Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review
Abstract
:1. Introduction
2. Key Challenges in Operation and Control of DC–AC Converters
2.1. Main Challenges in Grid-Following Converters
2.2. Main Challenges in Grid-Forming Converters
3. Overview of Control Optimization
3.1. Minimizing a Function: Global Minimum and Local Minimum
3.1.1. Local Optimization
- Converges to a local minimum or maximum, depending on the problem.
- Performs well for convex functions where the local minimum is also the global minimum.
- Exhibits fast convergence but may become trapped in suboptimal solutions if multiple minima exist.
3.1.2. Global Optimization
- Broadly explore the solution space to avoid becoming trapped in local minima.
- Typically require higher computational effort.
- Suitable for nonlinear, non-convex, or highly constrained problems.
3.1.3. Key Differences Between Local and Global Optimization
3.2. Optimal Controllers
3.3. Optimized Controllers
4. Literature Review on Optimization Techniques for DC–AC Converters
4.1. Most Common Techniques for Low-Level Control of DC–AC Converters
4.1.1. Model-Based Optimization
Model Predictive Control
- is the control input and is the system output;
- is the reference signal;
- Q and R are weighting matrices;
- N is the prediction horizon;
- x is the system state, and , and define the system dynamics.
Linear Matrix Inequalities
- is the decision variable vector;
- , …, are symmetric matrices of appropriate dimensions;
- denotes negative semi-definiteness, ensuring is negative semi-definite.
- Stability analysis: using the Lyapunov function approach, system stability can be formulated as an LMI feasibility problem;
- Robust control: designing controllers that guarantee stability and performance under uncertainties, e.g., and control;
- State-feedback and observer design: finding gain matrices that satisfy performance and stability constraints.
Adaptive Control
- is the calculated parameter vector;
- is the internal signals vector;
- r is the reference signal;
- is the adaptation rate vector;
- is an auxiliary vector;
- is the reference model output;
- y is the system output.
4.1.2. Heuristics and Metaheuristics
Genetic Algorithms
Particle Swarm Optimization
- is the velocity of particle i at time step t;
- w is the inertia weight, controlling the influence of the previous velocity;
- and are acceleration coefficients that balance exploration (searching new regions of the space) and exploitation (refining known good solutions);
- and are random numbers sampled from a uniform distribution in ;
- is the personal best position of particle i, representing the best solution found by that particle;
- is the global best position found by the entire swarm.
Simulated Annealing
- If , the new solution is always accepted.
- If , the solution is accepted with probability , allowing occasional uphill moves to escape local minima.
Hybrid GA–PSO and Evolutionary Algorithms
4.1.3. Data-Driven and AI-Based Optimization
Reinforcement Learning
- is the policy that maps states to actions;
- is the reward at time step t;
- is the discount factor;
- T is the time horizon.
Deep Learning (DL)
- is the desired output;
- , represents the input data;
- is the neural network output;
- N is the number of training samples.
4.2. Stability Analysis of Optimization-Based Control Techniques
4.3. Comparison of Key Optimization Techniques and Summary
5. Future Directions and Emerging Trends
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Challenge | GFM Converters | GFL Converters |
---|---|---|
Synchronization | Self-regulated; does not rely on an external reference | Requires external grid voltage for synchronization |
Voltage and frequency regulation | Must generate and stabilize voltage and frequency | Follows grid voltage and frequency |
Control complexity | Higher due to frequency control, droop control, virtual inertia, and power-sharing algorithms | Lower, as the reference is externally defined |
Transition handling | Must ensure smooth transitions between grid-connected and islanded operation | Typically remains grid-connected; islanding can cause instability |
Inertia and stability | Requires synthetic inertia and damping techniques to compensate for low system inertia | Contributes to inertia issues in weak grids but does not directly compensate |
Load variation response | Directly responsible for stabilizing sudden load changes | Reacts to changes but relies on the grid for stability |
Multi-inverter coordination | Requires precise coordination to avoid circulating currents and phase mismatches | Less critical since synchronization is externally dictated |
Standards | Description | Comments |
---|---|---|
IEEE 1547 | Provides criteria and requirements for the interconnection of distributed energy resources with electric power systems. | Establishes uniform standards for performance, operation, testing, safety, and maintenance of interconnections. Ensures reliable integration of DERs into the grid. |
IEEE 519 | Defines recommended practices and requirements for harmonic control in power systems. | Specifies acceptable levels of voltage and current harmonics to maintain power quality and mitigate interference issues in microgrids. |
UL 1741 | Standard for inverters, converters, controllers, and interconnection system equipment for distributed energy resources. | Ensures compliance with safety and performance requirements for grid-tied inverters and converters. Often used in conjunction with IEEE 1547. |
IEC 61850 | Standard for communication networks and systems in substations. | Defines protocols and data models for substation automation, supporting seamless communication between protection and control devices in microgrids. |
EN 50160 | Specifies voltage characteristics of electricity supplied by public distribution networks in Europe. | Ensures consistency in voltage levels, waveform quality, and power frequency across different grid conditions. |
EN 50549-1 | Defines requirements for the connection of generating plants to distribution networks. | Focuses on grid connection of renewable energy systems up to and including Type B generation, ensuring compliance with grid stability criteria. |
NPR 9090 | Dutch practice guideline for low-voltage DC installations. | Provides design and safety considerations for DC microgrids, emphasizing proper isolation and protection mechanisms. |
Aspect | Local Optimization | Global Optimization |
---|---|---|
Search Scope | Limited to a local region | Searches the entire solution space |
Convergence Speed | Fast for convex functions | Typically slower due to broader search |
Risk of Becoming Stuck | High (in local minima) | Lower (designed to escape local minima) |
Computational complexity | Lower | Higher |
Suitable Problems | Convex and smooth functions | Nonlinear, non-convex, and complex problems |
Example Methods | Gradient Descent, Newton’s Method | Genetic Algorithms, Particle Swarm, Reinforcement Learning |
Optimization Technique | Key Characteristics | Advantages | Limitations | Ref. | Comments |
---|---|---|---|---|---|
Model Predictive Control | Utilizes system models to predict future states and optimize control actions | Ensures optimal performance | Requires highly accurate models; stability, design, and implementation are challenging | [100,101] | Effective for systems with well-defined dynamics; may perform poorly when unmodeled dynamics are significant |
Linear Matrix Inequalities | Employs convex optimization techniques for controller design | Provides robust control solutions with low computational burden; may achieve optimal performance | Limited to linear or linearized systems; may be conservative | [102,103] | Suitable for systems where linear approximations are valid |
Adaptive control | May require a reference model (if direct-type); operates by estimating or measuring system states; can be designed with simplified reduced-order models | Robust to matched and unmatched dynamics; delivers satisfactory performance (may not be optimal); can stabilize unstable systems | Challenging to design initial gains; stability, design, and implementation are nontrivial | [104,106] | Suitable for various systems; non-minimum phase zeros and frequency-rich references may pose challenges |
Genetic Algorithm | Mimics natural selection to find optimal solutions | Does not require explicit models; flexible and simple to implement | May converge to local optima; computationally demanding; usually implemented offline | [107,108] | Useful for complex optimization problems with large search spaces; easily combinable with other metaheuristics |
Particle Swarm Optimization | Simulates the social behavior of swarms to explore optimal solutions | Simple implementation; efficient search capabilities; faster convergence compared to similar metaheuristics | May suffer from premature convergence; requires parameter tuning; computationally demanding; usually implemented offline | [110,111] | Effective for optimizing microgrid operations with multiple renewable sources; easily combinable with other metaheuristics |
Reinforcement Learning | Learns optimal control policies through interaction with the environment | Handles complex, nonlinear dynamics; adaptable and potentially highly robust to uncertainties | Requires extensive, high-quality training data; usually implemented offline | [117,118] | Promising for systems with high uncertainty and variability; computationally intensive (especially for training) |
Neural network-based control | Utilizes neural networks to model and control system behavior | Capable of approximating complex functions; adaptable and potentially highly robust to uncertainties | Prone to overfitting with deep architectures; requires extensive high-quality data; usually implemented offline | [119,120] | Promising for systems where traditional modeling is challenging; needs substantial computational resources (especially for training) |
Optimization Technique | Boundary Conditions | Challenges |
---|---|---|
Model Predictive Control | Requires an accurate system model; works best with systems that have well-defined state-space representations | Computationally intensive for high-dimensional systems; stability and feasibility depend on tuning and constraints; may struggle with harsh unmodeled dynamics |
Linear Matrix Inequalities | Limited to convex optimization problems; most effective for linear or linearized systems | May be conservative; not suitable for highly nonlinear systems; require accurate system characterization |
Adaptive control | Can handle system uncertainties; typically assumes system dynamics are slowly time-varying or, at least, much slower than the adaptation rate | Requires appropriate parameter initialization; stability depends on adaptation laws; non-minimum phase systems pose challenges |
Genetic Algorithm | Effective for global optimization in high-dimensional spaces; does not require explicit system models | High computational cost; convergence is not guaranteed to be globally optimal; often requires offline implementation |
Particle Swarm Optimization | Works well for problems with large search spaces; requires a well-defined fitness function | Susceptible to premature convergence; requires parameter tuning; typically implemented offline due to computational demands |
Reinforcement Learning | Suitable for highly nonlinear and uncertain environments; can adapt to dynamic conditions | Requires extensive training data; computationally expensive for real-time applications; stability and safety are difficult to guarantee |
Neural network-based control | Can approximate complex nonlinear functions; does not require explicit system equations | Prone to overfitting; requires large datasets for training; computationally expensive, limiting real-time feasibility |
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Hollweg, G.V.; Singh Chawda, G.; Chaturvedi, S.; Bui, V.-H.; Su, W. Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review. Energies 2025, 18, 1429. https://doi.org/10.3390/en18061429
Hollweg GV, Singh Chawda G, Chaturvedi S, Bui V-H, Su W. Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review. Energies. 2025; 18(6):1429. https://doi.org/10.3390/en18061429
Chicago/Turabian StyleHollweg, Guilherme Vieira, Gajendra Singh Chawda, Shivam Chaturvedi, Van-Hai Bui, and Wencong Su. 2025. "Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review" Energies 18, no. 6: 1429. https://doi.org/10.3390/en18061429
APA StyleHollweg, G. V., Singh Chawda, G., Chaturvedi, S., Bui, V.-H., & Su, W. (2025). Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review. Energies, 18(6), 1429. https://doi.org/10.3390/en18061429