Smart Photovoltaic Research Articles

A Novel ConvXGBoost Method for Detection and Identification of Cyberattacks on Grid-Connected Photovoltaic (PV) Inverter System

The integration of solar Photovoltaic (PV) systems into the AC grid poses stability challenges, especially with increasing inverter-based resources. For an efficient operation of the system, smart grid-forming inverters need to communicate with the Supervisory Control and Data Acquisition (SCADA) system. However, Internet-of-Things devices that communicate with SCADA make these systems vulnerable. Though many researchers proposed Artificial-Intelligence-based detection strategies, identification of the location of the attack is not considered by these strategies. To overcome this drawback, this paper proposes a novel Convolution extreme gradient boosting (ConvXGBoost) method for not only detecting Denial of Service (DoS) and False Data Injection (FDI) attacks but also identifying the location and component of the system that was compromised. The proposed model is compared with the existing Convolution Neural Network (CNN) and decision tree (DT) strategies. Simulation results demonstrate the effectiveness of the proposed method for both the smart PV and PV fuel cell (PV-FC) systems. For example, the proposed model is efficient with an accuracy of 99.25% compared to the 97.76% of CNN and 99.12% of DT during a DoS attack on a smart PV system. Moreover, the proposed method can detect and identify the attack location faster than other models.

TOWARDS RENEWABLE URBAN LANDSCAPES: EXPLORING PHOTOVOLTAIC PANEL INTEGRATION - A CASE STUDY

The global shift towards sustainable practices and adopting renewable energy sources, notably photovoltaic (PV) panels, in urban planning is increasingly imperative. This paper delves into this emerging trend, focusing on integrating PV systems in smart cities. It presents a comprehensive case study conducted in AlZahia, a town located in Sharjah (Longitude: 55.4209317 and Latitude: 25.3462553), United Arab Emirates (UAE), examining the adoption of photovoltaic panels and smart applications to provide insights into their potential for cost savings and sustainability benefits. The study evaluates four pivotal aspects: building wall design and installation, window features, roof structures, and building orientation. Additionally, it investigates the incorporation of smart applications such as automated curtains, smart lighting systems, motion-activated cameras and lights, and a gas detection system during the design phase. The study results demonstrate that the implementation of PV panels and smart applications can yield substantial energy savings of approximately 52 and 36%, respectively, accompanied by a significant reduction in the carbon footprint. These findings provide practical guidance for contractors and developers seeking sustainable and energy-efficient facilities. They also underscore these technologies' feasibility and economic viability, empowering homes to fulfill a significant portion of their energy requirements through renewable sources, thus reducing reliance on grid electricity.

Enhancing Energy Efficiency in Photovoltaic Systems through Smart Technology Integration: Innovations and Future Perspectives

Photovoltaic (PV) technology, which converts solar radiation into electricity, has become a key player in the global transition to clean energy. As demand for renewable energy rises, innovations in smart artificial intelligence (AI), the Internet of Things (IoT), and big data analytics are being utilized to enhance the efficiency and reliability of PV systems. The integration of these technologies into PV systems is explored in this review, focusing on how they enhance fault detection, real-time monitoring, and energy optimization. It discusses the role of AI in improving system design and performance, real-time applications leveraging IoT control and data management, and the application of big data analytics for predictive maintenance and energy forecasting. Additionally, the review addresses the environmental and economic benefits of smart PV systems, highlighting their potential for reducing carbon emissions, stabilize energy grids, and provide long-term financial savings. Despite the technological and economic challenges, smart PV systems are poised to play a critical role in future global energy infrastructures by contributing to sustainable development goals and optimizing renewable energy production.

A Novel Stochastic Volt/VAR/Pressure Optimization-Based Conservation Voltage Reduction Technique in Integrated Electricity and Natural Gas Systems with PV Smart Inverters

A Novel Stochastic Volt/VAR/Pressure Optimization-Based Conservation Voltage Reduction Technique in Integrated Electricity and Natural Gas Systems with PV Smart Inverters

Solar Energy Integration in Urban Gujarat: A Pathway to IGBC Certification

The integration of solar energy in urban Gujarat is a key strategy for sustainable development and energy challenges in growing cities. This paper explores the potential of retrofitting non-certified green buildings with solar technologies to meet IGBC certification standards, focusing on the Surat Solar City initiative. Gujarat's abundant solar resources and progressive policies are enabling urban areas to transition to renewable energy solutions through rooftop solar PV systems, building-integrated photovoltaics (BIPV), and smart energy systems. Innovative technologies like transparent solar cells, solar paint, and hybrid systems are enhancing energy efficiency and reducing carbon footprints. Government incentives and net metering policies are driving adoption. Solar retrofitting projects in Gujarat, particularly in Surat, serve as scalable models for integrating renewable energy into urban infrastructure. Key Words: Solar retrofitting, IGBC certification, Smart energy systems, BIPV, Energy storage

Determination of yearly energy savings by CVR in combination with photovoltaic inverter control functions in distribution network

The effect of CVR is examined on unbalanced three-phase IEEE-13 and IEEE-123 distribution system with PV inverter integration on a yearly basis.In earlier work, the daily load demands of various customers were taken to determine the substation demand, loss reduction, and energy savings by the proposed scheme. In this paper,the yearly load patterns of consumers and seasonal variations of PV system have been taken to examine the impact of the CVR scheme on the distribution networks. Voltage-dependent load models for time series simulation of 8760 hours have been taken for study purposes. Various control functions of smart PV inverter such asVolt-Watt, Volt-VAr, Volt-VAr(hysteresis), andcombi mode has been used for the necessary control of voltage and reactive power. Several case studies have compared gross yearly substation demand and losses to evaluate the most effective control required to achieve maximum savings in energy.

Parameter estimation of photovoltaic power generation system and digital twin model construction

Abstract Photovoltaic power generation system is an essential part of modern power system, but it has intermittency and limitations and will also significantly reduce the stability of power system operation. For this reason, this paper proposes a new method for estimating the parameters of a photovoltaic power generation system. It constructs a digital twin model of photovoltaic panels for accurate real-time system monitoring. In order to construct a more realistic twin model, an improved iterative method is proposed to analyze and calculate the unknown parameters of the relationship between the internal characteristics of the PV panels. A comparative analysis with the adaptive war strategy is carried out, which verifies the effectiveness of the proposed method, reduces the operating errors of the real object and the twin, makes an essential contribution to the application of the digital twin technology in the photovoltaic power generation system, and lays a smart PV power system foundation.

Constructing Bionic Perovskite Smart Photovoltaic Windows with Switchable Colors and High Cycling Stability.

Smart photovoltaic windows (SPWs) provide a high-efficiency and energy-saving strategy owing to the dual capabilities of electricity generation and sunlight modulation achieved by tunable colors and transmittances. Due to the deterioration of chromic process on photovoltaic layers, SPWs usually suffer from poor cycling stability. Moreover, thermochromic SPWs with a multilayer structure usually change transmittance without reversible color transitions. To address these issues, inspired by chameleon skin, bionic SPWs are designed and constructed by integrating hydrogel, CsPbBr3 semitransparent perovskite solar cells (ST-PSCs), and transparent polymer film. The SPWs realize reversible transitions between transparent green (25°C) and opaque yellow (45°C) states in a short duration (2min) under natural conditions. By optimizing perovskite film and ultrathin-metal electrodes, CsPbBr3 ST-PSCs achieve a good trade-off between transmittance and efficiency, delivering the highest photovoltaic efficiency (8.35%) and a record light utilization efficiency (4.43). Ultimately, the multilayer SPWs maintain stable optical properties and more than 88% initial conversion efficiency after 100 transition cycles, presenting excellent cycling stability. This study proposes a novel approach and device structure for SPWs with high cycling stability, switchable colors, and switchable transmittances. It also paves the way for smart photovoltaic deployment in buildings and many other fields.

Smart PV Monitoring and Maintenance: A Vision Transformer Approach within Urban 4.0

The advancement to Urban 4.0 requires urban digitization and predictive maintenance of infrastructure to improve efficiency, durability, and quality of life. This study aims to integrate intelligent technologies for the predictive maintenance of photovoltaic panel systems, which serve as essential smart city renewable energy sources. In addition, we employ vision transformers (ViT), a deep learning architecture devoted to evolving image analysis, to detect anomalies in PV systems. The ViT model is pre-trained on ImageNet to exploit a comprehensive set of relevant visual features from the PV images and classify the input PV panel. Furthermore, the developed system was integrated into a web application that allows users to upload PV images, automatically detect anomalies, and provide detailed panel information, such as PV panel type, defect probability, and anomaly status. A comparative study using several convolutional neural network architectures (VGG, ResNet, and AlexNet) and the ViT transformer was conducted. Therefore, the adopted ViT model performs excellently in anomaly detection, where the ViT achieves an AUC of 0.96. Finally, the proposed approach excels at the prompt identification of potential defects detection, reducing maintenance costs, advancing equipment lifetime, and optimizing PV system implementation.

Smart Photovoltaic Windows for Next-Generation Energy-Saving Buildings.

The global energy system transforming from fossil fuels to renewable green energy through the adaption of innovative and dynamic green technologies. Energy-saving buildings (ESBs) are attracting extensive attention as intelligent architectures capable of significantly reducing the energy consumption for heating, air-conditioning, and lighting. They provide comfortable working and living environment by regulating and harnessing solar energy. Smart photovoltaic windows (SPWs) offer a promising platform for designing ESBs due to their unique feature. They can modulate solar energy based on dynamic color switching behavior under external stimuli and generate electrical power by harvesting solar energy. In this review, the-state-of-art of strategies and technologies are summarized putting SPWs toward high-efficiency ESBs. The SPWs are systematically categorized according to the working principle and functional component. For each type of SPWs, material and architecture engineering are focused on to optimize operation mode, optical modulation capability, photovoltaic performance and durability for giving ESBs flexible manipulation, extraordinary energy-saving effect, and high electricity power. In addition, the challenges and opportunities in this cutting-edge research area are discussed, with the aim of promoting the development of advanced multifunctional SPWs and their application in high efficiency ESBs.

Integration of distributed PV into smart grids: A comprehensive analysis for Germany

Modern smart grids typically combine physical and communication networks for efficient information exchange and innovative applications. Aligned with digitalization and advancements in smart grids, the integration of photovoltaic (PV) systems comprises a variety of regulatory and technological aspects. However, no previous study has conducted an extensive and systematic analysis of the PV-grid integration framework, particularly for one country. To fill this gap, this paper uses Germany as an example to present a comprehensive, state-of-the-art analysis of integrating distributed PV systems into smart grids, focusing on the regulation and technical implementation of the German Smart Meter Infrastructure and PV control interfaces. Starting from a standardization perspective, this analysis utilizes the Smart Grid Architecture Model to identify crucial roles, components and processes specifically in Germany. Furthermore, it outlines the current implementation of PV integration into distribution networks at a national level. The results of this study show the overall complexity of PV integration in the smart grid context, confirm the feasibility of the German integration approach, and highlight the necessity of deploying standardized information models and communication technologies. These key findings can help market participants with different roles to identify potential technical bottlenecks or other critical points in the regulation and technical implementation. For instance, the proposed in-depth analysis framework provides an orientation for characterizing the PV integration or, more generally, the grid integration scenario of renewables in other countries.

Distribution of Sports Load by the Smart Photovoltaic Cell Switching System

The article presents a summary of the new energy scenario, based on distributed generation and on the integration into the electricity networks of the generators of small and medium power. They are based on clean technologies and renewable energy, including cogeneration, micro-generation with renewables (non fossil energy sources, such as wind, solar, geothermal, tidal, hydroelectric, biomass), energy storage systems, energy efficiency techniques, and flexibility and demand responsibility techniques. The perspective shows a new system, where thousands or millions of users are their own generators, becoming producers and consumers of electricity. All these generators will be interconnected through an electricity network totally interactive and intelligent. For this new environment a sophisticated control and communications technologies are required to ensure the correct operation of the electricity networks, the creation of new models of power distribution, and advanced technology development of energy storage, power electronics and superconductor devices. The main technology that can lead to this new revolution, equivalent to the technology of silicon, which is revolutionizing the conventional limits of electricity and electronics, is nanotechnology, as well as its applications in the development of new nanostructured materials. The new energy systems will present smaller scale, will be easier to build and dismantle, decentralized, renewable, with localized and decentralized energy production.

Robust Local Coordination Control of PV Smart Inverters With SVC and OLTC in Active Distribution Networks

Robust Local Coordination Control of PV Smart Inverters With SVC and OLTC in Active Distribution Networks

A Decentralized Voltage Regulation Scheme Using Improved Volt‐Var Function of PV Smart Inverter

With the growing distributed PV installation rate in distribution systems, voltage regulation difficulties such as local voltage violations and fluctuations have become common. To solve the voltage regulation problems, the local voltage regulation method using volt‐var (VV) function is effective for its high regulation speed, high accuracy, and flexibility. However, there are still hurdles on real application, such as parameter setting difficulties, insufficient voltage fluctuation mitigation effect, and unfairness of reactive power generation between PV customers. To further improve the VV function, this paper proposes a PID closed‐loop based VV function and mode‐switching function of PV smart inverter (SI). To validate the proposed methods, numerical simulations were conducted using a 6‐feeder distribution system model based on the JST‐CREST 126 distribution feeder model. According to the simulation result, the proposed method can better mitigate the local voltage violation compared to basic VV function based on the VV curve, also improve the fairness between PV customers, while the total reactive power generation and tap operations increases for the aim of more adequate voltage regulation. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Hybrid uncertainty approach for management of energy storage-embedded soft open points in distribution grids

Energy storage-embedded soft open point (ESOP) is an innovative electronic device that incorporates energy storage capabilities and is designed to interconnect two or three neighboring distribution feeders. Its primary functions include active power control, reactive power compensation, and grid voltage regulation. On the other hand, distribution grids have been equipped with other distributed energy resources (DERs) such as smart PV inverters, renewable energy sources (solar and wind), and so on. Therefore, this article proposes a novel management scheme for the mentioned devices in distribution grids based on a convex formulation to take powerful solvers like CPLEX into account. Since there are a set of uncertainties associated with the model, including renewable resources, loads, and market price, a hybrid framework is developed based on the unscented transformation (UT) method and information gap decision theory (IGDT). In other words, the uncertainties of renewable resources and loads are captured by the UT method which could provide a correlation among uncertain parameters, while the IGDT method is employed to model the uncertainty associated with the market price. The hybridization of these methods assists in reducing the discrepancy between the actual and predicted values of uncertain parameters. The model is applied to a 33-bus grid, considering various case studies. The proposed method results in a substantial operational cost reduction of approximately 26.82 %. Additionally, it ensures a flatter voltage profile across the network, with the minimum voltage reaching 0.985 p.u.

Volt–Var curve determination method of smart inverters by multi-agent deep reinforcement learning

Reactive power control of PV inverters can be applied to mitigate the voltage increase caused by reverse power flow and voltage fluctuations caused by PV output fluctuations in the distribution system. This paper focuses on the Volt–Var control of PV smart inverters to minimize power losses. It proposes a multi-agent type cooperative voltage control framework to optimize the blind band and slope of the VVC. The proposed method utilizes the optimized VVC to eliminate voltage deviations using only local information. The optimization problem is formed as a Markov decision process and solved using a deep reinforcement learning algorithm called multi-agent soft-actor critic, which achieves fast control that fully accounts for uncertainty while achieving global optimization. Therefore, the proposed method can contribute to reducing power loss, which is a system-wide problem, with only local information. Case studies were conducted using a small and medium-sized distribution network model with IEEE 33 buses and 12 demand cases. The comparison results demonstrate that the proposed method is superior to other benchmark methods in terms of minimizing power losses and mitigating voltage variations, as the proposed method can reduce power losses by about 9% while achieving avoidance of voltage deviations.

A SOCP-Based ACOPF for Operational Scheduling of Three-Phase Unbalanced Distribution Systems and Coordination of PV Smart Inverters

The proliferation of distributed energy resources (DERs) imposes new challenges to distribution system operation, e.g., power quality issues. To overcome these challenges and enhance system operation, it is critical to effectively utilize all available resources and accurately characterize unbalanced distribution networks in operational tools. This paper proposes a convex second-order-cone programming (SOCP)-based AC optimal power flow (ACOPF) model for three-phase unbalanced distribution networks, including smart inverters and Volt-VAr controller (VVC) devices. Reactive power-voltage (Q-V) characteristics of smart inverters of solar photovoltaic (PV) units are also modeled. Moreover, the settings of Q-V characteristics of VVC are co-optimized within the proposed ACOPF, considering the allowable range of the IEEE 1547-2018 standard. Furthermore, dynamic analyses are conducted to verify the stability of optimal settings of VVC. The proposed models are tested on an actual 1747-node primary distribution feeder in Arizona. The results illustrate the effectiveness of the proposed ACOPF for unbalanced systems in providing an optimal solution while capturing the non-linearity and non-convexity of ACOPF. By co-optimizing settings, system operation is improved due to the flexibility of adjusting reactive power output from PV units with VVC. The time-domain simulations show that the optimal settings cause no stability issue for the distribution system.

Highly efficient and high color rendering index multilayer luminescent solar concentrators based on colloidal carbon quantum dots

Compared to the silicon-based photovoltaics, large-area luminescent solar concentrators (LSCs) can be used as smart PV window for building-integrated photovoltaics (BIPVs). However, because of the low quantum yield of the fluorophores, and reabsorption energy loss, the current obtained large-area LSCs have low optical efficiency. Herein, we demonstrated highly efficient and semitransparent multilayer LSCs based on three types of carbon quantum dots (C-dots). The blue and green C-dots were synthesized using vacuum heating approach and the orange C-dots were synthesized using solvothermal approach. The as-prepared B-, G- and O-C-dots have the QYs of 90 %, 73 % and 72 %, respectively, with large Stokes shift (0.37–0.73 eV). By optimizing the concentration of the C-dots in each layer, the best LSC (100 cm2) with multilayer structure exhibited an external optical efficiency of 9.1 %, and a power conversion efficiency (PCE) of 6.0 %, which are higher than that of most of reported LSCs. The simulations indicate that optimized multilayer LSCs could have a theoretical external optical efficiency over 12 % for 1 m2 devices. Besides high optical efficiency, the multilayer LSCs also have high Color Rendering Index (above 80) and high average visible transmission (above 70 %), which make the as-prepared LSCs suitable for potential commercial BIPVs.

Toward optimal voltage/VAR control with smart PVs in active distribution networks

Rapidly increasing penetration of distributed PVs brings great challenges to distribution networks. Driven by the revised IEEE 1547 standard, inverter-based smart PVs provide customized control and voltage regulation capabilities. This paper presents a comprehensive study investigating voltage/VAR control considering network reconfiguration, VAR optimization, and smart PVs. To do so, a second-order cone programming-based VAR optimization and network reconfiguration (VONR) model is formulated. The enhanced necessary radiality condition, and the extensions for multiple smart PV operation modes and multi-period operation are provided. A comprehensive case study considering various network and smart PV operation modes, and hourly VONR operation is conducted. The results show that implementing VONR with smart PV reactive power dispatching optimizes the scheduling plans, resulting in a reduction of network loss by 59.97 % and 30.37 % for the 33-bus and 123-bus test systems, respectively, compared to the no-control operation.

A Case Study of the Use of Smart EV Charging for Peak Shaving in Local Area Grids

Electricity storage systems, whether electric vehicles or stationary battery storage systems, stabilize the electricity supply grid with their flexibility and thus drive the energy transition forward. Grid peak power demand has a high impact on the energy bill for commercial electricity consumers. Using battery storage capacities (EVs or stationary battery systems) can help to reduce these peaks, applying peak shaving. This study aims to address the potential of peak shaving using a PV plant and smart unidirectional and bidirectional charging technology for two fleets of electric vehicles and two comparable configurations of stationary battery storage systems on the university campus of Saarland University in Saarbrücken as a case study. Based on an annual measurement of the grid demand power of all consumers on the campus, a simulation study was carried out to compare the peak shaving potential of seven scenarios. For the sake of simplicity, it was assumed that the vehicles are connected to the charging station during working hours and can be charged and discharged within a user-defined range of state of charge. Furthermore, only the electricity costs were included in the profitability analysis; investment and operating costs were not taken into account. Compared to a reference system without battery storage capacities and a PV plant, the overall result is that the peak-shaving potential and the associated reduction in total electricity costs increases with the exclusive use of a PV system (3.2%) via the inclusion of the EV fleet (up to 3.0% for unidirectional smart charging and 8.1% for bidirectional charging) up to a stationary battery storage system (13.3%).