To simulate a Multi Microgrid system within MATLAB that has includes designing the individual microgrids, its control systems, power management strategies, and the interactions among several microgrids. We can be utilized MATLAB or Simulink along with more toolboxes like Simscape Power Systems to design electrical modules, control systems, and communication amongst the grids.
We will instruct you through the step-by-step guide to simulate Multi Microgrid projects using MATLAB:
Steps to Simulate Multi Microgrid system in MATLAB
Step 1: Install Required Toolboxes
Make sure that we have the essential toolboxes are installed on the machine in MATLAB:
- Simulink
- Simscape Power Systems (for designing electrical components)
- MATLAB Control System Toolbox (for control strategies)
- MATLAB Optimization Toolbox (optional, for enhancing power flow or economic dispatch)
Step 2: Model Individual Microgrids
Each microgrid involves:
- Power Generation: It would contain renewable energy sources (solar PV, wind turbines), energy storage systems (batteries), or conventional generators (diesel, gas).
- Load Demand: Distinct kinds of loads such as residential, commercial, or industrial can be designed.
- Control System: For voltage regulation, frequency control, and power sharing, we can execute the controllers.
Below is a simple configuration for a solar PV system along with a battery and a local load:
- Solar PV Array: We can utilize the Solar Array block from Simscape Power Systems.
- Battery: To store excess energy by using the Battery block.
- Load: Utilize a resistive or inductive load from the Simulink library.
- Inverter and Converter: We need to use power electronics converters for interfacing amongst the DC and AC portions of the system.
- Controller: Execute a basic Proportional-Integral (PI) controller for the inverter to control voltage and frequency.
Example MATLAB/Simulink components for a single microgrid:
% Solar PV Array
pv_array = ‘powerlib/Sources/Solar Array’;
% Battery Storage
battery = ‘powerlib/Energy Storage/Battery’;
% Load
load = ‘powerlib/Elements/Resistive Load’;
% Inverter
inverter = ‘powerlib/Power Electronics/Voltage Source Inverter’;
Step 3: Develop Control Strategies for Microgrids
We can execute the control strategies like:
- Primary control: Controls local voltage and frequency (Droop control).
- Secondary control: Make sure power balance among the microgrids.
- Tertiary control: Handles an energy trading amongst microgrids or the utility grid such as economic dispatch.
Use PI controllers or advanced control techniques like Model Predictive Control (MPC) in Simulink, we can execute these controllers.
Example of a droop control implementation for frequency regulation:
P_ref = 1; % Reference active power
Q_ref = 0; % Reference reactive power
V_ref = 1; % Reference voltage
f_nom = 50; % Nominal frequency
% Droop control equations
P = measureActivePower(); % Measured active power
Q = measureReactivePower(); % Measured reactive power
delta_f = k_p * (P_ref – P); % Frequency deviation
delta_V = k_q * (Q_ref – Q); % Voltage deviation
% Adjust output based on droop characteristics
frequency = f_nom + delta_f;
voltage = V_ref + delta_V;
Step 4: Create Multiple Microgrids
After designing one microgrid then we can simulate the system making several microgrids. Every single microgrid can have diverse generation sources, loads, and control strategies. We can associate them through the power lines or AC/DC buses.
- AC Microgrid Interconnection: We can utilize transformers, transmission lines, and breakers to associate numerous microgrids.
- DC Microgrid Interconnection: We need to use DC-DC converters to attach DC microgrids.
Step 5: Power Flow Management and Energy Trading
For multi-microgrid systems, energy sharing or power trading among the microgrids can be handled utilizing the methods such as:
- Decentralized Control: Every single microgrid functions independently however distribute the energy according to the predefined rules.
- Centralized Control: A central controller handles power flows and economic dispatch among the microgrids.
- Peer-to-Peer (P2P) Energy Trading: Execute the algorithms for energy trading rely on market prices demand, and supply.
Example: Power flow amongst two microgrids with droop control:
% Power flow calculation between Microgrid 1 and Microgrid 2
P_mg1 = measureActivePower(microgrid1);
P_mg2 = measureActivePower(microgrid2);
power_flow = P_mg1 – P_mg2; % Positive means flow from Microgrid 1 to 2
Step 6: Simulate Scenarios
We can replicate diverse situations like:
- Islanded Operation: Each microgrid functions independently without being associated to the key grid.
- Grid-Connected Operation: Microgrids can swap the power including the key utility grid.
- Fault Conditions: Mimic the faults such as short circuits or grid disturbances to experiment the reliability and control strategies of the microgrids.
Example situation of a fault within a microgrid:
% Simulate a fault at t = 1 second
fault = ‘powerlib/Elements/Three-Phase Fault’;
set_param(fault, ‘FaultTime’, ‘1’);
Step 7: Monitor and Analyze the System
Observe crucial parameters like:
- Voltage and frequency stability within each microgrid.
- Power exchange amongst microgrids.
- Battery state of charge (if energy storage is utilized).
- Economic dispatch (optimal power distribution between microgrids).
We need to utilize the scopes, data logging, or Simulink’s visualization tools to monitor system behavior.
Example code for observing power flow:
% Scope for power flow monitoring
scope = ‘powerlib/Elements/Scope’;
Step 8: Optimize the Multi-Microgrid System (Optional)
To enhance the power flow and economic dispatch among the microgrids, we can utilize the optimization methods like:
- Linear programming or Mixed-Integer Linear Programming (MILP) for answering the economic dispatch issue.
- Genetic algorithms or Particle Swarm Optimization for reducing the power losses or increasing renewable energy utilization.
Below is an instance of an optimization problem for power dispatch:
% Optimization problem: Minimize generation cost
objective = @(P) gen_cost(P); % Generation cost function
P_opt = fmincon(objective, P_init, A, b, Aeq, beq, lb, ub); % Optimization function
Step 9: Run the Simulation
When the design is configured then we set up the simulation metrics like simulation time, solver settings, and step sizes. Then execute the simulation to monitor the system’s behavior.
Instance of configuring the simulation parameters:
sim_time = 10; % Simulation time in seconds
set_param(‘model’, ‘StopTime’, num2str(sim_time));
sim(‘model’);
Step 10: Visualize and Analyze Results
At the end, envision the outcomes of the simulation by using the MATLAB plots or Simulink scopes. We can monitor power flows, voltage levels, frequency stability, and other performance parameters.
Example of plotting outcomes:
% Plot voltage and frequency of microgrid
time = out.tout;
voltage = out.yout{1}.Values.Data;
frequency = out.yout{2}.Values.Data;
figure;
subplot(2,1,1);
plot(time, voltage);
title(‘Voltage in Microgrid’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
subplot(2,1,2);
plot(time, frequency);
title(‘Frequency in Microgrid’);
xlabel(‘Time (s)’);
ylabel(‘Frequency (Hz)’);
By following these procedures, you can simulate a multi-microgrid system using MATLAB, which encompassing power generation, control, and energy trading among the grids. You can further modify the system by inserting more complexity, integrating renewable energy sources, and then executing the advanced optimization methods. phdprime.com is your ultimate destination for completing your research work. Reach out to us for all your simulation requirements.
