How to implement Reinforcement Learning (RL) for Microgrid Energy Management with EV Charging & Solar PV in Simulink?

Microgrid energy management involves balancing fluctuating solar photovoltaic (PV) generation, dynamic electricity tariffs, and unpredictable Electric Vehicle (EV) charging demands. Implementing Reinforcement Learning (RL) with MATLAB Reinforcement Learning Toolbox allows an autonomous agent to optimize power dispatch between battery storage, solar generation, and grid interaction.

Step 1: Define State Space & Action Space

Construct a MATLAB script defining the environment state variables and agent control actions:

• State Vector (S): [Solar PV Generation, EV Station Load, Battery State of Charge (SOC), Grid Electricity Price, Hour of Day]
• Action Vector (A): [Battery Charge/Discharge Power (-P_max to +P_max), Grid Power Import/Export]

Step 2: Create DDPG Agent in MATLAB

Deep Deterministic Policy Gradient (DDPG) is effective for continuous action spaces in microgrid power dispatch.

% Observation and Action specifications
obsInfo = rlNumericSpec([5 1], 'LowerBound', -inf, 'UpperBound', inf);
actInfo = rlNumericSpec([1 1], 'LowerBound', -50, 'UpperBound', 50); % kW power

% Create DDPG Agent Networks
actorNet = buildActorNetwork(obsInfo, actInfo);
criticNet = buildCriticNetwork(obsInfo, actInfo);

actor = rlContinuousDeterministicActor(actorNet, obsInfo, actInfo);
critic = rlQValueFunction(criticNet, obsInfo, actInfo);

agentOpts = rlDDPGAgentOptions(...
    'SampleTime', 300, ... % 5-minute dispatch intervals
    'DiscountFactor', 0.99, ...
    'ExperienceBufferLength', 1e6);

agent = rlDDPGAgent(actor, critic, agentOpts);

Step 3: Define Reward Function

Formulate a reward function that penalizes grid electricity costs and battery degradation while rewarding renewable solar utilization:

Reward = - (Cost_Grid * P_grid) - (Cost_Degradation * |P_battery|) + Penalty_Unmet_EV_Load

Step 4: Connect to Simulink Environment

Link the RL Agent block to your Simscape Electrical microgrid model containing the solar PV array, battery energy storage system (BESS), and EV fast-charging stations. Train the agent using train(agent, env, trainOpts) across 1,000 simulated days.