From 975e27741d91b556cb7beb86ced1d394fb032f36 Mon Sep 17 00:00:00 2001 From: Sadam93 Date: Fri, 31 May 2024 11:22:07 -0400 Subject: [PATCH] clean and update HEMS code --- HEMS.py | 122 +++++++------------------------------------------------- 1 file changed, 15 insertions(+), 107 deletions(-) diff --git a/HEMS.py b/HEMS.py index 3ac9d6e..17b56c3 100644 --- a/HEMS.py +++ b/HEMS.py @@ -7,15 +7,9 @@ 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 +from matplotlib import pyplot as plt # 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') @@ -28,9 +22,9 @@ 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 @@ -38,16 +32,16 @@ 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), +model.EV_Dur = py.Set(initialize=py.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), +model.EV_Dur1 = py.Set(initialize=py.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), +model.t_second = py.Set(initialize=py.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), +model.ist_interval = py.Set(initialize=py.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), +model.second_interval = py.Set(initialize=py.RangeSet(model.beta.value + 1, model.t_final.value), doc='time interval after departure') # @@ -65,8 +59,9 @@ model.PV = py.Param(model.h, model.t, initialize=dict(zip(list(itertools.product [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 @@ -125,10 +120,6 @@ model.DischRate_ESS = py.Param(model.h, initialize=dict(zip(list(itertools.produ 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") @@ -217,14 +208,13 @@ model.water_use = py.Param(model.h, model.t, 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 @@ -317,47 +307,6 @@ model.Const_3 = py.Constraint(model.h, model.t, rule=Status_Power, # -# -# 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') - - # # @@ -412,10 +361,6 @@ model.Const_EV_6 = py.Constraint(model.h, model.t, rule=Power_EV_Disch, doc='Dis 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') @@ -426,14 +371,6 @@ def SoC_EV2(model, h, i): 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 @@ -490,15 +427,9 @@ model.Const_EV_12 = py.Constraint(model.h, [model.beta.value], rule=EV_final_SoC # -# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# 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 @@ -506,15 +437,11 @@ model.Const_ESS_1a = py.Constraint(model.h, [model.t_first.value], rule=SoC_ESS1 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 ') @@ -614,8 +541,6 @@ def EWH_power(model, 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 - ( @@ -640,24 +565,14 @@ model.Const_EWH_5 = py.Constraint(model.h, model.t_second, rule=EWH_temp_2, doc= # # # 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 @@ -671,13 +586,10 @@ 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 @@ -799,10 +711,10 @@ for j in model.h: SEWH[j - 1].append(py.value(v)) for i in model.Buy_price: - Buyprice.append(value(model.Buy_price[i])) + Buyprice.append(py.value(model.Buy_price[i])) for i in model.Sell_price: - Sellprice.append(value(model.Sell_price[i])) + Sellprice.append(py.value(model.Sell_price[i])) # alone # for k in range(len(model.h)): @@ -895,8 +807,7 @@ for i in model.Sell_price: # ax[2,0].set_ylabel('Power (kW)') # ax[0, k].set_title(f"Home {k+1}") # -# # pl.tight_layout() -# + # plt.show() @@ -914,7 +825,4 @@ for k in range(len(model.h)): ax[1].set_ylabel('Electricity price ($/W/h)') plt.suptitle(f"Home {k+1}") plt.show() - - -# print('this is the end of code') # #