Photovoltaic Generation Research Articles

ESTADO DINÁMICO DE UNA MICRORRED TRIFÁSICA INCLUYENDO GENERACIÓN FOTOVOLTAICA BAJO TRANSITORIOS EN LA FRECUENCIA FUNDAMENTAL DE OPERACIÓN

This technical note analyses the transient response of a microgrid with photovoltaic (PV) generation controlled by DC/AC inverters and a dq0 current controller to regulate the power flow, voltage and current at the point of common connection (PCC).

Optimal capacity allocation and scheduling strategy for CSP+PV hybrid standalone power plants

Solar photovoltaic (PV) power generation, as an important clean technology, has been widely adopted globally, especially in remote island areas where access to the main power grid is unavailable. PV power can serve as the primary energy source for standalone island grids. However, due to its dependence on sunlight, PV power output fluctuates, particularly during nighttime and under poor lighting conditions, necessitating the integration of energy storage technology or alternative power generation methods to ensure continuous power supply. Concentrating solar power (CSP) generation, as an emerging technology, can provide efficient power output when solar radiation is abundant and ensure continuous power supply through thermal energy storage systems during adverse weather conditions or nighttime. Although CSP offers advantages such as dispatchability, its high construction and maintenance costs may pose challenges in commercial deployment. Hybrid solar power plants combining both PV and CSP technologies leverage the strengths of both, ensuring more stable and economically viable power output. This study establishes a model for hybrid solar power plants, considering the impact of PV and CSP component capacities and proportions on performance and costs, i.e., capacity allocation. To maximize the overall benefits of standalone microgrids while ensuring the stability of the power station, a capacity allocation method guided by economic dispatch is proposed. Through iterative analysis, the optimal configuration is determined to minimize the system's equivalent annual costs. Simulation experiments validate the financial feasibility of hybrid solar power plants and the reliability of the proposed configuration method.

Performance analysis of hybrid off-grid renewable energy systems for sustainable rural electrification

Delivering sustainable power to rural communities in sub-Saharan Africa, particularly in Somalia, where many areas are far from the electrical grid and suffer from energy shortages, necessitates the deployment of appropriate technologies. This study evaluates the techno-economic and environmental viability of a hybrid renewable energy system (HRES) comprising a 15 kWp photovoltaic (PV) generator, 10 kW wind turbine (WT), 25 kW diesel generator (DG), and a 72-kWh battery energy storage system (BESS). The HRES is proposed to supply sustainable power to a rural community in Somalia. The techno-economic assessment of the proposed HRES and a comparative analysis of other configurations—namely the PV/WT/BESS and PV/DG/BESS systems is conducted using HOMER and MATLAB software. The results indicate that the proposed PV/WT/DG/BESS HRES demonstrates superior techno-economic performance compared to the other configurations. The lowest net present cost of $96,899.16 and the levelized cost of energy (LCOE) of $0.090/kWh were achieved. This system attains 25 % excess electricity, maintains a 0 % unmet electric load, and demonstrates robust environmental performance with a renewable penetration rate of 91.8 %. Approximately 53.34 % of greenhouse gas emissions are reduced compared to the PV/DG/BESS configuration. The study confirmed the significant impacts of all sensitivity parameters on operational costs, LCOE, fuel prices, fuel intake, and renewable fraction. The overall results indicate superior performance in the optimal system case, offering a feasible and suitable choice for the sustainable electrification of rural communities.

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.

Increasing Renewable Energy Penetration on Low-Voltage Networks: An Expert Knowledge Approach

While South Africa is deemed one of the countries with the highest irradiation levels, it still utilises coal as its primary energy source due to its abundance. Due to the world-wide drive towards carbon neutrality, residential, commercial, agricultural, and industrial consumers are considering small-scale embedded generation systems. The National Rationalised Specifications 097-2-3 document specifies the scale of the embedded generation capacity a consumer is allowed to install. However, specifications do not yet make the required provisions for the addition of energy storage. The effective collective management of the grouped small-scale embedded generation systems could provide a high level of energy security and increase the percentage of renewable energy generation in the total energy mix. Potential challenges come into play when considering the stochastic nature of photovoltaic generation and its effect on the storage capacity and the dispersion in load profiles of the residential units typically present on a low-voltage network. This paper contributes by investigating the utilisation of photovoltaic generation in conjunction with storage as the basis for virtual power plant control, with the aim to safely increase renewable energy penetration and improve energy security, all while remaining within the South African low-voltage regulatory limits. A two-level virtual power plant controller is proposed with the dispersed energy storage units as the primary controllable resources and the dispersed photovoltaic generation as the secondary controllable resources. The objective of the controller is to achieve nodal energy management, energy sharing, and ancillary service provision and finally to increase renewable energy penetration. A representative single-feeder low-voltage network is simulated, and test cases of 50% and 75% renewable energy penetration are investigated as the basis for evaluation. The proposed controller architecture proved to maintain network integrity for both test cases. The adaptability of the controller architecture was also confirmed for a changed feeder topology; in this case, it was a multi-feeder topology. Future work is warranted to inform policy on the allowed levels of renewable energy penetration to be based not only on demand but also on the level of energy storage present in a network.

Review of Recent Offshore Floating Photovoltaic Systems

Photovoltaic (PV) power generation is a form of clean, renewable, and distributed energy that has become a hot topic in the global energy field. Compared to terrestrial solar PV systems, floating photovoltaic (FPV) systems have gained great interest due to their advantages in conserving land resources, optimizing light utilization, and slowing water evaporation. This paper provides a comprehensive overview of recent advancements in the research and application of FPV systems. First, the main components of FPV systems and their advantages as well as disadvantages are analyzed in detail. Furthermore, the research and practical applications of offshore FPV systems, including rigid floating structures and flexible floating structures, are discussed. Finally, the challenges of offshore FPV systems are analyzed in terms of their stability and economic performance. By summarizing current research on FPV systems, this overview aims to serve as a valuable resource for the development of offshore FPV systems.

Quantification of Uncertainty in PV Generation Prediction through Machine Learning Regularization Techniques

Quantification of Uncertainty in PV Generation Prediction through Machine Learning Regularization Techniques

Comparative Study of Zinc Sulfide Thin Films Fabricated by Spin Coating and Rf Magnetron Sputtering as a Buffer Layer for 2nd Generation Photovoltaics

Comparative Study of Zinc Sulfide Thin Films Fabricated by Spin Coating and Rf Magnetron Sputtering as a Buffer Layer for 2nd Generation Photovoltaics

Generación renovable eólica y fotovoltaica en Ecuador: Una revisión sistemática de literatura

INTRODUCTION. Photovoltaic and wind renewable generation has grown exponentially in recent years worldwide, in Ecuador this technology has not been the exception, as several photovoltaic and wind power plants have been installed, increasing the generation of renewable energy. OBJECTIVE. This article presents a systematic review of the literature on operational photovoltaic and wind power plants in Ecuador, offering a perspective on the renewable sources connected to the national energy system. METHOD. Through a bibliographic search in the Scopus database, 8 publications were obtained that met the eligibility criteria, in addition to reports issued by government entities, to ensure that the information and technical aspects are accurate. RESULTS. The documentary analysis allows us to know the current status of photovoltaic and wind power plants, identifying their installed capacity, technology used, and operational characteristics. DISCUSSION AND CONCLUSIONS. The information collected provides a solid basis for understanding the current status and development prospects of photovoltaic and wind power generation in Ecuador. Despite certain limitations related to the availability of data in open sources, it was possible to obtain significant information on the main renewable plants in the country. These results serve as a basis for future research aimed at the expansion and optimization of renewable energies in the national context..

AIoT-Based Visual Anomaly Detection in Photovoltaic Sequence Data via Sequence Learning

Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data.

Long-Term Optimal Scheduling of Hydro-Photovoltaic Hybrid Systems Considering Short-Term Operation Performance

Integrating photovoltaic power stations into large-capacity hydropower stations is an efficient and promising method for regulating large-scale photovoltaic power generation. However, constrained by the time step length, traditional long-term scheduling of hydro-PV hybrid systems does not adequately consider short-term operational performance indicators, resulting in sub-optimal scheduling plans that fail to coordinate the consumption of photovoltaic power and the utilization of water resources in the basin. To address this, this study established a long-term optimal scheduling model for hydro-PV hybrid systems. This model overcomes the limitation of the time step length in long-term scheduling by incorporating long-term power generation goals and short-term operation performance targets into the long-term optimal scheduling process based on scheduling auxiliary functions. In case studies, the optimised model balanced the long-term power-generation goals and short-term operational performance targets by redistributing energy across different periods. Compared to optimization models that did not consider short-term operation performance, in a typical normal year, the model effectively reduced the electricity curtailment volume (28.54%) and power shortage volume (10.91%) of the hybrid system while increasing on-grid electricity (0.03%). Similar improvements were observed in wet and dry years. These findings provide decision support for hydropower scheduling in the context of large-scale photovoltaic power integration.

Distribution Grid Hosting Capacity Improvement using Reactive Power Control Optimization Algorithm of Inverter-based Photovoltaic Generation System

This paper uses a reactive power control optimization algorithm to present an inverter-based photovoltaic generation system for increasing the distribution grid host capacity. The main objective of this study is to regulate bus voltages by providing sufficient minimal reactive power consumption, which is obtained by a Particle Swarm Optimization-based optimization algorithm. A modeling study of the system network is developed using DIgSILENT PowerFactory, and the control algorithm is implemented in MATLAB. The proposed approach is efficient for various scenarios of the IEEE 13-bus test system and practical distribution network. The analysis results are compared with outcomes from other control methods, including technical and economic assessments. The performance of the proposed optimization-based power system has shown that the proposed algorithm method yielded significantly superior mitigated voltage rises and increased hosting capacity compared to those achieved by existing control methods of a specific distribution network.

Determination of I-V curves for photovoltaic generator by an analytical method; Akbaba model

In order to maximize the amount of electrical energy generated and ensure effective use, the conventional I-V or V-I characteristics derived from the diode model of Photovoltaic (PV) Solar Panels are important. Unfortunately, these features cannot be extracted using linear equations. It follows that the answer requires extremely drawn-out, time-consuming procedures. Many studies focus on calculating maximum power extraction. The analytical solutions to these equations are also nonexistent. Programming on computers can assist in solving the equations. The Akbaba Model was created specifically for the PVG to address these challenges. The Akbaba Model relies heavily on coefficients A, B, and C in this equation. The diode model serves as the true model in this case, and the coefficients A, B, and C are calculated. Another advantage of this model is that, because its qualities have partially deteriorated with time, the genuine I-V characteristics of the PV Panels can be recovered by measuring again.

Based on Deep Learning Model and Flink Streaming Computing Short Term Photovoltaic Power Generation Prediction for Suburban Distribution Network

With the advancement of global energy internet construction, accurate prediction of new energy generation power such as photovoltaic is an important foundation for ensuring the safety and economic working of new power systems. A short-term photovoltaic power generation prediction method for suburban distribution networks based on deep learning model fusion and Flink flow calculation is proposed to address the challenges of complex power grids, diversified disturbance factors, and isolated monitoring points. This method uses Bi directional Long Short Term Memory(BiLSTM) to extract cross sequential nonlinear characteristic of photovoltaic power generation time series data. Compared with standard LSTM, BiLSTM can consider both historical and future information simultaneously, thus extracting richer extracted features from power generation time series data. This method also integrates attention mechanism to capture the importance distribution of historical temporal features for power generation prediction, effectively solving the problem of long-term temporal dependence in standard LSTM models. The Flink streaming computing framework embeds a trained BiLSTM-Attention photovoltaic power generation prediction model, enabling real-time prediction and monitoring analysis of photovoltaic power generation at various monitoring points in the suburban distribution network. This article uses a dataset of a suburban photovoltaic power station for validation, and trains the model with historical power generation data, meteorological factors, weather types, seasons, and other information as inputs. The BiLSTM-Attention fusion model studys the temporal characteristics of power generation, and has high accuracy in predicting short-term photovoltaic power generation in different scenarios. The Flink streaming computing platform can not only process high throughput predicted power data, but also has low time delay.

A Short-term PV Power Prediction and Uncertainty Analysis Model Based on CEEMDAN and AHA-BP

The stochastic and intermittent nature of photovoltaic (PV) generation brings a series of scheduling problems to the power system. An effective prediction of PV power is essential to minimize the impact of uncertainty. Therefore, this paper presents an integrated prediction model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the artificial hummingbird algorithm (AHA), and the BP neural network (BPNN) for predicting power generation from PV power plants, and a methodology for uncertainty analysis by using the nonparametric kernel density estimation (NPKDE). First, one month of PV power is decomposed into an array of components using CEEMDAN. Then, the weights and thresholds of the BPNN are optimized by using AHA. These components are trained using the BPNN. Finally, the final prediction results are obtained by superimposing the components, and NPKDE is employed to compute the probability density and confidence interval of the prediction error. The proposed prediction method demonstrates superior predictive performance in comparison with other models. Also, the NPKDE approach better describes the accuracy of the probability density distribution.

Energy management and demand side management framework for nano-grid under various utility strategies and consumer’s preference

This research proposes a day-ahead scheduling utilizing both demand side management (DSM), and Energy Management (EM) in a grid-tied nanogrid comprises of photovoltaic, battery, and diesel generator for optimizing the generation cost and the energy not supplied (at grid-outage). Wider terminology is introduced to combine both load controllability (considered in traditional DSM), and interval capability to accommodate additional loads defined as flexible, non-flexible, and semi-flexible intervals. Moreover, the user selection for EM or combined operation of EM with DSM at different degrees of interval flexibility is defined as user preference. In addition, three utility’s operations are considered denoted as fixed rate pricing (FRP), time-of-use (ToU) pricing, and FRP with grid-outage. Hence, the suggested framework utilizes the opportunities of generation diversity, the electricity pricing strategy, and the load flexibility. The obtained result show that, DSM with flexible intervals reduces the cost by 21.02%, 25.23%, and 18.15% for FRP, ToU, and FRP with grid-outage scenarios respectively. And cost reduction by 20.41%, 22.42%, and 17.81% for DSM with semi-flexible intervals and 16.24%, 21.15%, and 13.8% for DSM with non-flexible intervals. This cost reduction is associated with full utilization of renewable energy generation and reduction of the energy from/to battery which enhances its lifetime or reduces the required battery size during design stage for cost and provisions saving in flexible and semi-flexible intervals. A hybrid optimization technique of Moth-flame optimization algorithm, and Lagrange’s multiplier is proposed and confirms its effectiveness with detailed comparison with other techniques.

FUZZY LOGIC-BASED POWER MANAGEMENT IN GRID-CONNECTED SOLAR ENERGY SYSTEMS

With the ever-increasing application of photovoltaic systems on the power grid, energy flow management and grid stability have become a key factor, especially under varying conditions like variable cloud cover and fluctuating load demands. The paper looks forward to proposing a fuzzy logic approach to the management of energy regarding a solar energy system connected to a grid, aiming at obtaining an optimised power distribution among PV generation, the grid, and battery storage. The proposed system will be driven by five input parameters, PV value, grid availability, load demand, battery state of charge, and tariff rate, to regulate the energy flow activity within different scenarios. The simulation results developed in the MATLAB Simulink environment proved the effectiveness of the applied controller in maintaining energy stability and fulfilling the requirements of a load within seven case studies for a 24-hour duration. The work points out the potential of fuzzy logic, besides reliability, in enhancing the efficiency of various renewable energy systems integrated with conventional energy supply grids.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0799/a.php" alt="Hit counter" /></p>

Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system

With a target of 35 % renewable energy in the country’s energy mix by 2030, the goal of this research is to support the Libyan government’s plan to maximize the use of environmentally friendly and sustainable energy sources. This will be accomplished by making effective use of the region’s plentiful natural resources. The feasibility of wind and solar energy has been established by local research, and the presence of highlands that can store pumped hydropower (PHS) makes hybrid renewable energy systems (HRES) even more feasible. These systems might offer a sizable edge over competitors in the regional energy market. In this study, PHS was utilized to stabilize electricity production in addition to storing energy. A hydropower turbine provides electricity to the load, and the PHS system is powered by a PV/wind supply. By lowering the volatility of wind and photovoltaic power generation, the suggested system could offer a more dependable renewable energy source without requiring complicated control mechanisms. This strategy could encourage a wider adoption of renewable energy, which could be especially advantageous for developing nations. Under particular load and climate conditions, the energy production of different solar PV array and wind turbine farm configurations was estimated using the System Advisor Model (SAM) software. The ideal HRES configuration consists of a 290 MW wind farm, a 154 MW solar PV field, and a 508 MW PHS system. This configuration will provide 100 % of the annual electrical demand of 513 GWh, plus a 20 % safety margin. The levelized cost of energy (LCOE), estimated at $211.65/MWh, is reduced by this configuration. It is anticipated that the $929.02 million initial investment will be recovered in 11 years, and the system will turn a profit in the following 9 years. Also, the planned HRES will stop 532.17 tons of CO2 emissions from being released each year.

Research and Case Application of Zero-Carbon Buildings Based on Multi-System Integration Function

This study focuses on developing and implementing zero-carbon buildings through the integration of multiple systems to meet China’s carbon neutrality goals. It emphasizes the significant role of the building sector in carbon emissions and highlights the challenge of increasing energy consumption conflicting with China’s “dual carbon” targets. To address this, the research proposes a comprehensive framework that combines multifunctional envelope structure (MES) systems, photovoltaic power generation, energy storage, direct current (DC) systems, flexible energy management (PEDF), and regional energy stations. This framework integrates different technologies such as phase change materials, radiation cooling, and carbon mineralized cement, aiming to reduce carbon emissions throughout the building’s lifecycle. The method has been successfully applied in the Yazhou Bay Zero Carbon Post Station project in Sanya, Hainan, with precise calculations of carbon emission reductions. The carbon emission calculations revealed a reduction of 44.13 tons of CO2 annually, totaling 1103.31 tons over 25 years, primarily due to the rooftop photovoltaic systems. It demonstrates that the multi-system integration can reduce carbon emissions and contribute to China’s broader carbon neutrality goals. This approach, if widely adopted, could accelerate the transition to carbon-neutral buildings in China.

Fuel Cell-Based Distributed Robust Optimal Scheduling for Combined Heat and Power Supply

At present, the safe operation of integrated energy systems is significantly affected by the considerable uncertainty inherent to wind and photovoltaic power generation. Based on this, this paper proposes an optimal scheduling model for integrated electricity, heat, and hydrogen-based energy systems on distributed robust optimization (DRO). Firstly, a combined heat and power microgrid system considering hydrogen energy systems was constructed based on the thermoelectric cogeneration characteristics of fuel cells and electrolyzers. Then, a data-driven two-stage distribution robust optimization scheduling model is built by combining typical historical data of wind power output, photovoltaic power output, and load. The results show that the distributed robust method reduces the running cost by 6% compared to the deterministic method. The proposed method and model are capable of meeting the demand for thermoelectric loads within the microgrid in a more cost-effective manner, thereby achieving stable and independent operation of the system.