# NOTE: for two homes # 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 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') # # model = py.ConcreteModel() # # i think it should be outside the loop # 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') # # 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') # # 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") # # 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") # # 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") # 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 # # NOTE:Variable # 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) # # 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 # # 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) # # 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)) # # 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') # # NOTE:Constraints # 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') # # # 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') # # 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') # # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 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') # # 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') # # # 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') # # 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') # 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) # # 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)) # 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))}") # # 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 # 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() # if you want to plot each home alone in the figure 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() # print('this is the end of code') # #