Modeling and Simulink of SYNERGISTIC DISTRIBUTED ADAPTIVE VOLTAGE RESTORAGE SECONDARY CONTROL
Implementation Plan:
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Step 1: Initially we design a DC Microgrid model.
Step 2: Then, we implement Type-II fuzzy logic with genetic algorithms to make communications by selecting appropriate trade-off.
Step 3: Next, we implement secondary control algorithms with capability to adapt fluctuations, to make sustaining dynamic changes in the system.
Step 4: Then, we integrate the Lithium-ion battery technology and develop a heuristic algorithm into the microgrid for a stable lifetime.
Step 5: Next, we stabilize the frequency using hierarchical distributed coordinated control in battery energy storage systems (BESS).
Step 6: Then, we implement improved SOC-based droop control to prevent the overuse.
Step 7: Finally we plot the performance metrics for the following:
7.1: Total operation cost ($\hour) vs Time of the day
7.2: Power (kW) vs Time of the day
7.3: Voltage Magnitude (p.u.) vs Time of the day
7.4: Absolute current magnitude (p.u.) vs Time of the day
7.5: Time (sec) vs. Power Available at PCC (Watts)
7.6: Time (sec) vs. Reactive Power (Var)
7.7: Time (sec) vs. Voltage at PCC (Volt)
7.8: Time (sec) vs. Fault Identification Rate (%)
7.9: Time (sec) vs. RMS line current (A)
7.10: Time (sec) vs. RMS line voltage (V)
Software Requirements:
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1. Development Tool: Matlab-R2020a/Simulink and above
2. Operating System: Windows-10 (64-bit)
Note: –
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1. We make a simulation based process only, not a real time process.
2. If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
3. Please note that this implementation plan does not include any further steps after it is put into implementation.
4. If the above plan satisfies your requirement, please confirm us soon.
We perform the EXISTING process based on the Reference 3 Title: Optimal control of source–load–storage energy in DC microgrid based on the virtual energy storage system
