t, rule=EV_SoC_limit2, doc='Maximum SoC of EV')
def EV_final_SoC(model, h, i):
return model.Energy_EV[h, i] == model.Energy_EV_dep[h]
model.Const_EV_12 = py.Constraint(model.h, [model.beta.value], rule=EV_final_SoC,
doc='Final SoC of EV at departure time')
# </editor-fold>
# <editor-fold desc="Energy Storage System Constraints">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
def SoC_ESS1(model, h, i):
return model.Energy_ESS[h, i] == model.Energy_ESS_In[h]
# #+ (
# model.P_ESS_Charge[h, i] * model.Ch_Effc_ESS[h] - model.P_ESS_Disch[h, i]) * model.time_d
model.Const_ESS_1a = py.Constraint(model.h, [model.t_first.value], rule=SoC_ESS1,
doc='SoC of ESS') # need to check wather we need to put in the brackets or not
def SoC_ESS2(model, h, i):
return model.Energy_ESS[h, i] == model.Energy_ESS[h, i - 1] + (
model.P_ESS_Charge[h, i] * model.Ch_Effc_ESS[h] - model.P_ESS_Disch[h, i]) * model.time_d
model.Const_ESS_1b = py.Constraint(model.h, model.t_second, rule=SoC_ESS2, doc='SoC of ESS')
def Power_ESS_Charge(model, h, i):
return model.P_ESS_Charge[h, i] == model.P_ESS_Charge_Grid[h, i] + model.PV_Battery[h, i]
model.Const_ESS_2 = py.Constraint(model.h, model.t, rule=Power_ESS_Charge,
doc='Charging power of ESS ')
def Power_ESS_Charge_limit1(model, h, i):
return model.P_ESS_Charge[h, i] <= model.ChargeRate_ESS[h] * model.S_ESS_Charge[h, i]
model.Const_ESS_2a = py.Constraint(model.h, model.t, rule=Power_ESS_Charge_limit1,
doc='Charging power of ESS (Upper limit)')
def Power_ESS_Charge_limit2(model, h, i):
return model.P_ESS_Charge[h, i] >= 0.2 * (model.ChargeRate_ESS[h] * model.S_ESS_Charge[h, i])
model.Const_ESS_2b = py.Constraint(model.h, model.t, rule=Power_ESS_Charge_limit2,
doc='Charging power of ESS (Lower limit)')
def Power_ESS_Disch_limit1(model, h, i):
return model.P_ESS_Disch[h, i] <= (model.DischRate_ESS[h] * model.S_ESS_Disch[h, i])
model.Const_ESS_4 = py.Constraint(model.h, model.t, rule=Power_ESS_Disch_limit1,
doc='Discharging power of ESS (Upper limit)')
def Power_ESS_Disch_limit2(model, h, i):
return model.P_ESS_Disch[h, i] >= 0.2 * (model.DischRate_ESS[h] * model.S_ESS_Disch[h, i])
model.Const_ESS_5 = py.Constraint(model.h, model.t, rule=Power_ESS_Disch_limit2,
doc='Discharging power of ESS (Lower limit)')
def Status_ESS(model, h, i):
return model.S_ESS_Disch[h, i] + model.S_ESS_Charge[h, i] <= 1
model.Const_ESS_6 = py.Constraint(model.h, model.t, rule=Status_ESS,
doc='Charging and discharging will not occur at same time')
def Power_ESS_Disch(model, h, i):
return (model.P_ESS_Disch[h, i] * model.DischEffc_ESS[h]) == (
model.P_ESS_Disch_Home[h, i] + model.P_ESS_Disch_Grid[h, i])
model.Const_ESS_7 = py.Constraint(model.h, model.t, rule=Power_ESS_Disch,
doc='Discharging power of ESS to Home and Grid')
def ESS_SoC_limit1(model, h, i):
return model.Energy_ESS[h, i] >= 0.2 * model.Cap_ESS[h]
model.Const_ESS_8 = py.Constraint(model.h, model.t, rule=ESS_SoC_limit1, doc='Minimum SoC of ESS')
def ESS_SoC_limit2(model, h, i):
return model.Energy_ESS[h, i] <= model.Cap_ESS[h]
model.Const_ESS_9 = py.Constraint(model.h, model.t, rule=ESS_SoC_limit2, doc='Maximum SoC of ESS')
def ESS_final_SoC(model, h, i):
return model.Energy_ESS[h, i] >= model.End_En_ESS[h]
model.Const_ESS_10 = py.Constraint(model.h, [model.t_final.value], rule=ESS_final_SoC,
doc='Final SoC of ESS at departure time')
# </editor-fold>
# <editor-fold desc="Electric Water Heater (EWH) Constraints">
def EWH_limit1(model, h, i):
return model.tetta_EWH_wat[h, i] <= model.tetta_up[h]
model.Const_EWH_1 = py.Constraint(model.h, model.t, rule=EWH_limit1, doc='Maximum Limit')
def EWH_limit2(model, h, i):
return model.tetta_EWH_wat[h, i] >= model.tetta_low[h]
model.Const_EWH_2 = py.Constraint(model.h, model.t, rule=EWH_limit2, doc='Minimum Limit')
def EWH_power(model, h, i):
return model.P_EWH[h, i] == model.Q[h] * model.S_EWH[h, i]
model.Const_EWH_3 = py.Constraint(model.h, model.t, rule=EWH_power, doc='Electric water heater power')
#
# # NOTE: I write this function different from the GAMS
def EWH_temp_1(model, h, i):
return model.tetta_EWH_wat[h, i] == model.tetta_amb[h, i] + model.Q[h] * model.S_EWH[h, i] * model.R[
h] * model.time_d - (
(model.M[h] - model.water_use[h, i]) / model.M[h]) * (
model.tetta_amb_int[h] - model.tetta_EWH_int[h]) * py.exp(-model.time_d / (model.R[h] * model.C[h]))
model.Const_EWH_4 = py.Constraint(model.h, [model.t_first.value], rule=EWH_temp_1, doc='EWH Model')
def EWH_temp_2(model, h, i):
return model.tetta_EWH_wat[h, i] == model.tetta_amb[h, i] + model.Q[h] * model.S_EWH[h, i] * model.R[
h] * model.time_d - (
(model.M[h] - model.water_use[h, i]) / model.M[h]) * (
model.tetta_amb[h, i] - model.tetta_EWH_wat[h, i - 1]) * py.exp(
-model.time_d / (model.R[h] * model.C[h]))
model.Const_EWH_5 = py.Constraint(model.h, model.t_second, rule=EWH_temp_2, doc='EWH Model')
# </editor-fold>
# <editor-fold desc="PV Constraints"> # need to include the PV equation of omer paper
# the below equation did not work got an error ( i dont know why!!!)
# def PV_production(model, h, i):
# return model.PV[ h,i] == model.PV_Grid[ h,i] + model.PV_Home[ h,i]
#
#
# model.Const_PV = py.Constraint(model.h, model.t, rule=PV_production, doc='PV Production')
def PV_production(model, h, i):
return model.PV_Grid[h, i] + model.PV_Home[h, i] + model.PV_Battery[h, i] == model.PV[h, i]
model.Const_PV = py.Constraint(model.h, model.t, rule=PV_production, doc='PV Production')
# </editor-fold>
# NOTE: I need to include the third term which is in the paper: daily peak demand * charge of the power
def objective_rule(model):
return sum((model.P_Buy_Grid[h, i] * model.Buy_price[i]) - (
model.P_Sell_Grid[h, i] * model.Sell_price[i]) for i in
model.t for h in model.h) * model.time_d
model.objective = py.Objective(rule=objective_rule, sense=py.minimize, doc='Definition of objective function')
# <editor-fold desc="Solver">
model.write('model2.lp', io_options={'symbolic_solver_labels': True})
opt = py.SolverFactory('cplex')
opt.options["mipgap"] = 0.09 # 0.155 for load and 0.8 for other load
result = opt.solve(model, tee=True)
# result = opt.solve(model, tee = True)
# model.pprint()
print(result)
# </editor-fold>
# <editor-fold desc="Check optimility">
if (result.solver.status == py.SolverStatus.ok) and (
result.solver.termination_condition == py.TerminationCondition.optimal):
print('optimal solution') # Do something when the solution in optimal and feasible
elif result.solver.termination_condition == py.TerminationCondition.infeasible:
print('solution is not feasible') # Do something when model in infeasible
else:
# Something else is wrong
print('Solver Status: ', result.solver.status)
print('Sum of all homes objectives = ', py.value(model.objective))
# </editor-fold>
for h in model.h:
print(
f"Objective of Home {h} : " f"{py.value((sum((model.P_Buy_Grid[h, i] * model.Buy_price[i]) - (model.P_Sell_Grid[h, i] * model.Sell_price[i]) for i in model.t) * model.time_d))}")
# <editor-fold desc="Ploting">
# Plotting for automatic ploting
time = [i for i in model.t]
pbuy = [[] for j in model.h]
psell = [[] for j in model.h]
PEVCharge = [[] for j in model.h]
PESSCharge = [[] for j in model.h]
PEVDischHome = [[] for j in model.h]
PESSDischHome = [[] for j in model.h]
SEVCharge = [[] for j in model.h]
SEVDisch = [[] for j in model.h]
PEVDischGrid = [[] for j in model.h]
PESSDischGrid = [[] for j in model.h]
PVHome = [[] for j in model.h]
PVGrid = [[] for j in model.h]
PVBattery = [[] for j in model.h]
PPV = [[] for j in model.h]
baseload = [[] for j in model.h]
EnergyESS = [[] for j in model.h]
EnergyEV = [[] for j in model.h]
tettaEWHwat = [[] for j in model.h]
SEWH = [[] for j in model.h]
PEWH = [[] for j in model.h]
Buyprice = [] # cost = [i for i in model.c.values()] <<< check this
Sellprice = []
for j in model.h:
for k, v in model.P_Buy_Grid.items():
if k[0] == j:
pbuy[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.P_Sell_Grid.items():
if k[0] == j:
psell[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.P_EV_Charge.items():
if k[0] == j:
PEVCharge[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.P_EV_Disch_Home.items():
if k[0] == j:
PEVDischHome[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.P_EV_Disch_Grid.items():
if k[0] == j:
PEVDischGrid[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.P_ESS_Charge.items():
if k[0] == j:
PESSCharge[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.P_ESS_Disch_Home.items():
if k[0] == j:
PESSDischHome[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.P_ESS_Disch_Grid.items():
if k[0] == j:
PESSDischGrid[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.PV_Home.items():
if k[0] == j:
PVHome[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.PV_Grid.items():
if k[0] == j:
PVGrid[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.PV_Battery.items():
if k[0] == j:
PVBattery[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.PV.items():
if k[0] == j:
PPV[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.base_load.items():
if k[0] == j:
baseload[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.Energy_ESS.items():
if k[0] == j:
EnergyESS[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.Energy_EV.items():
if k[0] == j:
EnergyEV[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.P_EWH.items():
if k[0] == j:
PEWH[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.tetta_EWH_wat.items():
if k[0] == j:
tettaEWHwat[j - 1].append(py.value(v))
for j in model.h:
for k, v in model.S_EWH.items():
if k[0] == j:
SEWH[j - 1].append(py.value(v))
for i in model.Buy_price:
Buyprice.append(value(model.Buy_price[i]))
for i in model.Sell_price:
Sellprice.append(value(model.Sell_price[i]))
# alone
# for k in range(len(model.h)):
# fig, ax = plt.subplots(5, 3, figsize=(10, 10))
# ax[0, 0].bar(time, pbuy[k], label='Buying power')
# ax[0, 0].bar(time, psell[k], label='Selling power')
# ax[0, 0].legend(loc='best', fontsize='small', ncol=3)
# ax[0, 1].bar(time, baseload[k], label='Base load', color='r')
# ax[0, 1].legend(loc='best', fontsize='small', ncol=3)
# ax[0, 2].plot(time, Buyprice, label='Buyprice')
# ax[0, 2].plot(time, Sellprice, label='Sellprice')
# ax[0, 2].legend(loc='best', fontsize='small', ncol=3)
# ax[1, 0].bar(time, PEWH[k], label='EWH Power', color='r')
# ax[1, 0].legend(loc='best', fontsize='small', ncol=3)
# ax[1, 1].plot(time, tettaEWHwat[k], label='EWH Temp')
# ax[1, 1].legend(loc='best', fontsize='small', ncol=3)
# ax[1, 2].bar(time, SEWH[k], label='Status of EWH')
# ax[1, 2].legend(loc='best', fontsize='small', ncol=3)
# ax[2, 0].bar(time, PESSCharge[k], label='ESS Charging power', color='r')
# ax[2, 0].legend(loc='best', fontsize='small', ncol=3)
# ax[2, 1].bar(time, PESSDischHome[k], label='ESS Disch to home')
# ax[2, 1].bar(time, PESSDischGrid[k], label='ESS Disch to grid')
# ax[2, 1].legend(loc='best', fontsize='small', ncol=3)
# ax[2, 2].plot(time, EnergyESS[k], label='Energy of ESS', color='g')
# ax[2, 2].legend(loc='best', fontsize='small', ncol=3)
# ax[3, 0].bar(time, PVHome[k], label='PV to Home')
# ax[3, 0].legend(loc='best', fontsize='small', ncol=3)
# ax[3, 1].bar(time, PVGrid[k], label='PV to Grid')
# ax[3, 1].bar(time, PVBattery[k], label='PV to battery')
# ax[3, 1].legend(loc='best', fontsize='small', ncol=3)
# ax[3, 2].bar(time, PVHome[k], label='PV to Home')
# ax[3, 2].bar(time, PVGrid[k], label='PV to Grid')
# ax[3, 2].legend(loc='best', fontsize='small', ncol=3)
# ax[4, 0].bar(time, PEVCharge[k], label='EV Charging power', color='r')
# ax[4, 0].legend(loc='best', fontsize='small', ncol=3)
# ax[4, 1].bar(time, PEVDischHome[k], label='EV Disch to home')
# ax[4, 1].bar(time, PEVDischGrid[k], label='EV Disch to grid')
# ax[4, 1].legend(loc='best', fontsize='small', ncol=3)
# ax[4, 2].plot(time, EnergyEV[k], label='Energy of EV', color='r')
# ax[4, 2].legend(loc='best', fontsize='small', ncol=3)
# ax[4, 0].set_xlabel('Time (step)')
# ax[4, 1].set_xlabel('Time (step)')
# ax[4, 2].set_xlabel('Time (step)')
# ax[0, 2].set_ylabel('price ($/W/h)')
# ax[0, 0].set_ylabel('Power (kW)')
# ax[1, 0].set_ylabel('Power (kW)')
# ax[2, 0].set_ylabel('Power (kW)')
# ax[3, 0].set_ylabel('Power (kW)')
# ax[4, 0].set_ylabel('Power (kW)')
# ax[2, 2].set_ylabel('Energy (kWh)')
# ax[4, 2].set_ylabel('Energy (kWh)')
# plt.suptitle(
# f" Home {k + 1} : " f" Cost is : {py.value((sum((model.P_Buy_Grid[k + 1, i] * model.Buy_price[i]) - (model.P_Sell_Grid[k + 1, i] * model.Sell_price[i]) for i in model.t) * model.time_d))}")
# pl.tight_layout()
# plt.show()
# code for if you want to plot all the home togathoer
Update HEMS.py code
2024-05-31 10:58:57 -04:00
# fig, ax = plt.subplots(6, len(model.h), figsize=(10,10))
# for k in range(len(model.h)):
# ax[0, k].plot(time, pbuy[k], label='Buying power')
# ax[0, k].plot(time, psell[k], label='Selling power')
# # ax[0, k].plot(time, baseload[k], label='Base load')
# ax[0, 0].legend(loc='best')
# ax[1, k].plot(time, Buyprice, label='Buyprice')
# ax[1, 0].legend(loc='best')
# ax[1, k].plot(time, Sellprice, label='Sellprice')
# ax[1, 0].legend(loc='best')
# ax[2, k].plot(time, PEWH[k], label='EWH Power')
# ax[2, k].plot(time, tettaEWHwat[k], label='EWH Temp')
# ax[2, 0].legend(loc='best')
# ax[3, k].plot(time, PESSCharge[k],label='ESS Charging power')
# ax[3, k].plot(time, PESSDischHome[k], label='ESS Disch to home')
# ax[3, k].plot(time, PESSDischGrid[k], label='ESS Disch to grid')
# ax[3, 0].legend(loc='best')
# ax[4, k].plot(time, PVHome[k], label='PV to Home')
# ax[4, k].plot(time, PVGrid[k], label='PV to Grid')
# ax[4, k].plot(time, PPV[k], label='All PV Production')
# ax[4, 0].legend(loc='best')
# ax[5, k].plot(time, PEVCharge[k], label='EV Charging power')
# ax[5, k].plot(time, PEVDischHome[k], label='EV Disch to home')
# ax[5, k].plot(time, PEVDischGrid[k], label='EV Disch to grid')
# ax[5, 0].legend(loc='best')
# # ax[6, k].plot(time, EnergyESS[k], label='Energy of ESS')
# # ax[6, k].legend(loc='best')
# # ax[7, k].plot(time, EnergyEV[k], label='Energy of EV')
# # ax[7, 0].legend(loc='best')
# ax[4, 5].set_xlabel('Time (step)')
# ax[1, 0].set_ylabel('Electricity price ($/W/h)')
# ax[0, 0].set_ylabel('Power (kW)')
# ax[2,0].set_ylabel('Power (kW)')
# ax[0, k].set_title(f"Home {k+1}")
#
# # pl.tight_layout()
#
# plt.show()
Add HEMS
2024-04-06 17:08:29 -04:00
# if you want to plot each home alone in the figure
Update HEMS.py code
2024-05-31 10:58:57 -04:00
bar_width =0.4
time1 = np.array(time)
for k in range(len(model.h)):
fig, ax = plt.subplots(2,constrained_layout=True, )
ax[0].bar(time1 - bar_width/2 , pbuy[k], width=bar_width, label='Base')
ax[0].bar(time1 + bar_width/2 , psell[k], width=bar_width, label='Controllable Load')
ax[1].plot(time, Buyprice, label='buying price')
ax[1].plot(time, Sellprice, 'y-+', label='selling price')
ax[1].legend(loc='upper left')
ax[1].set_xlabel('Time (hour)')
ax[1].set_ylabel('Electricity price ($/W/h)')
plt.suptitle(f"Home {k+1}")
plt.show()
Add HEMS
2024-04-06 17:08:29 -04:00
# print('this is the end of code')
# # </editor-fold>
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