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Advanced Control Strategies for Photovoltaic Energy Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: 30 August 2026 | Viewed by 3950

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Special Issue Editors

Dr. Ana Cabrera-Tobar

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Guest Editor

Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
Interests: photovoltaic systems; microgrids; power electronics; renewable energy technologies, energy resilience, model predictive control

Dr. Alberto Dolara

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Guest Editor

Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
Interests: photovoltaic systems; MPPT algorithms; renewable energies; power electronics; wireless power transfer; electric vehicles
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Special Issue Information

Dear Colleagues,

The global transition toward sustainable energy systems has set photovoltaic (PV) technology as a key pillar for the use of renewable energy adoption. However, the widespread integration of PV systems into power grids introduces challenges, such as intermittent generation, power quality issues, and system reliability concerns, correlated to the availability of the primary energy source and operational disturbances. To overcome these challenges, advanced control strategies are essential for optimizing performance, enhancing resilience, and ensuring the reliable integration of PV systems into modern energy infrastructures.

This Special Issue explores novel methodologies, algorithms, and technologies aimed at improving the operation, management, and resilience of PV systems.

Topics of interest include, but are not limited to, the following:

Adaptive control techniques;
Strategies for hybrid energy storage system control;
Fault detection and mitigation;
Grid-supportive control;
Artificial intelligence and machine learning-based optimization;
Approaches for enhancing energy efficiency and system stability;
Innovations that strengthen the resilience of PV systems to disruptions, ensuring sustainable operation under varying conditions.

Contributions covering theoretical advancements, experimental validations, and practical applications are particularly encouraged.

Dr. Ana Cabrera-Tobar
Dr. Alberto Dolara
Guest Editors

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Keywords

photovoltaic energy systems
advanced control strategies
resilience
photovoltaic grid integration
artificial intelligence
energy storage optimization
fault detection and mitigation
hybrid energy systems

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Published Papers (6 papers)

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Research

25 pages, 2354 KB

Open AccessArticle

Intelligent ANFIS-MPPT Control via Double-Diode Model Training: Implementation and Validation Under Tropical Irradiance Profiles in Cúcuta, Colombia

by Jhon Lizarazo, Aldo Pardo and Ivaldo Torres

Energies 2026, 19(10), 2259; https://doi.org/10.3390/en19102259 - 7 May 2026

Viewed by 210

Abstract

An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) controller, trained with real-world data, is proposed and experimentally validated for photovoltaic systems operating under highly variable climatic conditions. Unlike conventional approaches relying on synthetic data, this work integrates a double-diode model (DDM) with high-fidelity measurements from a class-A pyranometer to capture the stochastic nature of the tropical environment. The controller was implemented on a Raspberry Pi 5 platform, governing a 100 kHz DC–DC converter connected to a 340 W photovoltaic array in Cúcuta, Colombia. Experimental results demonstrate a near instantaneous tracking time of 100 ms, significantly outperforming the 240 ms achieved by the conventional Perturb and Observe (P&O) method. Under rapid irradiance fluctuations, the system reached a peak static efficiency of 96.4% and a dynamic efficiency of 94.5%. Furthermore, a 10-h comparative energy analysis revealed that the ANFIS engine harvested 926.6 Wh, representing an 8.21% increase in cumulative energy yield compared to the P&O algorithm. This study confirms that intelligent MPPT controllers trained with real physical data can operate robustly on low-cost, non-deterministic embedded platforms, providing a resilient solution for energy optimization in modern PV microgrids. Full article

(This article belongs to the Special Issue Advanced Control Strategies for Photovoltaic Energy Systems)

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29 pages, 4802 KB

Open AccessArticle

Performance and Robustness Evaluation of the Resonance Suppression Strategy for the Photovoltaic Grid-Connected System Based on the Entropy Weight Method

by Chuang Liu, Pengcheng Li, Guoqing Liu, Heling Yang and Cong Sun

Energies 2026, 19(8), 1886; https://doi.org/10.3390/en19081886 - 13 Apr 2026

Viewed by 325

Abstract

There are numerous broadband resonance phenomena during the operation of new energy grid-connected systems. Therefore, the performance and adaptability of resonance suppression strategies for different resonance scenarios are of great significance. This paper proposed a comprehensive evaluation method based on the entropy weight method to assess the performance and robustness of resonance suppression strategies for photovoltaic (PV) grid-connected systems. Corresponding performance indicators were constructed considering the dynamic response characteristics of PV grid-connected systems. The six suppression strategies were comparatively analyzed in terms of performance and robustness under three scenarios: the LCL (inductor–capacitor–inductor)-type PV grid-connected system, the PV grid-connected system with SVG, and the newly built PV grid-connected system with SVG. This work effectively evaluates the performance and robustness of different suppression strategies, identifies the deficiencies of individual strategies, and provides a theoretical basis for designing flexible resonance suppression strategies with parameter adaptability. Full article

(This article belongs to the Special Issue Advanced Control Strategies for Photovoltaic Energy Systems)

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16 pages, 4108 KB

Open AccessArticle

Simplification of ANN-Based Adaptive Load Prediction and Offline Controller for Photovoltaic Heating Systems

by Shimin Xu, Yaxiong Wang, Shengli An and Qingzong Su

Energies 2026, 19(5), 1305; https://doi.org/10.3390/en19051305 - 5 Mar 2026

Viewed by 314

Abstract

This study examines how strongly demand-load prediction and adaptive load control in photovoltaic heating systems rely on computationally intensive artificial neural network (ANN) models. To streamline the computational workflow and reduce runtime resource requirements, we propose an ANN load-prediction-and-validation algorithm coupled with a corresponding offline control strategy. By optimizing the algorithmic structure and shifting heavy computations away from online execution, the proposed method substantially lowers the operational computational burden while preserving predictive accuracy, enabling efficient real-time load prediction and adaptive control. Based on a modelling study of a monocrystalline PV string comprising two 330 W modules connected in series, the proposed simplified prediction method produced annual cumulative energy outputs of 139.9, 391.2, 320.2, 251.4, and 154.1 kW·h across the five irradiance intervals [200, 400), [400, 600), [600, 800), [800, 1000), and [1000, ∞), respectively. Compared with a conventional artificial neural network (ANN)-based prediction approach, the corresponding deviations were 1.1%, −0.1%, 0.0%, 0.1%, and −0.4%, the total annual cumulative energy outputs across all intervals was 1256.7 kW·h with a mean deviation of −0.07%. Moreover, the simplified load-control strategy required only 3.57% of the computational resources consumed by the conventional ANN method. In addition, the method rapidly reallocates computational resources in response to changes in real-time input data, thereby minimizing redundant computation. Overall, the results demonstrate that the proposed framework markedly reduces computational complexity without sacrificing accuracy, providing an effective alternative to traditional ANN-based solutions and facilitating the practical deployment of photovoltaic heating systems. Full article

(This article belongs to the Special Issue Advanced Control Strategies for Photovoltaic Energy Systems)

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31 pages, 5687 KB

Open AccessArticle

A Hybrid Ensemble Learning Framework for Accurate Photovoltaic Power Prediction

by Wajid Ali, Farhan Akhtar, Asad Ullah and Woo Young Kim

Energies 2026, 19(2), 453; https://doi.org/10.3390/en19020453 - 16 Jan 2026

Cited by 1 | Viewed by 628

Abstract

Accurate short-term forecasting of solar photovoltaic (PV) power output is essential for efficient grid integration and energy management, especially given the widespread global adoption of PV systems. To address this research gap, the present study introduces a scalable, interpretable ensemble learning model of PV power prediction with respect to a large PVOD v1.0 dataset, which encompasses more than 270,000 points representing ten PV stations. The proposed methodology involves data preprocessing, feature engineering, and a hybrid ensemble model consisting of Random Forest, XGBoost, and CatBoost. Temporal features, which included hour, day, and month, were created to reflect the diurnal and seasonal characteristics, whereas feature importance analysis identified global irradiance, temperature, and temporal indices as key indicators. The hybrid ensemble model presented has a high predictive power, with an R² = 0.993, a Mean Absolute Error (MAE) = 0.227 kW, and a Root Mean Squared Error (RMSE) = 0.628 kW when applied to the PVOD v1.0 dataset to predict short-term PV power. These findings were achieved on standardized, multi-station, open access data and thus are not in an entirely rigorous sense comparable to previous studies that may have used other datasets, forecasting horizons, or feature sets. Rather than asserting numerical dominance over other approaches, this paper focuses on the real utility of integrating well-known tree-based ensemble techniques with time-related feature engineering to derive real, interpretable, and computationally efficient PV power prediction models that can be used in smart grid applications. This paper shows that a mixture of conventional ensemble methods and extensive temporal feature engineering is effective in producing consistent accuracy in PV forecasting. The framework can be reproduced and run efficiently, which makes it applicable in the integration of smart grid applications. Full article

(This article belongs to the Special Issue Advanced Control Strategies for Photovoltaic Energy Systems)

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19 pages, 2609 KB

Open AccessArticle

Adaptive Energy Management System for Green and Reliable Telecommunication Base Stations

by Ana Cabrera-Tobar, Greta Vallero, Giovanni Perin, Michela Meo, Francesco Grimaccia and Sonia Leva

Energies 2025, 18(23), 6115; https://doi.org/10.3390/en18236115 - 22 Nov 2025

Viewed by 636

Abstract

Telecommunication Base Transceiver Stations (BTSs) require a resilient and sustainable power supply to ensure uninterrupted operation, particularly during grid outages. Thus, this paper proposes an Adaptive Model Predictive Control (AMPC)-based Energy Management System (EMS) designed to optimize energy dispatch and demand response for a BTS powered by a renewable-based microgrid. The EMS operates under two distinct scenarios: (a) non-grid outages, where the objective is to minimize grid consumption, and (b) outage management, aiming to maximize BTS operational time during grid failures. The system incorporates a dynamic weighting mechanism in the objective function, which adjusts based on real-time power production, consumption, battery state of charge, grid availability, and load satisfaction. Additionally, a demand response strategy is implemented, allowing the BTS to adapt its power consumption according to energy availability. The proposed EMS is evaluated based on BTS loss of transmitted data under different renewable energy profiles. Under normal operation, the EMS is assessed regarding grid energy consumption. Simulation results demonstrate that the proposed AMPC-based EMS enhances BTS resilience. Full article

(This article belongs to the Special Issue Advanced Control Strategies for Photovoltaic Energy Systems)

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22 pages, 8021 KB

Open AccessArticle

Advanced Single-Phase Non-Isolated Microinverter with Time-Sharing Maximum Power Point Tracking Control Strategy

by Anees Alhasi, Patrick Chi-Kwong Luk, Khalifa Aliyu Ibrahim and Zhenhua Luo

Energies 2025, 18(18), 4925; https://doi.org/10.3390/en18184925 - 16 Sep 2025

Viewed by 1140

Abstract

Partial shading poses a significant challenge to photovoltaic (PV) systems by degrading power output and overall efficiency, especially under non-uniform irradiance conditions. This paper proposes an advanced time-sharing maximum power point tracking (MPPT) control strategy implemented through a non-isolated single-phase multi-input microinverter architecture. The system enables individual power regulation for multiple PV modules while preserving their voltage–current (V–I) characteristics and eliminating the need for additional active switches. Building on the concept of distributed MPPT (DMPPT), a flexible full power processing (FPP) framework is introduced, wherein a single MPPT controller sequentially optimizes each module’s output. By leveraging the slow-varying nature of PV characteristics, the proposed algorithm updates control parameters every half-cycle of the AC output, significantly enhancing controller utilization and reducing system complexity and cost. The control strategy is validated through detailed simulations and experimental testing under dynamic partial shading scenarios. Results confirm that the proposed system maximizes power extraction, maintains voltage stability, and offers improved thermal performance, particularly through the integration of GaN power devices. Overall, the method presents a robust, cost-effective, and scalable solution for next-generation PV systems operating in variable environmental conditions. Full article

(This article belongs to the Special Issue Advanced Control Strategies for Photovoltaic Energy Systems)

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