Photovoltaic Generation Research Articles

The Impact of Ambient Weather Conditions and Energy Usage Patterns on the Performance of a Domestic Off-Grid Photovoltaic System

Solar photovoltaic (PV) systems are growing rapidly as a renewable energy source. Evaluating the performance of a PV system based on local weather conditions is crucial for its adoption and deployment. However, the current IEC 61724 standard, used for assessing PV system performance, is limited to grid-connected systems. This standard may not accurately reflect the performance of off-grid PV systems. This study aims to evaluate how ambient weather conditions and energy usage patterns affect the performance of an off-grid PV system. This study uses a 3.8 kWp building-integrated photovoltaic (BIPV) system located at SolarWatt Park, University of Fort Hare, Alice, as a case study. Meteorological and electrical data from August and November are analyzed to assess the winter and summer performance of the BIPV system using the IEC 61724 standard. The BIPV system generated 376.29 kWh in winter and 366.38 kWh in summer, with a total energy consumption of 209.50 kWh in winter and 236.65 kWh in summer. Solar irradiation during winter was 130.18 kWh/m2, while it was 210.24 kWh/m2 during summer. The average daily performance ratio (PR) was 44.01% in winter and 28.94% in summer. The observed decrease in PR during the summer month was attributed to the higher levels of solar irradiance experienced during this time, which outweighs the increased AC energy output. The low-performance ratio does not indicate technical issues but rather a mismatch between the load demand and PV generation. The results of this study highlight the need for a separate method to assess the performance of off-grid PV systems.

A novel method of high-density urban block form generation based on multi-objective solar performance optimization: A case study of Nanjing

Efficiently harnessing solar radiation in high-density urban environments to optimize block forms holds significant potential for advancing urban low-carbon development. This paper introduces a novel method for generating high-density block forms based on comprehensive solar utilization. Grasshopper was used to compile an automated workflow for block form generation, solar simulation, and multi-objective optimization algorithms. Leveraging maximum solar radiation within and surrounding the block and effective photovoltaic utilization ratio are the three objectives in the multi-objective optimization process. From 60,000 generated samples, 1200 block forms were selected as the Pareto fronts. As the plot ratio of the generated blocks increases from 3 to 7, the annual mean solar radiation around the block decreases from 891.3 kWh/m2 to 863.5 kWh/m2, a reduction of only 3.1 %. When the number of blocks within the block increases from 1 to 4, the average solar radiation inside the block increases from 484.8 kWh/m2 to 516.1 kWh/m2, an increase of about 6.5 %. Additionally, as site coverage increased from 30 % to 60 %, the average solar radiation inside the block decreased from 549.2 kWh/m2 to 459.8 kWh/m2, a decrease of approximately 17 %. Notably, the photovoltaic power generation fell from 90.3 kWh/m2 to 61.2 kWh/m2, a decrease of about 32 %. The results indicate that the grid modeling method and multi-objective optimization algorithm adopted in this study can significantly improve the diversity of block morphology and achieve multiple solar performance requirements. The findings support designers in swiftly assessing building volume rationality and solar utilization potential at the early stages of urban design.

A study on the microstructure and power generation performance of colored BIPV modules

The need for greenhouse gas reduction and carbon neutrality is increasing, and the Building Integrated Photovoltaic (BIPV) power generation system is emerging as a key element. However, colored BIPV modules that enhance the aesthetic value of buildings have challenges in maintaining power generation efficiency due to their varied optical properties. This work investigates the structural, elemental, and power generation of colored BIPV modules fabricated using Plasma Enhanced Chemical Vapor Deposition (PECVD), inorganic pigment dyeing (HAZE), and color dot printing (DOT) in comparison to a conventional BIPV module without color treatment. Comprehensive structural and elemental analyses were performed, and optical properties were assessed. Power generation performance was measured using an Outdoor-exposed Power Generation Evaluation (OPGE) test at various tilt angles and azimuths. The results indicated a 57.2 % decrease in power output for the PECVD module, while the DOT and HAZE modules showed a reduction of approximately 30 % compared to the reference module. The power output of the colored modules decreased compared to the conventional ones due to higher light reflectance. However, to enhance efficiency, an improved optical design was developed, reducing light reflectance by over 57 % on average. This provides the potential for optimizing both the aesthetic and functional aspects.

Can combined wind and solar power meet the increased electricity load on heatwave days in China after the carbon emission peak? A case study in southern Hebei

Wind and photovoltaic (PV) power are the fastest-growing renewable energy sources; however, they are vulnerable to weather extremes. In the context of global warming, the frequency of extreme weather events, particularly heatwaves (HW), is anticipated to increase. Compared to normal summer days, HW days not only cause a rise in electricity demand but also exhibit a complementary growth in wind/PV generation. Therefore, determining whether the increase in wind/PV generation on HW days can meet the corresponding surge in electricity load is pivotal for replacing fossil fuel-based electricity. To address this question, this study conducts a case study for southern Hebei Province, which has the highest combined wind/PV installations in China, for the period 2031–2040 under the scenario that China will achieve its carbon peak (2030) and carbon neutrality goals as planned. We use recently developed load and wind power models and calibrate a boosting ensemble learning model to simulate PV generation. The results reveal that the total increase in wind/PV generation on HW days can fully meet the total increase in electricity consumption starting in 2039. However, due to the uneven distribution of wind/PV generation and load changes throughout the HW days, 9 GWh of energy storage capacity is required to ensure electricity supply in the early morning hours. Under China's climate change adaptation strategy, Hebei Province can harness wind/PV energy to address peak load demands during HW conditions and initiate climate-resilient urban development pilot projects.

Research on the Application of New Energy Photovoltaic Power Generation Technology

With the rapid development of technology and economy, the demand for energy in society is increasing. People are gradually realizing that fossil energy is limited, and the development of new energy may also face situations where it cannot meet social needs. The problem of resource shortage is gradually exposed to people. Therefore, the development of usable new energy has become an urgent problem for society to solve. At present, electricity is the most widely used energy source worldwide and photovoltaic power generation technology is gradually becoming well-known. As an emerging industry, the development of photovoltaic power generation still requires continuous promotion by national and social policies to be extended to various industries and ensure the stability of its energy supply. This article mainly outlines the principles, characteristics, and advantages of photovoltaic power generation, and briefly explains the current technology types and application aspects of photovoltaic power generation to contribute to its promotion and better serve all aspects of social life with new energy.

Dynamic Determination Method of Line Drop Compensator Parameters for Voltage Regulators Based on Mixture of Experts Using Real‐Time Information

In this paper, a method for determining line drop compensator (LDC) parameters for step voltage regulators and on‐load tap changers is proposed to avoid voltage violations in distribution systems with voltage fluctuations due to photovoltaic (PV) generation and electric vehicle (EV) charging. Distribution system operators need to effectively solve voltage problems caused by the widespread installation of distributed energy resources with the existing voltage regulators to construct an efficient and rational infrastructure. Our focus was on improving the method for determining the LDC parameters for the existing voltage regulators based on LDC control. A mechanism to dynamically change the LDC parameters depending on the situations in the distribution system was developed by using a mixture of experts, one of the machine learning techniques, and real‐time voltage and power flow information from information technology switches. The power flow calculations were performed using a distribution system model constructed based on the topology, loads, and generation characteristics of an actual distribution system in the Hokuriku region. The effectiveness of the proposed method was evaluated in terms of its improved effect on PV and EV hosting capacity, as well as the impact on the frequency of tap operations of voltage regulators. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Photovoltaic Maximum Power Point Tracking Technology Based on Improved Perturbation Observation Method and Backstepping Algorithm

Photovoltaic power generation systems mainly use the maximum power tracking (MPPT) controller to adjust the voltage and current of the solar cells in the photovoltaic array, so that the photovoltaic array runs at the maximum power point (MPP) to achieve the purpose of maximum power output. At present, photovoltaic power stations mainly adopt the traditional method to track the maximum power point, but this fixed step method easily causes output power oscillation of the photovoltaic array when tracking the maximum power point, and it easily falls into the local extreme point under partial shadow conditions. In order to solve these problems, this paper proposes an improved perturbation observation method and backstepping method (IP&O-backstepping) to replace the traditional method applied to the MPPT controller to optimize the operating state of the solar cell, thereby improving the output power point of the photovoltaic array and increasing the output power of the photovoltaic array. The algorithm first uses the improved perturbation and observation (IP&O) method to search the maximum power point of the photovoltaic array and output the reference voltage. Secondly, the reference voltage is input into the backstepping algorithm for voltage tracking. Finally, the algorithm tracks the reference voltage and makes the photovoltaic array operate at the maximum power point. The simulation is carried out by using MATLAB/Simulink. The IP&O-backstepping algorithm is compared with the intelligent algorithm and the traditional method, and the results show that compared to the above algorithm, the IP&O-backstepping algorithm can not only track the maximum power point of the photovoltaic array, but also has a faster tracking speed, and the output power has almost no oscillation when the photovoltaic array runs at the maximum power point.

Considering Embodied Greenhouse Emissions of Nuclear and Renewable Power Plants for Electrolytic Hydrogen and Its Use for Synthetic Ammonia, Methanol, Fischer-Tropsch Fuel Production.

Recent concerns surrounding climate change and the contribution of fossil fuels to greenhouse gas (GHG) emissions have sparked interest and advancements in renewable energy sources including wind, solar, and hydroelectricity. These energy sources, often referred to as "clean energy", generate no operational onsite GHG emissions. They also offer the potential for clean hydrogen production through water electrolysis, presenting a viable solution to create an environmentally friendly alternative energy carrier with the potential to decarbonize industrial processes reliant on hydrogen. To conduct a full life cycle analysis, it is crucial to account for the embodied emissions associated with renewable and nuclear power generation plants as they can significantly impact the GHG emissions linked to hydrogen production and its derived products. In this work, we conducted a comprehensive analysis of the embodied emissions associated with solar photovoltaic (PV), wind, hydro, and nuclear electricity. We investigated the implications of including plant-embodied emissions in the overall emission estimates of electrolysis hydrogen production and subsequently on the production of synthetic ammonia, methanol, and Fischer-Tropsch (FT) fuels. Results show that average embodied GHG emissions of solar PV, wind, hydro, and nuclear electricity generation in the United States (U.S.) were estimated to be 37, 9.8, 7.2, and 0.3 g CO2 e/kWh, respectively. Life cycle GHG emissions of electrolytic hydrogen produced from solar PV, wind, and hydroelectricity were estimated as 2.1, 0.6, and 0.4 kg of CO2 e/kg of H2, respectively, in contrast to the zero-emissions often used when the embodied emissions in their construction were excluded. Average life cycle emission estimates (CO2 e/kg) of synthetic ammonia, methanol, and FT-fuel from solar PV electricity are increased by 5.5, 16, and 49 times, respectively, compared to the case when embodied emissions are excluded. This change also depends on the local irradiance for solar power, which can result in a further increase of GHG emissions by 35-41% in areas of low irradiance or reduce GHG emissions by 21-25% in areas with higher irradiance.

Expansion Control of Alkali-Activated Materials Using Waste Glass Cullet from Photovoltaic Panels as Fine Aggregates

Glass cullet (GC) generated from the disposal of photovoltaic (PV) panels are typically landfilled, and effective GC utilization methods must be established for PV generation. In this study, alkali-activated material (AAM) mortars were prepared from the paste of fine blast-furnace slag powder, fly ash, and sodium orthosilicate (SO) and mixed with crushed sand and GC to investigate the potential use of GC as a fine aggregate in AAM. The replacement of crushed sand with GC did not considerably affect the flowability of the mortar, whereas the compressive strength decreased with the increasing GC replacement rates. Although expansion due to the alkali–silica reaction (ASR) was observed in mortars wherein GC replaced crushed sand, the expansion can be controlled by reducing the amount of mixed SO, autoclaving the GC, performing preleaching to remove the Si that causes the ASR, and replacing the blast-furnace slag with fly ash. By enforcing measures against the expansion, the possibility of using GC as fine aggregate is enhanced considerably, thus increasing the feasibility of continuous PV production.

Optimization of shared energy storage configuration for village-level photovoltaic systems considering vehicle charging management

Distributed renewable energy is more abundant in rural areas, and a large amount of distributed photovoltaic grid-connected power brings challenges to the stable of the power grid. How to promote the self-generation and self-consumption of distributed renewable energy has become an urgent problem. In this paper, a village-level distributed photovoltaic power generation system including energy storage and electric vehicles is constructed. With the goal of minimizing the photovoltaic grid-connected power and maximizing the annual comprehensive revenue, the planning model of energy storage capacity allocation for village-level distributed power generation system is constructed. Considering the charging management for different numbers of electric vehicles, the optimal energy storage capacity allocation strategy is solved using the improved particle swarm algorithm.Five scenarios are set up as examples to be analyzed.The conclusions are:(1)After the configuration of a reasonable energy storage,the grid-connected generation of the system is significantly reduced,and the annual comprehensive revenue of the system is significantly increased.(2)After considering charging management for different numbers of electric vehicles, the capacity and power of the energy storage configuration can be significantly reduced, thus reducing the energy storage investment cost and operation and maintenance cost, and further significantly improving the annual comprehensive revenue of this system.

Research on fault diagnosis for three-phase rectifier device in direct current transmission system based on continuous wavelet transform and vision transformer

Abstract As a core component of photovoltaic power generation systems, the three-phase rectifier device plays a crucial role, and its failure can potentially reduce energy conversion efficiency and output quality. Presently, the performance of fault time-domain signal diagnosis methods based on three-phase rectifier circuits is challenging to enhance. This paper proposes a novel fault detection method for three-phase rectifier devices based on Vision Transformer, referred to as CWT-ViT, to address this issue. This method transforms time-domain fault signals into images through Continuous Wavelet Transform (CWT), subsequently inputting these images into a Vision Transformer model. Relying on its powerful self-attention mechanism and fully connected layers, it realizes the extraction and learning of rectifier device image features. A Simulink simulation model of a three-phase bridge controllable rectifier circuit is established for fault injection to collect fault signals. Fault diagnosis experiments demonstrate that the proposed diagnostic method achieves a prediction accuracy of 98.6%, maintaining a relatively high precision level. In comparison to four excellent classification models currently available: AlexNet, RepVGG, ResNet, and GoogLeNet, the proposed method demonstrates superior diagnostic performance. Additionally, this paper conducts ablation experiments to meticulously analyze the impact of each module in the fault diagnosis process. This research achieves more precise and efficient fault diagnosis in photovoltaic power generation systems, thereby reducing downtime and maintenance costs for actual equipment and enhancing the stability of photovoltaic power generation systems. This research provides an innovative, intelligent solution for the intelligent operations and maintenance of photovoltaic power generation.

Anomaly detection of photovoltaic power generation based on quantile regression recurrent neural network

Distributed photovoltaic (PV) power generation systems are widely spread. Moreover, due to the randomness of meteorological conditions and the complexity of installation environments, it is difficult to eliminate the interference of factors such as meteorological fluctuations in the monitoring of abnormal states of PV equipment. Based on this, this paper proposes a PV power generation anomaly detection method based on Quantile Regression Recurrent Neural Network (QRRNN). First, the characteristics of solar irradiance on clear days are analyzed, and the clear day masking method is used to eliminate the interference of cloudy and rainy weather. Then, the output correlation of different power stations is analyzed to obtain PV stations with high output correlation as the horizontal reference, which is used to exclude interferences such as permanent faults at the power stations. At the same time, vertical comparison of the output curves of the station under test on different clear days is conducted to eliminate interference factors such as weather and environmental conditions. Subsequently, the metered active power output data, which is free from interference, is input into the QRRNN model to obtain the normal active power output range of the PV. The power threshold of the normal output range is utilized to identify anomalies in PV power generation. Finally, simulation analysis of actual PV system data is conducted, and the results show that the method can effectively identify PV power generation anomalies and has high accuracy in PV fault detection.

Combined cycle gas turbine (CCGT) plants utilizing methane-hydrogen blends represent a significant element in Australia’s journey toward achieving net-zero emissions

To deliver 570 TWh of dispatchable electricity to the grid in Australia, it is necessary to build up wind and solar photovoltaic power plants generating capacity of 325 GW, and a long-term hydrogen energy storage system comprising 50–150 GW of electrolyzers, 50 TWh of hydrogen storage, and 165 GW of hydrogen power generation. While the addition of other renewable energy producers and energy storage technologies effective on shorter scales are certainly welcome and positively impact the above demand, the inclusion of inter-annual variability and safety negatively impacts these numbers. Fuel cells are currently less advanced compared to electrolyzers and hydrogen storage in salt caverns. Expanding hydrogen power generation will require exploring additional opportunities. Here we advocate for methane-hydrogen Combined Cycle Gas Turbine (CCGT) plants. Their adaptability to effortlessly integrate methane and green hydrogen mixtures positions them as a key player in the evolution toward net zero. This transition will be complete once a fully established wind and solar photovoltaic generation, coupled with an efficient hydrogen energy storage system, is in place. Notably, these CCGT plants consistently yield the lowest Life Cycle Analysis (LCA) carbon dioxide (CO2) emissions during the grid’s progression toward net zero.

Energy system models should consider evolving charging profiles

Abstract Globally, sales of battery electric vehicles (BEVs) are surpassing records every year, and their growing charging needs will ultimately reshape power infrastructure planning practices. While studies have analyzed the impact of light-duty BEVs on the electricity sector, they have overlooked the prospective evolution in charging profiles. We developed a framework for analyzing passenger vehicle electrification futures accounting for the evolution in charging infrastructure, BEVs technical features, and socio-demographics. We soft-link a BEV charging profiles generator with an energy system optimization model to analyze a light-duty vehicle (LDV) electrification scenario in the U.S. from 2020-2050. Compared to static charging profiles, common in prior work, evolving profiles lead to substantial differences in projected power plant installed capacity (up to ~300 GW more solar PV) and activity (up to ~460 TWh more solar PV generation). Hence, future studies should consider not only different charging profiles (e.g., day, night, uncontrolled) but also how these evolve over time.

Photo-voltaic system with battery employing parallel converters operation for charging stations and distributed loads amidst anomalous grid

In this work, solar photovoltaic array-based generation unit is connected to non-ideal distribution network and powering plug-in electric vehicle (PEV) charging infrastructure and other local loads connected to AC common bus while ensuring reliable operation of parallel connected voltage source converters under dynamics. A photovoltaic array connected via voltage source inverter, which feeds power to loads and battery is used to act as power management entity and provides reactive power and harmonic power support via converter. In context of widespread incorporation of PEVs and renewable energy sources (RES) into grid, inherent intermittency and dynamic behavior pose significant challenges in establishing a resilient and flexible control framework. Consequently, a solution is proposed that employs dual low pass filter integrated along an adaptive noise elimination filtering algorithm, serving as a primary processing stage. Additionally, integrator-based frequency locked loop control strategy is implemented in conjunction to mitigate power quality concerns, regulate voltage levels, and correct power factor discrepancies. Furthermore, system ensures continuous operation, providing uninterrupted power to local loads even during fluctuating conditions while simultaneously functioning as a seamless PEV charging station. Additionally, a comparative analysis of controller performance is summarized in a tabular format, alongside a power quality assessment. The analysis includes the evaluation of total harmonic distortion (THD) for both grid currents and grid voltages, which are measured at 2.36 % and 3.60 %, respectively. These values are well within the limits established by IEEE 519 standard, demonstrating compliance with the required power quality criteria. This RES based charging structure topology is tested using a hardware prototype under dynamics and impactful performance is observed through obtained results.

Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba

As the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, this paper proposes a novel hybrid predictive model that integrates Optimized Variational Mode Decomposition (VMD), Vision Mamba (Vim) for extracting features from sky images, and advanced mechanisms like Patch Embedding and Variate-wise Cross-Attention. Initially, the proposed model employs SAO-optimized VMD to decompose the photovoltaic power series into high, medium, and low-frequency components. Subsequently, these components are patched to serve as input for the subsequent layers. In the third step, exogenous variables, including meteorological and image data, are introduced and processed through Variate Embedding combined with cross-attention mechanisms to capture the intricate interactions between these variables. Finally, by integrating the outputs from all processing steps through normalization and feed-forward layers, the final predictive results are produced. Experimental evaluations across different seasons demonstrate significant enhancements in forecasting accuracy, with the model achieving Root Mean Square Error (RMSE) values of 0.3587 in spring, 0.4376 in summer, 0.3544 in autumn, and 0.3493 in winter. Similarly, Mean Absolute Error (MAE) and Mean Squared Error (MSE) across these seasons underscore the model's effectiveness. This model offers new technical means for photovoltaic power forecasting and provides valuable decision support for the optimization and management of photovoltaic power systems.

Supervisory control strategy for dual battery assisted solar water pumping using a 3-stage interleaved boost converter

This article proposes effective power management and battery protection strategies for a solar photovoltaic (SPV) powered dual-battery supported standalone water pumping approach propelled by a permanent magnet brushless direct current (PMBLDC) motor. A drift-avoidance perturb and observe MPPT algorithm is employed in conjunction with a 3-stage interleaved boost converter (TSIBC) and proposes a switching control scheme to enable double-stage solar photovoltaic generation system (SPGS) to operate at its maximum accessible power point (MPP) irrespective of any variation in climatic condition. The proposed dual battery control scheme (DBCS), which integrates a primary battery with an auxiliary battery, is connected to the 310 V DC link. This dual battery setup utilizes a parallel-active configuration facilitated by two bidirectional DC-DC converters (BDCs). The core idea behind dualization aims to maintain the state of charge (SoC) of the primary battery within upper and lower limits with the help of an auxiliary battery unit and regulate the DC link voltage at 310 V during fluctuations generated in the protection period. This scheme significantly extends the battery's lifespan, achieving an estimated 800 to 1000 cycles by maintaining its depth of discharge (DoD) at a level of 70 %. A centralized power handling unit (CPHU) is integrated for both the BDCs to facilitate the operating mode selection and regulate DC link voltage by alternatively charging/discharging both batteries. This approach enhances overall system efficiency by eliminating the need to oversize the solar array by up to 46 %, which would have been necessary to fully charge both batteries simultaneously. Previously, the DC link voltage regulation relied solely on MPPT control, resulting in significant deviations (up to ±38.43 %) during battery overcharging/deep-discharging (SoC ≥ 90 %/SoC ≤ 20 %). This work proposes a solution using an auxiliary battery controller within the DBCS, achieving near-zero (±0.5 %) DC link voltage deviation under all operating conditions. The effectiveness of the suggested DBCS controller is validated through a time-domain simulation conducted in MATLAB/Simulink, alongside real-time hardware-in-the-loop (HIL) digital simulator using the OPAL-RT platform. The acquired results corroborate enhanced performance for the standalone water pumping system (WPS) and demonstrate a seamless shift from the normal operational mode to battery protection mode, while ensuring steady regulation of the DC link voltage.

Identification of transformer overload and new energy planning for enterprises based on load forecasting.

The new energy system constructed by energy storage and photovoltaic power generation system can effectively solve the problem of transformer overload operation in some enterprises. It can not only reduce the cost of electricity, but also realize low-carbon emission reduction. However, due to its low return on investment, the willingness of enterprises to install new energy is not high. In this paper, we first establish a load forecasting model to users whose transformers are overloaded or about to be overloaded, which are potential customers with new energy installation needs. Then, Optimal configuration models of PV and energy storage systems based on nonlinear programming are developed for these potential customers. The optimal installed capacity of the PV energy storage and the optimal charging and discharging strategy for the energy storage system can be obtained. This optimization strategy ensures that the electricity consumption of the enterprise does not exceed the rated capacity, and effectively reduces the enterprise's basic tariff and electricity price to achieve cost reduction and efficiency. Finally, taking a building materials production factory as an example, we obtain the optimal plan for the new energy capacity, as well as the economic benefits of the plan and the specific strategy of energy storage charging and discharging for this factory.

Analysis of Inverter Efficiency Using Photovoltaic Power Generation Element Parameters

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants.

Effects of the Operating Point of Photovoltaic Strings on Sizing of DC‐ and AC‐Connected Energy Storage Systems for Power Smoothing

Photovoltaic (PV) power generators are constantly exposed to operating condition variations. While the electrical characteristic of a PV generator has exactly one maximum power point (MPP) during homogeneous operating conditions, several MPPs may exist during nonuniform operating conditions. Since highly varying global MPP voltage causes large fluctuations in the inverter reference voltage, it would be beneficial to keep the operating point of the inverter all the time close to the nominal MPP voltage to secure more predictable and straightforward operation of the PV system. This article presents a study of the effects of the operating point on the operation of PV strings equipped with energy storage systems (ESS). A scenario in which the MPP closest to the nominal MPP voltage is used as the operating point is compared to operation in the global MPP. The effects of inverter sizing on the selection of the operating point are also studied. Moreover, DC and AC connections of the ESSs are compared. The study is based on measured current–voltage curves of a 23 module PV string. The results show that, in practice, there are no major differences in sizing of DC‐connected ESSs between the two MPPs.