feat: first commit

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/flexibility.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.9 (venv)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyCompatibilityInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ourVersions">
+        <value>
+          <list size="2">
+            <item index="0" class="java.lang.String" itemvalue="3.10" />
+            <item index="1" class="java.lang.String" itemvalue="3.9" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyPep8Inspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="E111" />
+        </list>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="list.__getitem__" />
+          <option value="hub.catalog_factories.data_models.cost.capital_cost.CapitalCost.__getitem__" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (venv)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/flexibility.iml" filepath="$PROJECT_DIR$/.idea/flexibility.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

optimized.py

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+import math
+
+import cvxpy as cp
+import pandas as pd
+data = pd.read_csv('new_file.csv')
+demand = data['Q_tot_mpc'].to_list()
+demand_watts = [x * 1000 for x in demand]
+t_out = data['T_out'].to_list()
+p_electricity = data['Electricity_Price'].to_list()
+data['Datetime'] = pd.to_datetime(data['Datetime'])
+cp_water = 4182 # J/kgK
+# Heat Pump Sizing
+hp_cap = max(demand_watts) * 0.7
+hp_cop_curve_coefficients = [1.039924, 0.0146, 6e-06, -0.05026, 0.000635, -0.000154]
+hp_nominal_efficiency = 2.5
+hp_delta_t = 5
+# TES Sizing
+volume = round(max(demand_watts) * 3.6e3 / (1000 * cp_water * 15))
+height = 2
+d = math.sqrt((4 * volume) / (math.pi * height))
+ua = 0.28
+
+# problem setting
+time_horizon = 1440  # Number of time steps (15-minute intervals)
+time_step_size = 900  # Time step size in seconds (15 minutes)
+initial_temperature = 40
+lower_limit_TES = 40
+upper_limit_TES = 55
+
+# Define problem variables
+storage_charge = cp.Variable(time_horizon, nonneg=True)  # Energy from heat pump to tank
+storage_discharge = cp.Variable(time_horizon, nonneg=True)  # Energy from tank to house
+heat_pump_energy = cp.Variable(time_horizon, nonneg=True)  # Heat pump energy
+tank_temperature = cp.Variable(time_horizon)  # Tank temperature (°C)
+
+# Define problem parameters
+thermal_demand = demand_watts  # Heating demand
+
+electricity_price = p_electricity
+# Calculate the change in tank temperature
+temperature_change = (time_step_size / (1000 * volume *
+                                        cp_water)) * (storage_charge[:-1] - storage_discharge[:-1])
+# Calculate the electricity cost
+electricity_cost = ((heat_pump_energy[:-1] * time_step_size) / (hp_nominal_efficiency * 3600)) @ electricity_price[:-1]
+
+# Define the objective function
+objective = cp.Minimize(cp.sum_squares(thermal_demand - storage_discharge)) + cp.Minimize(cp.sum(electricity_cost)) + cp.Minimize(cp.sum(heat_pump_energy))
+# Define the constraints
+constraints = [heat_pump_energy <= hp_cap, tank_temperature >= lower_limit_TES,
+               tank_temperature <= upper_limit_TES, tank_temperature[0] == initial_temperature,
+               storage_charge <= heat_pump_energy]
+
+# Specify the tank temperature in the next time step
+constraints.extend([tank_temperature[i+1] == tank_temperature[i] + temperature_change[i] for i in range(time_horizon - 1)])
+# Create the optimization problem
+problem = cp.Problem(objective, constraints)
+# Solve the problem
+problem.solve(solver=cp.GUROBI)
+
+# Get the optimized values
+optimized_storage_charge = storage_charge.value
+optimized_storage_discharge = storage_discharge.value
+optimized_heat_pump_energy = heat_pump_energy.value
+optimized_tank_temperature = tank_temperature.value
+electricity_consumption = (optimized_heat_pump_energy*time_step_size)/(hp_nominal_efficiency*3600)
+optimized_cost = electricity_consumption * electricity_price / 1000
+output = pd.DataFrame(index=data['Datetime'])
+output["optimized_storage_charge"] = optimized_storage_charge
+output["optimized_storage_discharge"] = optimized_storage_discharge
+output["optimized_heat_pump_energy"] = optimized_heat_pump_energy
+output["demand"] = thermal_demand
+output["T"] = optimized_tank_temperature
+output["electricity_consumption"] = electricity_consumption
+output["electricity_cost"] = optimized_cost
+out_file = output.to_csv("results_multi_objective.csv")

rule_based.py

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+import math
+
+import pandas as pd
+data = pd.read_csv('new_file.csv')
+demand = [0] + data['Q_tot_mpc'].to_list()
+t_out = [0] + data['T_out'].to_list()
+data['Datetime'] = pd.to_datetime(data['Datetime'])
+p_electricity = data['Electricity_Price'].to_list()
+cp = 4182 # J/kgK
+# Heat Pump Sizing
+hp_cap = max(demand) * 0.7 * 1000
+hp_cop_curve_coefficients = [1.039924, 0.0146, 6e-06, -0.05026, 0.000635, -0.000154]
+hp_nominal_efficiency = 2.5
+hp_delta_t = 5
+# TES Sizing
+volume = round(max(demand) * 3.6e6 / (1000 * cp * 15))
+height = 2
+d = math.sqrt((4 * volume) / (math.pi * height))
+ua = 0.28
+
+variable_names = ["t_sup_hp", "t_tank", "t_ret", "m_ch", "m_dis", "q_hp", "hp_cop", "hp_electricity",
+                  "electricity_cost"]
+num_hours = len(demand)
+variables = {name: [0] * num_hours for name in variable_names}
+t_sup_hp, t_tank, t_ret, m_ch, m_dis, q_hp, hp_cop, hp_electricity, electricity_cost = [variables[name] for name in variable_names]
+t_tank[0] = 40
+for i in range(len(demand) - 1):
+  t_tank[i + 1] = t_tank[i] + ((m_ch[i] * (t_sup_hp[i] - t_tank[i])) + (ua * (t_out[i] - t_tank[i])) / cp - m_dis[i] * (t_tank[i] - t_ret[i]))  * (900 / (1000 * volume))
+  # hp operation and tank charging
+  if t_tank[i + 1] < 40:
+    q_hp[i + 1] = hp_cap
+    m_ch[i + 1] = q_hp[i + 1] / (cp * hp_delta_t)
+    t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cp)) + t_tank[i + 1]
+  elif 40 <= t_tank[i + 1] < 55 and q_hp[i] == 0:
+    q_hp[i + 1] = 0
+    m_ch[i + 1] = 0
+    t_sup_hp[i + 1] = t_tank[i + 1]
+  elif 40 <= t_tank[i + 1] < 55 and q_hp[i] > 0:
+    q_hp[i + 1] = hp_cap
+    m_ch[i + 1] = q_hp[i + 1] / (cp * hp_delta_t)
+    t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cp)) + t_tank[i + 1]
+  else:
+    q_hp[i + 1], m_ch[i + 1], t_sup_hp[i + 1] = 0, 0, t_tank[i + 1]
+  if q_hp[i + 1] > 0:
+    t_out_fahrenheit = 1.8 * t_out[i] + 32
+    t_tank_fahrenheit = 1.8 * t_tank[i] + 32
+    hp_cop[i + 1] = (1 / (hp_cop_curve_coefficients[0] +
+                    hp_cop_curve_coefficients[1] * t_tank_fahrenheit +
+                    hp_cop_curve_coefficients[2] * t_tank_fahrenheit ** 2 +
+                    hp_cop_curve_coefficients[3] * t_out_fahrenheit +
+                    hp_cop_curve_coefficients[4] * t_out_fahrenheit ** 2 +
+                    hp_cop_curve_coefficients[5] * t_tank_fahrenheit * t_out_fahrenheit)) * hp_nominal_efficiency
+    hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
+    electricity_cost[i + 1] = hp_electricity[i + 1] * p_electricity[i + 1]
+  else:
+    hp_cop[i + 1] = 0
+    hp_electricity[i + 1] = 0
+    electricity_cost[i + 1] = 0
+
+  # storage discharging
+  if demand[i + 1] == 0:
+    m_dis[i + 1] = 0
+    t_ret[i + 1] = t_tank[i + 1]
+  else:
+    if demand[i + 1] > 0.5 * max(demand):
+      factor = 6
+    else:
+      factor = 4
+    m_dis[i + 1] = (max(demand) * 1000) / (cp * factor)
+    t_ret[i + 1] = t_tank[i + 1] - (demand[i + 1] * 1000) / (m_dis[i + 1] * cp)
+
+output = pd.DataFrame(index=data['Datetime'])
+output["demand"] = [x * 1000 for x in demand][1:]
+output["q_hp"] = q_hp[1:]
+output["hp_cop"] = hp_cop[1:]
+output["hp_electricity_consumption"] = hp_electricity[1:]
+output["m_ch"] = m_ch[1:]
+output["m_dis"] = m_dis[1:]
+output["t_sup_hp"] = t_sup_hp[1:]
+output["t_tank"] = t_tank[1:]
+output["t_return"] = t_ret[1:]
+out_file = output.to_csv("results_rule_based.csv")
+
+
+
+
+
+