From 91daa0dd0ca67e416304c8bbedd33459e338e228 Mon Sep 17 00:00:00 2001
From: Sadam93 <sadamengr15@gmail.com>
Date: Fri, 31 May 2024 11:11:33 -0400
Subject: [PATCH] updating basic exmaple code, coming all example

---
 basic_example.py | 100 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 100 insertions(+)

diff --git a/basic_example.py b/basic_example.py
index eadc9b9..1167301 100644
--- a/basic_example.py
+++ b/basic_example.py
@@ -88,3 +88,103 @@ ax1.set_xlabel('Time (hour)')
 ax1.set_ylabel('Electricity price ($/W/h)')
 plt.suptitle("Energy management system-Example (4)")
 plt.show()
+
+
+'''
+# another way do the coding, using excel sheet for the data and call them in the code
+'''
+
+
+# import matplotlib.pyplot as plt
+# import pandas as pd
+# import pyomo.environ as py
+# # from pyomo.environ import *
+#
+# # model
+# # from pyomo.core import value
+#
+# model = py.ConcreteModel()
+# data = pd.read_excel('input_data.xlsx', sheet_name='set')
+#
+# # Set
+# model.t = py.Set(initialize=data.time, doc='time')
+#
+# # Parameter
+# model.pb = py.Param(model.t, initialize=dict(zip(data.time.values, data.base_load2.values)), doc='Base load')
+# model.c = py.Param(model.t, initialize=dict(zip(data.time.values, data.price.values)), doc='cost')
+# model.dt = py.Param(initialize=1, doc='time duration ')
+# model.pmax = py.Param(initialize=5, doc='Maximum Power ')
+# model.emax = py.Param(initialize=8, doc='Maximum Controllable Energy ')
+# model.plmax = py.Param(initialize=6, doc='Maximum Controllable power')
+# # for the simplicity of the equation
+#
+# # Variable
+# model.pl = py.Var(model.t, bounds=(0, model.plmax))
+#
+#
+#
+# # Constraints
+# # note:We can write the constraint like this
+# # model.c1 = py.Constraint(expr=sum([pl[i] for i in t]) * dt == 8000)
+#
+# # note:Other way to write equations of constraints
+# def Conctrol_power(model, i):
+#     return sum([model.pl[i] for i in model.t]) * model.dt == model.emax
+# model.c1 = py.Constraint( rule=Conctrol_power, doc='Controllable power')
+# def total_power(model, i):
+#     return (model.pb[i] + model.pl[i]) <= model.pmax
+# model.c2 = py.Constraint(model.t, rule=total_power, doc='Total power')
+#
+#
+# def objective_rule(model):
+#     return sum([(model.pb[i] + model.pl[i]) * model.c[i] for i in model.t])
+# model.objective = py.Objective(rule=objective_rule, sense=py.minimize, doc='Define objective function')
+#
+# # Solve
+# opt = py.SolverFactory('cplex')
+# result = opt.solve(model)
+# print(result)
+# # model.pprint()
+#
+# # Print variables
+# print('------------Controllable variable Solution------------------------')
+# for i in model.t:
+#     print('x[%i] = %i' % (i, py.value(model.pl[i])))
+# print('objective function = ', py.value(model.objective))
+#
+# # Plotting
+#
+# # Plotting
+# time = []
+# pbase = []
+# pload = []
+# cost = []
+#
+# for ll in range(len([model.t, model.pb, model.pl, model.c])):
+#     if ll == 0:
+#         for i in model.t:
+#             time.append(py.value(model.t[i]))
+#     elif ll == 1:
+#         for i in model.pb:
+#             pbase.append(py.value(model.pb[i]))
+#     elif ll == 2:
+#         for i in model.pl:
+#             pload.append(py.value(model.pl[i]))
+#     else:
+#         for i in model.c:
+#             cost.append(py.value(model.c[i]))
+#
+# fig, (ax0, ax1) = plt.subplots(nrows=2, ncols=1)
+#
+# ax0.plot(time, pbase, 'b-', label='Base')
+# ax0.plot(time, pload, 'g--', label='Controable Load')
+# ptotal = [pbase[i] + pload[i] for i in range(len(pbase))]
+# ax0.plot(time, ptotal, 'r', label='Total Load')
+# ax0.set_ylabel('Electric power (W)')
+# ax0.legend(loc='upper left')
+# ax1.plot(time, cost, 'y-+', label='Cost')
+# ax1.legend(loc='upper left')
+# ax1.set_xlabel('Time (hour)')
+# ax1.set_ylabel('Electricity price ($/W/h)')
+# plt.suptitle("Energy management system-Example (5)")
+# plt.show()