Recent Trends of Control Strategies for Hybrid Optimization Techniques in Solar Power Microgrids: A Comparative Review

Abstract

Solar powered microgrids are becoming a majortrend of modern distributed generation systems due to their clean, renewable and decentralized nature but, the inconsistentcharacteristics of photovoltaic generation comes with challenges in achieving stable, efficient, and reliable operation. To solve these issues, advanced control strategies integrated with hybrid optimization techniques have gained significant attention. Hybrid approaches, combining metaheuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Reptile Search Algorithm (RSA), Aquila Optimizer (AO), and Artificial Neural Networks (ANN), have been applied to improve maximum power point tracking (MPPT), voltage and frequency regulation, energy storage scheduling, and power quality enhancement. This paper presents a comprehensive comparative review of recent control strategies employed in solar microgrids with an emphasis on hybrid optimization frameworks. Various methods are analyzed based on performance indicators such as tracking efficiency, stability, convergence rate, robustness, and computational burden. Furthermore, the review highlights the emerging trends of AI-assisted optimization, real-time digital simulation, and cyber-physical resilience for solar microgrids. The study concludes by outlining future research directions toward scalable, adaptive, and intelligent control for resilient solar microgrid operation. This paper provides a comparative review of recent control strategies for solar microgrids with emphasis on hybrid optimization techniques.

Introduction

Clean and sustainable energy demands have accelerated the adoption of renewable-based distributed generation, with solar PV emerging as a leading option due to its modularity, low cost, and scalability. The integration of PV with microgrids—systems combining generation, storage, loads, and control—has enhanced reliable and flexible power supply under both grid-connected and islanded modes.

However, solar microgrids face challenges from PV intermittency, leading to instability, voltage fluctuations, load-shedding, and ineffective utilization of resources. To address these issues, recent research emphasizes advanced control and optimization techniques, especially metaheuristic and hybrid algorithms. Techniques such as PSO, GA, GWO, HHO, RSA, and hybrid combinations (PSO-GA, ANN-HHO, Fuzzy-PSO, RSA-AO) show improved performance in MPPT, energy management, stability enhancement, and resilience.

Globally and in India, the integration of renewable energy—especially solar—has grown rapidly. India shows a steeper growth trend, driven by strong policies and rapid solar park development, highlighting the need for efficient control and optimization for solar microgrids.

Solar Microgrid Background

A solar microgrid typically consists of PV arrays, converters, energy storage systems (ESS), loads, and hierarchical controls:

• Primary control: droop-based power sharing and V/f regulation

• Secondary control: restores voltage and frequency

• Tertiary control: optimizes energy flow and cost through the grid

Traditional control strategies struggle with nonlinearities caused by shading, irradiance variation, and fluctuating loads. Therefore, hybrid optimization and AI-based control are increasingly used for improving MPPT, ESS scheduling, and overall system reliability.

Control & Optimization Problems

The main operational challenges fall into four categories:

1. Maximum Power Point Tracking (MPPT)

• Necessary for maximizing PV output under changing solar conditions

• Metaheuristic MPPT (PSO, GWO, SSO, hybrid GWO-NM, CS-GWO, MPSO-PID) improves global peak tracking, especially in partial shading.

2. Energy Management & Scheduling (EMS)

• Coordinates PV, ESS, and grid to minimize cost and enhance reliability

• Uses mixed-integer programming, reinforcement learning, and hybrid optimizers (PSO-GA, ANN-HHO, Aquila, HHO).

3. Voltage and Frequency Stability

• Critical in islanded or weak-grid scenarios

• Optimization helps tune droop, PI/MPC, and virtual inertia to reduce deviations and enhance dynamic response.

4. Microgrid Resilience

• Addresses uncertainties from renewable intermittency, forecasting errors, and cyber-physical threats

• AI-based hybrid strategies improve fault recovery and long-term robustness.

Mathematical Modeling

The microgrid is represented using compact mathematical models of:

• PV generator (single-diode model)

• DC-DC boost converter

• Grid-tied inverter (dq-frame model)

• Battery/ESS models

• System-level power balance equations

• Uncertainty modeling for solar irradiance and load

These models form the basis for hybrid optimization in MPPT, ESS scheduling, and voltage-frequency control.

Hybrid Solar Microgrid Operation and Control

A hybrid microgrid combining PV + ESS + STATCOM requires hierarchical control:

• Primary: droop control, virtual impedance

• Secondary: centralized/decentralized recovery of voltage/frequency

• Tertiary: EMS and optimal power flow (MPC, heuristic, metaheuristic)

STATCOM provides fast reactive power compensation, while ESS handles active power balancing. Their coordinated control enhances voltage stability in weak-grid conditions.

AI-based approaches such as adaptive droop, fuzzy logic, and reinforcement learning improve adaptability under uncertainties.

Types of Control Methods

1. MPPT Control

Ensures maximum energy extraction under varying solar conditions.
Advanced MPPT methods (fuzzy, adaptive, metaheuristic) outperform traditional P&O and INC in fast-changing environments.

2. Voltage & Frequency Control

Maintains stable voltage and frequency using coordinated STATCOM-ESS control and hierarchical control strategies.

Main Contributions of the Review

1. Classification of control strategies for solar microgrids, with emphasis on hybrid and intelligent optimization.

2. Comparative evaluation of methods based on efficiency, robustness, computational complexity, and scalability.

3. Identifying research gaps, such as the need for RTDS testing, cyber-physical resilience, and AI-driven predictive optimization.

Conclusion

The review has presented a detailed comparative study on classical, heuristic, and hybrid control optimization strategies employed in PV-ESS-STATCOM integrated microgrids. Classical controllers like PI, PID, fuzzy logic, and ANN were easy to use and implement; therefore, they were favored, which made them widely applied; however, these controllers did not perform well under uncertain and nonlinear conditions. This is where metaheuristic, such as PSO, GA, DE, HHO, RSA, AO, and WOA, have entered the picture as powerful techniques with a robust and flexible framework for solving multi-objective control problems. Hybrid methods, which combine conventional and metaheuristic methods, have shown excellent adaptation and convergence capabilities that are particularly useful under dynamic and uncertain grid conditions. The comparative performance analysis shows that whereas metaheuristic are strong on convergence and global search ability, classical techniques, on the other hand, are still very important when it comes to fast response and simplicity. Hybrid methods carry the merit of increasing robustness, decreasing overshoot, and improving dynamic stability. Yet the challenges of scalability, real-time implementation, and integration with cyber-physical systems remain. The rapid rise in desirability of this technology is illustrated by the installation of PV-based hybrid microgrids across the world from remote locales like Ta?u Island (American Samoa) to industrial sites like the Fekola Gold Mine (Mali) and community resilience centers in the United States. The technologies could however be a success only if suitable control and optimization strategy choices are exercised.

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Copyright © 2025 Yibenthung Odyuo, Ritula Thakur. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.