PV Plant Research Articles

Analyses of the Effectiveness of Power Optimizers in Heterogeneous PV Plants

Analyses of the Effectiveness of Power Optimizers in Heterogeneous PV Plants

Modular Multilevel VSC for Solar PV Plant with Battery Energy Storage

Modular Multilevel VSC for Solar PV Plant with Battery Energy Storage

Analysis of CSP/PV Hybrid Power Plants in High Latitude Regions

This study aims to explore the potential of combined CSP and PV systems in high latitude areas. Several performance metrics are evaluated and compared with standalone CSP or PV plants to identify the benefits and challenges of deploying such hybrid plants. Six different sites have been selected and the ASDELSOL hybrid power plant simulation tool, developed by the Thermodynamics and Renewable Energies Group at the University of Seville, has been used. This simulation tool allows performing dynamic performance simulations of hybrid solar plants under various operation strategies, along with conducting economic evaluations. We have analyzed a hybrid solar plant composed of 50 MW PTC and 75 MW PV, with 10 hours of TES and a 15 MW electric heater to transfer excess PV energy to the TES tanks in a 50 MW base load operation strategy, where the main objective of the PV plant is to cover the self-consumption of the PTC plant. Results obtained show that PV/PTC hybridization reduces LCOE and increases CF compared to standalone PV or PTC plants. These improvements are more pronounced in regions where the contribution of PTC plants is lower. In high latitude regions, an N-S orientation of PTC fields achieves higher production than an E-W orientation. However, a combination of both orientations can optimize their production and performance. Finally, it is concluded that it is necessary to carry out feasibility studies in high latitude locations before dismissing them outright, as it has been observed that, in certain regions, hybrid PV/PTC solar plants can offer an effective alternative.

A protection scheme for the transmission line connecting large-scale centralized solar PV plant

A protection scheme for the transmission line connecting large-scale centralized solar PV plant

Thermographic inspections of solar photovoltaic plants in India using Unmanned Aerial Vehicles: Analysing the gap between theory and practice

Aerial inspection of solar PV plants using Unmanned Aerial Vehicles (UAVs) is gaining traction due to benefits such as no downtime and cost-effectiveness. This technology is proven to be the low-cost alternative to conventional approaches involving visual inspection and I-V curve tracing to identify physical damages and underperforming strings, respectively. Though the use of UAVs for thermographic solar PV inspection is a popular alternative in developed countries, its use in developing economies experience various challenges. Studies emphasizing these challenges especially in the context of rapid evolution of drones are limited. To overcome this limitation, literature scoping, a one-on-one survey, focus group discussion, and a flight campaign using a UAV with a thermal payload is conducted in India to identify the limitations. These are further categorized into Technical, Behavioural, Implementation, Pre-deployment, Deployment, and Post-deployment categories. The relevance and significance of each challenge are analysed using a hybrid multi-criteria framework developed in this study. Findings of this study highlight the importance of drone regulations, technology readiness, and workshops for drone pilots, industry professionals, and solar developers in India. This study aid developing economies in devising strategies that can promote the use of UAVs for solar PV plant commissioning activities.

Two decades of progressive cost reduction: A paradigm for distributed solar photovoltaics and energy efficiency

The increasing deployment of renewable energy resources has led to massive energy cost effectivity worldwide in the past decade. The emergence of this cost revolution led electricity consumers to increasingly adopt distributed renewable energy resources to decrease dependence on traditional power grids. This paper applies the integrated resource planning framework, the objective of which is to design a least-cost electricity system by looking at renewable energy resources, efficient appliances, and demand response management strategies to reduce electricity bills. The results show that the commercial entity can save its electricity bill by $0.16 if it installs 6 MW solar PV over the lifetime of the solar PV plants. Besides, it was observed that wind turbines were not economically feasible to install at the site because of low wind resources. Biogas power plant is too expensive mainly because of the cost of fuel (waste). It also shows that by retrofitting 2000 compact fluorescent lamps (CFLs) with light-emitting diodes (LEDs), the company can save $0.15 million. By shifting between 0.5 MW and 1.4 MW of heating and cooling demand to periods of low tariff costs, the company can save $0.013 million annually. The climate transition plan for this company relies on PV, efficiency interventions and demand response. The study also demonstrates that an integrated resource planning framework can be used to plan a mini-grid.

Unveiling the impact of climate diversity on solar PV soiling

ABSTRACT Advanced knowledge of PV soiling can aid in the economic profitability of PV plants and efficient generation. This study introduces a novel approach for evaluating energy losses from soiling deposition on PV modules. Experimental investigations conducted under real outdoor conditions in Jaipur (India) were used to validate the model’s accuracy, demonstrating close agreement (with MAPE < 3.6%). Annual soiling trends were also investigated for seven cities reflecting diverse climatic zones (hot-dry, warm-humid, composite, temperate, and cold) and pollutant (PM10) concentrations. The proposed model was also tested with three different cleaning thresholds using rainfall intensities of 1, 3, and 5 mm/day and energy yield reductions due to soiling were simulated. Consequently, the findings of this study underscore the significance of seasonal variations in soiling profiles for different cities and, thus, highlight the need to develop location-specific cleaning strategies for PV systems that also account for the soiling removal due to rain events. The significant contribution of this research is a simplistic modeling approach that addresses the relationship of PV soiling with the input parameters such as wind speed (WS), relative humidity (RH), rainfall, particulate matter (PM10), and tilt angle. Hence, the proposed method can calculate soiling losses worldwide irrespective of geographical differences.

The Effect of Increasing Aggregation Levels of Electrical Consumption Data on Renewable Energy Community (REC) Analyses

The growing number of renewable energy communities (RECs) exemplifies the potential of citizen-driven actions towards a more sustainable future. However, obtaining hourly measured consumption data from REC members remains challenging, hindering accurate feasibility studies for the development of communities. This study examines the impact of estimating hourly consumption from aggregated data on REC analysis results. A case study with real consumption data from diverse users, representative of a typical community in Tuscany, Italy, was analysed to investigate various influencing factors. Multiple scenarios were simulated: two open-source tools estimated energy production from the community’s PV plants, and two REC configurations were considered—one with consumers and prosumers and another with consumers and a producer (with the same total installed power). Additionally, three locations were evaluated to consider the importance of geographical positioning. The study revealed that the impact of consumption data aggregation is more significant in scenarios with low energy sharing, such as the scenario where prosumers were replaced with a producer. Geographical positioning showed no major discrepancies in energy and economic outcomes, implying that using estimated hourly consumption data from aggregated data has a relevant impact regardless of location. Furthermore, different weather files did not affect the impact of aggregated consumption data.

A model for energy predictions and diagnostics of large-scale photovoltaic systems based on electric data and thermal imaging of the PV fields

The aim of this investigation is the development of robust models for the performance prediction and automatic monitoring of large photovoltaic systems, based on historical and real-time electric and thermal data. This issue is increasingly important due to the worldwide diffusion of large photovoltaic systems and their need to identify and predict failures and malfunctions, in order to promptly assess the convenience of maintenance actions. The present model describes the response to irradiance and temperature conditions of both modules and inverters and also it is able to predict shading conditions able to affect the energy yield. The model has been validated against real electric measurements in 6 large PV plants located in southern Italy and it demonstrated to be able to predict the real time power production within a 4.1 % error. Even more importantly, the model and its comparison with subhourly measurements over several years has demonstrated its effectiveness in detecting downtime conditions caused by inverter or string problems. Simulations and measurements revealed that missed energy production due to electrical grid coupling downtime can exceed 50 % on certain days and that the shading conditions (up to 5 % of the daily energy production) can be easily detected and separated from component problems, thus avoiding false alarms. Finally, the analysis of aerial infrared images allowed to further test the model in failure detection capability, assess the relationship between thermal anomalies and underperformance conditions and in predicting the yearly deterioration rate at the PV plants.

Mitigation of Photovoltaics Penetration Impact upon Networks Using Lithium-Ion Batteries

The paper conducts a comprehensive analysis of the impact of very large-scale photovoltaic generation systems on various aspects of power systems, including voltage profile, frequency, active power, and reactive power. It specifically investigates IEEE 9-bus, 39-bus, and 118-bus test systems, emphasizing the influence of different levels of photovoltaic penetration. Additionally, it explores the effectiveness of battery energy storage systems in enhancing system stability and transient response. The transition to PV generation alters system stability characteristics, impacting frequency response and requiring careful management of PV plant locations and interactions with synchronous generators to maintain system reliability. This study highlights how high penetration of photovoltaic systems can improve steady-state voltage levels but may lead to greater voltage dips in contingency scenarios. It also explores how battery energy storage system integration supports system stability, showing that a balance between battery energy storage system capacity and synchronous generation is essential to avoid instability. In scenarios integrating photovoltaic systems into the grid, voltage levels remained stable at 1 per unit and frequency was tightly controlled between 49.985 Hz and 50.015 Hz. The inclusion of battery energy storage systems further enhanced stability, with 25% and 50% battery energy storage system levels maintaining strong voltage and frequency due to robust grid support and sufficient synchronous generation. At 75% battery energy storage system, minor instabilities arose as asynchronous generation increased, while 100% battery energy storage system led to significant instability and oscillations due to minimal synchronous generation. These findings underline the importance of synchronous generation for grid reliability as battery energy storage system integration increases.

A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system.

BESS and the ancillary services markets: A symbiosis yet? Impact of market design on performance

The evolution of ancillary services markets (ASM) and balancing products is ongoing. The aim of the evolution is to integrate the products over the national boundaries and to open the ASM to distributed energy resources (DERs). Among DERs, battery energy storage systems (BESS) are increasing their importance. In this work, we investigate by means of numerical simulations the effect of different evolutions in the regulatory framework on the performance of a BESS providing ancillary services. The analyzed regulatory barriers are selected based on ongoing evolution in EU market design. The following parameters are involved: power vs energy-intensive services, symmetry of procurement, time definition and distance to delivery. The considered case study is a BESS associated to a large-scale energy district including load, a cogeneration plant, and a PV plant. Results are given in terms of energy flows, economics, operational efficiency, and reliability of service provision. Where both reliability, provision of large flexibility volumes, and good economic performance are achieved, we say that there is a symbiosis between BESS and the markets. This way, the best ASM arrangements abating regulatory barriers for BESS are defined.

Investigation of condition monitoring system for grid connected photovoltaic (GCPV) system with power electronics converters using machine learning techniques

Power systems protection has become more vital in recent years to ensure stability, reliability, security, and power quality due to the exponential growth of grid-connected photovoltaic (GCPV) systems. As a result, several nations have set new grid codes for the grid integration of PV plant installations to overcome these concerns. Investigating Fault Ride Through (FRT) capacity is one of the primary criteria for grid codes. For 3-phase GCPV systems, fault detection and classification approaches are proposed in this study. Firstly, different faults that occurred in the GCPV system are categorized and compared, with the critical and analytical evaluation of grid codes, particularly FRT requirements such as Low Voltage Ride Through (LVRT) and High Voltage Ride Through (HVRT) for different nations. Further, a detailed classification and a comparison of the existing FRT techniques are presented for better control methods based on system complexity, detection accuracy, and other evolutionary criteria. To ensure smooth grid operation, accurate fault detection and condition monitoring of the systems are required. This paper discusses a machine learning (ML) based technique for detecting faults in 3-phase GCPV systems. Multi-peak phenomena caused by FRT capabilities and anti-islanding detection are the main problems experienced while integrating a PV system into the local grid. The fault classification technique is developed using weighted K-nearest neighbor (WKNN) and fine Gaussian support vector machine (FGSVM) based ML approaches utilizing Wavelet Transform. The proposed ML-based findings show that the fault detection algorithm-based classification accuracy has significantly improved.

Concept and Modeling of Participation Opportunity for Large-Scale Renewable Energy Plants

Citizen participation in the energy transition is desirable for many reasons: it increases acceptance of renewable energies, fulfils the wish to make one's own contribution to the energy transition and generates relevant parts of the necessary capital for the transformation of the energy system. This paper presents a scalable participation concept for the German legal framework which allows citizens to invest in and partially consume electricity from large-scale renewable energy plants at the time of generation. The developed concept provides a method for direct financial involvement via a direct contract with local electricity suppliers and virtual self-consumption by matching demand with PV generation – without the need for property ownership or new subsidies. Different consumer types and participants shares of a large-scale PV plant are modelled and simulated for the years 2019-2021. The financial advantage of citizen participation depends on the type of energy generation plant, yearly (solar) yield, on the remuneration for the surplus energy quantity, on the electricity procurement costs, on the customer's energy consumption, on the amount, and time flexibility and others. This paper shows that all analysed consumer types have the potential to profit from the model every year, although simulations are dependent on variable annual factors and assume the energy supplier's short-term procurement. The approach’s scalability and organizational simplicity could expedite the development of local participatory projects and contribute significantly to Germany's energy transition.

Local versus outsider developers: Impact on development duration and its’ implications for community acceptance of solar PV plants in South Korea

This study focused on community acceptance of small to medium solar PV plant projects in South Korea, which has rarely been the subject of research despite reports of disputes. It set out to determine whether there is a difference in community acceptance based on a project being developed by local residents or by outsiders. We used the duration of development for each plant as a proxy variable for the level of acceptance. We interpreted longer development duration as corresponding to a lower level of community acceptance when other characteristics of solar PV plants were controlled for. Analyses using a censored regression model indicated that the development duration was shorter (with statistical significance) when plants were developed by local residents rather than by outsiders. Further investigation revealed local resident involvement had more effect on reducing the development duration for plants exploiting forested land, the type for which community acceptance was known to be low. The effect was not evident when the plants were developed using existing buildings.

Health status identification method of PV plant sensors based on abnormal data analysis

Health status identification method of PV plant sensors based on abnormal data analysis

Optimizing virtual energy sharing in renewable energy communities of residential users for incentives maximization

Renewable energy communities (RECs) are considered a promising tool for putting the citizens at the center of the energy transition, while also promoting self-sufficiency coming from local resources and decarbonization through high penetration of renewables. A key challenge when operating RECs is represented by the number of decision variables to consider depending on the number and type of community participants and distributed technologies, while also considering the associated uncertainties. Moreover, the monetarization of energy shared in the community for benefitting residential users is crucial. The contribution of this paper is to present an innovative stochastic linear programming model for optimizing the energy sharing in RECs to maximize revenues associated with the incentives for the energy shared as established by the Italian regulation. The REC under study consists of a condominium with a PV plant installed on the rooftop, and air conditioning and battery storage systems installed in each apartment. The problem is to find the optimal control strategies for air conditioning systems and batteries with a 15-minute time-step, which maximize the expected revenue from energy sharing while meeting the users’ comfort requirements and preventing users’ bills from increasing. Numerical results demonstrate the effectiveness of the optimization model to maximize the energy shared and the related revenues through the optimal control of installed assets. The combined optimized strategies of both air conditioning and batteries allow for finding the best performance of the REC in terms of maximization of the energy shared. In this latter case, the expected total revenue for users for the energy sharing increases by 59.7 %, 38.7 % and 12.6 % as compared to the baseline case with no optimal control, the case with control of air conditioning only, and the case with control of batteries only, respectively.

A novel algorithm MPPT controller based on the herd horse optimization for photovoltaic systems under partial shadow conditions

In recent years, a significant scientific issue has been the creation of maximum power point tracking (MPPT) methods to increase the energy production of PV plants. Moreover, to try to cope with the unparalleled operating conditions of PV plants, many bio-inspired meta-heuristic algorithms have already been suggested in the literature, but their implementation is often complex and difficult. In this sense, we propose a novel algorithm for monitoring the (MPPT), using the newly meta-heuristic approach of herd horse optimization (HHO). A DC/DC boost converter is utilised in the suggested controllers to extract the most power possible from the PV resource. The system is programmed and modelled using the MATLAB/SIMULINK software, which also studies four shadow models and a 3S1P topography of single-junction solar arrays. Considering partial shading conditions (PSC), the success of the power values in the global maximum power point (GMPP) of the proposed method is between 99.64% and 99.07%. Besides the time to capture the GMPP by the proposed algorithm is between 0.396 s and 1.666 s, shorter than that of the CSA and FPA algorithms. Comparison with the CSA and FPA optimizers confirmed the quality of the MPPT-based HHO algorithm for GMPP extraction in different (PSC).

Control of Aggregated Virtual Synchronous Generators for PV Plants Considering Communication Delays

Control of Aggregated Virtual Synchronous Generators for PV Plants Considering Communication Delays

Waste Assessment of 1.275 MWP PV Plant: Case of Northern Cyprus

PV technologies gained significant importance during the last 2 decades by providing clean and renewable electricity. However, PV panels complete their operational life in 25-30 years and transform into hazardous waste for both human health and environment. Management of PV Waste is an important environmental issue which requires a detailed inventory of the PV Waste. In this study, the PV Waste inventory of 1.275 MWP PV Plant installed in Serhatköy Region of Northern Cyprus, on May 2011, is investigated. Results showed that, this plant will complete its operational lifetime in 13-18 years and generate the first bulk PV Waste in Northern Cyprus. Inventory analysis of the Serhatköy PV Plant revealed that 63476 kg of glass,16807 kg of aluminum, 9230 kg of steel, 6640 kg of EVA, 807 kg of silicon, and 746 kg of copper can be recycled and recovered. Also, this study revealed that Northern Cyprus can catch the PV Waste Management targets of the European Union with a down-cycling PV Waste Strategy.