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https://github.com/nrel-sienna/psi-cambodia


https://github.com/nrel-sienna/psi-cambodia

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README 

        
# PSI-Cambodia



This repository contains an example of how to execute a power system scheduling simulation in [Sienna](https://github.com/NREL-Sienna/) using the [PowerSimulations.j](https://github.com/NREL-Sienna/PowerSimulations.jl) package, expanding on the data assembled for the [PowNet](https://github.com/kamal0013/PowNet) application for the power

grid in Cambodia. [![DOI](https://zenodo.org/badge/278169749.svg)](https://zenodo.org/badge/latestdoi/278169749)



![](https://github.com/kamal0013/PowNet/blob/master/fig2_Cambodia_grid.jpg)



This simulation is based on three open-source data and modeling tools used to model power systems with renewable resources such as wind and solar:



1. The [RE-Data Explorer](re-explorer.org/) can be used to investigate where to site wind and solar plants and to download wind and solar time-series resource files for selected latitude & longitudes where the plants will be located.



    - This repository already contains resource data files in the *REDE_resource_data/Input/* folder from the RE-Data Explorer for 5 hypothetical wind and solar plants in Cambodia.



2. The [System Advisor Model (SAM)](https://sam.nrel.gov/) can be used to model the power output (MW) from a wind or solar power plant, using the hourly or subhourly resource data from the RE-Data Explorer as inputs.



    - There are multiple ways for users to access SAM, including but not limited to:

        - A [downloadable GUI](https://sam.nrel.gov/download.html), which can be used for manual analysis of individual locations, i.e., power plants

        - A Python interface called [PySAM](https://nrel-pysam.readthedocs.io/en/main/index.html), which can be used to programmatically process multiple locations and/or plant configurations

        - The [Renewable Energy Potential (reV) tool](https://github.com/NREL/reV), which can be used for very large scenario analysis and batch runs

        

    - In the *REDE_resource_data* folder, there is a Python script that uses PySAM to process multiple solar and wind time-series resource files downloaded from the RE-Data Explorer for locations in Cambodia. This notebook is based on a PySAM example notebook, available [here](https://github.com/NREL/pysam/blob/main/Examples/PySAMWorkshop.ipynb). 

    

    - This processing has already been complete for the 5 example plants, and the outputs are available in the *REDE_resource_data/Output/* folder. However, if users would like to add other wind and solar locations and process those, here are the required steps:

        1. Add wind or solar resource files from the RE-Data Explorer to the *REDE_resource_data/Input/* folders

        

        2. Update the *REDE_resource_data/RE_plant_config.csv* file, which is a manually generated .csv file to make it easier to load the same plant metadata into both PySAM (to generate the plant-specific power profiles) and Sienna\Data (to attach the hypothetical solar and wind plants to the existing PowerSystem.jl model). It contains the following 4 pointers:

            - type (used by Sienna\Data): either "Solar_PV" or "Wind_WT" 

            - node (used by Sienna\Data): Acronym of the node in the PowNet system that the plant should connect to (for example, the closest node based on the latitude/longitude of the plant). 

            - resource_file (used by PySAM): Resource file (from RE-Data Explorer or the [National Solar Radiation Database (NSRDB)](https://nsrdb.nrel.gov/)) 

            - specification_file (used by PySAM): Plant configuration file, [exported from the SAM GUI](https://nrel-pysam.readthedocs.io/en/latest/inputs-from-sam.html). Example wind and solar plant specification files are already included in the *REDE_resource_data/Input* folders for the following configurations:

                 * Solar: (1) A 100 MWdc fixed (12 degree ~= Cambodia's latitute) tilt system or (2) a 100 MWdc one-axis tracking plant

                 * Wind: Models plant with Gamesa G114 2.0 MW turbines, which is selected to emulate the 'T200' representative turbine in the RE-Data Explorer's wind technical potential study (more info Table 12, page 23 [here](https://www.nrel.gov/docs/fy17osti/66861.pdf)); A review of Cambodia's mean wind speeds in the RE-Data Explorer indicates that the T200 or perhaps T237 representative turbines are a good choice for Cambodia.

                 

        3. Then, run the `REDE_timeseries_prep.py` script by navigating to the *REDE_resource_data* folder in a terminal with [conda](https://docs.conda.io/projects/continuumio-conda/en/latest/user-guide/install/index.html) installed, and running:

            ```

                conda env create --name pysam_env --file=environment.yml

                conda activate pysam_env

                python REDE_timeseries_prep.py

            ```

            This will install PySAM and run process the plant data. (Note: There are two ways to install PySAM: `pip` and `conda`. If you decide to install PySAM on your own rather than using the `environment.yml` file, it is recommended to use `pip` due to delays updating the `conda` version of PySAM: `pip install nrel-pysam`.)



3. Finally, [Sienna](https://github.com/NREL-Sienna/) can be used to run simulations of the Cambodia system, including the new renewable resources. 



This example includes two pieces of code:

 - Data preparation using Sienna's [PowerSystems.jl](https://github.com/nrel-sienna/PowerSystems.jl) package and

 construction of a `System`. This includes pulling in the wind and solar power profiles. 

 - Example `Simulation` of a day-ahead unit-commitment scheduling sequence using Sienna's [PowerSimulations.j](https://github.com/NREL-Sienna/PowerSimulations.jl) package.



Examples are provided in `.jl` script format. To autogenerate ipython notebooks, execute

the following commands from a Julia REPL:



```julia

] activate . # Activates the required environment

include("literate.jl")

```



From there, users can walk step-by-step through the generated example, PSI-Cambodia.ipynb, using Jupyter Notebooks.