Monte Carlo Q-Learning to Operate a Battery

I have a vision for using machine learning for optimal control of energy systems.  If a neural network can play a video game, hopefully it can understand how to operate a power plant.

In my previous role at ENGIE I built Mixed Integer Linear Programming models to optimize CHP plants.  Linear Programming is effective in optimizing CHP plants but it has limitations.

I’ll detail these limitations in future post – this post is about Reinforcement Learning (RL).  RL is a tool that can solve some of the limitations inherent in Linear Programming.

In this post I introduce the first stage of my own RL learning process. I’ve built a simple model to charge/discharge a battery using Monte Carlo Q-Learning. The script is available on GitHub.

I made use of two excellent blog posts to develop this.  Both of these posts give a good introduction to RL:

Features of the script
 

As I don’t have access to a battery system I’ve built a simple model within Python.  The battery model takes as inputs the state at time t, the action selected by the agent and returns a reward and the new state.  The reward is the cost/value of electricity charged/discharged.

def battery(state, action):  # the technical model
    # battery can choose to :
    #    discharge 10 MWh (action = 0)
    #    charge 10 MWh or (action = 1)
    #    do nothing (action = 2)

    charge = state[0]  # our charge level
    SP = state[1]  # the current settlement period
    action = action  # our selected action
    prices = getprices()
    price = prices[SP - 1]  # the price in this settlement period

    if action == 0:  # discharging
        new_charge = charge - 10
        new_charge = max(0, new_charge)  
        charge_delta = charge - new_charge
        reward = charge_delta * price
    if action == 1:  # charging
        new_charge = charge + 10
        new_charge = min(100, new_charge)
        charge_delta = charge - new_charge
        reward = charge_delta * price
    if action == 2:  # nothing
        charge_delta = 0
        reward = 0

    new_charge = charge - charge_delta
    new_SP = SP + 1
    state = (new_charge, new_SP)
    return state, reward, charge_delta

The price of electricity varies throughout the day.
The model is not fed this data explicitly – instead it learns through interaction with the environment.
 
One ‘episode’ is equal to one day (48 settlement periods).  The model runs through thousands of iterations of episodes and learns the value of taking a certain action in each state.  
 
Learning occurs by apportioning the reward for the entire episode to every state/action that occurred during that episode. While this method works, more advanced methods do this in better ways.
def updateQtable(av_table, av_count, returns):
    # updating our Q (aka action-value) table
    # ********
    for key in returns:
        av_table[key] = av_table[key] + (1 / av_count[key]) * (returns[key] - av_table[key])
    return av_table
The model uses an epsilon-greedy method for action selection.  Epsilon is decayed as the number of episodes increases.
Results
 
Figure 1 below shows the the optimal disptach for the battery model after training for 5,000 episodes.  
Figure 1 – Electricity prices [£/MWh] and the optimal battery dispatch profile [%]
I’m happy the model is learning well. Charging occurs during periods of low electricity prices. It is also fully draining the battery at the end of the day – which is logical behavior to maximise the reward per episode.  
 

Figure 2 below shows the learning progress of the model.

Figure 2 – Model learning progress
Next steps
 
Monte Carlo Q-learning is a good first start for RL. It’s helped me to start to understand some of the key concepts.
 
Next steps will be developing more advanced Q-learning methods using neural networks.

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