Hybrid Metaheuristic Secondary Distributed Control Technique for DC Microgrids
Abstract
:1. Introduction
- A secondary distributed control approach for a DC-MG, introducing a novel weighting coefficient that simultaneously eliminates bus voltage fluctuations and ensures equitable current allocation across multiple ESs.
- A novel hybrid algorithm combining PSO and GWO is introduced to enhance coefficient selection for the distributed control strategy in the microgrid. This advanced algorithm optimizes the control coefficient, thereby ensuring that the control objectives are effectively achieved. By leveraging the strengths of both PSO and GWO, the proposed method provides a robust solution for fine-tuning the control coefficient, which enhances the overall performance and reliability of the DC-MG.
- A state-space model of a DC-MG incorporating eigenvalue observation analysis is developed to assess the effects of the optimized secondary distributed control on the microgrid’s stability. This analysis provides valuable insights into the system’s stability dynamics, helping to understand how the control strategy influences overall system performance.
- A real-time testing setup is constructed using MATLAB/Simulink® and implemented on a Speedgoat™ real-time target machine to validate the practical performance of the proposed approach in real-world applications.
2. Mathematical Model DC Microgrid Systems
2.1. Model of Buck Converter
2.2. Buck Converter Primary Controls
3. Secondary Distributed Controls
3.1. Communication Graph
3.2. Control Objectives
3.3. Proposed Design for Secondary Control
4. Enhanced Tuning Technique for Secondary Distributed Control
4.1. Particle Swarm Optimization (PSO)
4.2. Grey Wolf Optimizer (GWO)
4.3. Hybrid PSO-GWO Algorithm
4.4. Implementation of Hybrid PSO-GWO for Distributed Secondary Control
Algorithm 1 HPSO-GWO Algorithm Implementation |
Run PSO to evaluate the fitness of all particles (47) Sort and index the fitness values of each particle. if = then stop else end if for current particle do if rand(0,1) < then assign values to a, d, and c ▹To avoid getting trapped in local minima else run PSO to evaluate the fitness of all particles end if Evaluate the fitness of all wolves if < then Compute new wolves position, (41) Substitute this position to PSO particles Run PSO else update the wolf position end if end for |
5. Results and Discussion
5.1. Stability Analysis
5.2. Control Objectives Realization
5.3. Validation through Real-Time Experimental Simulation
5.3.1. Current Allocation and Voltage Recovery Evaluation
5.3.2. Performance of Proposed Secondary Control during Varying Power Demand
5.3.3. Performance of Secondary Control during Communication Delay
5.4. Comparison with Alternative Secondary Distributed Control Methods
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
RESs | Renewable Energy Sources |
DC-MG | DC Microgrid |
MGs | Microgrids |
AC | Alternating Current |
ESs | Energy Sources |
MHO | Meta-Heuristic Optimization |
PSO | Particle Swarm Optimization |
GWO | Grey Wolf Optimization |
HPSO-GWO | Hybrid Particle Swarm Optimization-Grey Wolf Optimization |
S-S | State-Space |
CPL | Constant Power Load |
DC | Direct Current |
DERs | Distributed Energy Resources |
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Methods | Control Objective Realization | Robustness | Scalability | Communication Type | Implementation Complexity |
---|---|---|---|---|---|
[17,27,28] | good | moderate | moderate | reduced | complex |
[22,25] | good | moderate | low | all-to-all | simple |
[23,29] | better | low | low | all-to-all | simple |
[24] | good | moderate | moderate | reduced | simple |
[20] | good | moderate | moderate | reduced | complex |
[26] | good | low | high | none | complex |
Proposed Technique | excellent | high | moderate | reduced | complex |
Rated Bus Voltage | 48 V | |
Voltage Source | 100 V | |
Switching Frequency | 10 kHz | |
Converter Capacitance | C | 200 μF |
Converter Inductance | L | 10 mH |
Load | 3 , 5 , 5 | |
HPSO-GWO Algorithm Parameters | ||
No. of search agents | 30 | |
Inertia constant | w | 0.5 + rand()/2 |
Max. count of iterations | 500 | |
No. of design variables | 1 | |
Primary Controls | ||
Current Loop | 2.5, 5 | |
Voltage Loop | 0.248, 2 | |
Droop resistance | 1 |
−137.25 | − 198.70 + i347.11 | |
12.46 + i120.71 | −198.70 − i347.11 | |
12.46 − i120.71 | −6.16 | |
−12.76 | − 1.45 | |
−6.5 + i55.60 | −3.33 | |
−6.5 + i55.60 | −0.80 + i4.08 | |
−4.5 + i66.35 | −0.80 − i4.08 | |
−4.5 + i66.35 | −370.06 | |
−3.41 | −370.06 | |
−3.41 | − 0.80 + i4.08 | |
— | −0.80 − i4.08 | |
— | −3.35 | |
— | −3.35 |
Switching Frequency | 10 kHz | |
Sampling Frequency | 20 kHz | |
Voltage Source | 100 V | |
Nominal Bus Voltage | 48 V | |
Converter Inductance | L | 20 mH |
Converter Capacitance | C | 120 μF |
Resistive Load | 4 , 6 | |
Line Resistance | 0.3 , 0.4 , 0.6 , 0.7 | |
Constant Power Load | 300 W | |
Primary Controls | ||
Current Loop | 0.05, 148 | |
Voltage Loop | 0.259, 38 | |
Droop resistance | 1 | |
Secondary Controls | ||
Variation Coefficient for Voltage | 1.35 | |
Variation Coefficient for Current | 7.8 |
Control Objectives | Proposed | ||||||
---|---|---|---|---|---|---|---|
Voltage Recovery | 2 s | 1.3 s | 1.72 s | 3 s | 1 s | 1.1 s | ≤0.4 s |
Current Allocation | 2.8 s | 1.5 s | 2 s | 3.4 s | 1.6 s | 2.1 s | ≤1.4 s |
Robustness | low | high | moderate | low | high | moderate | very high |
Implementation Complexity | simple | complex | simple | simple | complex | complex | complex |
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Lasabi, O.; Swanson, A.; Jarvis, L.; Khan, M.; Aluko, A. Hybrid Metaheuristic Secondary Distributed Control Technique for DC Microgrids. Sustainability 2024, 16, 7750. https://doi.org/10.3390/su16177750
Lasabi O, Swanson A, Jarvis L, Khan M, Aluko A. Hybrid Metaheuristic Secondary Distributed Control Technique for DC Microgrids. Sustainability. 2024; 16(17):7750. https://doi.org/10.3390/su16177750
Chicago/Turabian StyleLasabi, Olanrewaju, Andrew Swanson, Leigh Jarvis, Mohamed Khan, and Anuoluwapo Aluko. 2024. "Hybrid Metaheuristic Secondary Distributed Control Technique for DC Microgrids" Sustainability 16, no. 17: 7750. https://doi.org/10.3390/su16177750