Smart Photovoltaic Research Articles

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

In order to discuss the allocation effect of anintelligent photovoltaic cell switching system on sports load, thispaper, based on the ZigBee sensing model, designs theinformation allocation module for collecting sports load statusbased on the spectral characteristic analysis of model parametersand the real-time observation of sports load status, and obtainsthe initial data. In-depth analysis of the correlation analysis ofintelligent photovoltaic cell switching system on sports loadallocation, and judge the influence degree of sports loadallocation. The results show that in the monitoring of sports loadstatus, the data acquisition frequency of 28 to 30 kHz can reach -50 dB, and the maximum oxygen uptake VO2, the number ofmax indicators reached 33.21 ± 3.09 and O2, the number of Pindicators is 362.57 ± 98.71, and the number of METs is 9.06 ±1.21. Therefore, we show that the smart photovoltaic cellswitching system has a positive correlation with the sports loaddistribution, and the smart photovoltaic cell switching system hasa practical effect on the sports load distribution.

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%).

Multiple control strategies for smart photovoltaic inverter under network voltage fluctuations and islanded operation

This article proposes a central control system that communicates with both grid-tied and off-grid control systems to offer various control strategies for operating a smart photovoltaic (PV) inverter. The target is to connect two sets of PV panels and one set of battery storage unit to either a 440 V/60 Hz utility grid or to feed local loads at 380 V/50 Hz using a smart inverter. When the smart PV inverter is connected to the grid, on the one hand, it injects fixed and programmed active power into the grid under all operating conditions, both normal and critical conditions, and on the other hand, by reciprocal exchanging reactive power with the grid, it addresses balanced and unbalanced fluctuations of the grid voltage. Also, when the smart PV inverter is disconnected from the grid, it can operate as an islanded mode to feed time-varying three-phase loads. Nonlinear simulations of the time domain in MATLAB-Simulink software show that the set of control strategies offered for the smart PV inverter not only by controlling the active power leads to the sale of electricity to the grid, but also by controlling the reactive power problems on the grid side such as voltage drop as a result of single-line-to-ground (SLG) or line-to-line-to-ground (2LG) faults, balanced grid voltage fluctuations such as voltage sag-swell and unbalanced voltage conditions will be resolved. Also, variable three-phase loads are fed well in off-grid mode.

Technical and economic analyses of PV battery systems considering two different tariff policies

Installing batteries in solar photovoltaic (PV) houses is becoming commonplace and different tariff policies give residents more options to lower their energy bills. This paper develops two rule-based control strategies to operate solar PV battery systems under fixed flat or time-of-use tariff policies, aiming to increase PV self-consumption and self-sufficiency. The battery modelling considers the charging and discharging efficiencies and the battery energy efficiency. The payback period for solar PV battery systems under the two tariff policies is also analysed considering various economic factors such as the capital cost of solar PV systems, the capital and maintenance costs of the batteries, the annual discount rate and the increases or decreases in the retail prices of grid electricity and the feed-in tariff. The analyses are conducted using actual PV energy and smart meter data from a real case study house in Geelong, Australia. Results indicate that when battery capacity is increased, PV self-consumption and self-sufficiency grow under both tariff policies, but this trend is limited by constrained PV generation due to seasonal conditions. Additionally, increasing solar PV system size for fixed battery capacity increases PV self-sufficiency, but decreases PV self-consumption. Results of economic analysis demonstrate that the payback period for a standalone solar PV system increases as its capacity grows. Moreover, the payback period for PV batteries can be slightly shorter or even longer than using solar PV systems alone under both tariff policies, which is economically unattractive. Considering the benefits that batteries can bring to residents and electricity networks, local governments need to be more proactive in providing financial subsidies for residents to install batteries.

Design and Implementation of IoT based PV Integrated Power Monitoring System

The awareness of usage of power and effective utilization of power is based on intelligent decision, which can help the user as well as supplier. In this paper the design and implementation of IOT based PV integrated system is presented. A hardware prototype is developed with PV module connected to boost converter, Arduino Uno controller, ESP 8266 Wi-Fi module, and Thing speak to monitor a real-time power. The Arduino Uno serves as the central controller, gathering data from smart power meters and PV modules for solar energy generation. The collected data is wirelessly delivered to the ThingSpeak, which provides cloud-based storage, data analysis, and visualization. With these users may remotely monitor usage of power, track solar energy generation, and get notifications for energy utilization.

Autonomous and smart cleaning mobile robot system to improve the maintenance efficiency of solar photovoltaic array

A solar photovoltaic (PV) array is part of a PV power plant as a generation unit. PV array that are usually placed on top of buildings or the ground will be very susceptible to dirt and dust. Thus, this dirt and dust will be able to reduce the performance and work efficiency of the generation unit. Cleaning PV arrays by manpower requires high effort, cost, and risk, especially in higher location. This study presents the design of a mobile robot that is used to replace human labor to clean PV arrays. That way, the PV array maintenance steps can reduce operational costs and risks. This intelligent controlled mobile robot can maneuver safely and efficiently over PV arrays. gyroscope and proximity sensors are used to detect and follow the sweep path over the entire PV array area. Proportional integral derivative (PID) control test makes the robot can stabilize in about 5.72 seconds to keep on the track. The smart PV cleaning robot has average operation time about 13 minutes in autonomous mode and 20-24 minutes in manual mode. The operation of the robot is effective to give more efficiency on the use of energy, time, and maintenance costs of PV array system.

Challenges and Optimization of Building-Integrated Photovoltaics (BIPV) Windows: A Review

PV windows are seen as potential candidates for conventional windows. Improving the comprehensive performance of PV windows in terms of electrical, optical, and heat transfer has received increasing attention. This paper reviews the development of BIPV façade technologies and summarizes the related experimental and simulation studies. Based on the results of the literature research, the average comprehensive energy-saving rate of BIPV façades can reach 37.18%. Furthermore, limitations and optimization directions of photovoltaic integrated shading devices (PVSDs), photovoltaic double-skin façades, and photovoltaic windows are presented. To improve the energy-saving potential of windows as non-energy efficiency elements of buildings, smart PV windows are proposed to be the key to breakthrough comprehensive performance. However, not all switchable windows concepts can be applied to PV windows. Typical studies on smart windows and PV windows are sorted out to summarize the challenges and optimization of smart PV window technical solutions. Considering the technological innovations in smart PV windows, two requirements of energy-saving materials and building envelopes are put forward. The advances in materials and the building envelope are complementary, which will promote the sophistication and promotion of solar building technology.

Broadband Modulation, Self‐Driven, and Self‐Cleaning Smart Photovoltaic Windows for High Efficiency Energy Saving Buildings

AbstractSmart photovoltaic windows (SPWs) are an emerging green technology presenting energy‐saving by combining solar irradiance regulation and solar energy harvesting. The SPWs integrating extraordinary energy‐saving performance, stable and controllable operational mode, and diverse functionality are essential for devising high efficiency energy‐saving buildings (ESBs). However, the attainment of such features has yet to be realized in current iterations of SPWs. Herein, a conceptual demonstration of a split‐type broadband modulation, self‐driven, and self‐cleaning SPWs is presented by coupling a silicon solar cell with a multifunctional chromogenic unit (MCU) for creating highly efficient ESBs. Within the multilayer structured MCU, thermal‐responsive polymer stabilized liquid crystal (PSLC) and VO2@SiO2 nanoparticles act as chromic component, enabling broadband light modulation. Thanks to the excellent electrothermal effect of indium tin oxide (ITO), the phase transition of PSLC and VO2@SiO2 nanoparticles can be induced by the electrical power output generated by the silicon solar cell. Therefore, the transparency of SPWs can be manipulated according to the occupant's preference during the daytime. Moreover, a superhydrophobic SiO2 coating provides SPWs with self‐cleaning capability which effectively reduces water resource consumption and eliminates the inconvenience of window cleaning, while providing occupants with a clear view even in complex weather conditions.

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. This paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.