Merge pull request #16 from RAMP-project/development

Development

CHANGELOG.md

@@ -1,6 +1,18 @@
 Release History
 ===============
 
+0.3.1 (2021-06-23)
+------------------
+
+**|new|**       added "input_file_3" as an example of e-cooking loads
+
+**|changed|**   the way in which input files are called in the ramp_run script has been changed to be more explicit and user-friendly
+
+**|changed|**   the readme.md has been updated to describe the purpose of the 3 provided input files, 1: basic electric appliances, 2: DHW, 3: cooking
+
+**|changed|**   the pubs_list.md has been updated with two new publications
+
+
 0.3.0 (2021-05-28)
 ------------------
 

README.md

@@ -24,7 +24,16 @@ The model is developed in Python 3.6, and requires the following libraries:
 * random
 
 ## Quick start
-To get started, download the repository and simply run the "ramp_run.py" script. The console will ask how many profiles (i.e. independent days) need to be simulated, and will provide the results based on the default inputs defined in `input_file_1`.py and `input_file_2`. To change the inputs, just modify the latter files. Some guidance about the meaning of each input parameter is available in the `core.py` file, where the *User* and *Appliance* Python classes are defined and fully commented. 
+To get started, download the repository and simply run the "ramp_run.py" script. The console will ask how many profiles (i.e. independent days) need to be simulated, and will provide the results based on the default inputs defined in `input_file_x`.py. To change the inputs, just modify the latter files. Some guidance about the meaning of each input parameter is available in the `core.py` file, where the *User* and *Appliance* Python classes are defined and fully commented. 
+
+### Example input files
+Three different input files are provided as example representing three different categories of appliancces that can be modelled with RAMP.
+
+- `input_file_1.py`: represents the most basic electric appliances, is an example of how to model lightbulbs, radios, TVs, fridges, and other electric appliances. This input file is based on the ones used for [this publication](https://doi.org/10.1016/j.energy.2019.04.097).
+
+- `input_file_2.py`: shows how to model thermal loads, with the example of a "shower" appliance. The peculiarity of thermal appiances is that the nominal power can be provided as external input as a "csv" file (in this case, `shower_P.csv`). For the example "shower" appliance, the varying nominal power accounts for the effect of groundwater temperature variation throughout the year. This input file is based on that used for [this publication](https://doi.org/10.3390/app10217445).
+
+- `input_file_3.py`: represents an example of how to model electric cooking appliances. This input file is used in [this publication](https://doi.org/10.1109/PTC.2019.8810571)
 
 ## Authors
 The model has been developed by:

docs/pubs_list.md

@@ -7,7 +7,9 @@
 ## List of publications
 This is an up-to-date list of all publications featuring RAMP.
 
-*Giacomo Falchetta, Nicolò Stevanato, Magda Moner-Girona, Davide Mazzoni, Emanuela Colombo, Manfred Hafner, M-LED: Multi-sectoral Latent Electricity Demand Assessment for Energy Access Planning, FEEM Working Papers (2020), https://www.econstor.eu/handle/10419/223636*
+*Giacomo Falchetta, Nicolò Stevanato, Magda Moner-Girona, Davide Mazzoni, Emanuela Colombo, Manfred Hafner, The M-LED platform: advancing electricity demand assessment for communities living in energy poverty, Environmental Reasearch Letters (2021), https://doi.org/10.1088/1748-9326/ac0cab*
+
+*Nicolò Stevanato, Lorenzo Rinaldi, Stefano Pistolese, Sergio Balderrama, Sylvain Quoilin, Emanuela Colombo, Modeling of a Village-Scale Multi-Energy System for the Integrated Supply of Electric and Thermal Energy, Applied Sciences (2020), https://doi.org/10.3390/app10217445*
 
 *Francesco Lombardi, Sylvain Quoilin, Emanuela Colombo, Modelling distributed Power-to-Heat technologies as a flexibility option for smart heat-electricity integration, Proceedings of ECOS 2020, pp. 2369-2380, http://hdl.handle.net/11311/1143671*
 

ramp/input_files/input_file_3.py

@@ -0,0 +1,75 @@
+# -*- coding: utf-8 -*-
+
+#%% Definition of the inputs
+'''
+Input data definition 
+'''
+
+from ramp.core.core import User, np, pd
+User_list = []
+
+
+'''
+This example input file represents a single household user whose only loads
+are the "cooking" activities. The example showcases how to model electric cooking loads by means of 
+the Prefence Index and User Preference attributes.
+'''
+
+#Create new user classes
+HH = User("generic household",1,3)
+User_list.append(HH)
+
+#Create new appliances
+
+#Create Cooking appliances
+
+HH_lunch1_soup = HH.Appliance(HH,1,1800,2,70,0.15,60, thermal_P_var = 0.2, pref_index =1, fixed_cycle=1)
+HH_lunch1_soup.windows([12*60,15*60],[0,0],0.15)
+HH_lunch1_soup.specific_cycle_1(1800,10,750,60,0.15)
+HH_lunch1_soup.cycle_behaviour([12*60,15*60],[0,0])
+
+HH_lunch2_rice = HH.Appliance(HH,1,1800,2,25,0.15,20, thermal_P_var = 0.2, pref_index = 2, fixed_cycle=1)
+HH_lunch2_rice.windows([12*60,15*60],[0,0],0.15)
+HH_lunch2_rice.specific_cycle_1(1800,10,750,15,0.15)
+HH_lunch2_rice.cycle_behaviour([12*60,15*60],[0,0])
+
+HH_lunch2_egg = HH.Appliance(HH,1,1200,2,3,0.2,3, thermal_P_var = 0.2 , pref_index = 2)
+HH_lunch2_egg.windows([12*60,15*60],[0,0],0.15)
+
+HH_lunch2_platano = HH.Appliance(HH,1,1800,2,10,0.15,5, thermal_P_var = 0.2, pref_index = 2, fixed_cycle=1)
+HH_lunch2_platano.windows([12*60,15*60],[0,0],0.15)
+HH_lunch2_platano.specific_cycle_1(1800,5,1200,5,0.15)
+HH_lunch2_platano.cycle_behaviour([12*60,15*60],[0,0])
+
+HH_lunch2_meat = HH.Appliance(HH,1,1200,2,7,0.15,3, thermal_P_var = 0.2, pref_index = 2)
+HH_lunch2_meat.windows([12*60,15*60],[0,0],0.15)
+
+HH_lunch3_beansnrice = HH.Appliance(HH,1,1800,2,45,0.2,30, thermal_P_var =0.2 , pref_index = 3, fixed_cycle=1)
+HH_lunch3_beansnrice.windows([12*60,15*60],[0,0],0.15)
+HH_lunch3_beansnrice.specific_cycle_1(1800,10,750,35,0.2)
+HH_lunch3_beansnrice.cycle_behaviour([12*60,15*60],[0,0])
+
+HH_lunch3_meat = HH.Appliance(HH,1,1200,2,10,0.2,5, thermal_P_var = 0.2, pref_index = 3)
+HH_lunch3_meat.windows([12*60,15*60],[0,0],0.15)
+
+HH_lunch_yuca = HH.Appliance(HH,1,1800,1,25,0.15,10, thermal_P_var = 0.2, pref_index =0, fixed_cycle=1)
+HH_lunch_yuca.windows([13*60,14*60],[0,0],0.15)
+HH_lunch_yuca.specific_cycle_1(1800,10,750,15,0.15)
+HH_lunch_yuca.cycle_behaviour([12*60,15*60],[0,0])
+
+HH_breakfast_huminta = HH.Appliance(HH,1,1800,1,65,0.15,50, thermal_P_var = 0.2, pref_index =0, fixed_cycle=1)
+HH_breakfast_huminta.windows([6*60,9*60],[0,0],0.15)
+HH_breakfast_huminta.specific_cycle_1(1800,5,750,60,0.15)
+HH_breakfast_huminta.cycle_behaviour([6*60,9*60],[0,0])
+
+HH_breakfast_bread = HH.Appliance(HH,1,1800,1,15,0.15,10, thermal_P_var = 0.2, pref_index =0, fixed_cycle=1)
+HH_breakfast_bread.windows([6*60,9*60],[0,0],0.15)
+HH_breakfast_bread.specific_cycle_1(1800,10,1200,5,0.15)
+HH_breakfast_bread.cycle_behaviour([6*60,9*60],[0,0])
+
+HH_breakfast_coffee = HH.Appliance(HH,1,1800,1,5,0.15,2, thermal_P_var = 0.2, pref_index =0)
+HH_breakfast_coffee.windows([6*60,9*60],[0,0],0.15)
+
+HH_mate = HH.Appliance(HH,1,1800,1,30,0.3,2, thermal_P_var = 0.2, pref_index =0)
+HH_mate.windows([7*60,20*60],[0,0],0.15)
+

ramp/ramp_run.py

@@ -29,11 +29,12 @@
 from core.stochastic_process import Stochastic_Process
 from post_process import post_process as pp
 
+# Define which input files should be considered and run. 
+# Files are specified as numbers in a list (e.g. [1,2] will consider input_file_1.py and input_file_2.py)
+input_files_to_run = [1,2,3]
+
 # Calls the stochastic process and saves the result in a list of stochastic profiles
-# In this default example, the model runs for 2 input files ("input_file_1", "input_file_2"),
-# but single or multiple files can be run restricting or enlarging the iteration range 
-# and naming further input files with progressive numbering
-for j in range(2,3):
+for j in input_files_to_run:
     Profiles_list = Stochastic_Process(j)
 
 # Post-processes the results and generates plots