Multi year ramp 2025

RAMP_New_test.py

@@ -0,0 +1,118 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov  1 10:28:55 2024
+
+@author: nilsl
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt 
+import numpy as np
+from openpyxl import load_workbook
+from ramp import UseCase, User
+import time
+import datetime
+
+start = time.time()
+
+
+
+workbook = load_workbook(filename="example_excel_usecase_filled_final.xlsx")
+workbook.save(filename="example_excel_usecase_filled_new_final.xlsx")
+
+use_case = UseCase()  # creating a new UseCase instance
+
+use_case.load("example_excel_usecase_filled_new_final.xlsx")
+
+#dictionary for the user and appliance limits
+user_lim = {}
+user_lim['household_low'] = 15
+user_lim['household_low_growth'] = 15
+user_lim['household_med'] = 15
+user_lim['household_med_growth'] = 15
+user_lim['household_high'] = 7
+user_lim['household_high_growth'] = 7
+user_lim['IGA'] = 3
+user_lim['Church'] = 2
+user_lim['PL'] = 1
+app_lim  = {}
+app_lim['light'] = 8
+app_lim['radio'] = 2
+app_lim['tv'] = 1
+app_lim['decoder'] = 1
+app_lim['dvd'] = 1
+app_lim['charger'] = 3
+app_lim['woofer'] = 1
+app_lim['freezer'] = 1
+app_lim['speaker'] = 1
+app_lim['light_fresh'] = 8
+app_lim['radio_fresh'] = 2
+app_lim['tv_fresh'] = 1
+app_lim['decoder_fresh'] = 1
+app_lim['dvd_fresh'] = 1
+app_lim['charger_fresh'] = 4
+app_lim['woofer_fresh'] = 1
+app_lim['freezer_fresh'] = 1
+app_lim['speaker_fresh'] = 1
+app_lim['laptop_fresh'] = 1
+
+app_lim['Appliance_1'] = 2
+app_lim['Appliance_2'] = 2
+app_lim['Appliance_3'] = 2
+app_lim['Appliance_4'] = 2
+app_lim['Appliance_5'] = 5
+
+
+
+Load_ECOS = np.mean(np.load("Load_ECOS.npy").reshape((24,60)),axis = 1)
+
+n_days = 30
+date_start = "2024-01-01"
+use_case.date_start = date_start
+use_case.initialize(num_days=n_days, force=True)
+n_years = 5
+profile = use_case.generate_daily_load_profiles(flat=True, num_years = n_years, num_app_lim=app_lim, num_user_lim=user_lim, load_growth='yes')
+
+
+
+
+
+if n_years == 1:
+    Load = profile.reshape((n_days,60*24))
+    Load_mean_day = np.mean(Load, axis = 0).reshape((24,60))
+    Load_RAMP = np.mean(Load_mean_day, axis = 1)
+    plt.plot(Load_RAMP)
+    plt.show()
+
+else:
+    Load = profile.iloc[:].to_numpy()
+    Load_mean_day = np.zeros((n_years,1440))
+    for n in range(n_years):
+        Load_year_n = np.transpose(Load[:,n])
+        Load_year_n_reshape = Load_year_n.reshape((n_days,1440))
+        Load_mean_day[n,:] = np.mean(Load_year_n_reshape, axis = 0)
+
+    Load_plot = np.mean(Load_mean_day.reshape((n_years,24,60)),axis=2)
+    fig, ax = plt.subplots()
+    time_axis = [datetime.datetime(2024, 1, 1, 8) + datetime.timedelta(hours=i) for i in range(24)]  # 08:00 to 08:00 next day
+    tick_times = [time_axis[0] + datetime.timedelta(hours=i) for i in range(0, 24, 2)]
+    plot_legend = []
+    for n2 in range(n_years):
+        plt.plot(time_axis,Load_plot[n2,:], label = str(n2))
+        plot_legend.append("Year" + str(n2)) 
+    plt.xticks(fontsize = 11)
+    plt.yticks(fontsize = 11)
+
+    ax.set_xticks(tick_times)
+    ax.set_xticklabels([dt.strftime('%H:%M') for dt in tick_times], rotation=45)
+    plt.legend(plot_legend, fontsize = 12)
+    plt.xlabel("Time [Hour]", fontsize = 13)
+    plt.ylabel("Electrical Demand [kW]", fontsize = 13)
+    plt.tight_layout()
+    plt.show()
+
+
+
+
+
+

ramp/core/core.py

@@ -112,6 +112,7 @@ def __init__(
         if self.random_seed:
             random.seed(self.random_seed)
 
+
     @property
     def date_start(self):
         """Start date of the daily profiles generated by the UseCase instance"""
@@ -386,8 +387,9 @@ def calc_peak_time_range(self, peak_enlarge=None):
         # The peak_time is randomly enlarged based on the calibration parameter peak_enlarge
         return np.arange(peak_time - rand_peak_enlarge, peak_time + rand_peak_enlarge)
 
+    """Added a new input here: num_years"""
     def generate_daily_load_profiles(
-        self, days=None, flat=True, cases=None, verbose=False
+        self, days=None, flat=True, cases=None, verbose=False, num_years = 1, num_user_lim = None, num_app_lim = None, load_growth = 'no'
     ):
         """
         Iterate over the days and generate a daily profile for each of the days
@@ -407,14 +409,77 @@ def generate_daily_load_profiles(
         -------
         daily_profiles: numpy array
         """
-        if cases is not None:
+        if num_years > 1:
+            if load_growth == 'yes': #add new appliances if the promotion year has come
+                app_gr_df = pd.read_excel("Appliance_growth_example_final.xlsx")
             results = {}
-            for case in cases:
-                profiles = self.generate_daily_load_profiles(days=days, flat=True)
-                results[f"case {case}"] = pd.Series(
+            original_user_info = {}
+            new_user_info = {}
+            for y in range(num_years):
+                #print("Year ",y)
+                #print("Number of days: ", self.days)
+                for user in self.users:     #for each user category
+                    #increase the number of users in the user category based on the uniform distribution for all users 
+                    if user.num_users < num_user_lim[user.user_name]:
+                        #print("The old number of users: ",user.user_name, user.num_users)
+                        user_increase = int(np.heaviside(y,0)*round(random.uniform(0,1))) # heaviside function is used to make sure that no increases are made in year 0
+                        if user.num_users + user_increase > num_user_lim[user.user_name]:
+                            user_increase = num_user_lim[user.user_name] - user.num_users
+                        user.num_users = random.choice([user.num_users,user.num_users,user.num_users+user_increase])
+                        #print("The new number of users:", user.num_users,"\nUser Increase:", user_increase)
+                        #if user.num_users == num_user_lim[user.user_name]:
+                        #    print("Maximum number of users reached")
+
+                    for app in user.App_list:
+                        if app.number < num_app_lim[app.name]:
+                            #print("The old number of apps: ",app.name, app.number)
+                            app_increase = int(np.heaviside(y,0)*round(random.uniform(0,1)))
+                            if app.number + app_increase > num_app_lim[app.name]:
+                                app_increase = num_app_lim[app.name] - app.number
+                            app.number = random.choice([app.number,app.number,app.number+app_increase])
+                            #print("The new number of apps: ",app.number,"\nApp increase: ",app_increase)
+                            #if app.number == num_app_lim[app.name]:
+                                #print("Max number of appliances reached")
+                for user in self.users:
+                    for n_gr in range(app_gr_df.shape[0]): 
+                        if app_gr_df.loc[n_gr,'growth_name'] == user.user_name and app_gr_df.loc[n_gr,'growth_year'] == y: #check which appliances have to be adjusted in numbers
+                            user_new = User(user.user_name + "_growth", user.num_users, user.user_preference) #define a new user instance
+                            self.add_user(user_new)
+                            user_new.add_appliance(
+                                number=int(app_gr_df.loc[n_gr,'number']),
+                                power=float(app_gr_df.loc[n_gr,'power']),
+                                num_windows=int(app_gr_df.loc[n_gr,'num_windows']),
+                                func_time=int(app_gr_df.loc[n_gr,'func_time']),
+                                time_fraction_random_variability=float(app_gr_df.loc[n_gr,'time_fraction_random_variability']),
+                                func_cycle=int(app_gr_df.loc[n_gr,'func_cycle']),
+                                #fixed=app_prom_df.loc[n_prom,'fixed'],
+                                #flat=app_prom_df.loc[n_prom,'flat'],
+                                name=app_gr_df.loc[n_gr,'name'],
+                                occasional_use=float(app_gr_df.loc[n_gr,'occasional_use']),
+                                window_1 = np.array([app_gr_df.loc[n_gr,'window_1_start'],app_gr_df.loc[n_gr,'window_1_end']],dtype=np.intc),
+                                window_2 = np.array([app_gr_df.loc[n_gr,'window_2_start'],app_gr_df.loc[n_gr,'window_2_end']],dtype=np.intc),
+                                window_3 = np.array([app_gr_df.loc[n_gr,'window_3_start'],app_gr_df.loc[n_gr,'window_3_end']],dtype=np.intc),
+                                random_var_w = float(app_gr_df.loc[n_gr,'random_var_w'])
+                                )
+                #Uncomment to have two sets of users to compare the original users with the final ones
+# =============================================================================
+#                 if y == 0:
+#                     for user in self.users:
+#                         original_user_info[user.user_name] = user.num_users
+#                 elif y == num_years-1:
+#                     for user in self.users:
+#                         new_user_info[user.user_name] = user.num_users
+#                         print(user.App_list)
+#                     print(original_user_info)
+#                     print(new_user_info)
+# =============================================================================
+                self.save("multiyear output year " + str(y))  # just for checking the use_case. Can be commented out
+                profiles = self.generate_daily_load_profiles(days=days, flat=True, num_years = 1)
+                results[f"y {y}"] = pd.Series(
                     index=self.datetimeindex, data=profiles
                 )
-            answer = Plot(pd.concat(results, axis=1))
+
+            answer = pd.concat(results, axis=1)
         else:
             if self.days is None:
                 if days is not None:
@@ -613,30 +678,30 @@ def load(self, filename: str) -> None:
 
                 # assign windows arguments
                 for k in WINDOWS_PARAMETERS:
-                    if "window" in k:
-                        w_start = row.get(k + "_start", np.NaN)
-                        w_end = row.get(k + "_end", np.NaN)
+                    if "window" in k: 
+                        w_start = row.get(k + "_start", np.nan)
+                        w_end = row.get(k + "_end", np.nan)
                         if not np.isnan(w_start) and not np.isnan(w_end):
                             appliance_parameters[k] = np.array(
                                 [w_start, w_end], dtype=np.intc
                             )
                     else:
-                        val = row.get(k, np.NaN)
+                        val = row.get(k, np.nan)
                         if not np.isnan(val):
                             appliance_parameters[k] = val
 
                 # assign duty cycles arguments
                 for duty_cycle_params in DUTY_CYCLE_PARAMETERS:
                     for k in duty_cycle_params:
                         if "cw" in k:
-                            cw_start = row.get(k + "_start", np.NaN)
-                            cw_end = row.get(k + "_end", np.NaN)
+                            cw_start = row.get(k + "_start", np.nan)
+                            cw_end = row.get(k + "_end", np.nan)
                             if not np.isnan(cw_start) and not np.isnan(cw_end):
                                 appliance_parameters[k] = np.array(
                                     [cw_start, cw_end], dtype=np.intc
                                 )
                         else:
-                            val = row.get(k, np.NaN)
+                            val = row.get(k, np.nan)
                             if not np.isnan(val):
                                 appliance_parameters[k] = val
 
@@ -667,7 +732,8 @@ def __init__(
         # TODO check type of Usecase
         self.usecase = usecase
         self.user_name = user_name
-        self.num_users = num_users
+        """Changed self.num_users = num_users to self._num_users = num_users"""
+        self._num_users = num_users
         self.user_preference = user_preference
         self.rand_daily_pref = 0
         self.load = None
@@ -822,6 +888,16 @@ def num_days(self):
             if self.usecase.is_initialized is True:
                 answer = self.usecase.num_days
         return answer
+
+    """Added a new propery with a setter: num_users"""
+    @property
+    def num_users(self): 
+        return self._num_users
+
+    @num_users.setter
+    def num_users(self, new_num_users):
+        self._num_users = new_num_users
+
 
     def save(self, filename: str = None) -> Union[pd.DataFrame, None]:
         """Saves/returns the model database including all appliances as a single pd.DataFrame or excel file.
@@ -1046,7 +1122,7 @@ def generate_aggregated_load_profile(
         """
 
         self.load = np.zeros(1440)  # initialise empty load for User instance
-        for _ in range(self.num_users):
+        for _ in range(self._num_users):
             # iterates for every single user within a User class.
             self.load = self.load + self.generate_single_load_profile(
                 prof_i, peak_time_range, day_type