Source code for the paper
Joint Control of Manufacturing and Onsite Microgrid System via Novel Neural-Network Integrated Reinforcement Learning Algorithms
by Yang, J., Sun, Z., Hu, W. and Steimeister, L.
Accepted at Applied Energy.
The run files are
- experiments_comparison.py
compares the efficiency of optimal solution selected by reinforcement learning, by mixed-integer programming routine strategy and by benchmark random policy.
- mip_plot.ipynb, plot_average_experiments.ipynb
plot the comparison of total energy cost and total production throughput in units for the optimal policy and mixed-integer programming policy; also plot the average over 3 times of these experiments.
The main files are
- microgrid_manufacturing_system.py
simulates the joint operation of microgrid and manufacturing system.
- reinforcement_learning.py
reinforcement learning via two layer fully connected neural network.
- Simple_Manufacturing_System-Pure_Q-Learning.py, 1st_on.npy, 2nd_on.npy, both_off.npy, both_on.npy
learn the microgrid-manufacturing system using pure Q-learning. This is to compare with our new method.
- Simple_Manufacturing_System_routine_strategy.py
learn the microgrid-manufacturing system using routine strategy via linear mixed-integer programming.
- mip-solver.xlsx
solving the mixed-integer programming total cumulative energy cost and total production units given the mixed-integer programming solution.
The auxiliary files are
- projectionSimplex.py
proximal operator to the simplex D^c={(x_1, x_2), 0\leq x_i\leq 1, x_1+x_2\leq 1}.
- SolarIrradiance.csv, WindSpeed.csv, rate_consumption_charge.csv
1 year data in 8640 hours (360 days * 24 hours) for solar irradiance, wind speed and rate of consumption charge.
- real-case parameters-experimental-use.xlsx
the scaled real-case parameters for the manufacturing system and the microgrid used in the experiment.