Photovoltaic Energy Research Articles

Construction of an Anode Material for Sodium-Ion Batteries with an Ultrastable Structure.

The reserves of sodium resources are much larger than those of lithium resources, and they are widely distributed and easy to produce and can be widely used in photovoltaic energy storage and other industries on the premise of this advantage. However, how to produce hard carbon anodes at a low cost for the preparation of energy storage materials requires continuous exploration and experimentation to find the optimal solution. Here, we used waste sour date shell biomass as a precursor for a hard carbon anode obtained by simple acid treatment and two pyrolyses. It is shown that acid washing after prepyrolysis has a significant effect on the electrochemical performance of the sour date shell-derived hard carbon (ZJ), constructing a stable structure that makes it easier for sodium ions to be embedded and dislodged, and the carbon particles are homogeneous and free of bonding and electrode cracking. The ZJ-1500-HCl pyrolyzed at 1500 °C has a high reversible capacity of 329.1 mAh g-1, 94.1% initial Coulombic efficiency (ICE), 90.54% capacity retention efficiency cycling 300 cycles at a current density of 300 mA g-1, and a resistivity below 10 Ω. It has good cycle stability and a good multiplier performance. It is expected to be used in practical production and photovoltaic energy storage in the future.

Developing a photovoltaic energy generation forecast system using neural networks

Developing a photovoltaic energy generation forecast system using neural networks

Fuzzy logic controller of a step-up boost converter applied to a photovoltaic system

the constantly increasing global energy need and the polluting nature of fossil energies have increased the interest in the development of renewable energies. Among them, photovoltaic energy. The function of a photovoltaic (PV) system is to ensure the conversion of solar energy into electrical energy and the conditioning of this energy in order to adapt it to various needs. The performance of a PV system depends essentially on the quality of the conditioning of the energy, which allows the optimal transfer of the energy of the PV generator, and the management of the energy produced to meet energy needs by incorporating of converters. In this article, we present an intelligent approach to improve and optimize the performance of a photovoltaic system, by the fuzzy MPPT control. Our system consists of a photovoltaic panel, a Boost-type DC-DC converter con-sidered as an adaptation stage between the PV and the load. The IC technique was compared with FLC to evaluate the best and most accurate controller for MPPT. The results suggest that the use of FLC could improve the MPPT and increase the sTableility of the PV system. System modeling and simulation performed with Matlab/Simulink.

Technical characteristics and development trend of third generation solar cells

The future of global sustainable energy systems is anticipated to heavily rely on photovoltaic solar energy (PV). This paper provides an analysis of third-generation solar cells, exploring their underlying working mechanisms, evaluating their performance metrics, and discussing both their advantages and disadvantages. Third-generation solar cells, which include perovskites, dye-sensitized cells, and organic photovoltaics, offer promising advancements over traditional silicon-based cells, such as higher efficiencies and potential for lower production costs. However, challenges like material stability, scalability, and long-term reliability hinder their widespread adoption. In offering a comprehensive examination of these advanced solar technologies, the paper aims to contribute to the understanding and progress of renewable energy solutions. It underscores the importance of continued research and innovation in overcoming the existing limitations of third-generation solar cells. The transition towards a future that relies more heavily on sustainable energy sources hinges on the ability to develop and implement efficient, reliable, and cost-effective solar technologies. The paper identifies these bottleneck issues and suggests pathways for overcoming them.

Optimizing power output in hybrid photovoltaic/wind systems: a nonlinear back-stepping approach for enhanced efficiency and stability

Abstract This paper investigates the challenge of controlling hybrid renewable energy systems (HRES), specifically those combining wind energy and photovoltaic sources, under varying environmental conditions such as fluctuating wind speeds and partial shading. The primary objective is to develop a robust backstepping control strategy that enhances the system’s stability and energy efficiency while ensuring seamless grid integration through the use of dual-fed induction generators. The study uses advanced modeling techniques, including maximum power point tracking for wind turbines and particle swarm optimization for photovoltaic systems, to optimize energy capture. A detailed simulation framework was designed to validate the effectiveness of the control strategy under different climatic scenarios. Quantitative results show that the wind turbine achieved over 95% power recovery, the DC link voltage remained stable within 0.5% of the reference, and photovoltaic energy extraction was optimized with 98% accuracy, even under partial shading. These findings indicate that the proposed control strategy significantly enhances the performance, reliability, and adaptability of the HRES. This work offers a promising contribution to the integration of renewable energy sources into the electrical grid, supporting a more sustainable energy future.

Robust Distributed Load Frequency Control for Multi-Area Power Systems with Photovoltaic and Battery Energy Storage System

The intermittent power generation of renewable energy sources (RESs) interrupts the balance between power generation and demand load due to the increased frequency fluctuation, which challenges the frequency stability analysis and control synthesis of power generation systems. This paper proposes a robust distributed load frequency control (DLFC) scheme for multi-area power systems. Firstly, a multi-area power system is constructed by integrating photovoltaic (PV) and battery energy storage systems (BESSs). Then, by employing the linear matrix inequality (LMI) technique, the sufficient condition capable of ensuring that the proposed controller satisfies H∞ robust performance in the sense of asymptotic stability is derived. Finally, testing is conducted on a four-area renewable power system, and results verify the strong robustness of the proposed controller against load disturbance and intermittence of RESs.

Innovative electric heating system for a hybrid solar cooker (photovoltaic/thermal) using photovoltaic energy with battery storage

Innovative electric heating system for a hybrid solar cooker (photovoltaic/thermal) using photovoltaic energy with battery storage

Study of Photodegradation of Organic Solar Cells Under Brazilian Climate Conditions

The increasing technical and economic viability of photovoltaic solar energy technologies includes modules with organic photovoltaic (OPV) cells, which have shown significant efficiency increases, reaching 20% for research devices. This study investigated the photodegradation and associated loss mechanisms in OPV devices under tropical conditions in Brazil. The electrical and optical characteristics of the modules were correlated with chemical and structural changes when exposed to sunlight. Electrical parameters were monitored over time on external test benches and measured in solar simulators, while changes in the optical transmission and absorption of the films were analyzed. Scanning electron microscopy, energy-dispersive spectroscopy, and Fourier-transform infrared spectroscopy were used to study the physical and chemical properties of the materials. We found that photodegradation causes bound breakage in the active layer, altering the carbon structure and consequently reducing the module’s output power. The primary reasons for the activation and progression of this mechanism are high temperature and elevated solar irradiance. Therefore, we demonstrate that understanding these mechanisms is essential for the development of more sustainable OPVs in tropical climates.

Energy Storage Solutions to De-Carbonize the Electric Supply in Jordan

This paper aims to estimate the size of Energy Storage Systems (ESS) required de-carbonizing the electrical network in Jordan. Load profile in addition to the PV and Wind energy profiles were studied and used as input data to the simulation model. Different cases of RE penetration levels as a percentage of load demand were considered and entered to the simulation model as well. Other factors, such as allowed over-generation and ESS efficiency also provided and calculations carried out accordingly. In addition to the technical consideration, an economic analysis approach added to the study. Results show that staying with the current level of RE penetrations, i.e. at PV 11% and W 9% (RE level 20% of load demand) and 5% curtailment, no need to install ESS to absorb the RE intermittency and variability. As RE penetration level increases, the need for ESS becomes necessary, i.e. to reach 100% RE penetration and de-carbonize the electrical network accordingly, an ESS of 138,900.00MWH/ 7,386.57MW PHES would be required at PV 65% and Wind 35% penetration level and 25% curtailment with initial CAPEX investment of JOD 15,367,314,444.10. Economic feasibility study shows that with the current RE energy prices and ESS CAPEX, reaching the 100% RE penetration level would not be the optimum solution as there will be a negative cost impact, unless these prices reduced to a certain level making the solution feasible.

A self-powered interface circuit for piezoelectric and photovoltaic energy extracting

This paper presents a high-efficiency multi-source energy harvesting system consisting of a piezoelectric rectifier, a photovoltaic maximum power point tracking circuit and a fast self-startup circuit for powering wireless sensor nodes. Synchronous electric charge extraction techniques (SECE) are utilized to extract the piezoelectric energy when the deflection of piezoelectric energy harvester beam reaches its peak. Compared with other circuits, the proposed SECE technique can achieve piezoelectric and photovoltaic extraction simultaneously with a concise flyback topology structure. A novel maximum power point tracking method is adopted to enhance the robustness of operation against changing operating condition and improve the photovoltaic extraction efficiency. To remove the need for a battery, a simple and efficient self-startup circuit is introduced. The energy harvesting circuit is fabricated in 180 nm CMOS process. Measurement results show a fast self-startup at a relatively low light intensity of 120 lux is realized. The energy harvesting circuit is capable of outputting 189 μW power at 3.3 V piezoelectric open circuit voltage and 0.6 V photovoltaic input voltage.

Dual-objective optimization of solar driven alkaline electrolyzer system for on-site hydrogen production and storage: Current and future scenarios

Achieving cost competitiveness of renewable hydrogen could accelerate the transition to the deeply decarbonized energy system. In this article, we develop and apply a dual-objective optimization model to explore the photovoltaic (PV) hydrogen production pathway and costs development from the present through 2050. The model is applied for optimal capacity allocation of the megawatt-scale off-grid PV-Hydrogen system to achieve maximum production at the minimal levelized cost of hydrogen (LCOH). Methodology includes a smoothing control strategy. Simulation is performed utilizing measured meteorological data for one year with hourly resolution and considering electrolyzer's load flexibility constraint. It has been found that the smoothing control strategy is indispensable for maximizing PV energy utilization, enhancing the electrolyzer's capacity factor and reducing the power curtailments. The analysis shows that the off-grid solar hydrogen in Algeria lacks economic competitiveness currently. Components CAPEX reduction turns out to be the fundamental condition towards the future LCOH decrease. LCOH could decline from 4.2 $/kg in 2025 to 2.24 $/kg in 2050 under central assumptions and to roughly 1.4 $/kg under optimistic assumptions. Alkaline electrolysis step cost could reduce by 0.28 $/kg every decade. Hydrogen storage autonomy could rise the LCOH by 7.1 c$ per day of autonomy in 2050.

Two-Stage Locating and Capacity Optimization Model for the Ultra-High-Voltage DC Receiving End Considering Carbon Emission Trading and Renewable Energy Time-Series Output Reconstruction

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model.

Developing an energy storage systems selection methodology for peak shaving purposes in photovoltaic micro-installations

This study investigates the selection methodology for peak shaving purposes in photovoltaic micro-installations. Due to the increasing share of photovoltaic micro-installations in the national electricity production and the consequent changes in the country's energy policy, the need for energy storage is becoming increasingly apparent. Therefore, based on data collected throughout the study of a photovoltaic installation conducted in years 2020-2024 and data on market electricity prices, the correlation between production, market electricity prices, and energy value was analyzed. The analysis established interconnections between these values and identified factors influencing their changes. It was observed that the market price of electricity is relatively low during hours of increased photovoltaic energy production. By comparing the correlations between energy production and electricity prices over different years, an increasing impact of the increasing share of photovoltaic energy production in national electricity production on the market price of electricity was noted, highlighting the importance of energy storage. For the described household, an energy storage system was selected with a view to meeting the energy demand during hours of higher market electricity prices. The selected model was Lithium-Iron-Phosphate battery with a capacity of 5 kWh. Furthermore, the cost effectiveness of household-scale energy storage systems was examined. Implementing the proposed solutions could result in increased auto-consumption and presumably improve cost-effectiveness of photovoltaic installations in the future.

Optimization of PV benefits for electric vehicles charging station using a deterministic method and fuzzy inference system method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of −863.4c€/day, whereas the FIS method delivers greater savings of −898.7c€/day. For fast charging, the deterministic approach yields savings of −524.6c€/day, while the FIS method results in even higher savings of −540.7c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20 %, compared to 81.60 % with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Optimal scheduling of distributed generation in smart microgrids: A comprehensive model and efficient algorithm

Abstract This study proposes an optimal scheduling model for distributed generation (DG) within smart microgrids, incorporating various distributed energy resources (DERs) such as photovoltaic panels, wind turbines, biomass generators, and energy storage systems. To address the complexities of the scheduling problem, we design a hybrid optimization algorithm combining Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). This hybrid algorithm leverages the global search capabilities of GA and the local search efficiency of PSO to achieve robust and efficient convergence to near-optimal solutions. A comprehensive case study based on a real-world smart microgrid system demonstrates the effectiveness of the proposed model and algorithm. The results indicate significant reductions in total operational costs, enhanced renewable energy utilization, reduced grid dependency, and improved system reliability. This research highlights the potential for broader implementation of the model, contributing to the advancement of smart grid technologies and the transition towards sustainable energy systems.

Advancing global solar photovoltaic power forecasting with sub-seasonal climate outlooks

Given the high weather dependency of solar photovoltaic energy, accurate weather information ranging from days to weeks in advance are required for stable plant operation and management. This study emphasizes that sub-seasonal climate outlooks can advance global solar power forecasting. We evaluate the capacity factor (CF) for standard test conditions (CFstc), calculated from atmospheric reanalysis of surface solar irradiance and ambient air temperature, by comparing it with actual CF from two solar plants in Korea. Results confirm CFstc as a reliable estimate of actual CF. Application of this methodology to four-week outlooks from two state-of-the-art sub-seasonal climate forecast systems shows a significant anomaly correlation coefficient (ACC) skill for global solar power forecasting at least one week in advance (5–11 forecast days). Regional ACC skills vary at extended lead times, with South Asia, eastern South America and eastern Australia maintaining ACC > 0.6 for up to two weeks (12–18 forecast days). Notably, useful ACC skill persists for up to four weeks (26–32 forecast days) across 70.9 % of global land areas. These findings suggest that sub-seasonal climate outlooks can provide decision-making information in developing more reliable solar energy strategies, highlighting the importance of transdisciplinary collaboration between renewable energy and meteorological sectors.

Sub-nA photovoltaic energy harvesting circuit for miniaturized battery-less sensor edges

In this letter, we report on the photovoltaic energy harvesting circuit for miniaturized, battery-less sensor devices. To accumulate nanowatt power from ultra-small photovoltaic cells, it is essential to reduce the minimum operating current of the energy harvesting circuit. To reduce the minimum operating current, we propose a dual-capacitance voltage monitor (DCVM) which can overcome the issue of the voltage drop caused by the shoot-through current for switching. We designed the energy harvesting circuit including the DCVM in a 180 nm CMOS standard process, and successfully demonstrate the minimum operating current of 1 nA.

Designing an intelligent smart energy monitoring system for optimizing the utilization of PV energy

Designing an intelligent smart energy monitoring system for optimizing the utilization of PV energy

A frequency control strategy with DC microgrid systems for EV stations

ABSTRACT Fluctuations in power supply and demand lead to frequency deviations, which have detrimental effects on the operation of electrical devices and equipment. Therefore, the implementation of effective frequency control mechanisms is essential for enhancing the strength and dependability of microgrid systems. Research aims to develop a frequency control mechanism for an electrical grid system that integrates both wind and photovoltaic (PV) energy sources. The proposed system utilises a Doubly Fed Induction Generator (DFIG) system with frequency controller for regulating frequency and balancing source and demand of power in the grid. To optimise the solar power output, a Luo converter is implemented, which offers high efficiency with improved conversion of power. Additionally, an adaptive neuro fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller is engaged to ensure optimal regulation of photovoltaic system. DC link obtained from the PV system is then utilised for powering DC loads and charging electric vehicles (EVs) effectively. In order to accomplish this, a bidirectional converter is used, which permits power flow in both ways and facilitates effective EV charging from the microgrid system. The validation of research is conducted on MATLAB software, and the results proves greater converter efficiency of 94.93%, with reduced total harmonic distortion (THD) of 1.24%.

A Case Study on Deep Learning for Photovoltaic Power Forecasting Combining Satellite and Ground Data

The increasing demand for clean energy presents challenges in energy supply management, largely due to their intermittency. Photovoltaic power generation, in specific, is greatly affected by weather factors, which may render power grids susceptible to instability, quality and balance issues. In this context, photovoltaic power generation forecasting is crucial not only to enhance the management of diverse energy sources through generation planning, but also to ensure widespread adoption of photovoltaic energy. To address the predictability issue in generation, this study aims to investigate the combination of satellite data with meteorological data to predict the energy generation potential in photovoltaic panels within 30, 60, 120, and 180-minute horizons. For this purpose, images from the GOES-16 satellite are used in combination with data from a ground-based weather station, located at Florianópolis – Santa Catarina – Brazil. The data is fed to a convolutional neural network, where convolutions are employed to extract features from the satellite images, aiming to establish a relationship with solar irradiation. The output of the convolutional network serves as input for a multilayer perceptron network, which utilizes the data to predict the Global Horizontal Irradiance (GHI). Our results support that models incorporating satellite images provide forecasts approximately 41% better for the 30-minute horizon and 21% better for the 180-minute horizon, when compared to models without satellite images.