ECE Master Project - Scalable Network-Based RL Environment for High-Level EV Natural Disaster Response Simulation
In situations of natural disasters/phenomenons, blackouts can occur. To combat this, EV (electric vehicles) drivers can use their vehicles as batteries for customers/ clients. To simulate the difficulty of this challenge of getting to as many customers as possible until the blackout period is over, a RL environment was created. This environment features random scenarios based on node network size, amount of the houses (can't be larger than node size), blackout data, and housing models to simulate a blackout response in a high level matter.
Example of an random agent in action.
Load represents the power needed at the moment from the node.
Current Charge represents the power the agent (or car) has.
Values on edges represents the miles of the edge cost to go through.
Blue node represents buffers. Green node represents homes. Grey node represent blackout out node.
More details can be found in Action & State Space section.
Clone the project
git clone https://github.com/Natemen96/ECEMasterProject.gitGo to the project directory
cd ECEMasterProject/RL python main.pyWARNING: This is a large install (~2GB) and will take approximately 30 mins depending on internet speed. Depending on your set up, you may find it easier to install the package yourself. The list can be found in the conda_env folder. See 'conda_env/rl_window.yml' for the most up to date package list. Works best on Windows.
For easy installing, using a conda first make sure you have conda installed.
After conda is installed do the following to set up the environment
conda env create -f conda_env/rl_window.yml --name rl_env #or any name you prefer
conda activate rl_env #verify it work make sure to run the program within the conda env.
Housing Data can be download from pecan street/dataport
Start at "Home" (a node) travel to each location in exchange for power (main resource for EV), unload power in exchange for reward, travel back to home in exchange for power. The agent can restore power at "Home" and earn more reward depending on how much power it got back. The more power it restores for itself the more it's rewarded.
Once power is restored in a node with a power charging station, the agent can restore energy there as well. Some nodes will be empty depending on parameters.
graph_env: Randomly generated a fully connected graph n nodes
Args:
n (int, optional): [number of nodes]. Defaults to 5.
numofhome (int, optional): [number of homes for agent starting
point]. Defaults to 1.
numofblackout (int, optional): [number of houses with blackout].
Defaults to 1.
numofblackoutws (int, optional): [number of blackout houses with
solarpower]. Defaults to 0.
numofchargingstation (int, optional): [number of charging
station]. Defaults to 0.
max_actions (int, optional): [max number of action can be done
times 7. By default 3 (7) = 21]. Defaults to 3.
blackout_str (str, optional): [Reads Blackout data from npy
file. Blackout data from the 2021 Texas Power Crisis, a file for
each country]. Defaults to 'Brazoria'.
agents (list, optional): [Agent objects]. Defaults to [None].
nonsolarhouse_data_paths (list, optional): [Path for npy file
with nonsolar data info. Added to a node per path.]. Defaults to
[None].
solarhouse_data_paths (list, optional): [Path for npy file with
solarhouse data info. Added to a node per path.]. Defaults to
[None].
data_sample (int, optional): [how many data sample it take from
data in paths, samples taken evenly throughout data. By default
(0/6. 1/6 ... 6/6 0/6 ...) sample]. Defaults to 6.BasicAgent: Basic Agent that act as skeleton for other agent. Won't work by itself.
Args:
car ([dict]): [car information in the form of a python
dictionary. Required dict information: model, make, year,
avg_energy_consumption(kWh/mile), battery_capacity(kWh)]SmartQLAgent: Smart Agent that uses Qtable to make decisions
Args:
car ([dict]): [car information in the form of a python
dictionary. Required dict information: model, make, year,
avg_energy_consumption(kWh/mile), battery_capacity(kWh)]
sample_rate (int, optional): [decides how often a sample of the
qtable is taken. If left to default it's every 1/100 of total ep]
. Defaults to 100.
qtabletraining (bool, optional): [Flag for turning qtable
training on]. Defaults to True.
quiet (bool, optional): [Flag for turning data collection on].
Defaults to True.RandomDataAgent: Used for data collection of random agent. Doesn't
use Observation.
Args:
car ([dict]): [car information in the form of a python
dictionary. Required dict information: model, make, year,
avg_energy_consumption(kWh/mile), battery_capacity(kWh)]Reward given for unloading, recharging. Diversity of power restoration will result in more reward. Reward lost for running out of energy. Episode is over when EV is out of energy or network is completely powered.
Each Agent will have 24 actions per ep by default.
e+2 Actions:
e - Move (at cost) to Node if possible
1 - Unload (at cost)
1 - Recharge (no cost, expect an action)
2*n + 6*n + 300*n + c States:
n - Statuses of Each Node
Key - Assignment
0 - Buffer (Blue)
1 - Blackout (Darkgrey)
2 - Blackout with Solar (Lightgrey)
3 - Powered (Blue)
4 - Charging Station (Yellow)
5 - Home (Green)
n - Cost of each edge
n - Is the path reachable or not?
0 - No
1 - Yes
c - Charge State (400 possible values by default)
where n is the number of nodes states, e is the number of edges, and c number of is Charge states Each nodes was designed to be based on a house dataset. Some houses that are solar powered will need less power from EVs compared to non-solar powered homes. The cost of house node can be derived from the housing data.
Percentage of blackout based on texas blackout data, as well as change per day.
At a minimum a Q-Learning was used to find the best solutions.
Can be scaled to multiple EV (MARL would be needed), & a larger network. MARL has not been fully tested and is likely very buggy.
During a test run of 50k Eps the smart agent that was support with Qtables had 263.13% improvement compared to a random agent (that does random actions).
scripts/Data/TexasBlackout.csv
-csv file of data collected from https://poweroutage.us/area/state/texas during the 2021 Texas Power Crisis with the following
- Metrics: ID,County,Customers Tracked,Customers Out,Outage %,DateTime
ECEMasterProject/RL/Data/
Avg_House_0/
- npy files of average daily power per every 15 mins of various houses provide by Dataport
Avg_House_1/
- npy files of next day regression of Avg_House_0 per house
Avg_House_n/
- npy files of concatenation of Avg_House_0 adn Avg_House_1 per houses
County/
- npy files of Texas County that were effect the most during the 2021 Texas Power Crisis
RL/agent/Sim_Data
- csv file result from various RL training, only stored reward.
RL/agent/agent_jsons/
-various json file of EV information
-Metrics: model, make, year, avg_energy_consumption(kWh/mile), battery_capacity(kWh), source
models/
KM/
-Classifier models trained using Time Series K Means stored in h5
LSTM/
-Old LSTM regression model trained on one house stored in h5
LSTM_nonsolar/
-LSTM regression model trained on non solar data stored in h5
LSTM_solar/
-LSTM regression model trained on solar data stored in h5
Qtables/
-Qtables from RL training stored in h5
notebooks/
analysis_c0.ipynb
-Analysis of classifier and regression model on solar houses data
analysis_c1.ipynb
-Analysis of classifier and regression model on nonsolar houses data
cluster.ipynb
-Creation and Analysis of classifier that can distinguish between solar and nonsolar data
compare_qtable_analyis.ipynb
-Comparing Qtable Smart Agent with Random Agent Baseline
D_LSTM_HousingData.ipynb
-Creation and analysis of regression on avg housing data where the original data sample occurs every 15 mins -> 98 sample per day
Dp1_analysis.ipynb
-Creation and analysis of regression on one house
EveryNyHouseAvg.ipynb
-Creation and analysis of regression on all house samples with model trained on one house (house 1)
LSTM_cluster0.ipynb
-Creation and analysis of regression on one house using most of the solar housing data
LSTM_cluster1.ipynb
-Creation and analysis of regression on one house using most of the nonsolar housing data
qtable_analyis_4_19.ipynb
-Analysis of Smart Agents performance
qtable_analyis_4_22.ipynb
-Analysis of Smart Agents performance
qtable_analyis.ipynb
-Analysis of Smart Agents performance
random_agent_analyis.ipynb
-Analysis of Random Agents performance
Texas_Blackout.ipynb
-Analysis of recorded Blackout DataSome edges can be blocked off and restored later as days pass (simulate for natural disasters)
Add testing
DRL and Classic Optimization techniques
Debug for MARL
Convert to OpenAI environment
Learned more about making DL/ML models.
Learned more about data analysis from exploring data and results from DL/ML training.
Learned about networkx and it's capabilities for graph theory related for projects.
Learned more about making reinforcement learning environments and agents.
Learned more about data scraping as I needed to scrap blackout data during the 2021 Texas Power Crisis
Learned how to make a gif using images in python.
Thank you to Professor Qin of my alma mater, Purdue University for his amazing support and sharing his expertise for this project.
Thank you to DataPort and Pecan Street for providing housing data to build models with.
Thank you to Matt Leacock the creator of the board game, Pandemic for inspiring this Reinforcement Learning Environment.
