EV-Ecosim platform
+A grid-aware co-simulation platform for the design and optimization of electric vehicle charging stations. +Paper: https://doi.org/10.36227/techrxiv.23596725.v2
+
Requirements
+GiSMo SLAC GridLAB-D installation (master branch): https://github.com/arras-energy/gridlabd. +This GridLAB-D version is required for the python co-simulation functionality. Recommended use with AWS EC2 SLAC +GiSMo HiPAS GridLAB-D AMI (beauharnois-X).
+Folder descriptions
+ambient_data
+Hosts ambient temperature data for capturing the effects of environmental conditions on subsystems, such as battery, +transformers, charging stations.
+base_load_data
+Includes existing base case building/home load (usually uncontrollable) within the distribution grid. This work uses +proprietary Pecan Street Data. Below is an exmaple data prototype for the base load data. Note that column fields are +case-sensitive. The data used in the original paper has a minute resolution, as is the power system simulation. ``
+
batt_sys_identification
+Battery system identification module. Hosts the class for generating battery system identification parameters
+from experimental data. This module leverages a genetic algorithm to optimize the battery model parameters.
+The battery model is a 2nd order RC Equivalent circuit model (ECM). One can this module to generate custom NMC
+battery parameters by uploading experimental data to the batt_sys_identification/data folder and running the module.
+The module will generate a .csv file with the battery parameters in the batt_sys_identification/params folder.
+The data prototype is shown below. Note that column fields are case-sensitive.
The module will save a new .csv file with an additional field for the corrected open circuit voltage (OCV) values;
+this field (column) will be labelled ocv_corr within the new battery data csv, including the existing columns as shown
+in the data prototype above.
Once the battery parameters are generated, they can be used in the battery_data folder and configs/battery.json can
+be modified so the model runs using the new custom parameters.
charging_sim
+This contains the configs folder which includes the configuration files for all the relevant modules.Hosts the implementation of the physical modules, including:
battery.py - Battery cell module.
+batterypack.py - Battery pack module.
+batteryAgingSim.py - Battery aging module.
+controller.py - Controller module.
+chargingStation.py - Charging station module.
+electricityPrices.py - Electricity prices module.
+optimization.py - Optimization module.
+orchestrator.py - Simulation orchestrator module.
+solar.py - Solar PV module.
+utils.py - Hosts utility functions used by some modules.
+simulate.py - Offline DER control optimization for cost minimization (this is run for offline mode (no state feedback)).
+DLMODELS
+This includes legacy load forecasts models developed (not needed).
+elec_rates
+Includes .csv files for electricity Time-of-use (TOU) rates. The input data prototype for electricity rates +is shown below. User must upload a normal full-year sized data (for 365 days) to avoid +any errors.
+
The data required must be in the format shown above. The electricityPrices.py module will read the data and sample
+prices during optimization and simulation. The data should be one full year of TOU rate prices at 15 minute resolution.
+The electricityPrices.py module can also help with downscaling the data to 15 minute resolution if the data is at a
+much coarser resolution. The module will save the downscaled data in the elec_rates folder.
feeders
+Library of IEEE test feeders and PNNL taxonomy feeders for distribution systems in the GridLAB-D .glm format.
+IEEE feeders have spot loads specified at primary distribution level. PNNL taxonomy feeders have spot loads specified at
+primary or secondary distribution level.
feeder_population
+Scripts for populating base feeder models with time-varying loads and resources using the load data in base_load_data.
+feeder_population.py generates the necessary files for a co-simulation run based on the parameters specified in
+feeder_population/config.txt. This module uses the
+Requires residential load data not included in repo (limited access).
solar_data
+Includes solar irradiance data for capturing the effects of environmental conditions on overall system cost. Default +data for solar irradiance is from the National Solar Radiation Database (NSRDB) for the San Francisco Bay Area. +The data prototype is from the National Renewable Energy Laboratory (NREL) and is shown below. Note that column fields +are case-sensitive.
+
Month labels are indexed from 1 to 12, inclusive; 1 - January, 12 - December. The original data is in hourly resolution. +The EV-Ecosim data prototype is in 15 minute intervals by default, with irradiance oversampled 4 times from hourly +dataset. The GHI represents the “Global Horizontal Irradiance” in W/m^2, which is the total amount of shortwave radiation +received from above by a surface horizontal to the ground.
+test_cases
+Co-simulation cases.
+base_case- Reads voltage from GridLAB-D and writes power injections at each timestep (no EV charging or DER).
rlsf - base_case plus implements a recursive least squares filter to estimate network model online (not used)
battery - base_case plus transformer thermal model plus DER integration (included battery and solar).
transformer - base_case plus simulation of transformer thermal model for each transformer in GridLAB-D model (not used).
analysis
+Scripts for plotting and analysis of co-simulation results. Includes post optimization and simulation cost +calculation modules and voltage impacts on the distribution grid.
+plot_results.py - This module is used post-simulation to parse the voltages from the power-simulation to calculate the percentage
+voltage violations per ANSI C84.1. The file also generates voltage distribution plots. A user can modify the
+SIMULATION_FOLDER variable which is the string of the path where the powerflow simulation output voltages at each node
+exist.
load_post_opt_costs.py - This module is calculates the levelized cost of energy and populates into tables/cost matrices, which are saved in the
+respective files and folders. The module also generates plots for the cost analysis.
cost_analysis.py - This module contains the CostEstimator class, which estimates the cost of the different grid and DER components
+from the simulation.
How to run
+Create a new environment using conda env create --name <your env name> -f environment.ymlOR
+install packages listed in the environment manually (RECOMMENDED)
+Ensure gridlabd is installed by following recommended installation method.
For battery test case:
+-
+
Navigate to
test_cases/battery/feeder_populationand runfeeder_population.py. This uses the +test_cases/battery/feeder_population/config.txtsettings to prepare the power system and populate the secondary +distribution network with time-varying base loads, EV charging stations, with the required transformers.
+Once confirmed that
feeder_population.pyhas run successfully and generates the requiredIEEE123_secondary.glmand +IEEE123_populated.glmfiles, you are done with the initial pre-simulation run prep.
+Now navigate one level of out
/feeder_populationand run scenarios.py usingpython3 scenarios.py
+
For base case:
+-
+
Navigate to
EV50_cosimulation/feeder_populationand runfeeder_population.py. This uses the +./feeder_population/config.txtsettings to prepare the power system and populate the secondary distribution network
with time-varying base loads
+Navigate to
test_cases/base_caseand run master_sim.py usingpython3 master_sim.py
+
Post-simulation analysis
+-
+
This is done with the modules in the
analysisfolder. Please see theanalysisfolder section for more details.
+
)
+
+
+
+[//]: # (
)
+
+## Authors
+Emmanuel Balogun: ebalogun@stanford.edu, Lily Buechler: ebuech@stanford.edu
+
+
+## Requirements
+
+GiSMo SLAC GridLAB-D installation (master branch): https://github.com/arras-energy/gridlabd.
+This GridLAB-D version is required for the python co-simulation functionality. Recommended use with AWS EC2 SLAC
+GiSMo HiPAS GridLAB-D AMI (beauharnois-X).
+
+## Folder descriptions
+
+### ambient_data
+
+Hosts ambient temperature data for capturing the effects of environmental conditions on subsystems, such as battery,
+transformers, charging stations.
+
+
+### base_load_data
+
+Includes existing base case building/home load (usually uncontrollable) within the distribution grid. This work uses
+proprietary Pecan Street Data. Below is an exmaple data prototype for the base load data. Note that column fields are
+case-sensitive. The data used in the original paper has a minute resolution, as is the power system simulation. ``
+
+