RAMP Introduction

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An open-source bottom-up stochastic model for generating multi-energy load profiles (RAMP Website , RAMP Documentation)

What is RAMP

RAMP is a bottom-up stochastic model for the generation of high-resolution multi-energy profiles, conceived for application in contexts where only rough information about users’ behaviour are obtainable. Those may range from remote villages to whole countries. RAMP provides an easy and intuitve API for building up stochastic profiles.

https://github.com/RAMP-project/RAMP/blob/master/docs/figures/Example_output.jpg?raw=true

Requirements

RAMP has been tested on macOS, Windows and Linux.

For running RAMP, a couple of things are needed:

  1. The Python programming language, version 3.6 or higher

  2. A number of Python adds-on packages:

Quick start

There are different ways to build a model using RAMP!

Example python input files

Three different input files are provided as example representing three different categories of appliancces that can be modelled with RAMP. To have a look to the python files, you can download them using the “download_example” function:

from ramp import download_example

download_example("the specfic folder directory to save the files")

  • 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.

  • 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.

  • input_file_3.py: represents an example of how to model electric cooking appliances. In this input file two different kind of meals are modelled: 1) short and repetitive meals (e.g. breakfast); and 2) main meals (e.g. lunch, dinner). Repetitive meals do not vary across days, whilst main meals do so. In particular, every household can randomly choose between 3 different types of main meal every day. Such variability in meal preferences is modelled by means of two parameters: the user preference and the preference index. The user preference defines how many types of meal are available for each user to choose every day (e.g. 3). Then, each of the available meal options is modelled separately, with a different preference index attached. The stochastic process randomly varies the meal preference of each user every day, deciding whether they want a “type 1” meal, or a “type 2”, etc. on a given day. This input file is used in this publication

Spreadsheet input files

It is also possible to use spreadsheets as input files. To do so you need to run the ramp command with the option -i:

ramp -i <path to .xlsx input file>

If you already know how many profile you want to simulate you can indicate it with the -n option:

ramp -i <path to .xlsx input file> -n 10

will simulate 10 profiles. Note that you can use this option without providing a .xlsx input file with the -i option, this will then be equivalent to running python ramp_run.py from the ramp folder without being prompted for the number of profile within the console.

Other options are documented in the help of ramp, which you access with the -h option

ramp -h

If you have existing python input files, you can convert them to spreadsheet. To do so, go to ramp folder and run

python ramp_convert_old_input_files.py -i <path to the input file you wish to convert>

For other example of command lines options, such as setting date ranges, please visit this section of the documentation

Building a model with a python script

# importing functions
from ramp import User,calc_peak_time_range,yearly_pattern

# Create a user category
low_income_households = User(
 user_name = "low_income_household", # an optional feature for the User class
 num_users = 10, # Specifying the number of specific user category in the community
)

You can add appliances to a user category by:

# adding some appliances for the household
radio = low_income_household.add_appliance(
 name = "Small Radio", # optional feature for the appliance class
 number = 1, # how many radio each low income household holds
 power = 10, # RAMP does not take care of unit of measures , watt
 func_time = 120, # Total functioning time of appliance in minutes
 num_windows = 2, # in how many time-windows the appliance is used
)

The use time frames can be specified using the ‘window’ method for each appliance of the user category:

# Specifying the functioning windows
radio.windows(
 window_1 = [480,540], # from 8 AM to 9 AM
 window_2 = [1320,1380], # from 10 PM to 11 PM
)

Now you can generate your stochastic Profiles:

# generating load_curves
load = low_income_household.generate_aggregated_load_profiles(
   prof_i = 1, # the ith day profile
   peak_time_range = calc_peak_time_range(), # the peak time range
   Year_behaviour = yearly_pattern(), # defining the yearly pattern (like weekdays/weekends)
)

Contributing

This project is open-source. Interested users are therefore invited to test, comment or contribute to the tool. Submitting issues is the best way to get in touch with the development team, which will address your comment, question, or development request in the best possible way. We are also looking for contributors to the main code, willing to contibute to its capabilities, computational-efficiency, formulation, etc.

To contribute changes:

  1. Fork the project on GitHub

  2. Create a feature branch (e.g. named “add-this-new-feature”) to work on in your fork

  3. Add your name to the AUTHORS file

  4. Commit your changes to the feature branch

  5. Push the branch to GitHub

  6. On GitHub, create a new pull request from the feature branch

When committing new changes, please also take care of checking code stability by means of the qualitativte testing functionality.

How to cite

Please cite the original Journal publication if you use RAMP in your research:

F. Lombardi, S. Balderrama, S. Quoilin, E. Colombo, Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model, Energy, 2019, https://doi.org/10.1016/j.energy.2019.04.097

More information

Take a look to RAMP Website!

License

Copyright 2019 RAMP, contributors listed in Authors

Licensed under the European Union Public Licence (EUPL), Version 1.2-or-later; you may not use this file except in compliance with the License.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Note

This project is under active development!