PV Power Generation Research Articles

Effect of Saharan dust episodes on the accuracy of photovoltaic energy production forecast in Hungary (Central Europe)

In order to meet global sustainability goals, in particular, the rapid decarbonisation of the energy sector in combination with geopolitical energy security issues, as well as further improvement in regional air quality-the so-called renewable energy sources are becoming increasingly critical and important. Photovoltaic power (PV) generation is clearly the most widely deployed (non-hydro) renewable energy source globally, which still has a significant growth potential in many countries, including Hungary.However, due to the intermittent nature and the strong dependency of photovoltaic power production on meteorological factors, continuous adjustment of electric power in the electric grid is needed. This, in turn, requires the most accurate forecast of the photovoltaic energy to be produced in the ultra-short term (15 min) and short term (1 day) in order to minimise last-minute and high-price energy acquisition or unplanned kicks-in of gas-fired auxiliary power plants.In this paper, we evaluate the impacts of large-scale dust transport episodes on the accuracy of forecasts of PV power generation. The numbers and intensities of Saharan dust storm events reaching Central Europe have increased over the last decade, hitting a new record in 2022 with 16 (!) African dust episodes observed over Hungary. We have shown that the semi-direct effect of atmospheric dust particles on high-level cloud formation rather than their direct irradiance-reducing effect is responsible for the reduced accuracies of e short-term (24-h) PV energy production forecasts during these events.

A multi-factor spatio-temporal correlation analysis method for PV development potential estimation

The impact of wind speed on PV power generation is influenced by the monsoon and the complex topography and environmental factors found in mountainous areas. This complexity can reduce the correlation between PV power and irradiance. To better understand the impact of wind speed, we conducted an analysis in various topographical regions of the Hengduan Mountain in China. Our findings show that high wind speeds can enhance PV utilization, particularly in plateau and hill regions. This is due to the blocking and lifting effect of the north-south oriented mountains on the east-west wind, as well as the channeling effect on the north-south wind. To improve the efficiency of utilizing solar energy, it is important to consider the spatial distribution of wind speed when developing PV. Correlation analysis of multi-factors on a spatial and temporal scale can also offer insight. Furthermore, planning the layout of PV systems with meteorological resources in mind can enhance PV utilization in complex mountainous terrain.

Research on Operation and Maintenance Strategy of New Energy Photovoltaic Power Generation System Considering Extreme Weather Conditions

Abstract This paper employs the gray correlation analysis method to compute the correlation between irradiance, ambient temperature, wind speed, and photovoltaic power generation efficiency. This method relies on screened data for data normalization, and after processing, it determines the degree of similarity between the factors. We propose a dynamic combined maintenance strategy for PV power generation systems that considers reliability, overhaul resources, and overhaul time constraints. We propose a two-layer optimization strategy based on extreme weather factors and failure-related characteristics, and we use empirical analysis to investigate the maintenance strategy’s effectiveness. The analysis reveals a positive correlation between irradiance and PV power generation efficiency, with a multiplicative increase in the range of 1-3. In the correlation analysis of ambient temperature and wind speed variables, the power generation curve is not consistent with its trend, which is related to the influence of extreme temperature and extreme wind speed, and the meteorological factors affecting the PV power generation efficiency change at different moments of the day, where the correlation coefficient at 6:45 is 0.912. When the failure correlation strength increases to a value of 0.1, the number of repairs rises to 8. When the weather accessibility ratio is 0.2, the reduction in system availability is up to 62%. The two-layer optimization strategy, which considers extreme weather factors and failure-related characteristics, is both economical and universal.

Detection of shading for short-term power forecasting of photovoltaic systems using machine learning techniques

This paper presents a mahcine learning based solar power forecast methode that can take into account shading related fluctuations. The generated PV power is difficult to predict because there are various fluctuations. Such fluctuations can be weather related when a cloud passes over the array. But they can also occur due to shading caused by stationary obstacles, and this paper addresses this form of shading. In this work an approach is presented that improves the forecast under such fluctuations caused by shading. A correction of the prediction could successfully reduce error due to shading. The evaluation of the model is based on five sets of recorded shading data, where shading resulted from intentionally placed structures. The correction uses internal inverter data and irradiance values of the previous day to perform the correction and was able to reduce the RMSE of four 10 kWp systems with different orientation and tilt angle under shading and thus improve the prediction accuracy by up to 40%. The model can detect how intense the shading is and correct the forecast by itself.

Assessment of New Energy Consumption Capacity of Grid Based on Edge Computing

Distributed photovoltaic grid integration brings many negative effects to the distribution network, and carrying out the research on its consumption capacity (CC) in the distribution network is the key to guarantee the safe and stable operation of the distribution network. This paper takes the maximum power access mode and minimum network loss as the optimization objectives, and takes voltage deviation, voltage fluctuation and short circuit capacity as the constraints to construct the evaluation model of distributed PV power generation CC. In view of the above problems, this paper uses edge computing to construct the evaluation of new energy (NE) CC of the grid, and applies it to the IEEE33 node network and the actual network. Finally, the established mathematical model and algorithm are validated (relative to Monte Carlo, the evaluation time of edge computing and time series production simulation is shorter, 13.11s and 23s, respectively).

Novel MPPT algorithm with MIWO for PV Power Generation System under Partial Shading

Novel MPPT algorithm with MIWO for PV Power Generation System under Partial Shading

The Application of PVSyst for Design of Solar Photovoltaic Power Generation at School Building

The need of electrical power at the school building may be partly fulfilled by solar PV power generation. The panels are installed on the rooftop of the building. For the purpose of optimal design of the generation system, PVSyst is employed for this task. This paper proposes the design of solar PV generation using PVSyst. The design result is then confirmed with the real system. Some adjustments will be carried out to align the design with the real system. For the design, the data of electric load is recorded. The capacity of solar intensity and other parameters were also recorded. With the data in hand, the design of solar PV generation may be carried out using PVSyst. With the design results, the real system may be implemented referring the document of design. Since the capacity of the available panels is limited some application may not be completely implemented. Besides the contribution in the form of load sharing, the system may be used by the students to learn how the electricity can be generated using the resources that are freely available

Optimal Day Ahead Active and Reactive Power Management in Residential Buildings using Particle Swarm Optimization

This paper introduces an innovative Active and Reactive Residential Building Energy Management (AR-RBEM) system, seamlessly integrated with a smart microgrid and the Electrical Power Grid (EPG) supplier. The proposed AR-RBEM computes optimal active and reactive power setpoints for each energy source over the next 24 hours based on predicted PV power generation and load consumption. The proposed strategy aims to achieve two primary objectives: minimize overall energy expenses, considering both active and reactive power consumption, and account for Energy Storage System (ESS) degradation costs. Additionally, it seeks to reduce the Peak-to-Average Ratio (PAR) and carbon emissions from the supplier’s perspective. To tackle this multi-objective problem, a metaheuristic algorithm which is Particle Swarm Optimization (PSO), is employed. To assess the effectiveness of the proposed approach, a comparative analyse is conducted, emphasizing the significance of concurrently managing active and reactive power compared to a system exclusively managing active power (A-RBEM). Both strategies are evaluated using Time of Use Tarriff (TOU) and real world data. The comparative analysis underscores the advantages of the proposed AR-RBEM, showcasing benefits in terms of cost reduction for both active and reactive power of 59,6%, a substantial 25.3% reduction in carbon emissions compared to the alternative strategy.

Study on the Equivalent Conversion and Utilisation Model of Solar Energy and its Benefits

Relying on the energy local area network (LAN), we study the equivalent conversion relationship between different “energy qualities” in the process of solar energy conversion and utilisation and its benefit model. Considering the impact of load demand characteristics and current energy consumption price factors on solar energy utilization, a multi-objective optimization model of solar energy conversion and utilization is constructed with the goals of maximizing solar energy utilization and maximizing the comprehensive benefits of system operation; then, the particle swarm optimization algorithm with quantum behaviour is used to optimize and solve the model, and a comparative analysis is carried out with the traditional single-energy supply mode. The results show that the proposed model can effectively improve the comprehensive utilisation and consumption of solar energy by the energy consumption system, and achieve higher energy sales benefits. Therefore, with the help of energy storage technology, the fluctuation and intermittency of the solar PV system output can be suppressed, and flexible grid connection can be realised, so as to enhance the grid-connected capacity of solar energy; at the same time, higher economic benefits can be obtained with the help of optimisation, coordination and control system operation. Optimised control of solar PV power generation systems under different scenarios ensures the safety and stability of the system’s power output and improves the grid-connected capability of solar PV power generation systems. The validity and feasibility of the proposed model and its operation mode are verified, providing a new idea for the development and utilisation of large-scale solar energy.

Control approach for photovoltaic inverters enhancing the primary grid using the virtual synchronous generator concept

This paper presents a control scheme for virtual synchronous generators (VSGs) in PV inverters, designed to enhance grid frequency and voltage. Through the skillful management of active and reactive power, this control scheme enables PV inverters to interact seamlessly with the main grid in response to grid events, including voltage and frequency fluctuations. The VSG controller is implemented using Matlab Simulink, and its effectiveness is rigorously assessed under various scenarios in a case study involves a 50 kVA rated PV inverter, a 50 kW rated PV system, and a 220 V grid phase voltage. In conditions of low power generation from the PV system (solar radiation of 200 W/m²) and high load power (120 kW and 37.5 kVAr), the load voltage drops to 202.4 V. The VSG controller successfully raises the grid voltage by 17.6 V, stabilizing it at 220 V. Conversely, in scenarios of high PV power generation (solar radiation of 1000 W/m²) and low load power (20 kW and 7.5 kVAr), the grid voltage surges by 10 V, reaching 230 V. The proposed control strategy adeptly fine-tunes the voltage, ultimately stabilizing at 220 V. Additionally, when the frequency deviates within ±0.4 Hz from the nominal frequency of 50 Hz, the proposed control effectively restores the frequency to its nominal value.

A systematic review of recent developments in IoT-based demand side management for PV power generation

A systematic review of recent developments in IoT-based demand side management for PV power generation

Control and Management Approach of Photovoltaic-Battery-Ultra-Capacitor Energy System in Standalone and Grid-Connected Modes

This paper deals with energy management for the photovoltaic-battery-ultra-capacitor energy system for a standalone and grid-connected. The developed energy system can operate in an isolated or connected mode according to the required power. Indeed, the photovoltaic generator is coupled with the DC bus voltage via a DC–DC power converter, to provide the maximum power from the photovoltaic panels. An energy storage system presented by batteries and ultracapacitors is employed to regulate the DC bus voltage through both DC–DC bidirectional power converters. The DC bus is connected to the AC bus through a three-phase power inverter to control the operating modes and ensure the energy demands. In effect, an energy management algorithm is developed to control the energy flow in the system for a standalone and grid-connected. Hence, the output voltage DC bus is controlled by the energy storage system using a Lyapunov approach to ensure high performance under load and irradiance profile variations. In this context, simulations have been carried out to prove the effectiveness of the proposed photovoltaic system. The developed energy system has been tested in standalone and grid-connected modes with the following scenarios: PV generator supplies the load, PV generator power is limited due to less solar lighting, the electric grid supplies the high load power, the surplus energy of the PV generator is injected into the electric grid, and standalone mode operation.

Optimal Configuration for Soft Open Point Based on Scenario Generation of Wind Power and Photovoltaic Output

Soft Open Point (SOP) is a new type of fully controlled power electronic device. The use of SOPs can greatly improve the efficiency, flexibility, and controllability of distribution networks. However, considering that SOPs are expensive and have high maintenance costs, the installation capacity and location of SOPs need to be optimized. In this paper, we propose an SOP optimization configuration method considering the stochasticity and correlation of wind and PV power generation. First, considering the uncertainty and negative correlation of wind and PV power output, the probability density distribution function is generated using Copula theory and historical data. Then, the optimal location of the SOPs is determined by a cluster discovery algorithm and the Power Loss Sensitivity Index (PLSI). Finally, the capacity of the SOP is determined by minimizing the integrated cost of the network over a year, and the capacity of the SOP is solved by the conic curve programming method. The superiority of the proposed method is verified in this study at the IEEE-33 node test system.

Optimized power flow control for PV with hybrid energy storage system HESS in low voltage DC microgrid

Due to the intermittent nature of solar irradiation, it is inevitable to integrate the system of energy storage in the PV standalone system. In this paper, Energy Storage System ESS composed of a battery bank and Supercapacitor SC has been employed to maintain power supply to the load. Because of the fast rate of charge/discharge of SC, it would handle the surge transient energy and that would reduce the required capacity of the battery, increase the life span, and lessen the maintenance cost. The control strategies ensure well power flow control between the PV system and ESS and thus meet the load demand, moreover sustain the DC-link voltage regulated. In the implemented control techniques, a protective improvement is proposed which would limit overcharging of the battery. That would minimize battery degradation and enhance the reliability of the system. This is performed by employing the battery uncompensated power to reduce the stress on the battery during the large fluctuations in the power due to load/irradiance variation. The control approach is based on providing optimal reference current for the ESS with fast dynamic response and reduce the computational burden when implementing in real-time, hence stable power could be delivered to the load. Moreover, the generated PV power is controlled based on the battery charging state and the demand power and this would ensure stable control of DC output voltage. The aforementioned points have led to vigorous PV system having a long-life battery and are capable of preserving energy balance between generations and load even at the abrupt load/solar irradiation variations. . A profile of solar irradiance for selected area has obtained and the system is tested under these data, and it has shown well dynamic response and this could be used for consideration of prototype implementation. The proposed control system is simulated by MATLAB/Simulink and the results show good performance under different scenarios of load/solar irradiation variations

A prediction model of dust accumulation on photovoltaic modules considering rainfall washing

AbstractThe power generation of the photovoltaic plant is related to the cleanliness of the photovoltaic modules. The accumulation of natural dust is the main source of pollution, which is affected by human activities and meteorological factors such as temperature, humidity, wind speed, and rainfall concentration in the current region. On the basis of particle swarm optimization (PSO) and the least‐squares support vector machine (LSSVM), the density of dust on photovoltaic modules was estimated. The authors proposed that the inertia weight decreased as a concave function to improve the efficiency of optimization and let the two positive constants change dynamically to improve convergence performance. The dust accumulation prediction model was established considering natural rainfall and the authors obtained the attenuation rate of the photovoltaic power output. Finally, the experiments in Hangzhou showed that the model can predict the density of accumulated dust quickly, which provides a theory for predicting PV power generation and managing the cleaning frequency.

Risk assessment of voltage overrun probability of photovoltaic access distribution system voltage based on quasi-Monte Carlo method

With the rapid development of renewable energy, distributed PV access to the distribution grid has become a common form. However, distributed PV access to the distribution network (DN) may cause voltage overrun problems, which brings challenges to the stability and reliability of the DN. In order to reasonably assess the impact of PV grid connection, the paper proposes a probabilistic trend calculation based on the quasi-Monte Carlo mode method to obtain the node voltage overrun probability under the premise of considering the stochastic nature of PV power generation and load fluctuation, and combines with the utility preference exponential function to characterize the overrun severity, to construct a comprehensive assessment model of voltage overrun probability risk. Taking the IEEE33 node system as an example, the simulation results show that the rational selection of access points effectively reduces the risk of overrun of the whole DN, and improves the safety of the distribution network.

A Study on Estimation Method of Fill Factor for PV Power Generation System from its Output Power

A Study on Estimation Method of Fill Factor for PV Power Generation System from its Output Power

Photovoltaic-Based Residential Direct-Current Microgrid and Its Comprehensive Performance Evaluation

The “dual carbon” strategy has drawn attention to distributed PV systems for their flexibility and variability, but the rising need for direct-current (DC) loads on the load side has created additional difficulties for microgrid system upgrades. In this article, a PV-based microgrid design approach for residential buildings is suggested, working on the assumption that distributed PV systems are given top priority to handle domestic DC needs. The residential DC microgrid system’s overall design concept is first put out, and the circuit system is then concentrated to supply the main idea for the ensuing verification of the system’s viability. Secondly, the actual power generation in the selected area was clarified by testing, and then the electricity consumption of DC loads accounted for about 20.03% of the total power consumption according to the survey of 100 users. In addition, the circuit system is subjected to spectral model measurements and physical measurements to verify the operational performance of the circuit system; the feasibility of the PV microgrid system is further verified using dual testing of the PV system and the circuit system. The test results show that the proposed DC microgrid system can accurately provide the required voltage for small household DC appliances, such as 24 V, 14 V, 5 V, etc. Finally, the system economics were analyzed, and the equipment payback years were estimated. The supply and demand of PV power generation and DC appliances can be balanced via the construction of a microgrid. This study offers a fresh concept for the use of PV technology. The concept behind this research can serve as a model for the creation and application of other new energy sources.

Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations

Accurate regional distributed PV power forecasting provides data support for power grid management and optimal operation. Distributed PV has the characteristics of large quantity, small capacity and difficulty in obtaining meteorological data. Statistical upscaling method is commonly used to forecast regional power. However, the current research ignores how to reasonably divide the sub-regions with similar output characteristics and mine the spatial and temporal correlation between different sub-regions. Therefore, this paper proposes a short-term regional distributed PV power forecasting method based on sub-region division considering spatio-temporal correlation. Firstly, the representative power plant is selected after dividing the sub-region by the AP clustering algorithm. Then, the GCN is used to extract spatial correlation features, and the LSTM is used to extract the evolution features of dynamic spatial correlation features, and the power forecasting models of representative plants in different weather types are established. Finally, the data integrity and similarity of the sub-region are scored, and the upscaling weight is determined to realize the power forecasting of the whole region. The distributed PV power generation data of Pingshan County, Hebei Province, China is used for simulation test. The results show that the forecasting method proposed has higher forecasting accuracy than the traditional model.

An IoT-based intelligent smart energy monitoring system for solar PV power generation

Abstract As the world’s attention turns to cleaner, more dependable, and sustainable resources, the renewable energy sector is rising quickly. The decline in world energy use and climate change are the two most significant factors nowadays. PV forecasting was essential to enhancing the efficiency of the real-time control system and preventing any undesirable effects. The smart energy management systems of distributed energy resources, the forecasting model of irradiation received from the sun, and therefore PV energy production might mitigate the impact of uncertainty on PV energy generation, improve system dependability, and increase the incursion level of solar power generation. Smart sensors and Internet of Things technologies are essential for monitoring and controlling applications in a broad range of fields. As a result, solar power generation forecasting was essential for microgrid stability and security, as well as solar photovoltaic integration in a strategic approach. This paper examines how to use IoT, a solar photovoltaic system being monitored, and shows the proposed monitoring system is a potentially viable option for smart remote and in-person monitoring of a solar PV system.