diff --git a/Courses/Data/PriceProfilePPA.csv b/Courses/Data/PriceProfilePPA.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cae57b2aeaeaeaa1d1f206c9254c2c1e3cb83bbf
--- /dev/null
+++ b/Courses/Data/PriceProfilePPA.csv
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diff --git a/Courses/Data/aveS.csv b/Courses/Data/aveS.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2babbbc9f8b5433f68061894d9a9d236ffd9b906
--- /dev/null
+++ b/Courses/Data/aveS.csv
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diff --git a/Courses/example_HyDesign.py b/Courses/example_HyDesign.py
new file mode 100644
index 0000000000000000000000000000000000000000..51519c8c9342b4edd18b9e8906a5f8c13ab2d87b
--- /dev/null
+++ b/Courses/example_HyDesign.py
@@ -0,0 +1,260 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Nov 10 12:05:52 2019
+
+@author: kdas
+"""
+
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import numpy as np
+
+from HyDesign.sizing import HPP, read_csv_Data
+
+if __name__ == "__main__":
+
+    # =============================================================================
+    # # Alessandra's data
+    # =============================================================================
+    input_wind_ts_filename = './Data/aveW.csv'
+    input_solar_ts_filename = './Data/aveS.csv'
+    input_price_ts_filename = './Data/PriceProfilePPA.csv'
+
+    start_date = pd.to_datetime('2018-09-01 00:00')
+    end_date = pd.to_datetime("2019-08-31 23:00")
+
+    timeFormat_wind = "%d/%m/%Y %H:%M"
+    timeFormat_solar = "%d/%m/%Y %H:%M"
+    timeFormat_price = '%d/%m/%Y %H:%M'
+    timename = 'Time'
+    timeZone_wind = 'Asia/Kolkata'
+    timeZone_solar = 'Asia/Kolkata'
+    timeZone_price = 'Asia/Kolkata'
+    timeZone_analysis = 'Asia/Kolkata'
+
+    # inputs
+    # Alessandra's data
+    parameter_dict = {
+        # hpp parameters
+        'hpp_grid_connection': 300,  # in MW
+        'hpp_land_area_available': 100,  # in square km
+        'hpp_lifetime': 25,  # [years]
+        'hpp_discount_factor': 0.07,
+        'hpp_BOS_soft_cost': 119940,  # [Eur/MW]
+        'hpp_grid_connection_cost': 37074,  # [Eur/MW]
+
+        # hpp wind parameters
+        'wind_rating_WT': 2.1,
+        'wind_nWT_per_string': 20,
+        'wind_lifetime_WT': 30,
+        'wind_rotor_diameter': 97,  # in m
+        'wind_hub_height': 120,  # in m
+        'wind_turbine_spacing': 5,  # in terms of Rotor Diameter
+        'wind_turbine_row_spacing': 10,  # in terms of Rotor Diameter
+        'wind_turbine_cost': 851000,  # [EUR/MW]
+        'wind_civil_works_cost': 116986,  # [Eur/MW]
+        'wind_fixed_onm_cost': 12805,  # Wind fixed O&M cost per year [Eur/MW]
+
+        # hpp solar parameters
+        'solar_lifetime_PV': 30,
+        'solar_PV_cost': 219000,  # [EUR/MW]
+        'solar_hardware_installation_cost': 241854,  # [Eur/MW]
+        'solar_fixed_onm_cost': 8149,  # Solar O&M cost per year [Eur/MW]
+
+        # hpp battery parameters
+        'battery_energy_cost': 181596,  # Eur/MWh
+        'battery_power_cost': 64190,  # Power conversion system cost[Eur/MW]
+        # Electric Balance of Plant, installation, commissioning cost [Eur/MW]
+        'battery_BOP_installation_commissioning_cost': 73360,
+        # Grid management control system cost[Eur/MW]
+        'battery_control_system_cost': 18340,
+        # Battery energy capacity maintenance cost [Eur/MWh] per year
+        'battery_energy_onm_cost': 10000,
+        'battery_depth_of_discharge': 0.9,
+    }
+
+    simulation_dict = {
+        'wind_as_component': 1,
+        'solar_as_component': 1,
+        'battery_as_component': 1,
+    }
+
+    ExampleHPP = HPP(
+        parameter_dict=parameter_dict,
+        simulation_dict=simulation_dict,
+    )
+
+    wind_power_t = read_csv_Data(
+        input_wind_ts_filename,
+        timename,
+        timeFormat_wind,
+        timeZone_wind,
+        timeZone_analysis,
+    )
+    solar_power_t = read_csv_Data(
+        input_solar_ts_filename,
+        timename,
+        timeFormat_solar,
+        timeZone_solar,
+        timeZone_analysis,
+    )
+    spot_price_t = read_csv_Data(
+        input_price_ts_filename,
+        timename,
+        timeFormat_price,
+        timeZone_price,
+        timeZone_analysis,
+    )
+
+    wind_power_t = wind_power_t.loc[start_date:end_date, 'Pwind'] / 100
+    solar_power_t = solar_power_t.loc[start_date:end_date, 'Psolar'] / 100
+    spot_price_t = spot_price_t.loc[start_date:end_date, 'Price']
+
+    capacity_factor_wind = ExampleHPP.calculate_Capacity_Factor(
+        wind_power_t, 1)
+    capacity_factor_solar = ExampleHPP.calculate_Capacity_Factor(
+        solar_power_t, 1)
+
+    # %% -----------------------------------------------
+    # Optimize the sizing of HPP (wind and solar)
+    # --------------------------------------------------
+
+    [hpp_wind_capacity, hpp_solar_capacity, P_HPP_t,
+     P_curtailment_t, hpp_investment_cost, hpp_maintenance_cost,
+     LCOE, NPV, IRR] = ExampleHPP.sizing_Wind_Solar(
+        wind_power_t, solar_power_t, spot_price_t)
+
+    # %%
+
+    AEP = P_HPP_t.mean() * 8760 / 1000
+    Curtailed_Energy = P_curtailment_t.sum() / 1000
+    # print("\033[H\033[J")
+    print()
+    print('Wind Capacity = %d MW' % hpp_wind_capacity)
+    print('Solar Capacity = %d MW' % hpp_solar_capacity)
+    print('AEP = %.0f GWh' % AEP)
+    print('Curtailed Energy = %.0f GWh' % Curtailed_Energy)
+    print('HPP Investment Cost = %.0f MEUR' % (hpp_investment_cost / 1e6))
+    print('HPP Maintenance Cost = %.0f MEUR' %
+          (hpp_maintenance_cost.sum() / 1e6))
+    print('HPP LCOE = %.2f EUR/MWh' % LCOE)
+    print('HPP NPV= %.0f MEUR' % (NPV / 1000000))
+    print('HPP IRR = %.0f%%' % (IRR * 100))
+
+    # %% -----------------------------------------------
+    # Eval a non-optimal HPP (wind and solar)
+    # --------------------------------------------------
+
+    [hpp_wind_capacity, hpp_solar_capacity, P_HPP_t,
+     P_curtailment_t, hpp_investment_cost, hpp_maintenance_cost,
+     LCOE, NPV, IRR] = ExampleHPP.eval_Wind_Solar(
+        wind_power_t, solar_power_t, spot_price_t,
+        Wind_MW=200, Solar_MW=300)
+
+    # %%
+
+    AEP = P_HPP_t.mean() * 8760 / 1000
+    Curtailed_Energy = P_curtailment_t.sum() / 1000
+    # print("\033[H\033[J")
+    print()
+    print('Wind Capacity = %d MW' % hpp_wind_capacity)
+    print('Solar Capacity = %d MW' % hpp_solar_capacity)
+    print('AEP = %.0f GWh' % AEP)
+    print('Curtailed Energy = %.0f GWh' % Curtailed_Energy)
+    print('HPP Investment Cost = %.0f MEUR' % (hpp_investment_cost / 1e6))
+    print('HPP Maintenance Cost = %.0f MEUR' %
+          (hpp_maintenance_cost.sum() / 1e6))
+    print('HPP LCOE = %.2f EUR/MWh' % LCOE)
+    print('HPP NPV= %.0f MEUR' % (NPV / 1000000))
+    print('HPP IRR = %.0f%%' % (IRR * 100))
+
+    # %% --------------------------------------------------
+    # Optimize the sizing of HPP (wind, solar and battery)
+    # -----------------------------------------------------
+
+    [hpp_wind_capacity,
+     hpp_solar_capacity,
+     hpp_battery_power_rating,
+     hpp_battery_energy_capacity,
+     P_RES_available_t,
+     P_HPP_t,
+     P_curtailment_t,
+     P_charge_discharge_t,
+     E_SOC_t,
+     hpp_investment_cost,
+     hpp_maintenance_cost,
+     LCOE,
+     NPV,
+     IRR] = ExampleHPP.sizing_Wind_Solar_Battery(wind_power_t,
+                                                 solar_power_t,
+                                                 spot_price_t)
+
+    # %%
+
+    AEP = P_HPP_t.mean() * 8760 / 1000
+    Curtailed_Energy = P_curtailment_t.sum() / 1000
+    # print("\033[H\033[J")
+    print()
+    print('Wind Capacity = %d MW' % hpp_wind_capacity)
+    print('Solar Capacity = %d MW' % hpp_solar_capacity)
+    print('Battery Storage Power Rating = %d MW' % hpp_battery_power_rating)
+    print(
+        'Battery Storage Energy Rating = %d MWh' %
+        hpp_battery_energy_capacity)
+    print('AEP = %.0f GWh' % AEP)
+    print('Curtailed Energy = %.0f GWh' % Curtailed_Energy)
+    print('HPP Investment Cost = %.0f MEUR' % (hpp_investment_cost / 1e6))
+    print('HPP Maintenance Cost = %.0f MEUR' %
+          (hpp_maintenance_cost.sum() / 1e6))
+    print('HPP LCOE = %.2f EUR/MWh' % LCOE)
+    print('HPP NPV= %.0f MEUR' % (NPV / 1000000))
+    print('HPP IRR = %.0f%%' % (IRR * 100))
+
+    # %% --------------------------------------------------
+    # Eval a HPP (wind, solar and battery)
+    # -----------------------------------------------------
+
+    [hpp_wind_capacity,
+     hpp_solar_capacity,
+     hpp_battery_power_rating,
+     hpp_battery_energy_capacity,
+     P_RES_available_t,
+     P_HPP_t,
+     P_curtailment_t,
+     P_charge_discharge_t,
+     E_SOC_t,
+     hpp_investment_cost,
+     hpp_maintenance_cost,
+     LCOE,
+     NPV,
+     IRR] = ExampleHPP.eval_Wind_Solar_Battery(
+        wind_power_t,
+        solar_power_t,
+        spot_price_t,
+        Wind_MW=200,
+        Solar_MW=300,
+        P_batt_MW=100,
+        E_batt_MWh=300
+    )
+
+    # %%
+
+    AEP = P_HPP_t.mean() * 8760 / 1000
+    Curtailed_Energy = P_curtailment_t.sum() / 1000
+    # print("\033[H\033[J")
+    print()
+    print('Wind Capacity = %d MW' % hpp_wind_capacity)
+    print('Solar Capacity = %d MW' % hpp_solar_capacity)
+    print('Battery Storage Power Rating = %d MW' % hpp_battery_power_rating)
+    print(
+        'Battery Storage Energy Rating = %d MWh' %
+        hpp_battery_energy_capacity)
+    print('AEP = %.0f GWh' % AEP)
+    print('Curtailed Energy = %.0f GWh' % Curtailed_Energy)
+    print('HPP Investment Cost = %.0f MEUR' % (hpp_investment_cost / 1e6))
+    print('HPP Maintenance Cost = %.0f MEUR' %
+          (hpp_maintenance_cost.sum() / 1e6))
+    print('HPP LCOE = %.2f EUR/MWh' % LCOE)
+    print('HPP NPV= %.0f MEUR' % (NPV / 1000000))
+    print('HPP IRR = %.0f%%' % (IRR * 100))
diff --git a/Courses/sizing.py b/Courses/sizing.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9415fcabf200f7fe59762399e24591d8c34c79b
--- /dev/null
+++ b/Courses/sizing.py
@@ -0,0 +1,924 @@
+#! /usr/bin/env python
+from __future__ import print_function, division, absolute_import, unicode_literals
+# Copyright (C) 2019 DTU Wind
+
+import os
+import logging
+import sys
+
+# from pathlib import Path
+import pandas as pd
+import numpy as np
+#from scipy.optimize import minimize
+#import matplotlib.pyplot as plt
+#from pulp import *
+import openpyxl
+import math
+
+
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+from datetime import date, timedelta, datetime
+import pickle
+from docplex.mp.model import Model
+import numpy_financial as npf
+
+#from landbosse.excelio import landbosse_input_dir
+
+
+class HPP(object):
+
+    """
+    A class used to represent a hybrid power plant
+
+    ...
+
+    Attributes
+    ----------
+
+    hpp_grid_connection : float
+        Grid connection capacity in MW
+    hpp_land_area_available : float
+        Total land area available for the HPP in sq. km.
+    wind_rating_WT : float
+        Rating of the wind turbine in MW
+    wind_nWT_per_string : int
+        Number of wind turbines connected in series in each string
+    wind_lifetime_WT : int/float
+        Lifetime of wind turbines in years
+    wind_rotor_diameter : float
+        Rotor diameter of the wind turbine in m
+    wind_hub_height : float
+        Hub height of the wind turbine in m
+    wind_turbine_spacing : int
+        Spacing between 2 wind turbines as a multiple of rotor diameter
+    wind_turbine_row_spacing : float
+        Spacing between 2 rows of WT strings in m
+    solar_lifetime_PV : int/float
+        Lifetime of the solar PV in years
+
+    Methods
+    -------
+    load_Exogenous_Data_Wind()
+        Load external data for wind power
+        Exogenous Data involve wind power timeseries
+    load_Exogenous_Data_Solar()
+        Load external data for solar power
+        Exogenous Data involve solar power timeseries
+    load_ExogenousData_Price()
+        Load external data for market price
+        Exogenous Data involve spot market price timeseries
+    load_Exogenous_Data_Sizing()
+        Load all the external data for sizing optimization
+        Exogenous Data involve wind power, solar power and spot market timeseries
+    load_Exogenous_Data_EMS()
+        Load all the external data for sizing optimization
+        Exogenous Data involve wind power, solar power, spot market, balancing market data, power forecast and spot market forecast
+    calculate_Capacity_Factor(ts, capacity)
+        Calculate the capacity factor of any power plant with input power time series in MW, ts and capacity in MW
+    sizing(wind_ts, solar_ts, price_ts)
+        calls sizing optimization method for either wind/solar or wind/solar/battery for sizing optimization depending on input from parameters_Simulation
+    sizing_Wind_Solar(wind_ts, solar_ts, price_ts)
+        performs sizing optimization for wind and solar sizing
+    sizing_Wind_Solar_Battery(wind_ts, solar_ts, price_ts)
+        performs sizing optimization for wind and solar sizing
+    calculate_LCOE(investment_cost, maintenance_cost_per_year, discount_rate, lifetime, AEP_per_year)
+        calculate the LCOE for given investment cost, maintenance cost per year, discount rate, lifetime, AEP per year
+    calculate_IRR(P_HPP_t, price_t, investment_cost, maintenance_cost_per_year)
+        calculate internal rate of return for given input of power generation from HPP, price timeseries, investment cost, maintenance cost per year
+    calculate_NPV(P_HPP_t, price_t, investment_cost, maintenance_cost_per_year, discount_rate)
+        calculate net present value for given input of power generation from HPP, price timeseries, investment cost, maintenance cost per year and discount rate
+    """
+
+    def __init__(
+            self,
+            parameter_dict,
+            simulation_dict={},
+    ):
+
+        # add all parameters in parameter_dict into the hpp object
+        parameter_list = []
+        for key, value in parameter_dict.items():
+            # This line define properties in the class
+            # it is equivalent to define them one-by-one
+            # self.hpp_grid_connection=hpp_grid_connection
+            self.__dict__[key] = value
+            parameter_list += [key]
+        self.parameter_list = parameter_list
+
+        simulation_list = []
+        for key, value in simulation_dict.items():
+            # This line define properties in the class
+            # it is equivalent to define them one-by-one
+            # self.hpp_grid_connection=hpp_grid_connection
+            self.__dict__[key] = value
+            simulation_list += [key]
+
+    # Define methods of hpp class
+    """
+    Load exogenous data for wind solar and market prices
+    """
+
+    def load_Exogenous_Data_Sizing(
+        self,
+        input_dir_ts,
+        input_wind_ts_filename,
+        input_solar_ts_filename,
+        input_price_ts_filename,
+        timename,
+        timeFormat_wind,
+        timeFormat_solar,
+        timeFormat_price,
+        timeZone_wind,
+        timeZone_solar,
+        timeZone_price,
+        timeZone_analysis,
+    ):
+        """
+        Method for reading wind power, solar power and price timeseries csv files;
+        convert the timeseries into specified time format;
+
+        Output
+        ------
+        data: Formatted timeseries
+
+        """
+        if wind_as_component == 1:
+            # wind_ts=self.load_Exogenous_Data_Wind()
+            wind_ts = read_csv_Data(
+                input_dir_ts + input_wind_ts_filename,
+                timename,
+                timeFormat_wind,
+                timeZone_wind,
+                timeZone_analysis,
+            )
+        else:
+            wind_ts = self.pd.Series([])
+
+        if solar_as_component == 1:
+            # solar_ts=self.load_Exogenous_Data_Solar()
+            solar_ts = read_csv_Data(
+                input_dir_ts + input_solar_ts_filename,
+                timename,
+                timeFormat_solar,
+                timeZone_solar,
+                timeZone_analysis,
+            )
+        else:
+            solar_ts = self.pd.Series([])
+
+        # price_ts=self.load_Exogenous_Data_Price()
+        price_ts = read_csv_Data(
+            input_dir_ts + input_price_ts_filename,
+            timename,
+            timeFormat_price,
+            timeZone_price,
+            timeZone_analysis,
+        )
+
+        return wind_ts, solar_ts, price_ts
+
+    def load_Exogenous_Data_EMS(self):
+        """
+        Method for reading wind power, solar power and price timeseries csv files;
+        convert the timeseries into specified time format;
+
+        Output
+        ------
+        data: Formatted timeseries
+
+        """
+        wind_ts = self.load_Exogenous_Data_Wind()
+        solar_ts = self.load_Exogenous_Data_Solar()
+        price_ts = self.load_Exogenous_Data_Price()
+
+        return wind_ts, solar_ts, price_ts
+
+    """
+    Derived methods of hpp class
+    """
+
+    def calculate_Capacity_Factor(self, ts, capacity):
+        """
+        Method for calculating capacity factor for a considered location
+
+        Input Parameters
+        ----------------
+        ts: timeseries data
+
+        Output Parameters
+        -----------------
+        cap_fact: capacity factor for a considered location
+        """
+        #cap_fact = sum(ts.values)/len(ts.values)/capacity
+        cap_fact = ts.mean() / capacity
+
+        return cap_fact
+
+    def sizing(self, wind_ts, solar_ts, price_ts):
+        """
+        High level method to calculate sizing of HPP (wind and solar),
+        with/without battery storage
+
+        Returns
+        -------
+        Capacity of Wind Power
+        Capacity of Solar Power
+        HPP power output timeseries
+        HPP power curtailment timeseries
+        HPP total CAPEX
+        HPP total OPEX
+        Levelised cost of energy
+        """
+        # extract parameters into the variable space
+        globals().update(self.__dict__)
+
+        if (wind_as_component == 1) and \
+           (solar_as_component == 1) and \
+           (battery_as_component == 0):
+            [hpp_wind_capacity, hpp_solar_capacity, P_HPP_t,
+             P_curtailment_t, hpp_investment_cost, hpp_maintenance_cost,
+             LCOE, NPV, IRR] = self.sizing_Wind_Solar(
+                wind_ts, solar_ts, price_ts)
+            return hpp_wind_capacity, hpp_solar_capacity, P_HPP_t, P_curtailment_t, hpp_investment_cost, hpp_maintenance_cost, LCOE, NPV, IRR
+
+        if (wind_as_component == 1) and \
+           (solar_as_component == 1) and \
+           (battery_as_component == 1):
+            [hpp_wind_capacity, hpp_solar_capacity, hpp_battery_power_rating,
+             hpp_battery_energy_capacity, P_RES_available_t,
+             P_HPP_t, P_curtailment_t, P_charge_discharge_t,
+             E_SOC_t, hpp_investment_cost, hpp_maintenance_cost,
+             LCOE, NPV, IRR] = self.sizing_Wind_Solar_Battery(
+                wind_ts, solar_ts, price_ts)
+            return hpp_wind_capacity, hpp_solar_capacity, hpp_battery_power_rating, hpp_battery_energy_capacity, P_RES_available_t, P_HPP_t, P_curtailment_t, P_charge_discharge_t, E_SOC_t, hpp_investment_cost, hpp_maintenance_cost, LCOE, NPV, IRR
+
+    def sizing_Wind_Solar(self, wind_ts, solar_ts, price_ts):
+        """
+        Method to calculate sizing of wind and solar
+
+        Returns
+        -------
+        Capacity of Wind Power
+        Capacity of Solar Power
+        HPP power output timeseries
+        HPP power curtailment timeseries
+        HPP total CAPEX
+        HPP total OPEX
+        Levelised cost of energy
+        """
+        # extract parameters into the variable space
+        globals().update(self.__dict__)
+
+        mdl = Model(name='Sizing')
+
+        time = price_ts.index
+
+        # Variables definition
+        P_HPP_t = mdl.continuous_var_dict(
+            time, lb=0, ub=hpp_grid_connection,
+            name='HPP power output')
+        P_curtailment_t = mdl.continuous_var_dict(
+            time, lb=0, name='Curtailment')
+        Wind_MW = mdl.integer_var(lb=0, name='Wind capacity')
+        Solar_MW = mdl.integer_var(lb=0, name='Solar capacity')
+
+        for t in time:
+            # Constraints
+            #mdl.add_constraint(((turbine_spacing * rotor_diameter * 0.001 * (nWT_per_string-1)) * (turbine_row_spacing * rotor_diameter * 0.001 * ((X*(1/(rating_WT*nWT_per_string)))-1)) ) + (land_SOL*Y) <= land_area_available)
+
+            mdl.add_constraint(
+                P_curtailment_t[t] >= wind_ts[t] * Wind_MW +
+                solar_ts[t] * Solar_MW -
+                hpp_grid_connection)
+            mdl.add_constraint(
+                P_HPP_t[t] == wind_ts[t] * Wind_MW +
+                solar_ts[t] * Solar_MW -
+                P_curtailment_t[t])
+
+        # Objective function
+        mdl.maximize(
+            
+            # E_u(NPV)
+            #mdl.sum(NPV[u]*weights[u] for u in unc) 
+
+            # Q_u(NPV, q) approximate by a N
+            #mdl.sum(NPV[u]**N *weights[u] for u in unc)**(1/N)
+
+            # 
+            #E_u(NPV) - 2*S_u(NPV)
+            
+            # CAPEX
+            -(wind_turbine_cost * Wind_MW + \
+              solar_PV_cost * Solar_MW + \
+              hpp_BOS_soft_cost * (Wind_MW + Solar_MW) + \
+              hpp_grid_connection_cost * hpp_grid_connection + \
+              solar_hardware_installation_cost * Solar_MW + \
+              wind_civil_works_cost * Wind_MW) + \
+            # revenues and OPEX
+            mdl.sum(
+                (mdl.sum(
+                    price_ts[t] * (wind_ts[t] * Wind_MW + \
+                                   solar_ts[t] * Solar_MW - \
+                                   P_curtailment_t[t])
+                    for t in time) + \
+                 - (wind_fixed_onm_cost * Wind_MW + \
+                    solar_fixed_onm_cost * Solar_MW)
+                 ) / np.power(1 + hpp_discount_factor, i)
+                for i in range(1, hpp_lifetime + 1)
+            )
+        )
+        # Solving the problem
+        sol = mdl.solve(log_output=False)
+
+        # print(mdl.export_to_string())
+        # sol.display()
+
+        P_curtailment_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_curtailment_t), orient='index')
+        P_HPP_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_HPP_t), orient='index')
+        AEP = P_HPP_ts[0].sum()
+        AEP_per_year = np.ones(hpp_lifetime) * AEP
+
+        # Investment cost
+        hpp_investment_cost = \
+            wind_turbine_cost * sol.get_value(Wind_MW) + \
+            solar_PV_cost * sol.get_value(Solar_MW) + \
+            hpp_grid_connection_cost * hpp_grid_connection + \
+            hpp_BOS_soft_cost * (sol.get_value(Wind_MW) + sol.get_value(Solar_MW)) + \
+            wind_civil_works_cost * sol.get_value(Wind_MW) + \
+            solar_hardware_installation_cost * sol.get_value(Solar_MW)
+        # Maintenance cost
+        hpp_maintenance_cost = np.ones(hpp_lifetime) * (wind_fixed_onm_cost * sol.get_value(
+            Wind_MW) + solar_fixed_onm_cost * sol.get_value(Solar_MW))
+
+        LCOE = self.calculate_LCOE(
+            hpp_investment_cost,
+            hpp_maintenance_cost,
+            hpp_discount_factor,
+            hpp_lifetime,
+            AEP_per_year)
+
+        P_HPP_ts_2 = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_HPP_t), orient='index')
+        IRR = self.calculate_IRR(
+            P_HPP_ts_2,
+            price_ts,
+            hpp_investment_cost,
+            hpp_maintenance_cost)
+        NPV = self.calculate_NPV(
+            P_HPP_ts_2,
+            price_ts,
+            hpp_investment_cost,
+            hpp_maintenance_cost,
+            hpp_discount_factor)
+        return sol.get_value(Wind_MW), sol.get_value(
+            Solar_MW), P_HPP_ts[0], P_curtailment_ts[0], hpp_investment_cost, hpp_maintenance_cost, LCOE, NPV, IRR
+
+    def sizing_Wind_Solar_Battery(self, wind_ts, solar_ts, price_ts):
+        """
+        Method to calculate sizing of wind and solar
+
+        Returns
+        -------
+        Capacity of Wind Power
+        Capacity of Solar Power
+        HPP power output timeseries
+        HPP power curtailment timeseries
+        HPP total CAPEX
+        HPP total OPEX
+        Levelised cost of energy
+        """
+
+        # extract parameters into the variable space
+        globals().update(self.__dict__)
+
+        mdl = Model(name='Sizing')
+
+        time = price_ts.index
+        # time set with an additional time slot for the last soc
+        SOCtime = time.append(pd.Index([time[-1] + pd.Timedelta('1hour')]))
+
+        # Variables definition
+        P_HPP_t = mdl.continuous_var_dict(
+            time, lb=0, ub=hpp_grid_connection, name='HPP power output')
+        P_curtailment_t = mdl.continuous_var_dict(
+            time, lb=0, name='Curtailment')
+        Wind_MW = mdl.integer_var(lb=0, name='Wind capacity')
+        Solar_MW = mdl.integer_var(lb=0, name='Solar capacity')
+        # Power charge/discharge from battery
+        # Lower bound as large negative number in order to allow the variable to
+        # have either positive or negative values
+        P_charge_discharge = mdl.continuous_var_dict(
+            time, lb=-10000, name='Battery power')
+        # Battery rated energy capacity
+        E_batt_MWh = mdl.integer_var(
+            lb=0, name='Battery energy capacity')
+        # Battery rated power capacity
+        P_batt_MW = mdl.integer_var(
+            lb=0, name='Battery power capacity')
+        # Battery energy level
+        E_SOC_t = mdl.continuous_var_dict(
+            SOCtime, lb=0, name='Energy level')
+
+        mdl.maximize(
+            # CAPEX
+            -(wind_turbine_cost * Wind_MW + \
+              solar_PV_cost * Solar_MW + \
+              battery_energy_cost * E_batt_MWh + \
+              (battery_power_cost + \
+               battery_BOP_installation_commissioning_cost + \
+               battery_control_system_cost) * P_batt_MW + \
+              hpp_grid_connection_cost * hpp_grid_connection + \
+              hpp_BOS_soft_cost * (Wind_MW + Solar_MW) + \
+              solar_hardware_installation_cost * Solar_MW + \
+              wind_civil_works_cost * Wind_MW) + \
+            # revenues and OPEX
+            mdl.sum(
+                (mdl.sum(
+                    price_ts[t] * P_HPP_t[t]
+                    for t in time) +\
+                 - (battery_energy_onm_cost * E_batt_MWh + \
+                    wind_fixed_onm_cost * Wind_MW + \
+                    solar_fixed_onm_cost * Solar_MW)
+                 ) / np.power(1 + hpp_discount_factor, i)
+                for i in range(1, hpp_lifetime + 1)
+            )
+        )
+        # Constraints
+        mdl.add_constraint(E_SOC_t[SOCtime[0]] == 0.5 * E_batt_MWh)
+
+        for t in time:
+            # Time index for successive time step
+            tt = t + pd.Timedelta('1hour')
+            # Time index for previous time step
+            ttt = t - pd.Timedelta('1hour')
+            # Delta_t of 1 hour
+            dt = 1
+
+            # Only one variable for battery
+            mdl.add_constraint(
+                P_HPP_t[t] == wind_ts[t] * Wind_MW +
+                solar_ts[t] * Solar_MW +
+                - P_curtailment_t[t] +
+                P_charge_discharge[t])
+
+            mdl.add_constraint(
+                E_SOC_t[tt] == E_SOC_t[t] -
+                P_charge_discharge[t] *
+                dt)
+            # Constraining battery energy level to minimum battery level
+            mdl.add_constraint(
+                E_SOC_t[t] >= (
+                    1 -
+                    battery_depth_of_discharge) *
+                E_batt_MWh)
+            # Constraining battery energy level to maximum battery level
+            mdl.add_constraint(E_SOC_t[t] <= E_batt_MWh)
+            # SOC at the end of the year has to be equal to SOC at the beginning
+            # of the year
+            mdl.add_constraint(
+                E_SOC_t[time[len(time) - 1]] == E_SOC_t[SOCtime[0]])
+            # Battery charge/discharge within its power rating
+            mdl.add_constraint(P_charge_discharge[t] <= P_batt_MW)
+            mdl.add_constraint(P_charge_discharge[t] >= -P_batt_MW)
+
+            # Solving the problem
+        sol = mdl.solve(log_output=False)
+
+        # print(mdl.export_to_string())
+        # sol.display()
+
+        P_HPP_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_HPP_t), orient='index')
+        P_HPP_ts = P_HPP_ts.reset_index()
+
+        P_curtailment_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_curtailment_t), orient='index')
+        P_curtailment_ts = P_curtailment_ts.reset_index()
+
+        P_charge_discharge_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_charge_discharge), orient='index')
+        mask = P_charge_discharge_ts.iloc[:, 0] < 0
+        P_charge_discharge_ts['positive'] = P_charge_discharge_ts.iloc[:, 0].mask(
+            mask)
+        P_charge_discharge_ts['negative'] = P_charge_discharge_ts.iloc[:, 0].mask(
+            ~mask)
+        P_charge_discharge_ts = P_charge_discharge_ts.reset_index()
+
+        P_RES_available_ts = wind_ts * \
+            sol.get_value(Wind_MW) + solar_ts * sol.get_value(Solar_MW)
+
+        E_SOC_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(E_SOC_t), orient='index')
+        E_SOC_ts = E_SOC_ts.reset_index()
+
+        AEP = P_HPP_ts[0].sum()
+        AEP_per_year = np.ones(hpp_lifetime) * AEP
+
+        # Investment cost
+        hpp_investment_cost = \
+            wind_turbine_cost * sol.get_value(Wind_MW) + \
+            solar_PV_cost * sol.get_value(Solar_MW) + \
+            battery_energy_cost * sol.get_value(E_batt_MWh) + \
+            (battery_power_cost +
+             battery_BOP_installation_commissioning_cost +
+             battery_control_system_cost) * sol.get_value(P_batt_MW) + \
+            hpp_grid_connection_cost * hpp_grid_connection + \
+            hpp_BOS_soft_cost * (sol.get_value(Wind_MW) +
+                                 sol.get_value(Solar_MW)) + \
+            solar_hardware_installation_cost * sol.get_value(Solar_MW) + \
+            wind_civil_works_cost * sol.get_value(Wind_MW)
+
+        # Maintenance cost
+        hpp_maintenance_cost = \
+            np.ones(hpp_lifetime) * (
+                wind_fixed_onm_cost * sol.get_value(Wind_MW) +
+                solar_fixed_onm_cost * sol.get_value(Solar_MW) +
+                battery_energy_onm_cost * sol.get_value(E_batt_MWh))
+
+        LCOE = self.calculate_LCOE(
+            hpp_investment_cost, hpp_maintenance_cost,
+            hpp_discount_factor, hpp_lifetime, AEP_per_year)
+
+        P_HPP_ts_2 = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_HPP_t), orient='index')
+        IRR = self.calculate_IRR(
+            P_HPP_ts_2, price_ts, hpp_investment_cost,
+            hpp_maintenance_cost)
+        NPV = self.calculate_NPV(
+            P_HPP_ts_2, price_ts, hpp_investment_cost,
+            hpp_maintenance_cost, hpp_discount_factor)
+
+        return sol.get_value(Wind_MW), sol.get_value(Solar_MW), sol.get_value(P_batt_MW), sol.get_value(
+            E_batt_MWh), P_RES_available_ts[0], P_HPP_ts[0], P_curtailment_ts[0], P_charge_discharge_ts[0], E_SOC_ts[0], hpp_investment_cost, hpp_maintenance_cost, LCOE, NPV, IRR
+
+    def eval_Wind_Solar(self,
+                        wind_ts, solar_ts, price_ts,
+                        Wind_MW, Solar_MW):
+        """
+        Method to evaluate the performance of a HPP: wind and solar
+
+        Returns
+        -------
+        Capacity of Wind Power
+        Capacity of Solar Power
+        HPP power output timeseries
+        HPP power curtailment timeseries
+        HPP total CAPEX
+        HPP total OPEX
+        Levelised cost of energy
+        """
+        # extract parameters into the variable space
+        globals().update(self.__dict__)
+
+        mdl = Model(name='Eval')
+
+        time = price_ts.index
+
+        # Variables definition
+        P_HPP_t = mdl.continuous_var_dict(
+            time, lb=0, ub=hpp_grid_connection,
+            name='HPP power output')
+        P_curtailment_t = mdl.continuous_var_dict(
+            time, lb=0, name='Curtailment')
+
+        for t in time:
+            # Constraints
+            #mdl.add_constraint(((turbine_spacing * rotor_diameter * 0.001 * (nWT_per_string-1)) * (turbine_row_spacing * rotor_diameter * 0.001 * ((X*(1/(rating_WT*nWT_per_string)))-1)) ) + (land_SOL*Y) <= land_area_available)
+
+            mdl.add_constraint(
+                P_curtailment_t[t] >= wind_ts[t] * Wind_MW +
+                solar_ts[t] * Solar_MW -
+                hpp_grid_connection)
+            mdl.add_constraint(
+                P_HPP_t[t] == wind_ts[t] * Wind_MW +
+                solar_ts[t] * Solar_MW -
+                P_curtailment_t[t])
+
+        # Objective function
+        mdl.maximize(
+            # CAPEX
+            -(wind_turbine_cost * Wind_MW + \
+              solar_PV_cost * Solar_MW + \
+              hpp_BOS_soft_cost * (Wind_MW + Solar_MW) + \
+              hpp_grid_connection_cost * hpp_grid_connection + \
+              solar_hardware_installation_cost * Solar_MW + \
+              wind_civil_works_cost * Wind_MW) + \
+            # revenues and OPEX
+            mdl.sum(
+                (mdl.sum(
+                    price_ts[t] * (wind_ts[t] * Wind_MW + \
+                                   solar_ts[t] * Solar_MW - \
+                                   P_curtailment_t[t])
+                    for t in time) + \
+                 - (wind_fixed_onm_cost * Wind_MW + \
+                    solar_fixed_onm_cost * Solar_MW)
+                 ) / np.power(1 + hpp_discount_factor, i)
+                for i in range(1, hpp_lifetime + 1)
+            )
+        )
+        # Solving the problem
+        sol = mdl.solve(log_output=False)
+
+        # print(mdl.export_to_string())
+        # sol.display()
+
+        P_curtailment_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_curtailment_t), orient='index')
+        P_HPP_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_HPP_t), orient='index')
+        AEP = P_HPP_ts[0].sum()
+        AEP_per_year = np.ones(hpp_lifetime) * AEP
+
+        # Investment cost
+        hpp_investment_cost = \
+            wind_turbine_cost * Wind_MW + \
+            solar_PV_cost * Solar_MW + \
+            hpp_grid_connection_cost * hpp_grid_connection + \
+            hpp_BOS_soft_cost * (Wind_MW + Solar_MW) + \
+            wind_civil_works_cost * Wind_MW + \
+            solar_hardware_installation_cost * Solar_MW
+        # Maintenance cost
+        hpp_maintenance_cost = np.ones(hpp_lifetime) * \
+            (wind_fixed_onm_cost * Wind_MW +
+             solar_fixed_onm_cost * Solar_MW)
+
+        LCOE = self.calculate_LCOE(
+            hpp_investment_cost,
+            hpp_maintenance_cost,
+            hpp_discount_factor,
+            hpp_lifetime,
+            AEP_per_year)
+
+        P_HPP_ts_2 = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_HPP_t), orient='index')
+        IRR = self.calculate_IRR(
+            P_HPP_ts_2,
+            price_ts,
+            hpp_investment_cost,
+            hpp_maintenance_cost)
+        NPV = self.calculate_NPV(
+            P_HPP_ts_2,
+            price_ts,
+            hpp_investment_cost,
+            hpp_maintenance_cost,
+            hpp_discount_factor)
+        return Wind_MW, Solar_MW, P_HPP_ts[0], P_curtailment_ts[
+            0], hpp_investment_cost, hpp_maintenance_cost, LCOE, NPV, IRR
+
+    def eval_Wind_Solar_Battery(self,
+                                wind_ts,
+                                solar_ts,
+                                price_ts,
+                                Wind_MW,
+                                Solar_MW,
+                                P_batt_MW,
+                                E_batt_MWh,
+                                ):
+        """
+        Method to calculate sizing of wind and solar
+
+        Returns
+        -------
+        Capacity of Wind Power
+        Capacity of Solar Power
+        HPP power output timeseries
+        HPP power curtailment timeseries
+        HPP total CAPEX
+        HPP total OPEX
+        Levelised cost of energy
+        """
+
+        # extract parameters into the variable space
+        globals().update(self.__dict__)
+
+        mdl = Model(name='Sizing')
+
+        time = price_ts.index
+        # time set with an additional time slot for the last soc
+        SOCtime = time.append(pd.Index([time[-1] + pd.Timedelta('1hour')]))
+
+        # Variables definition
+        P_HPP_t = mdl.continuous_var_dict(
+            time, lb=0, ub=hpp_grid_connection, name='HPP power output')
+        P_curtailment_t = mdl.continuous_var_dict(
+            time, lb=0, name='Curtailment')
+
+        # Power charge/discharge from battery
+        # Lower bound as large negative number in order to allow the variable to
+        # have either positive or negative values
+        P_charge_discharge = mdl.continuous_var_dict(
+            time, lb=-10000, name='Battery power')
+        # Battery energy level
+        E_SOC_t = mdl.continuous_var_dict(
+            SOCtime, lb=0, name='Energy level')
+
+        mdl.maximize(
+            # CAPEX
+            -(wind_turbine_cost * Wind_MW + \
+              solar_PV_cost * Solar_MW + \
+              battery_energy_cost * E_batt_MWh + \
+              (battery_power_cost + \
+               battery_BOP_installation_commissioning_cost + \
+               battery_control_system_cost) * P_batt_MW + \
+              hpp_grid_connection_cost * hpp_grid_connection + \
+              hpp_BOS_soft_cost * (Wind_MW + Solar_MW) + \
+              solar_hardware_installation_cost * Solar_MW + \
+              wind_civil_works_cost * Wind_MW) + \
+            # revenues and OPEX
+            mdl.sum(
+                (mdl.sum(
+                    price_ts[t] * P_HPP_t[t]
+                    for t in time) +\
+                 - (battery_energy_onm_cost * E_batt_MWh + \
+                    wind_fixed_onm_cost * Wind_MW + \
+                    solar_fixed_onm_cost * Solar_MW)
+                 ) / np.power(1 + hpp_discount_factor, i)
+                for i in range(1, hpp_lifetime + 1)
+            )
+        )
+        # Constraints
+        mdl.add_constraint(E_SOC_t[SOCtime[0]] == 0.5 * E_batt_MWh)
+
+        for t in time:
+            # Time index for successive time step
+            tt = t + pd.Timedelta('1hour')
+            # Time index for previous time step
+            ttt = t - pd.Timedelta('1hour')
+            # Delta_t of 1 hour
+            dt = 1
+
+            # Only one variable for battery
+            mdl.add_constraint(
+                P_HPP_t[t] == wind_ts[t] * Wind_MW +
+                solar_ts[t] * Solar_MW +
+                - P_curtailment_t[t] +
+                P_charge_discharge[t])
+
+            mdl.add_constraint(
+                E_SOC_t[tt] == E_SOC_t[t] -
+                P_charge_discharge[t] *
+                dt)
+            # Constraining battery energy level to minimum battery level
+            mdl.add_constraint(
+                E_SOC_t[t] >= (
+                    1 -
+                    battery_depth_of_discharge) *
+                E_batt_MWh)
+            # Constraining battery energy level to maximum battery level
+            mdl.add_constraint(E_SOC_t[t] <= E_batt_MWh)
+            # SOC at the end of the year has to be equal to SOC at the beginning
+            # of the year
+            mdl.add_constraint(
+                E_SOC_t[time[len(time) - 1]] == E_SOC_t[SOCtime[0]])
+            # Battery charge/discharge within its power rating
+            mdl.add_constraint(P_charge_discharge[t] <= P_batt_MW)
+            mdl.add_constraint(P_charge_discharge[t] >= -P_batt_MW)
+
+            # Solving the problem
+        sol = mdl.solve(log_output=False)
+
+        # print(mdl.export_to_string())
+        # sol.display()
+
+        P_HPP_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_HPP_t), orient='index')
+        P_HPP_ts = P_HPP_ts.reset_index()
+
+        P_curtailment_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_curtailment_t), orient='index')
+        P_curtailment_ts = P_curtailment_ts.reset_index()
+
+        P_charge_discharge_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_charge_discharge), orient='index')
+        mask = P_charge_discharge_ts.iloc[:, 0] < 0
+        P_charge_discharge_ts['positive'] = P_charge_discharge_ts.iloc[:, 0].mask(
+            mask)
+        P_charge_discharge_ts['negative'] = P_charge_discharge_ts.iloc[:, 0].mask(
+            ~mask)
+        P_charge_discharge_ts = P_charge_discharge_ts.reset_index()
+
+        P_RES_available_ts = wind_ts * \
+            Wind_MW + solar_ts * Solar_MW
+
+        E_SOC_ts = pd.DataFrame.from_dict(
+            sol.get_value_dict(E_SOC_t), orient='index')
+        E_SOC_ts = E_SOC_ts.reset_index()
+
+        AEP = P_HPP_ts[0].sum()
+        AEP_per_year = np.ones(hpp_lifetime) * AEP
+
+        # Investment cost
+        hpp_investment_cost = \
+            wind_turbine_cost * Wind_MW + \
+            solar_PV_cost * Solar_MW + \
+            battery_energy_cost * E_batt_MWh + \
+            (battery_power_cost +
+             battery_BOP_installation_commissioning_cost +
+             battery_control_system_cost) * P_batt_MW + \
+            hpp_grid_connection_cost * hpp_grid_connection + \
+            hpp_BOS_soft_cost * (Wind_MW +
+                                 Solar_MW) + \
+            solar_hardware_installation_cost * Solar_MW + \
+            wind_civil_works_cost * Wind_MW
+
+        # Maintenance cost
+        hpp_maintenance_cost = \
+            np.ones(hpp_lifetime) * (
+                wind_fixed_onm_cost * Wind_MW +
+                solar_fixed_onm_cost * Solar_MW +
+                battery_energy_onm_cost * E_batt_MWh)
+
+        LCOE = self.calculate_LCOE(
+            hpp_investment_cost, hpp_maintenance_cost,
+            hpp_discount_factor, hpp_lifetime, AEP_per_year)
+
+        P_HPP_ts_2 = pd.DataFrame.from_dict(
+            sol.get_value_dict(P_HPP_t), orient='index')
+        IRR = self.calculate_IRR(
+            P_HPP_ts_2, price_ts, hpp_investment_cost,
+            hpp_maintenance_cost)
+        NPV = self.calculate_NPV(
+            P_HPP_ts_2, price_ts, hpp_investment_cost,
+            hpp_maintenance_cost, hpp_discount_factor)
+
+        return Wind_MW, Solar_MW, P_batt_MW, E_batt_MWh, \
+            P_RES_available_ts[0], P_HPP_ts[0], P_curtailment_ts[0], \
+            P_charge_discharge_ts[0], E_SOC_ts[0], hpp_investment_cost, \
+            hpp_maintenance_cost, LCOE, NPV, IRR
+
+
+# =============================================================================
+#     def forecasting(self):
+#         return
+#
+#
+#     def energyMgmtSystem(self):
+#         return
+# =============================================================================
+
+    def calculate_LCOE(self,
+                       investment_cost, maintenance_cost_per_year,
+                       discount_rate, lifetime, AEP_per_year):
+        LCOE = (investment_cost + sum((maintenance_cost_per_year[i]) / np.power(1 + discount_rate, i) for i in range(
+            lifetime))) / sum(AEP_per_year[i] / np.power(1 + discount_rate, i) for i in range(lifetime))
+        return LCOE
+
+    def calculate_IRR(
+            self,
+            P_HPP_t,
+            price_t,
+            investment_cost,
+            maintenance_cost_per_year):
+        time = price_t.index
+        Revenue_t = sum(price_t.loc[t] * P_HPP_t[0].loc[t] for t in time)
+        Profit_t = Revenue_t - maintenance_cost_per_year
+        # print(Profit_t)
+        # print(investment_cost)
+        Cashflow = np.insert(Profit_t, 0, -investment_cost)
+        IRR = npf.irr(Cashflow)
+        return IRR
+
+    def calculate_NPV(
+            self,
+            P_HPP_t,
+            price_t,
+            investment_cost,
+            maintenance_cost_per_year,
+            discount_rate):
+        time = price_t.index
+        Revenue_t = sum(price_t.loc[t] * P_HPP_t[0].loc[t] for t in time)
+        Profit_t = Revenue_t - maintenance_cost_per_year
+        Cashflow = np.insert(Profit_t, 0, -investment_cost)
+        # print(Cashflow)
+        NPV = npf.npv(discount_rate, Cashflow)
+        return NPV
+
+
+def read_csv_Data(
+    input_ts_filename,
+    timename,
+    timeFormat,
+    timeZone,
+    timeZone_analysis,
+):
+    """
+    Method for reading timeseries csv files;
+    convert the timeseries into specified time format;
+
+    Output
+    ------
+    data: Formatted timeseries
+
+    """
+    data = pd.read_csv(input_ts_filename)
+    data[timename] = pd.to_datetime(data[timename], format=timeFormat)
+    data = data.set_index(timename, drop=True)
+
+    ts = data.tz_localize(
+        timeZone, ambiguous='infer').tz_convert(
+        timeZone_analysis).tz_localize(None)
+
+    return ts
diff --git a/Courses/sizing_exercise_v2.ipynb b/Courses/sizing_exercise_v2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f77b6a0ea98549a0056367565f86d5d9c8019a7d
--- /dev/null
+++ b/Courses/sizing_exercise_v2.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Code for asset sizing for HPP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "#sys.path.append(\"../\") # go to parent dir\n",
+    "!{sys.executable} -m pip install --user pandas\n",
+    "!{sys.executable} -m pip install --user numpy\n",
+    "!{sys.executable} -m pip install --user matplotlib\n",
+    "!{sys.executable} -m pip install --user sklearn\n",
+    "!{sys.executable} -m pip install --user openpyxl\n",
+    "!{sys.executable} -m pip install --user docplex\n",
+    "!{sys.executable} -m pip install --user numpy-financial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_wind_ts_filename = './Data/aveW.csv'\n",
+    "input_solar_ts_filename = './Data/aveS.csv'\n",
+    "input_price_ts_filename = './Data/PriceProfilePPA.csv'\n",
+    "\n",
+    "start_date = pd.to_datetime('2018-09-01 00:00')\n",
+    "end_date = pd.to_datetime(\"2019-08-31 23:00\")\n",
+    "\n",
+    "timeFormat_wind = \"%d/%m/%Y %H:%M\"\n",
+    "timeFormat_solar = \"%d/%m/%Y %H:%M\"\n",
+    "timeFormat_price = '%d/%m/%Y %H:%M'\n",
+    "timename = 'Time'\n",
+    "timeZone_wind = 'Asia/Kolkata'\n",
+    "timeZone_solar = 'Asia/Kolkata'\n",
+    "timeZone_price = 'Asia/Kolkata'\n",
+    "timeZone_analysis = 'Asia/Kolkata'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sizing import HPP, read_csv_Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_dict = {\n",
+    "        # hpp parameters\n",
+    "        'hpp_grid_connection': 300,  # in MW\n",
+    "        'hpp_land_area_available': 100,  # in square km\n",
+    "        'hpp_lifetime': 25,  # [years]\n",
+    "        'hpp_discount_factor': 0.07,\n",
+    "        'hpp_BOS_soft_cost': 119940,  # [Eur/MW]\n",
+    "        'hpp_grid_connection_cost': 37074,  # [Eur/MW]\n",
+    "\n",
+    "        # hpp wind parameters\n",
+    "        'wind_rating_WT': 2.1,\n",
+    "        'wind_nWT_per_string': 20,\n",
+    "        'wind_lifetime_WT': 30,\n",
+    "        'wind_rotor_diameter': 97,  # in m\n",
+    "        'wind_hub_height': 120,  # in m\n",
+    "        'wind_turbine_spacing': 5,  # in terms of Rotor Diameter\n",
+    "        'wind_turbine_row_spacing': 10,  # in terms of Rotor Diameter\n",
+    "        'wind_turbine_cost': 851000,  # [EUR/MW]\n",
+    "        'wind_civil_works_cost': 116986,  # [Eur/MW]\n",
+    "        'wind_fixed_onm_cost': 12805,  # Wind fixed O&M cost per year [Eur/MW]\n",
+    "\n",
+    "        # hpp solar parameters\n",
+    "        'solar_lifetime_PV': 30,\n",
+    "        'solar_PV_cost': 219000,  # [EUR/MW]\n",
+    "        'solar_hardware_installation_cost': 241854,  # [Eur/MW]\n",
+    "        'solar_fixed_onm_cost': 8149,  # Solar O&M cost per year [Eur/MW]\n",
+    "\n",
+    "        # hpp battery parameters\n",
+    "        'battery_energy_cost': 181596,  # Eur/MWh\n",
+    "        'battery_power_cost': 64190,  # Power conversion system cost[Eur/MW]\n",
+    "        # Electric Balance of Plant, installation, commissioning cost [Eur/MW]\n",
+    "        'battery_BOP_installation_commissioning_cost': 73360,\n",
+    "        # Grid management control system cost[Eur/MW]\n",
+    "        'battery_control_system_cost': 18340,\n",
+    "        # Battery energy capacity maintenance cost [Eur/MWh] per year\n",
+    "        'battery_energy_onm_cost': 10000,\n",
+    "        'battery_depth_of_discharge': 0.9,\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simulation_dict = {\n",
+    "        'wind_as_component': 1,\n",
+    "        'solar_as_component': 1,\n",
+    "        'battery_as_component': 1,\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ExampleHPP = HPP(\n",
+    "        parameter_dict=parameter_dict,\n",
+    "        simulation_dict=simulation_dict,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wind_power_t = read_csv_Data(\n",
+    "        input_wind_ts_filename,\n",
+    "        timename,\n",
+    "        timeFormat_wind,\n",
+    "        timeZone_wind,\n",
+    "        timeZone_analysis,\n",
+    "    )\n",
+    "solar_power_t = read_csv_Data(\n",
+    "        input_solar_ts_filename,\n",
+    "        timename,\n",
+    "        timeFormat_solar,\n",
+    "        timeZone_solar,\n",
+    "        timeZone_analysis,\n",
+    "    )\n",
+    "spot_price_t = read_csv_Data(\n",
+    "        input_price_ts_filename,\n",
+    "        timename,\n",
+    "        timeFormat_price,\n",
+    "        timeZone_price,\n",
+    "        timeZone_analysis,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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S2NPYwz8+Vc91y6u4O466gEi4XcL8ijw9V0AzI8hKJRAMKV470jGpFQAwq9hPU88QRoPUKOlvNZ6qvZEVS9w4NWJSKcMSmBgTALOTaGYHhvtHAtz7qzcpy/fynfeff1bsJxEWVhTomIBmRpCVSmBPYw+9wwEuW3x2PMCiptjP8FiI7sEYCsb6T9nrCrJwasTkaD+gznYHQcZbAkopvvroW5zoHOT+D11AWX7shWFTsagqnxOdupGcJvPJSiXw6mEjHnBphKCwheU7jmmugN3Vwhb+YmcsgUhtpC3ySjPaEvjNGw08trOJz1+/lHXTjI2Mh4UVRiO54x26kZwms8lSJdDO0uoCqgonTxWsiWeugN3VwhZOjZiM1EHUIjdzB8scbOnjaxv3cNmicj51zWJH7rGoSvcQ0swMsk4JjAVDbDvWyWVTxAPgtCUQU4aQU5aAUyMmI3UQtbBmCsQSE0kDhseC3PurHeR7PfzrB1fjdtkXBwgnvJuoRpPJZJ0SaOoeYngsxKqwrqGRqCjw4XFJ9LUCo4PGH+pMigmMdxCNZAmUQmjMjBtkDl9/Yh8HWvr43gdXJ1wUNhVF/hwqC33aEtBkPFmnBBq6jCf7umnyxd0uobrIH33V8IDNA+bDsVJE7X4qn84dBBkVHN5U38LDW0/wyasX8Y6lzo8yXVihewhpMp8sVAJGIG926fRFQ7UlMVQNj4+VtLFa2MJXCCoIY3GOvJyMKQPDmdc64qndzVQUePnCDUuTcr/lswrZ09jLH/c0J+V+Go0TRKUEROSYiLwlIjtFZLu5r0xEnhORg+bWpnmKztLQNYTbJeOB36moKc6NPibgRLWwhVOtIyLNF7bIQEtg67FOLppfRo47Oc82n7luCSvrivjEL97kP14+HFtNiUaTJsTy23KNUmq1UsoaM/llYJNSagmwyXyf9jR0DTGryH/GEJnJqCnxc6pnmFAoil9uJ5rHWYwrAZvTREf6AAFvhDGJGdZJtLlniIauIS6a78AIzkmoKPDx8F9ewm3n1fDtP+znS7/bzWhA1w1oMotEHpnWAw+ZPz8E3JGwNEmgoWswKlcQGK0jxoKK9oGR6Q/ubwVxQf7UWUdxMT5i0mZLwGoZ4YqwDHIzSwlsPWpYLE7UBEyFP8fN/XdewGeuXcx/b29gw0+30hNLgaFGk2KiVQIKeFZE3hCRe8x91Uopyxl6Coj4CCwi94jIdhHZ3tbWlqC4idPYNcTs0ujaOlguo6gmjPW3QF4FuNyJiBcZp9pJRxooY5FhnUS3Hu2kwOfhnJoIri2HcbmEL964jO994HzeON7Fu3+0mWPtOnVUkxlEqwSuUEqtAW4GPi0iV4V/qAxnaESfiVLqAaXUWqXU2spK5zM2pmI0EOJU73D0loBVNRxNwVhfizOuIHA2JhApHgDg9oCvOGMCw9uOdbJmXqljdQHR8J41s/nFxy+ma3CUO360edw60WjSmaiUgFKq0dy2Ao8C64AWEakBMLetTglpF6d6hgmp6DKDIKxqOJrgsFPVwuDciMlIA2XCySvNCEuga2CUt1v6uTjJrqBIrFtQxmOfvpzSPC/3/upNHSzWpD3TKgERyReRQutn4EZgD/A4sME8bAOw0Skh7eJ0emh07qCyfC8+jyu6MZNOVQuDczGBkb7J3UFguIQywBLYftyIWyQzKDwV88rz+YvL59PaN8Kp3hjbkWs0ScYTxTHVwKNmG14P8Cul1B9FZBvw3yJyN3Ac+IBzYtqDVSgWrSUgYqSSTjtwXilnLQGvQ5bASC+Uzp/88wzpJLrtWCdet4vzZk9dBZ5MVtYaintPYy81xYkPstFonGJaJaCUOgKcH2F/B3CdE0I5RUPXIC4xZgVEi1ErMM3T3FCX0WLBKUvA7YGcfPtjApEGyoSTVwadh+29pwNsPdrJ+XOK8ec4EJSPk+WzihCBvU093LDCoXWh0dhAVlUMN3QNUVOcG1MxUU1JFK0jxquFHbIEwJn+QSN9U8cEcstgML1TRAdHA+xp7EkbV5BFvs/Dwop89jY5NBFOo7GJrFMCdVG6gixqi3Np6RshOFXB2EASlIDdIyaDYxAYMjKAJiOvzGgyFwzYd1+b2XGim0BIcVEaBIUnsrK2mL2NPakWQ5PBBEOKf3xqn6P3yDIlEH2hmEVNiZ9gSNHaN4VLyLIE7JwtPBFfob0xgan6BllYBWPD3fbd12a2Hu1EBC6cl35dS1bWFtHUM0zXwGiqRdFkKPdvOsiPXznq6D2yRgmMBa0agdjm/1ppolPWCgyYRXCOuoNsnikwbD6hTpkimv79g7Yd6+ScWUUU+XNSLcpZrKw1rCztEtLEw+ZD7dz/wkHeu2a2o/fJGiUQa42AhZXZMWWtQH8ruDzgL0lAwmmw3RKwmsdNkyIKaZsmOhYMseNEd9JbRUSLlSG0t0m7hDSx0do3zH2/3smiygK+ecdKR+8VTYrojOBkDC2kw6m1lMCUlkCr2TLCQZ1qd0xgqqliFmneOmJPYw9DY8G0VQKl+V7qSnLZoy0BTQwEQ4r7Ht5J/8gYv/rLi8nzOvtnOmuUgFUjMCdGd1BRroc8r3vqgfP9bVDgcEsMn83D5qcaKGOR5jMFth0z5Eq3zKBwVtQWaUtAExP3bzrIliMdfOd957G0egpL3Sayxh3U0DUUc40AnC4Ym9YScDIoDIbbZrQPQkF7rheVJZDeMYGtR7tYUJFPZaEv1aJMysraIo62DzAwkr4ZVpr0ITwO8P61c5JyzyxSAoPMKvLHNXCktmSa4TL9bc4GheH0E7tdM3+nGihj4Ss0Yh1paAmEQoptxzq5aH76ZQWFs6q2GKVg/yntEtJMTTLjAOFkkRKIvoX0RGqKpxgzqZSRHZTvtDvINAvtigtEExgWMayBNJwpcLC1n56hsbR2BQGsrDvdPkKjmYzwOMCP7lrjeBwgnKxRAsYcgfh6uNQU59LePxJ5atRILwRHnLcE7G4nPdwLbi/kTOMey0vP/kFbj6VmiEyszCryU5bv1XEBzZR834wDfHP9qqTEAcLJCiUwFgzR3BO/Eqgt8aMUtETqCNlv1ggkIyYA9gWHpxooE06aWgLbjnZSVehjbll81l2yEBFW1hbpWgHNpJzoGOTfXjjIe9bUJS0OEE5WKIHTNQLxuoOsWoEISmC8ZYTD7iC/2d7BNndQ39TxAIs0tASUMuIB6xaUYXa3TWtW1hbzdkufnj+sichjOxtRCr5447KU3D8rlECsLaQnUlsyxXCZZLSMAPtHTE43UMYityTtAsMNXUM09wynvSvIYmVtEWNBxdstNrcC12Q8Sike29HIugVl1JWkpuV4liiB2IbJTMSyBCK2jkhGywiwPyYQrSVgzRRIowlZmVAfEI5VObxPu4Q0E3irsYcj7QPcsbouZTJkiRIYQuKoEbDI93ko8nsmtwTEBXnlCUo5DY7EBKJ0BwVHYGzQnvvawNajnRT5PSxLcgAtXuaX55PvdevgsOYsHtvRRI5buOXcWSmTIWuUwKwiP15P/P/c2pLcSSyBVkMBuBweaOLNN5SNXTGB6QbKWKRhwdjWY52snV+GK4VD5WPB5RJW1Bbp9hGaMwiGFE/sbuLqZVWU5HlTJkfUfxVFxC0iO0TkSfP9AhF5XUQOicgjIpK6f8U0xNNCeiI1xf5JLIE25+MBYOTs29lEbiTKmMB464j0yBBq7x/hSNtAxriCLFbWFlPf3Dv1XApNVvHq4Xba+kZS6gqC2CyB+4D6sPf/DPyLUmox0AXcbadgdpJIoZhFTckkYyYHWp3PDLLwFdsTE1AqtpgApE1wePt4fUB6VwpPZEVtEYOjQY51DKRaFE2a8NiOJgp8Hq47JwkPkVMQlRIQkdnArcCD5nsBrgV+ax7yEHCHA/IlTGB8jkBilkBtsZ/OgVGGxyb07ulPQt8gC7ssgbFBUMHo3EFpNlPgTwfbyfe6ObeuJNWixMQqPVtAE8bwWJBn9p7i5lWzUj4bO1pL4F+BvwGsROdyoFspZXXFagAi2jQico+IbBeR7W1tbYnIGhfNPcMEQ8oGd9AktQIDSegbZOEvOj0MJhGi6SBqkUaWgFKKF+pbuXJJZULxnVSwpLoAr9ulx01qAHi+voX+kQB3XJBaVxBEoQRE5DagVSn1Rjw3UEo9oJRaq5RaW1mZJLdJGKdrBBJ1B5m1AuFD50f6jadqp/sGWdhlCUTTPM5ifKZA6mMCe5t6OdU7nHLzOR5y3C6WzirQloAGMFxBVYU+LlnocFZhFETzOHU5cLuIHAN+jeEG+j5QIiJWl6PZQKMjEiZIQ5zDZCZiDZc5o5FcMgbMh2PXiMlo2khbeLzgLUgLS+C5fS2IwLXLM08JAKysKWZvUw8qjWouNMmne3CUl99u5fbza3GnQYbbtEpAKfUVpdRspdR84E7gBaXUXcCLwPvMwzYAGx2TMgEau40aAcudEy9VRUbP+ra+kdM7k9U3yMIuSyCa+cLhWAVjKWbT/hbWzC2lvCB95wdMxaq6IroGxyInGGiyhqfeamYsqNLCFQSJ1Ql8CfiCiBzCiBH8xB6R7KWha4jqwsRqBADyvB7yve4zlUCy+gZZ2DVictwSiLLYKq805ZZAc88Qexp7M9IVZLHCDA7v0XGBrGbjjiYWVeaPV5Knmpj+MiqlXlJK3Wb+fEQptU4ptVgp9X6l1Mh056cCO2oELCoLfbT1h1sCVt+gJMYEgiMQSPCrjiUmAGnRSXRTvfFd33BOdUrlSIRzagoR0RlC2UxD1yBbj3Vyx+q6tGl+mFkpFnHQkMAcgYlUFvpoP8MSsNxBSawTgMRdQpY1EbUlkHp30Kb6FuaW5bG4qiClciRCntfDwop8rQSymMd3NQGwPsUFYuHMaCUQCIZo7hlOODPIoqIggiWQWwruHFuuPy12dRKN1R2UW5ZSd9DgaIDNhzu47pyqtHl6ipdVdcW6h1AWs3FHE2vmljC3PH3mYMxoJXCq154aAYvKQt/ZMYFkBYXhdCA30bjASK+R8RNtv6O8Mhjqtm/IfYz8z8F2RgMhrs9gV5DFytoimnuG6RwYTbUomiRT39zLgZa+tAkIW8xoJWBXjYBFZYGPnqExRgLmH8OB9uSlh4KNlkCUHUQtcssAZU+hWhw8X99Cod+TMfMDpmLleOWwtgayjcd2NuJ2CbeeW5NqUc4gS5SAPZZARaGRmtjRbz7F9bcmLx4AYTMFbIgJRJseCmEFY8l3CYVCihf2t/GOpZXkuDN/uVoZIToukF2EQoondjZx1ZKKtEtxzvzfqilo6Bo0agRK4psjMJHKggm1AslsGQE2uoP6oo8HwOkUWCslNonsauimvX9kRriCAEryvNSV5Oo00Sxj27FOmnqG084VBDNcCTSaNQI+jz0NmioLw5TA2LDhVslESyBWd1Chab72NSd23zh4vr4Ft0u4elnyW444xaq6IrYf6zrtVtTMeB7b2USe180NK9LvYWZGK4GGriHqbHIFwWkl0N4/kvyWERAWE0jwKTLagTIWBebC7WtJ7L5xsKm+lbXzSlM6dMNu7rp4Hqd6h3nwlaOpFkWTBEYDIZ5+q5kbV1ST5/VMf0KSmdlKoNu+QjGA8gLjD1Fb30jyW0YAeHzg9tlgCfTFHhNw+5JuCTR0DbL/VN+McQVZXLW0kptXzeLfXjhIY3eEQUWaGcVLB1rpGRpjfRq6gmAGK4FAMERzd+JzBMLxedwU5+YYtQLJbhlhYUfriFjdQSJQOAv6k2sJWFXCmdwqYjK+etsKBOEbT+xNtSgah9m4q4myfC9XLK5ItSgRmbFKoKVvhEBI2ZYeajFeKzDeMiLJf6ASbSIXDBjtr2NRAmAogSRbAs/Xt7CwMp+FlZlbJTwZdSW5fOa6xTyzt4UXDyQ/4K5JDn3DYzy/r4XbzqtJ2+y29JTKBho67WkhPZGKAu+ZMYFkBoYh8XbS1rmxuIPAVAKn4r9vjPQNj/HakY4Z5woK5+NXLGRhZT7/8PjesyfWaWYEz+xtYSQQSqs2EROZuUrA5kIxi8pC/+mYgK8YcuxJP42aRC2B8eZxMQSGAQpmJTUw/MrBdsaCiusydHZANHg9Lr5++0qOdwzy4z8dSbU4GgfYuLOROWW5rJlbkmpRJmXGK4Fam2oELCoLTHdQMgfMh+MvTiwmEMtAmXAKZxlZSaPJGZT+/L4WinNzuHBeZg2Uj5Url1Ry67k1/ODFQ5w0rVfNzKC1b5jNh9pZf376dAyNxAxWAoNUF/lsqxGwqCj0MjAaJNiX5L5BFolaArHMFw5nvFbAeZdQMKR48UAr1y6vwpOmflQ7+ept5+B2Cd94cl+qRdHYyJO7mgkpuOOC2lSLMiVp/RsWCim+tnEPmw+1x3yu0ULa/k59VtVwqK8lNZaAryixOoFYO4haFJq++SRkCL15oouuwbEZmRUUiZriXD573RKe29fCC/uTX4uhcYaNOxtZWVvE4qoYf9eSTDSD5v0islVEdonIXhH5url/gYi8LiKHROQREbG9mufZfS3815bj/NPT9THPZW3sHqKuxN6gMJwuGJOBttRaAvHOqR2PCRTHdl4Sq4Yf2XYSf46Lq5bOnCrh6fjY5QtYVJnPPzy+TweJZwBH2wfY1dDDHWkcELaIxhIYAa5VSp0PrAZuEpFLgH8G/kUptRjoAu62UzClFPdvOojHJext6uWN49FPthoeC9LUbd8wmXAqC33kEMAz2pPcamELfxGoUPy+easTaMyWwCxj63BwuKl7iMd2NHLnRXMp8idpTkMa4PW4+Mb6VZzoHOQ/Xj6canE0U/D0W82c6Jg6frNxZyMi8K7z09sVBNENmldKqX7zbY75UsC1wG/N/Q8Bd9gp2PP1rexr7uXv37WCIr+Hn716LOpzf731BIGQcuRJsrLARznmH9L8FBR/jLeOiDMuYJ0Xa0zAX5KUqmGrlcLHr1zg6H3SkcsXV3DbeTX86KXDHGnrn/4ETdI52NLHp375Ju/+0Wb2TdIJVinFxp1NXLKgnFnFSc4ejIOoYgIi4haRnUAr8BxwGOhWSgXMQxoA2+wepRTf3/Q2c8vy+NC6udy5bi5/3HOK5p7pS+xHAyH+35+OcNH8Ui52oP98Wb6XSpelBFLhDrKayMWZITTSC64c8MS4OK2qYQcDw50Dozy89QS3r651JJ6TCXztXSvweVz87aNvxewC1TjPE7ubEQGPW7jzgS28eeJsD8Xuhh6Otg+kfUDYIioloJQKKqVWA7OBdcDyaG8gIveIyHYR2d7W1hbVOS/sb2VPYy/3XrMYj9vFhy+ZR0gpfvnaiWnP/f2bDTT3DHPvtUscScvyuF0s9JumYCrcQYl2ErXaSMfz3RTOgn7nlMBDrx5jaCzIJ96xyLF7pDtVhX7+9pZzeO1IJ795oyHV4mjCUErx5O4mLl1Yzm8/cRml+V7+/MHXeXVC4spjOxvxul3ctCq9hsdMRkzZQUqpbuBF4FKgRESslnizgcZJznlAKbVWKbW2snJ694xhBRxkTlku715jGBdzyvK4/pxqfrX1xJRBs0AwxI9eOsy5dcVctcQ5V81cn+mPT3a1MITNFIgzQyjWgTLhOGgJDIwEeGjLMa4/p5ql1emdTeE0H1w7h3Xzy/jHp+qN6nRNWrCvuZcjbQPcdl4tc8ry+M1fXcrs0lw++p/beH6fESsLhhRP7Grm2uVVFOdmRkwrmuygShEpMX/OBW4A6jGUwfvMwzYAG+0Q6KUDbexu6OHTVy8+o9fGRy+bT+fAKE/untwn/eTuZk50DnLvtYsdLc6Y7TWfwlNiCSQaE4ixjXQ4hTWOKYFfbztJ9+AYn7ome60AC5dL+Kf3rGJoNMg3de1A2vDErmY8LuGmVUaSRFWRn0fuuZTlswr5xC/e4PFdTbx6uJ32/hHWr84MVxBEZwnUAC+KyG5gG/CcUupJ4EvAF0TkEFAO/CRRYZRS/Oumg9SV5PKeNbPP+OyyReUsqSrgZ5uPRvSVhkKKH754iGXVhdzgcL+Zancfg/jBm+/ofSKScEygL/b0UIuCauO+NlcNjwZCPPjKES5eUMaauTO7QjhaFlcV8qlrFrFxZxMv6QZzKcdyBV2xpIKy/NPZ8KX5Xn758YtZM6+U+369g68/sY9Cv4drMqjdSTTZQbuVUhcopc5TSq1SSn3D3H9EKbVOKbVYKfV+pVTCduvLb7ex62Q3n75mMV7PmaKJCBsumz9puuiz+05xsLWfT12zCJfL2RLtSumhXRWlJnBnPcXH2zoi1oEy4ThUNbxxZyPNPcN88mptBYTzyasXsagyn68+tofB0cD0J2gcY+fJbhq6hnjXeWc/4Rf6c3joL9Zx1ZJKDrX2c/OqWfhz7O1U4CRpUzFsxQLqSnJ534WzIx7znjV1FPo9/OeEdFGlFP/2wiEWVORzW4T/JLspCXXTporpG0nBL6Yd7qC4YwLWhDH7lEAopPiPlw9zTk0R78ii4rBo8HncfOs959HQNcS/Pn8w1eJkNU/sasbrdnHDyshehlyvmx9/ZC1fvfUcPnf90iRLlxhpowReOdjOjhPdfPLqRWdZARZ5Xg8fXDuHP0xIF33p7Tb2NvXyyasX4XbYCgAoCHTRropPD5xPJi43eAsSSxGNtXmchWUJ2Jgh9Oy+Fg63DfDJqxeldZOtVLFuQRkfWjeXB185oofTp4hQSPHUW01cvaxyygJGr8fFx69cSK0DnQqcJC2UgGUF1BT7ef/ayFaAxUcunX9GuqhSin8zLYh3J2l8W+5oB+2qmPZUKAGIf6aAUqdTRONhvGrYHiWglOLfXz7M3LI8bjGDbZqz+fLNyykv8PGV379FIBhKtThZx7ZjnbT0jmRE9W88pIUS2HyogzeOd/GpqxdN2/Vzbnke1y2v4mEzXXTLkQ7ePNHNJ96xMDmTe4IBcka6aKfYGDOZCnyF8cUExoYgFIjfHTReNWyPEthypINdJ7v5q3cszIpuofFSnJvDP7xrJW819pzlCtU4zxO7m8jNcc/YhoZp8Zt3/6aDzCry84GL5kR1/EcvW0CHmS76wxcPUVno4/1rozs3YQY7EBRtqXIHgfFHPJ6YQLwDZSxsrhr+95cOU1Hg471rprb+NHDLubO4amklP3rpMGPaGkgagWCIp986xXXnVJHn9Ux/QgaSciWwu6Gbrcc6+curFkbd+//yxeUsrirgO8/sZ/OhDu65cmHyovHmWMkuUqgE4nUHjbeRjjNFFGybNbynsYdXDrZz9xULMiqTIlWICB++ZB6dA6P8z8HYW6tr4uPVwx10DozOWFcQpIES+K8tx8nzuqeNBYRjpYu29I5QmpfDn10810EJJ2AOmB/LrUhdNWe8g2XiHSgTTuEsW2YKvGL+IbszSutPA+9YWklxbg4bd0Ysztc4wJO7myj0eWZ05lpKlUDXwCiP72riPWvqYm4b/J4L6qgt9vPpaxaT70uimTZg9j8qqEytOyiemECv2Ysm3uwgsK1q+HjHABUFXkrzbR9DMWPxelzccu4snt3XousGksBIIMgf95zihpXVM9paTakSeGT7SUYDIT5y6fyYz833eXj1K9fx8SsX2i/YVJiWgLuwOoWB4ThiAqEQvPI9KJoNtRfEf2+bqoaPdQwwrzwFFdcZzvrVdQyOBnlun55A5jSvvN1O73BgRruCIIVKIBhS/HzLcS5ZWJZZDcMGWsHjp7ColPa+0dTI4CuCsQEIxvA0uPf30LwTrv0q5CTQ49ymquHjHYPMK8vOdtGJsG5+GTXFfh7f2ZRqUWY8T+5uoiQvhysWp2BuSBJJmRJ4cX8rjd1DbIjDCkgp/cZYyYpCP+39I4RCKWwdMRqlNRAYhRe+CdWr4LwPJHZvG2oFhseCNPcMa0sgDlwu4fbza3n57TY6B1L0EJIFDJnW1s2rapKTep5CUvave2jLMWYV+blhhbPN3mxnoBUKKqks9BEIKbqHxpIvw3g76SjjAtt/Cl3H4PqvGxXHiWApgQSqhk90GvMY5ldoSyAebl9dSyCkePot5+c9ZysvHmhlYDTIu87LjJkAiZASJXCkrZ9XDrZz18VzM69IqL8N8ivHB86nJDgcS/+g4V740/+BBVfB4usSv7cNlsCxdiOeoC2B+FhRU8TiqgLtEnKQJ3Y1UVHg4+KF5akWxXFS8hf4568dJ8ct3LkuiamddjHQCvmVVBQYSiAlaaJWds9Q5/THbv4+DHbADd+Ib5rYRGyYNXzcHNI9v1xbAvEgItyxupatxzpp6Jp64LkmdroHR3lhfyu3njsrKb3IUk3SlcDASIDfbm/glnNrxp+mM4ZQCAbaoaAqtZZA5TLIyYMn7jPcPJPR2wxbfgir3ptYRlA441XD8WenHOsYoDg3h5I8nR4aL7efb/TJemKXdgnZzcNbTzISCPGhZNYfpZCkK4FHdzTSNxKIKy005Qx1ggpCfoqVQFEtfGQjDHbCT94JLXsjH/fSt4xeQdf+b3vvX1iTsCWgrYDEmFuex5q5JbpwzGYCwRA/33KMyxaVs3xWAvU0GUTSlcDPtxxnVV0Ra+aWJPvWiWPWCFBQSaHPg8/jSl3V8Jx18LE/grjgZzfD8S1nft52AHb8HC66G8oW2HvvwuqEYgLHO3WNgB2sX13H/lN97D8VZ1txzVk8u6+Fpp5h/uJym39n0phoZgzPEZEXRWSfiOwVkfvM/WUi8pyIHDS3084FHBgJcKClj49cMj8ze8ebfYPIr0JEqCjwpa5qGKDqHLj7GWPg/c/vgAN/PP3Z81+HnHy46n/Zf9/CmrhbR4wGQjR2DWlLwAZuPa8Gt0t0gNhGfrb5KHPKcrk2g8ZDJko0lkAA+KJSagVwCfBpEVkBfBnYpJRaAmwy309Jx8AoJXk53J5BQ5jPYMBs3GUOmK8s9KWuatiiZC587BmoXA6//jPY+TCceA0OPAVX3Af5DhS6WFXDI/0xn9rQNUhI6cwgO6go8HHF4go27mxKTb3KDGNPYw/bjnWx4dL5WREQtohmxnCzUupN8+c+oB6oA9YDD5mHPQTcMd21eobG+MDaOZnbh8NyB+UbzaQqC1NsCVjkV8BHn4T5l8Njn4DffBQKZsEln3LmfuMTxmK3BsYzg3SNgC2sX11LY/cQb544e+62JjZ+uvko+V531C3tZwoxxQREZD5wAfA6UK2UsqKDp4Coqr7+/OJ5sdwyvRhoBVcO5Bqer4oCX+piAhPxFcJdv4UV642g7TVfAa9DT9sJ1Aoc69A1AnZy48pZ+HNcPKYDxAnR1jfCk7uaed+Fs2NuZpnpRK0ERKQA+B3wOaXUGZEopZQCItqjInKPiGwXke0+V4i5mewLNgvFrHz7ykIfHQOj6TPyz+OD9/0M/vIFWLPBufuMK4HYM4SOdwxS4PNQrruH2kKBz8P151Tz1O5mPWwmAX75+nFGgyE2XDY/1aIknaiUgIjkYCiAXyqlfm/ubhGRGvPzGqA10rlKqQeUUmuVUmtnl2d4ypXZMsKistCHUqRXDxeXG+outKcwbDLGW0fE7g4yuofmZWZiQJqyfnUdXYNjethMnIwEgvzitRNcvayShZUFqRYn6USTHSTAT4B6pdT3wj56HLAeNzcAG6e7Vp43Q2MBFv2tkH86a6CywHiaTXlwONkkUDVs1AhoV5CdWMNmtEsoPp7a3Ux7/0hWpYWGE40lcDnwYeBaEdlpvm4Bvg3cICIHgevN9zObgbbxzCAgtQVjqSTOWcOBYIiTnYPMy2SXYBri9bh4x9JKth/TweFYUUrxs83HWFSZz1VLZnbL6MmYdiSXUup/gMlsdxs6kmUIShlKID/MHVRg9OXPOiUAcU0Ya+oeJhBS2hJwgGWzCnl8VxP9IwEKkjlpL8N580QXbzX28M07VmWtizLDWnimkOFuCI6eYQlUFBruoPb+NIoJJIs4qoZPZwZpS8BullQZvuxDrbHXbmQzP918jEK/h/dcUJdqUVKGVgLR0nnE2JacbiqV5/WQ73VrSyBKjptKYH6FtgTsZok5ne/tlhjHjmYxTd1D/HHPKe68aE5y55SnGdn7L4+V1v3GtvKcM3anRdVwKiioNiabjfSDL7qMimMdg/hzXFRlWvfYDGBuWR5ej0tbAhN4u6WP5p5h/B4X/hw3uV43fo8bf46Ln20+ilIqM5tZ2ohWAtHSVm9kxExoxlZZ6KM9Wy0BMNJEo1QCxzsGmFeWn7W+Vydxu4RFlQXaEgijZ3CM9T/YzNBYcNJj3rmymjlZPutaK4Foaa2HiqVnjWesKPBxMBufvsKrhssXRXXK8Y5BFmhXkGMsrS7QGUJhbNzVyNBYkO/fuZryfB/DY0GGA0GGx0IMjwUZDYS4adWsVIuZcrQSiJbW/TDv0rN2Vxb6ePVwRwoESjExVg2HQorjnYNck0XdGZPNkqoCNu5sYmAkkNU+bjBSPx/eepJVdUWsX529Qd9o0IHhaBjuhd4Go1PnBCoLfPQMjTESmNzknJHE2D/oVO8wo4GQzgxyECs4rOMC8FZjD/XNvXzwouyYDpYIWglEQ9sBY1u14qyPrIKxjmxLE/WXgMcP/dEpASs9VNcIOIeVJqrjAsaISH+Oi/WZ2rY+iWglEA1t9ca26mxLwBo4ny5poic7B7n1/lecH0AuYmQIRWkJWC2ktSXgHDpDyGBgJMDjOxu59dzarOsIGg9aCURD637w5ELJ/LM+SrfWEc/ua2FvUy+bDyWhmVgMtQLHOgbwul3UFOc6LFT24nG7WFiRn/WWwFO7mxkYDfKhddk1FyBetBKIhrZ6qFwKrrO/LksJpMtcga1HjSD1vqYkzJ2NoWr4ePsgc8pys2piUypYWl2YndlqYTy87QSLqwq4cN60E281aCUQHa37zyoSsyi3OommgSWglGLr0U4A9jUnQwnEZgnoeIDzLKkqoKFriIGRQKpFSQkHTvWx40Q3d140R9ejRIlWAtMx1A19TRHjAQA+j5vi3Jy0qBo+1NpP1+AYJXk51Df3OT93tnDW6arhKVBKcbxjUE8TSwJWhtDhtuy0Bn697QQ5buHdWdwLKFa0EpgOKzNoEksA0mfW8OumFfChdXPpHwlwotPh4HBBdMNl2vpGGBoL6rnCSWBJtZUhlH1KYHgsyKM7Grlx5SzKC3RrkmjRSmA6psgMsqgo8KZFTGDr0U6qi3zcbFZBOu4SirJg7Nh4ZpC2BJxmXlkeXreLg63ZFxx+Zu8pugfH+JCuDYgJrQSmo7UecvKgePKFVVnoT7klYMUD1i0oZ2l1IW6XOB8cjrJg7HSNgLYEnMbjdrGwMp+DWWgJPLLtJHPKcrlsUXmqRckotBKYjtZ6qFwWMTPIorIg9e6gk51DnOodZt2CMvw5bhZXFiTREphaCRzvGMDjEupKdHpoMlhSXZh1lsDxjgFePdzBB9fOwaUz0GIimhnDPxWRVhHZE7avTESeE5GD5nbm5mK1TZ4ZZFFZ6GNgNMjgaOoyMl4zU0MvXlAGwIraIuctAatqOAp30OzSXDxu/cyRDJZUFXCycyil6zHZPLLtJC6B912oawNiJZrfyv8Ebpqw78vAJqXUEmCT+X7mMdhpBD2rplYCFWaaaHtf6lpHbD3aSWleDosrjcDgipoiTvUO0+FkrMKqGp4mMHxCZwYllaVmcPhw60CKJUkOY8EQv3mjgWuXVzGr2J9qcTKOaZWAUupPQOeE3euBh8yfHwLusFesNKHNHCQzjRIYrxruH3Zaokkx4gFl46bwitoiAOqbHXYLTFMroJQyawR0PCBZLK7KriljL+5vpa1vRDeLi5N47fNqpZTlAzgFVNskT3rRamYGRegeGk6qW0c09wxxonOQdQtOB8RW1BhKYF9zj7M3L6ye0h3UNThG33CAudoSSBrzy/PIcUvWVA7/ettJqgp9XLOsMtWiZCQJO2mVUgqYtCpJRO4Rke0isr2trS3R2yWXtv3gLYTi2VMeNqfM+KVL1UAPq0rYigcAlOZ7qS32s9fhuEAgv5pATzPBSQrTdGZQ8jF6CBVwMAssge7BUV5+u433rJmtY05xEu+31iIiNQDmtnWyA5VSDyil1iql1lZWZpimtjKDpik/L/LncM2yKjbuaiIQDCVJuNNsPdpJgc/DOebTv0UygsNvdvrxBAb4+m9fx3geOBNruLyOCSSXJdUFWWEJvLC/lWBIjdfGaGInXiXwOLDB/HkDsNEecdKMtv1TFomF8+4L6mjrG0nJlLGtRztZO7/0rOZsK2qKONzWz/AUM1YT5fUOIxC3bcebfPsP+89SBMfaBxGBOWU6PTSZLKkq5GTXIEOjM3vY0XP7Wqgu8nFuXXGqRclYokkRfRjYAiwTkQYRuRv4NnCDiBwErjffzywGOmCgbdr0UItrlldR5Pfw6I5GhwU7k47+EQ629rMuzBVksaK2iJAymmo5Qc/gGL9uNYJxX5h/jP/3pyP8+8uHzzjmeMcAtcW5+DzuSJfQOMTS6gKUmtk9hIbHgrz8dhs3rKjWtQEJEE120IeUUjVKqRyl1Gyl1E+UUh1KqeuUUkuUUtcrpSZmD2U+UbSLCMef4+bW82r4455TSe3guM2MQ1wcSQnUGE9HThWNvfR2K42hMgYqzuN61xusX13L//njAX75+vHxY451DOqeQSngdA+hmRsX2HyoncHRIDeu0K6gRNCRlMkYzwyKzhIAePcFsxkaC/LsvujaK9vB60c78Oe4OLeu5KzPZpfmUujzOBYXeL6+lYoCL7nn3o40bue7N1Vx7fIqvvrYHh7f1QQYloCOBySfeeX5Mz5D6Nm9LRT6PFyyULeJSAStBCajtR58RVAU/YzStfNKqSvJ5dEdTQ4KdiZbj3ayZm4pXs/Z/5Uul3BOTZEjlsBYMMRLB1q5ZlkVruW3ApBz6Bl+dNcaLppfxhce2cnGnY10DY7pzKAUkON2saAif8ZmCAVDik37W7h6eVXEta+JHv3tTUbbfqM+IIbBFC6X0cf8fw620drrfOFY7/AY+5p7I8YDLFbUFlHf3Gv7bIFtRzvpGw5w/Ypqo5iudD7sfxp/jpsHN6xl2axCPvfITkBnBqWKJTN4ytiOE120949y44qZWaKUTLQSiIRShiUQZTwgnDsuqCOkGHeHOMkbx7pQimmVwOBocDxf3y6er2/F63Fx5ZIKQ1Euvw2OvgwjfRT5c3joY+tYYP7x1xPFUsOSqgJOdM7MDKFn97WQ4xau1gViCaOVQCQG2mCoE6pWxHzq4qoCzptdnJQsodePdpLjFi6YM3n/vtOVw/a5hJQyTPHLFpWT5/UYO5fdAsFROPQ8ABUFPn75lxfztdtWjPey0SSXpdWFMzJDSCnFs3tPcemiCgr9OakWJ+PRSiASUbaLmIw7Vtext6nX8cyMrUc7OG92CbneydMvl1QX4LF5tsCh1n6Odwxy/TlhpviciyGvHPY/Nb6rpjiXj12xQM96TRFLqgzlO9PaSh9q7edYx6B2BdmEVgKRiLJx3GS86/xa3C5x1BoYGg2yu6FnSlcQGDOQF1fZO1vg+XqjQPy6c6pO73R7YOlNcPBZCI7Zdi9N/MyvyMfjkhk3YObZfUbX2hu0ErAFrQQi0Vpv9MoviG+RVRb6uHJJBRt3NDo27H3HiS4CITWtEgD720dsqm9hZW0RNcUTqoCX3wrDPXB8s2330sSPlSE00+YNP7v3FOfPKaG6SLeNtgOtBCLRtt+wAhJwY7z7gjqaeobHh7/bzetHO3EJXDhv+nk+K2qKaO0bsaXLaUf/CG+e6DrTFWSx8Brw5J7hEtKklqXVhRyaQe6gUz3D7Gro0a4gG9FKYCJWZlCc8QCLG1fMIt/r5jGHXEKvH+1gRW0RRVEExk7PFkjcGnjxQBshRWQl4M2DRdfA/qeN71GTchZXFXC8c9DR/lHJ5Ll6wxX0zpVaCdiFVgIT6W+B4e644wEWuV43N62q4em3mm3/BRwJBNlxopuLF0RXKbnSxvYRm+qNhl2r6ooiH7D8VuhtgOZdCd9LkzgzLUPo2b2nWFCRz6JKnXFmF1oJTKR1n7FN0BIAwyXUNxJgU/2knbbj4tE3GxkJhCL2C4pEcV4OdSW5Cc8WGAkE+dPbbVy7vHryjJ+lN4G44MDTCd1LYw9WD6GZEBzuGRpjy+EOblwxxfrTxIxWAhNpTSwzKJxLF5VTXeSzNUtod0M3X3t8L5ctKufa5VXTn2BiBIcTmzL22pFOBkaD3LBiivvmV8CcSwyXkCblzC83M4RmQFzgpQOtBEKKG7UryFa0EphIWz3klkF+4pWIbpewfnUdLx1otWXge3v/CJ/4+RtUFvj4wZ+tiWmS0oqaIo60DzA4Gn+H0031LfhzXFy2qGLqA5ffAi1vQdexuO+lsQevx8gQeuN416TT3zKF5/a1UFHgZfUUxZGa2NFKYCKtiWcGhfPeNbMJKcW7f/Qqm8ygVjwEgiHu/dWbdAyM8v8+fCFl+d6Yzl9RW4RKYLaAUopN9a1csbgSf840swGW3WJsD/whrntp7OW282p57UgnH/np6ymbg50oI4EgLx1o4/pzqs8anqRJDK0EwgmFoO2ALfEAi2WzCvnF3Rfj9bi4+6HtfOw/t3GsPfY+Pt/6w35eO9LJt95zLqvimKKUaPuI+uY+GruHpnYFWZQvMlpw61TRtOC+65fwf953HtuPdXHL/a/w2pHkT79LlC2HO+gfCWhXkANoJWDR8AY8eB2M9MDcS2y99GWLK/jDfVfy1VvPYevRTm78lz/x3WcORO2a2bizkZ/8z1E+etl83rNm6qH3kzG7NJcif/yzBSwr5ppo4xDLb4Hjr8LgzJs3lIl8YO0cHvv05RT6PPzZj1/jhy8ecqyQ0Qme29dCntc9vStSEzMJKQERuUlEDojIIRH5sl1CJZX+Vnjs0/DgtdDbBO9+AM59v+23yXG7+PiVC3nhi+/gtvNq+MGLh7j+/77MU7ubpxxOv7ephy/9bjfr5pfxd7fGH6wWESM4HKcl8Pz+Vs6fU0JVYZRVmstvBRU02kho0oJzaop4/DNXcOt5tXznmQN87KFtdA2MplqsKdnb1MNf/2YXv9newDXLqqZ3RWpixhPviSLiBn4I3AA0ANtE5HGl1D67hHOU4Bhs/TG89C0YG4LL74Or/hf4Ch29bVWRn+99cDUfunguX9u4l0//6k0KfR4uWlDGpQvLuWRhOStqi3C7hK6BUT7xizcoyfXyw7vWkBNDIDgSK2qKeXjrCYIhFbVfdTQQYndDN7tOdvPFG5ZGf7OaC6CwBvY/CeffGafEGrsp8Hm4/87VrFtQxjef2Met97/CV245h/NnlzC7NHfaWb2hkOJE5yD7T/UxOBogz+sm1+sxtjlu8rxu8rweSvNz4p4rHQiGeG5fCz/bfIytxzrJzXHzgYtm89nrlsR1Pc3UxK0EgHXAIaXUEQAR+TWwHphcCaiQ8Qc3GShltDYOjEBwxNgGho1tbxO8+E9GJtDi6+Gmb0NFchfYRfPLePIzV/Ds3lO8cqid1w538MJ+o56gyO9h3YJyOgZGaOkZ4ZG/uoTKQl/C91xRW8TQWJANP93KnLI8aov9zCr2U1uSS02xn6LcHA629LOvuZd9Tb3sa+7lUGsfY0GFCLxzVQyzXF0uI0C881fwm78Ajx88XmPrNrc5fvAXG32aztgWg6/AqDfQ2I4AH76wigtm+bnvkTf564dfByDP62ZpdQFLqwtZVl3I0upCQkrxdksfB07183ZrHwdb+hmKovhRBKoLfNSV5jK7NI+6Er+5zaUwNweXGNlzbhe4RHC7hJAy3D6/ev0ETT3D1JX4+do7F/DeC2dTnJsDqOT9/cgiRMVZ3i8i7wNuUkp93Hz/YeBipdS9k52zttattt+TJpV+JfOMP/7LbrYtEyhRWnqHee1IB1sOd7DlSAfHOwb55/eeywcvmmvL9Tv6R/jfG/dwvGOQUz3DdEzhCqgs9LGipogVtUWsqCli9ZwS5pTFOCayeRc8/hkYHTCVcLhCHgEyxyet0aQS+XrvG0qptY5c22klICL3APcALJ9demH9w/87MYljwe0Fj898CvWf/jknF+rWGk+iaczASIB8XyLG2tQMjwU51TNMc88wzT1DdA+OsbAynxW1RdH7/uNFKUMRjPTCULfRfXTY3A51wWjmV7hmKkpB/0iA1r5hRISqQh/5Xk9Cz0rBkKJvOEDP0BgjgSBKQUiBQqGUkYIcUjCr2E+VDVbvTEOu/EJaKoFLgX9QSr3TfP8VAKXUtyY7Z+3atWr79u1x3U+j0WiyFRFxTAkk4nTdBiwRkQUi4gXuBB63RyyNRqPRJIO4fQ1KqYCI3As8A7iBnyql9tommUaj0WgcJyGHs1LqaUB3CtNoNJoMRefgaTQaTRajlYBGo9FkMVoJaDQaTRajlYBGo9FkMVoJaDQaTRYTd7FYXDcT6QMOJO2G8VMMJDaLMTlkgpyZICNABdCeaiGiIFO+Ty2nvSxTSjnS3dK5ngSROeBU1ZudiMgDSql7Ui3HdGSCnJkgI4CIbNdr0z60nPYiIo61WtDuoMg8kWoBoiQT5MwEGTOJTPk+tZwZQrLdQRnxtKXJPvTa1KQzTq7PZFsCDyT5fhpNtOi1qUlnHFufSVUCSqmk/6JFGoEpBv8oIm+LSL2IfHaSczeIyEHztSFs/4Ui8pZ5zftFEhtIICI/FZFWEdkTtu87IrJfRHaLyKMiUhLtv8/cv0BEXjf3P2I2+UuISeRcLSKvichOEdkuIusmOTcp32W8pGJtgl6fen1Gh6PrUyk1Y18Yje0OAwsBL7ALWAH8BfBfgMs8rirCuWXAEXNbav5can62FbgEY0jTH4CbE5TzKmANsCds342Ax/z5n4F/jvbfZ37238Cd5s//AXzShu8zkpzPWv9+4BbgpVR+l5n00utTr890eMVtCUzyBBOVdheRr5jHHBCRd051zQQZH4GplBoFrBGYnwS+oZQKASilWiOc+07gOaVUp1KqC3gOuElEaoAipdRrylgl/wXckYiQSqk/AZ0T9j2rlAqYb18DZkf77zOfVq4Ffmse91CiMk4mJ8Z4sCLz52KgKcKpSfsuLfT61OvT2k2arc90W5txKQE5PWT+Zownlw+JyAqMJ4J/UUotBrqAuyOcuwJj9sBK4CbgRyLinuKaiVAHnAx732DuWwR80DQP/yAiS0zZ1orIg9OcW2f+PHG/k3wM4ykEEakVEatz62QylgPdYb+kTsr4OeA7InIS+C7wFVPOlH2Xen3q9RnG50ij9ZmOazNeS2CyJ5hotPt64NdKqRGl1FHgkHm9ya7pBD5gWBnR9h8DPwVQSm1X5rjMdEFE/g4IAL8EUEo1KaVuSa1UZ/BJ4PNKqTnA54GfQMq/S70+k4RenzGTdmszXiUwmeaMqN1F5HYR+cY05062PxEagTlh72eb+xqA35v7HgXOi+HcRs40fa39tiMiHwVuA+4yzdFoZewASkTEM2G/E2zg9Hf5G4wFGa2cTn2Xen2evd929PqMi7Rbm0nJDlJKPa6U+loy7jWByUZgPgZcYx7zDuDtCOc+A9woIqUiUooRCHtGKdUM9IrIJaZv8yPARrsFF5GbgL8BbldKDU5yWMR/n/kL+SLwPvO4DU7IaNKE8R2C8TRzMMIxKf0up0Ovz9jR6zM5JGNtxqsEJtOc0Wj3qbRupP1xY2pWawRmPfDfyhiB+W3gvSLyFvAt4ONwpp9QKdUJfBNjIW/DCNRZQadPAQ9imGOHMf2h8SIiDwNbgGUi0iAidwM/AAqB58RIb/sP89hxn+sU/z6ALwFfEJFDGD7YnyQi4xRy/iXwf0VkF/BPwD3msSn5Lk30+tTrM13XZ/qtzalShyZ7YfQcOgIs4HTq10oMcys87etTEc5daR7vM88/gpFKFvGa8cinX9n90utTv9L1lY5rM5F/zC0YZuph4O/MfQsx8moPmf8on7n/dgztap37d+Z5BwjLu410Tf3Sr3heen3qV7q+0m1tJrV3kEaj0WjSC91FVKPRaLIYrQQ0Go0mi4lZCUjkkud7zfdKRCqmOf8eMRpP7ReRrSJyxTTHrxaRdCo+0aQpk6zNX5r79ojRYCxnivP12tQ4xiTr8yciskuMRny/FZGCSc4VEfmqGA3u3haRF0Vk5TT3u1pELptWsBgDGpM1vLoAmA8cAyqmOP824A3rGIxmTyeAWVOc81HgB6kO5uhXer+mWJu3YDT/EuBhJmlUptemfjn5mmJ9FoUd8z3gy5Ocfy/wNJBnvr/RvJ5/inv+A/DX08kWqyUQsTxZKbVDKXUsivO/BPwvpVQ7gFLqTYwS6U8DiMhFIvKqqRm3ikgx8A2MPio7ReSDMcqryR4mW5tPKxOM7ItIjc5Ar02Ns0y2PnvBeNIHcjEa3kXiS8C9yizMU0o9C7wK3GWef5OIvGmuz00iMh/4BPB5c31eOZlgsc4YjlSefHEM56/EeNoKZzuwwawofAT4oFJqm4gUAYPA14C1Sql7Y5RVk11MuTZNN9CHgfsmOV+vTY2TTLo+ReRnGBbrPuCLE08011u+UurIhI+2AytFpBKjx9RVSqmjIlKmlOo0C/j6lVLfnUqwdAoMLwOalVLbAJRSvep0Lw2NJlF+BPxJKfVKHOfqtalxDKXUXwC1GFXV8ViUl2Cs7aPm9Sa2056SWJVATOXJIvKMaYpYLVv3ARdOOOxCYC8aTWJMujZF5O+BSuAL1od6bWqSzJR/O5VSQQwX0XvFaA+903x9w3QZDYjIwgnXtGd9xhjcmLI8mekDw7dj9OYoN9+vxgi+1ZjXOwJcZH5WaN7vvcBDqQ7s6Fd6vyZbmxh9d14Fcqc5X69N/XLsNcX6XGx+LhjzDr47yfmfBZ601jFwvXm9XIwHnJPAAvOzMnP7ReDr08oWxz8mUsnzZzF8XAGMrn0PTnH+JzFKnvebv3RXhX12EcaUol3mtgBj5Ns2YCeGTzbl/6H6lZ6vSdZmwHy/03x9bYrz9drUL8deE9cnhidmM/AWsAdjJkPRJOcK8PcYbSUOAC8D54Z9fjOww1yfz5n7lgK7zfV55WRy6bYRGo1Gk8WkU2BYo9FoNElGKwGNRqPJYrQS0Gg0miwmISUgInPMHhb7RGSviNxn7i8TkefMPhfPiTGyDRFZLiJbRGRERP56wrU+b15jj4g8LCL+RGTTaDQazfQkagkEgC8qpVZgFCx8WkRWAF8GNimllgCbzPcAnRiZRGdUsIlInbl/rVJqFUafjTsTlE2j0Wg005CQElBKNSujxwpKqT6Mirc6YD1G3xXM7R3mMa3KqLoci3A5D5BrztnMw0g11Wg0Go2D2BYTMBsWXQC8DlQrpZrNj04B1VOdq5RqxLAOTgDNQI8yGiRpNBqNxkFsUQJmD+zfAZ9TZlc8C2UUIkxZjGDGDNZjVNPVAvki8ud2yKbRaDSayUlYCZjdGX8H/FIp9Xtzd4uI1Jif1wCt01zmeuCoUqpNKTUG/B6YfhiCRqPRaBIi0ewgAX4C1Culvhf20ePABvPnDcDGaS51ArhERPLMa16HEV/QaDQajYMk1DbCHL/3Ckbvi5C5+28x4gL/DcwFjgMfUEZ/61kYPbCLzOP7gRVKqV4R+TpGG9UARg+MjyulRuIWTqPRaDTTonsHaTQaTRajK4Y1Go0mi9FKQKPRaLIYrQQ0Go0mi9FKQKPRaLIYrQQ0Go0mi9FKQKPRaLIYrQQ0Go0mi9FKQKPRaLKY/x9j0213kx+YsAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datetime import datetime\n",
+    "date_range = pd.date_range(datetime(2018,10,1),datetime(2018,10,3),freq='H')\n",
+    "\n",
+    "#df1 = wind_power_t['Pwind'].between_time(start_time='2018-09-01 04:00:00', end_time='2018-09-01 08:00:00') \n",
+    "plt.figure()\n",
+    "wind_power_t['Pwind'][date_range].plot()\n",
+    "solar_power_t['Psolar'][date_range].plot()\n",
+    "plt.figure()\n",
+    "spot_price_t['Price'][date_range].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Normalise Wind and Solar Power\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wind_power_t = wind_power_t.loc[start_date:end_date, 'Pwind'] / 100\n",
+    "solar_power_t = solar_power_t.loc[start_date:end_date, 'Psolar'] / 100\n",
+    "spot_price_t = spot_price_t.loc[start_date:end_date, 'Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "capacity_factor_wind = ExampleHPP.calculate_Capacity_Factor(wind_power_t, 1)\n",
+    "capacity_factor_solar = ExampleHPP.calculate_Capacity_Factor(solar_power_t, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2949527985922021"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "capacity_factor_wind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2347651599861256"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "capacity_factor_solar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[hpp_wind_capacity, hpp_solar_capacity, P_HPP_t,\n",
+    "     P_curtailment_t, hpp_investment_cost, hpp_maintenance_cost,\n",
+    "     LCOE, NPV, IRR] = ExampleHPP.sizing_Wind_Solar(\n",
+    "        wind_power_t, solar_power_t, spot_price_t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Wind Capacity = 129 MW\n",
+      "Solar Capacity = 347 MW\n",
+      "AEP = 1026 GWh\n",
+      "Curtailed Energy = 21 GWh\n",
+      "HPP Investment Cost = 353 MEUR\n",
+      "HPP Maintenance Cost = 112 MEUR\n",
+      "HPP LCOE = 31.95 EUR/MWh\n",
+      "HPP NPV= 72 MEUR\n",
+      "HPP IRR = 9%\n"
+     ]
+    }
+   ],
+   "source": [
+    "AEP = P_HPP_t.mean() * 8760 / 1000\n",
+    "Curtailed_Energy = P_curtailment_t.sum() / 1000\n",
+    "# print(\"\\033[H\\033[J\")\n",
+    "print()\n",
+    "print('Wind Capacity = %d MW' % hpp_wind_capacity)\n",
+    "print('Solar Capacity = %d MW' % hpp_solar_capacity)\n",
+    "print('AEP = %.0f GWh' % AEP)\n",
+    "print('Curtailed Energy = %.0f GWh' % Curtailed_Energy)\n",
+    "print('HPP Investment Cost = %.0f MEUR' % (hpp_investment_cost / 1e6))\n",
+    "print('HPP Maintenance Cost = %.0f MEUR' %\n",
+    "      (hpp_maintenance_cost.sum() / 1e6))\n",
+    "print('HPP LCOE = %.2f EUR/MWh' % LCOE)\n",
+    "print('HPP NPV= %.0f MEUR' % (NPV / 1000000))\n",
+    "print('HPP IRR = %.0f%%' % (IRR * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[hpp_wind_capacity,\n",
+    "     hpp_solar_capacity,\n",
+    "     hpp_battery_power_rating,\n",
+    "     hpp_battery_energy_capacity,\n",
+    "     P_RES_available_t,\n",
+    "     P_HPP_t,\n",
+    "     P_curtailment_t,\n",
+    "     P_charge_discharge_t,\n",
+    "     E_SOC_t,\n",
+    "     hpp_investment_cost,\n",
+    "     hpp_maintenance_cost,\n",
+    "     LCOE,\n",
+    "     NPV,\n",
+    "     IRR] = ExampleHPP.sizing_Wind_Solar_Battery(wind_power_t,\n",
+    "                                                 solar_power_t,\n",
+    "                                                 spot_price_t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Wind Capacity = 171 MW\n",
+      "Solar Capacity = 378 MW\n",
+      "Battery Storage Power Rating = 83 MW\n",
+      "Battery Storage Energy Rating = 271 MWh\n",
+      "AEP = 1199 GWh\n",
+      "Curtailed Energy = 20 GWh\n",
+      "HPP Investment Cost = 479 MEUR\n",
+      "HPP Maintenance Cost = 199 MEUR\n",
+      "HPP LCOE = 38.68 EUR/MWh\n",
+      "HPP NPV= 82 MEUR\n",
+      "HPP IRR = 9%\n"
+     ]
+    }
+   ],
+   "source": [
+    "AEP = P_HPP_t.mean() * 8760 / 1000\n",
+    "Curtailed_Energy = P_curtailment_t.sum() / 1000\n",
+    "# print(\"\\033[H\\033[J\")\n",
+    "print()\n",
+    "print('Wind Capacity = %d MW' % hpp_wind_capacity)\n",
+    "print('Solar Capacity = %d MW' % hpp_solar_capacity)\n",
+    "print('Battery Storage Power Rating = %d MW' % hpp_battery_power_rating)\n",
+    "print(\n",
+    "    'Battery Storage Energy Rating = %d MWh' %\n",
+    "    hpp_battery_energy_capacity)\n",
+    "print('AEP = %.0f GWh' % AEP)\n",
+    "print('Curtailed Energy = %.0f GWh' % Curtailed_Energy)\n",
+    "print('HPP Investment Cost = %.0f MEUR' % (hpp_investment_cost / 1e6))\n",
+    "print('HPP Maintenance Cost = %.0f MEUR' %\n",
+    "      (hpp_maintenance_cost.sum() / 1e6))\n",
+    "print('HPP LCOE = %.2f EUR/MWh' % LCOE)\n",
+    "print('HPP NPV= %.0f MEUR' % (NPV / 1000000))\n",
+    "print('HPP IRR = %.0f%%' % (IRR * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}