Photovoltaic Energy Research Articles

Photovoltaic power forecasting: A Transformer based framework

The accurate prediction of photovoltaic (PV) energy production is a crucial task to optimise the integration of solar energy into the power grid and maximise the benefit of renewable source trading in the energy market. This paper systematically and quantitatively analyses the literature by comparing different machine learning techniques and the impact of different meteorological forecast providers. The methodology consists of an irradiance model coupled with a meteorological provider; this combination removes the constraint of a local irradiance measurement. The result is a Transformer Neural Network architecture, trained and tested using OpenMeteo data, whose performance is superior to other combinations, providing a MAE of 1.22 kW (0.95%), and a MAPE of 2.21%. The implications of our study suggest that adopting a comprehensive approach, integrating local weather data, modelled irradiance, and PV plant configuration data, can significantly improve the accuracy of PV power forecasting, thus contributing to more effective technological and economic integration.

A Method for Fault Localization in Distribution Networks with High Proportions of Distributed Generation Based on Graph Convolutional Networks

To address the issues arising from the integration of a high proportion of distributed generation (DG) into the distribution network, which has led to the transition from traditional single-source to multi-source distribution systems, resulting in increased complexity of the distribution network topology and difficulties in fault localization, this paper proposes a fault localization method based on graph convolutional networks (GCNs) for distribution networks with a high proportion of distributed generation. By abstracting busbars and lines into graph structure nodes and edges, GCN captures spatial coupling relationships between nodes, using key electrical quantities such as node voltage magnitude, current magnitude, power, and phase angle as input features to construct a fault localization model. A multi-type fault dataset is generated using the Matpower toolbox, and model training is evaluated using K-fold cross-validation. The training process is optimized through early stopping mechanisms and learning rate scheduling. Simulations are conducted based on the IEEE 33-node distribution network benchmark, with photovoltaic generation, wind generation, and energy storage systems connected at specific nodes, validating the model’s fault localization capability under various fault types (single-phase ground fault, phase-to-phase short circuit, and line open circuit). Experimental results demonstrate that the proposed model can effectively locate fault nodes in complex distribution networks with high DG integration, achieving an accuracy of 98.5% and an AUC value of 0.9997. It still shows strong robustness in noisy environments and is significantly higher than convolutional neural networks and other methods in terms of model localization accuracy, training time, F1 score, AUC value, and single fault detection inference time, which has good potential for practical application.

CIRCULAR ECONOMY IN PHOTOVOLTAICS: AN ANALYSIS OF GLOBAL REGULATIONS AND THEIR IMPLICATIONS IN BRAZIL

As the market expands, the volume of waste generated by this sector is also growing. However, in many countries, regulations for the management of this waste at the end of its useful life are still inadequate, partly because the amount of solar modules discarded to date has been relatively low. This study reviews the value chain of solar photovoltaic energy, focusing on the analysis of regulations aimed at the management of photovoltaic waste at the end of its useful life, both at the national and international levels. To this end, a bibliometric review was conducted that analyzed 654 peer-reviewed articles published in English. The results provide an overview of the global regulations and standards applied to the treatment of photovoltaic waste, providing useful information for entrepreneurs and policy makers in evaluating future photovoltaic technologies and in the creation of guidelines for the management of this waste.

Research on the Smooth Switching Control Strategy of Electric Vehicle Charging Stations Based on Photovoltaic–Storage–Charging Integration

To facilitate seamless transitions between grid-connected and islanded modes in PV–storage–charging integration, an energy storage system converter is designated as the subject of investigation, and its operational principles are examined. Feed-forward decoupling, double closed-loop, constant-power (PQ), constant-voltage–constant-frequency (V/F), and constant-voltage charge and discharge control strategies are developed. The PQ and V/F control framework of the energy storage battery comprises an enhanced common current inner loop and a switching voltage outer loop. The current reference value output by the voltage outer loop and the voltage signal output by the current inner loop are compensated. The transient impact is reduced, and the smooth switching of the microgrid from the grid-connected mode to the island mode is realized, which significantly improves the power quality and ensures the uninterrupted charging of electric vehicles and the stable operation of the key load of the system. By constructing a simulation model of the photovoltaic energy storage microgrid on the MATLAB/Simulink platform, the practicability of the control strategy proposed in this paper is verified.

A dimension-enhanced residual multi-scale attention framework for identifying anomalous waveforms of fault recorders

The continuous introduction of technologies such as distributed generation, wind power, and photovoltaic energy poses challenges to identifying abnormal waveforms in power disturbances. Due to the constant increase in abnormal features, existing waveform recognition schemes for power disturbance abnormalities cannot meet the requirements of high accuracy and reliability. In this paper, a Dimension-Enhanced Residual Multi-Scale Attention Framework for identifying power disturbance abnormal waveforms is proposed. This framework first employs the Phase Adaptive Adjustment (PAA) method to address the phase offset problem of original recording data, then uses the Gramian Angle Field method to perform dimensionality expansion on the data processed by PAA, and finally utilizes the Residual Pyramid Squeeze Attention Network (ResPSANet) for identifying power disturbance abnormal waveforms. Experiments demonstrate that the proposed approach improves the performance of power disturbance abnormal waveform recognition by 10% compared to existing schemes.

A hybrid algorithm for photovoltaic maximum power point tracking using Artificial Neural Network and Kinetic Gas Molecular Optimization

The quest for efficient photovoltaic (PV) energy conversion systems has led to the development of Maximum Power Point Tracking (MPPT) algorithms. In this paper, we propose a novel hybrid approach that combines the power of Artificial Neural Network (ANN) with the optimization capability of Kinetic Gas Molecular Optimization (KGMO) for MPPT. We present a high-level outline of the proposed algorithm along with example MATLAB code snippets for each step, highlighting its potential for improving PV system performance. This paper proposes a metaheuristic optimized multilayer feed‐forward artificial neural network (ANN) controller to extract the maximum power from available solar energy. Firstly, to improve the maximum power point (MPP) delivered by PV arrays and to overcome the drawbacks in the conventional MPPT method under irradiation variation, a hybrid MPPT controller is designed, in which the input parameters include the PV array voltage and current. The output parameter is the duty cycle of the DC/DC boost converter. The proposed approach abbreviated as ANN-KGMO MPPT controller is based on the Kinetic Gas Molecular Optimization (KGMO) Algorithm which is useful to train the developed ANN and to evolve the connection weights and biases to get the optimal values of duty cycle converter corresponding to the MPP of a PV array. Finally, the performance of the proposed control system is confirmed by simulation tests on a 2 kW PV system. In addition, the performance of an ANN-KGMO -based MPPT controller is also compared to the conventional perturb and observe (P&O) method. To analyze the results, simulations are performed by using MATLAB software.

Preparation and Characterization of Transferable Encapsulated Dye-Sensitized Solar Cells

The increasing demand for sustainable energy as a means to combat the impact of climate change is addressed via a novel concept in the present work. Herein presented are developed transferable encapsulated dye-sensitized solar cells, canonically “solar capsules”, for photovoltaic applications on alternative surfaces, such as facades. The solar capsule assembly houses all the components necessary for photovoltaic energy conversion, enclosed within a semiconductor nanotubular array, making them truly unique in their construction. This capsule-style unit enables an easy transfer and draft onto a wide range of materials and surfaces for photovoltaic functionalization and applications. This type of dye-sensitized solar cell typically consists of transferred solar capsules and two additional electrodes. The design and construction of solar capsules means they have a high economic viability as they can seamlessly be up-scaled using commercially established techniques such as anodization and subsequent functionalization. This work demonstrates a working model of such transferable solar capsules by fabricating TiO2 nanotubes that are functionalized via facile dip- and spin-coating techniques in a wet lab at ambient conditions. These prototypes are characterized in bulk and are thoroughly investigated at the nanoscale for information on the chemical distribution of the constituents, as they may be influenced during the manufacturing process.

Impact of layout strategy and sector-coupling on the multi-criteria assessment of energy technology concepts for residential buildings

Energy system transformation needs decentralisation, decarbonisation and sector-coupling. Local energy systems are often customarily designed using heuristics for the technology layout. However, optimisation of the technology layout and operation is considered a powerful tool. Therefore, we investigate the influence of an optimised layout and the coupling of the electricity and heat sector on economic, ecological and technological criteria. The criteria comprise the annual costs, CO2 emissions, and the energy performance in terms of self-sufficiency and self-consumption. We consider typical energy technology concepts with fixed technology combinations for the heat and electricity supply of residential buildings. The optimisation is performed using a Mixed-Integer Linear Programming under the objective of minimal costs. The assessment is done using the Analytic Hierarchy Process. An optimised layout is found to reduce costs at the expense of emissions and energy performance. In comparison to the heuristic layout, especially the storage sizes differ among the layout strategies. Sector-coupling leads to CO2 emission savings and a higher self-consumption of the PV energy produced within the system. The multi-criteria assessment reveals higher performance scores for heuristically laid out concepts under a local sensitivity analysis of the criteria weights.

Research on stacking ensemble method for day-ahead ultra-short-term prediction of photovoltaic power

The intermittency and uncertainty of photovoltaic power output make it difficult to match real-time electricity demand, and accurate prediction of photovoltaic power is the basis for improving photovoltaic energy consumption. This article proposes a new stacking ensemble forecast model based on the time series generative adversarial network (TimeGAN-SEFM), to improve the accuracy of ultra-short-term forecasting of photovoltaic power. Firstly, a dual layer feature weighted clustering weighted fuzzy C-means clustering algorithm is proposed to accurately partition the original dataset according to weather types. Secondly, in the proposed TimeGAN-SEFM, a Particle Swarm Optimization based BP neural network is used as a meta learner to integrate three types of base learners, thereby improving the structural diversity of the model. The dataset for each type of weather is proportionally divided into multiple sub-datasets for training base learners. In response to the limitation that the amount of data used for training a single base learner is relatively small after multiple splitting of the original data, a TimeGAN augmentation network is introduced to perform multi-dimensional data augmentation on the sub-training sets, which improves the size and diversity of the dataset used for training base learners, thereby improving the predictive performance of the model. Finally, the predictive performance of the proposed TimeGAN-SEFM was evaluated using measurement data from a 20 MW photovoltaic power station in North China. The results show that TimeGAN-SEFM effectively improves the accuracy of photovoltaic prediction and helps promote real-time matching of photovoltaic energy and power demand, thereby improving the utilization of photovoltaic energy.

Comparative Analysis of Hydrogen Production and Economic Feasibility: Direct versus Indirect Coupling of Photovoltaic Systems with Electrolyzers

Hydrogen production using photovoltaics (PV) is essential for decarbonizing many sectors of the economy. The integration of PV and hydrogen electrolyzers is actively debated, with focus on direct versus indirect configurations and the option of storage. Direct configuration connects PV directly to the electrolyzer, offering simplicity and reduced installation costs but depends on the weather for efficient power transfer. Indirect configuration adds a power stage, increasing complexity and losses, but enabling maximum power tracking. Adding batteries allows storage of excess PV energy, extending hydrogen production. This study optimizes a PV generator to maximize annual hydrogen production in the direct configuration, then uses the same PV array for indirect configurations with and without batteries for a fair comparison. Results show that the indirect configuration with a battery yields 78% more hydrogen annually than without a battery and 109% more than the direct configuration. The indirect configuration with a battery uses 86.9% of PV energy for hydrogen production, yielding the highest profit at 2.53 € ⋅ W−1 (euros per watt‐peak of PV), compared to the direct and indirect configurations without a battery, which use 41.9% and 44.6% of PV energy and generate 1.49 and 1.83 € ⋅ W−1, respectively.

Integrated Photovoltaic Systems in Ecorubarchitecture

Implementation of integrated photovoltaic systems in building construction represents a strategic step in ecourbarchitectonic, functionally innovated formation of building with an altered esthetics and artistic understanding of spatial compositions. In such a concept, there are renewable energy sources which with modular solar panels bring about a high architectonic quality. They are an integral part of the structure, generating photovoltaic energy. The advent of BIPV technologies opened a very important city-building topic on future harmonic formation of the houses, their positioning and layout orientation in relation to the natural and artifact environment, as well as on the organic, environmental interpolation of physical structures into the existing urban fabric. In general, it is a strategic question of urbanity in terms of further formation of the appearance of houses, streets, squares and plazas in different climates. In the focus is a correct choice of the type of BIPV facades and building material which will improve its performance.

Modeling and Control of Dual Active Bridge‐Modular Multilevel Converter‐Based Solid State Transformer for Grid Integration of SPV and Battery Energy Storage

ABSTRACTThis article deals with the modeling and control of a solid‐state transformer (SST) based on a dual active bridge (DAB) and modular multilevel converter (MMC) for integrating solar photovoltaic (SPV) and battery energy storage (BES) systems into the grid. SST uses DABs for bidirectional DC‐DC conversion and an MMC for DC‐AC conversion. A hybrid control method is developed in this study, which combines proportional‐integral (PI) controllers and model predictive control (MPC) to achieve the multiple objectives of the SST. The DABs' control system uses PI controllers for power extraction from the SPV system and to regulate the charging and discharging of the BES according to power generation and demand fluctuations. MPC is applied to control the active power injection, regulate the DC‐link and sub‐module capacitor voltages of the MMC. Moreover, the developed hybrid control method ensures reliable SST performance even under adverse conditions like grid voltage distortion, unbalance, and frequency variations. An energy management strategy is developed based on total power generation, reference active power injection, and the state of charge of the BES. This strategy ensures accurate reference power injection to the grid despite dynamic changes in operating conditions. The article also presents a detailed mathematical model of the DAB and MMC components, and the stability of the control algorithm is analyzed theoretically. The effectiveness of the SST and the proposed hybrid control scheme is demonstrated through MATLAB/Simulink simulations and hardware‐in‐the‐loop experimental results under various operating conditions.

Effects of electric vehicles on energy sharing for optimal sizing of solar PV and battery energy storage

Energy sharing for homes with electric vehicles (EVs) enhances sustainability by optimizing energy usage, reducing peak demand, and integrating renewable energy sources, thereby lowering costs and improving energy resilience. This study investigates the effects of EVs on optimal sizing problem of solar photovoltaic (SPV) and battery energy storage system (BESS) for grid-tied homes which participate in energy sharing schemes. In this paper, it is assumed that the energy is shared between two homes: home-1 as the prosumer which has an EV and intends to buy SPV and BESS, and house-2 which is a consumer. The optimization problem is formulated to achieve the minimum cost of electricity (COE) for home-1 and to reduce the COE for home-2 while taking consideration of the design constraints over the project lifespan. A rule-based energy management system is developed for different sets of configurations to compare the economic and operational results. The optimization is done by incorporating realistic annual data of the irradiance, temperature, load, and uncertainties of EV. The developed optimization technique is general in nature and can be used for any grid tied homes willing to share the electricity. Sensitivity analyses on costs of SPV-BESS, home energy demand, and grid export constraints are provided. Uncertainty analyses investigates the price of energy sharing and solar PV generation. The impact of various EV models with their respective battery capacity is also analyzed. The results show that the proposed energy-sharing methodology reduces the COE for prosumer and consumer by 1.2 ¢/kWh and 3.6 ¢/kWh, respectively.

Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques

Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices and electricity output forecasts, incorporating relevant climatic variables to enhance accuracy. The jointly modeled climatic variables and the spot price constitute one of the innovative aspects of this work. Regarding practical application, we considered a hypothetical wind–photovoltaic plant located in Italy and used the relevant climate series to determine the quantity of energy produced. We forecast the quantity of energy as well as income through machine learning techniques and more traditional statistical and econometric models. We evaluate the results by splitting the dataset into estimation windows and test windows, and using a backtesting technique. In particular, we found evidence that ML regression techniques outperform results obtained with traditional econometric models. Regarding the models used to achieve this goal, the objective is not to propose original models but to verify the effectiveness of the most recent machine learning models for this important application, and to compare them with more classic linear regression techniques.

Unit commitment in solar‐based integrated energy distribution systems with electrical, thermal and natural gas flexibilities: Application of information gap decision theory

AbstractThe depleting oil reserves, air pollution and increasing energy demand, have overturned the focus of the scientific community to renewable energy sources. Among which the photovoltaic (PV) systems occupy more than half of the market share and are generally installed at the distribution level. The volatile and uncertain nature of these PV productions necessitates flexible resources in energy systems. To this end, the district heating systems have an outstanding flexibility on account of their high thermal inertia. This study investigates the optimal unit commitment scheduling for gas‐fired and non‐gas‐fired distributed generation units (NGU) in an integrated energy distribution system (IEDS) within the physical constraints of the electrical, natural gas and thermal energy distribution networks. Moreover, a planning‐based optimization framework is proposed to investigate the investment of battery storage systems in the electric distribution network under the high penetration of PV systems with the aim of enhancing flexibility and reducing the operating costs of the IEDS. In this framework, the information gap decision theory is deployed under risk‐averse and risk‐seeker strategies to deal with uncertain PV energy production. Additionally, the environmental emissions are considered in a multi‐objective approach. The IEDS is embodied through IEEE 33‐bus EDS, 20‐node natural gas network and an 8‐node district heating systems. Eventually, The proposed approach makes a noteworthy contribution to the advancement of solar energy systems in IEDS.

Optimizing shared charging services at sustainable bus charging hubs: A queue theory integration approach

Integrating solar photovoltaic (PV) systems into bus charging infrastructure offers a promising solution to mitigate carbon emissions and reduce grid loads. However, a mismatch between the output of solar PV energy and the electric bus (EB) charging demand can arise due to the instability and intermittency of solar PV energy. To address this issue, this study introduces a novel shared charging business mode that allocates charging facilities to private electric vehicles (PEVs), leveraging idle infrastructure to maximize solar PV utilization. We develop an optimization framework to jointly determine the EB charging schedules and the allocation of charging facilities between EBs and PEVs within an EB network integrated with solar PV systems. Additionally, a loss queue model with time-dependent arrival rates is proposed to model the stochastic nature of PEV charging demand. A case study is conducted on a bus network in Beijing to validate our proposed operational strategy. The findings demonstrate that integrating shared charging in bus depots equipped with solar PV systems improves solar PV utilization from 79.30 % to 92.91 %. This integration reduces CO2 emissions and overall daily system costs by 7.93 % and 14.82 %, respectively, without negatively impacting the grid load during peak periods.

Impact of new regulatory policies on photovoltaic energy projects in Brazil: a case study for the State of Mato Grosso do Sul

This study analyzed the impact of regulatory changes on the implementation of photovoltaic solar energy projects in the State of Mato Grosso do Sul, focusing on the amendments introduced by Law 14.300. The research explored solar generation conditions in various regions, highlighting the influence of geographic location on the productivity of photovoltaic systems. Using fuzzy logic, the attractiveness of solar energy was assessed in different scenarios, relating variables such as productivity, tariffs, and consumption. The results indicate that increases in productivity and tariffs are positively correlated with the attractiveness of solar energy. Additionally, an average increase in the payback period was observed in response to the new regulations. However, specific conditions, such as reduced installation costs, were identified that could mitigate this increase in payback. The study highlighted that adapting to the new regulatory policies could open significant opportunities for distributed generation, particularly in the agricultural sector, contributing to the energy and economic sustainability of the Mato Grosso do Sul region. Fuzzy logic was essential for modeling decision support systems, enabling a precise analysis of the regulatory policies' implications for the economic viability of solar energy projects.

An agile life cycle assessment for the deployment of photovoltaic energy systems in the built environment

BackgroundIn the context of urban energy transition, photovoltaic (PV) systems play an important role in electricity generation. However, PV technology has some environmental drawbacks that also need to be acknowledged and managed. Life cycle assessment (LCA) is widely used to assess the environmental impacts of systems, but LCA is very complex to perform. Therefore, this research work presents a proof of concept for a parameterized LCA tool for grid-tied photovoltaic systems in urban areas that allows non-experts in LCA to obtain LCA results reliably and quickly.ResultsThe resulting methodology is an integration of three preexisting tools: PVGIS, Brightway and Ecoinvent, plus a Breakeven point analysis. The first step of the approach consists of identifying the main parameters of photovoltaic systems: geographical, technological, and temporal. Once the non-expert practitioner sets the influential parameters, the tool assesses the greenhouse gas emissions over the life cycle of the PV panels per unit of supplied electricity, allocates the emissions per component, and calculates the point at which the avoided emissions compensate for those produced by the power system. The algorithm strives to find the optimal PV system configuration to reduce the environmental impact, providing decision-making support for promoters and policymakers in the context of the urban energy transition. Two case studies are presented to illustrate the proposed method’s applicability and benefits.ConclusionsThe production of PV panels was confirmed as the main source of emissions in this kind of installation. The reasons are analyzed, allowing for improved design. Furthermore, the estimated break-even point where savings of conventional electricity offset emissions shows the influence of the parameters on the system’s environmental performance.

Use of Sustainable Energy Sources and Technologies in Primary and Secondary School Buildings in Greece. Can They Eliminate their Carbon Footprint?

School buildings consume energy covering their energy demand. The most of them utilize fossil fuels and grid electricity while the use of renewable energies is rather limited. School buildings should eliminate their carbon emissions in order to achieve the global target for net-zero carbon emissions by 2050. The energy consumption and the carbon emissions in schools have been evaluated. School buildings in Greece consume less than 100 kWh/m2 year. Solar thermal energy, solar photovoltaic energy and high efficiency heat pumps can cover all the energy demand in school buildings in Greece replacing the use of conventional energy sources. These energy technologies are reliable, mature and cost-effective. There are 7,756 primary and secondary school buildings in Greece consuming 1.67 TWh/year. The total cost of energy renovation eliminating their carbon emissions is estimated at 1,156,000 €/school or 8.96 billion € for the 7,756 schools in the country. For achieving the target of net-zero carbon emissions by 2050, 310 Greek schools should be energy refurbished every year eliminating their carbon footprint in the next 25 years. Taking into account the availability of benign energy sources and technologies the main barrier for eliminating the carbon emissions in school buildings in Greece by 2050 is the high investment cost. Our results could be useful for the development of the required policies for decarbonization in school buildings.

Renewable energy integration impact on power quality of supply of transmission system

Electrical energy is known as an essential part of our day-to-day lives. Renewable energy resources can be regenerated through the natural method within a reasonably short time and can be used to bridge the gap in extended power outages. Achieving more renewable energy (RE) than the low levels typically found in today’s energy supply network will entail continuous additional integration efforts into the future. This study examined the impacts of integrating renewable energy on the power quality of transmission networks. This work considered majorly two prominent renewable technologies (solar photovoltaic and wind energy). To examine the effects, IEEE 9-bus (a transmission network) was used. The transmission network and renewable sources (solar photovoltaic and wind energy technologies) were modelled with MATLAB/SIMULINK®. The Newton-Raphson iteration method of solution was employed for the solution of the load flow owing to its fast convergence and simplicity. The effects of its integration on the quality of the power supply, especially the voltage profile and harmonic content, were determined. It was discovered that the optimal location, where the voltage profile is improved and harmonic distortion is minimal, was at Bus 8 for the wind energy and then Bus 5 for the solar photovoltaic source.