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This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV’s five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles.

Recent advancements in power electronics have significantly improved photovoltaic (PV) inverters by equipping them with sophisticated monitoring capabilities. These enhancements provide economic advantages by facilitating swift failure detection and lowering monitoring costs. Educating users on the economic repercussions of undetected failures in specific inverter monitoring systems is crucial. This paper introduces a novel metric, “Cost of Detection”, which assesses the financial impact of failures, considering the repair expenses and the “quality” of the monitoring system in place. The study analyzed fifteen inverter monitoring solutions, focusing on the variance in alerts generated by the manufacturers’ standard and extra monitoring features. Employing the Failure Mode and Effects Analysis (FMEA) method, alerts were prioritized based on their importance for two PV system scenarios: a low-power residential system (5 kWp) and a medium-power industrial/commercial system (100 kWp). Lisbon, Rome, and Berlin were chosen as the locations for these systems. The economic impact of system failures is evaluated annually for each capacity and city. Given the differing costs and annual yields, comparing their economic performance over time is essential. This comparison utilizes the Net Present Value (NPV), which estimates an investment’s worth by calculating the present value of all cash flows. The investment assessment includes only the costs of inverters and optimizers, excluding O&M expenses, licenses, and fees. Over five years, a higher NPV signifies a more economically advantageous solution. For residential systems, string inverters with optimizers have the highest NPV, surpassing those without optimizers by 17% across all three cities. The optimal monitoring solution in the industrial/commercial context was a string inverter with one optimizer for every two panels. Here, Rome emerged as the location with the most substantial NPV increase of 50%, followed by Berlin with 33% and Lisbon with 28%.

Large-scale optimization (LSO) problems among photovoltaic (PV) and concentrated solar power (CSP) systems are attracting increasing attention as they help improve the energy dispatch efficiency of PV and CSP systems to minimize power costs. Therefore, it is necessary and urgent to systematically analyze and summarize various LSO methods to showcase their advantages and disadvantages, ensuring the efficient operation of hybrid energy systems comprising different PV and CSP systems. This paper compares and analyzes the latest LSO methods for PV and CSP systems based on meta-heuristic algorithms (i.e., Particle Swarm Optimization, Genetic Algorithm, Enhanced Gravitational Search Algorithm, and Grey Wolf Optimization), numerical simulation and stochastic optimization methods (i.e., Constraint Programming, Linear Programming, Dynamic Programming Optimization Algorithm, and Derivative-Free Optimization), and machine learning-based AI methods (Double Grid Search Support Vector Machine, Long Short-Term Memory, Kalman Filter, and Random Forest). An in-depth analysis and A comparison of the essence and applications of these algorithms are conducted to explore their characteristics and suitability for PV and CSP or hybrid systems. The research results demonstrate the specificities of different LSO algorithms, providing valuable insights for researchers with diverse interests and guiding the selection of the most appropriate method as the solution algorithm for LSO problems in various PV and CSP systems. This also offers useful references and suggestions for extracting research challenges in LSO problems of PV and CSP systems and proposing corresponding solutions to guide future research development.

This study presents a new approach for Maximum Power Point Tracking (MPPT) by combining the honey badger algorithm (HBA) with a Genetic Algorithm (GA). The integration aims to optimize photovoltaic (PV) system performance in partial shading conditions (PSCs). Initially, the HBA is utilized to explore extensively and identify potential solutions while avoiding local optima. If necessary, the GA is then employed to escape local optima through selection, crossover, and mutation operations. On average, this proposed method has a 40% improvement in tracking time and 0.77% in efficiency compared with the HBA. In a dynamic case, the proposed method achieves a 4.81% improvement compared to HBA.

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects.

The primary disadvantage of solar photovoltaic systems, particularly in partial shadowing conditions (PSC), is their low efficiency. A power–voltage curve with a homogenous distribution of solar irradiation often has a single maximum power point (MPP). Without a doubt, it can be extracted using any conventional tracker—for instance, perturb and observe. On the other hand, under PSC, the situation is entirely different since, depending on the number of distinct solar irradiation levels, the power–voltage curve has numerous MPPs (i.e., multiple local points and one global point). Conventional MPPTs can only extract the first point since they are unable to distinguish between local and global MPP. Thus, to track the global MPP, an optimized MPPT based on optimization algorithms is needed. The majority of global MPPT techniques seen in the literature call for sensors for voltage and current in addition to, occasionally, temperature and/or solar irradiance, which raises the cost of the system. Therefore, a single-sensor global MPPT based on the recent red-tailed hawk (RTH) algorithm for a PV system interconnected with a DC link operating under PSC is presented. Reducing the number of sensors leads to a decrease in the cost of a controller. To prove the superiority of the RTH, the results are compared with several metaheuristic algorithms. Three shading scenarios are considered, with the idea of changing the shading scenario to change the location of the global MPP to measure the consistency of the algorithms. The results verified the effectiveness of the suggested global MPPT based on the RTH in precisely capturing the global MPP compared with other methods. As an example, for the first shading situation, the mean PV power values varied between 6835.63 W and 5925.58 W. The RTH reaches the highest PV power of 6835.63 W flowing through particle swarm optimization (6808.64 W), whereas greylag goose optimizer achieved the smallest PV power production of 5925.58 W.

This paper proposes a new technique for a single-direction solar tracker. The proposed design is based on a sun sensor system that controls the position of the solar panel. The sun sensors of the proposed design contain four photodiodes that are placed on the solar panel in specific angles and directions. The proposed system has several advantages such as the simplicity of implementing the system. This system combines the real-time tracking of sunlight and the low cost of applying a single-direction tracker. The prototyping experiment and Simulink MATLAB were applied to show the advantages of applying a single-direction tracker by following the angle of sunlight during the day. Real-time sun position and irradiation data were applied. The experimental results show that the proposed single-axis sun sensor PV tracker system generates around 20 more electric power than a fixed-structure PV system.

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model’s precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model’s predictive capabilities. Comparing the model’s predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach.

Under the dual-carbon target, the popularization and application of building integrated photovoltaic (BIPV) and ground source heat pump systems have made active buildings a research hotspot in the field of architecture and energy. Aiming at this issue, based on the building energy consumption model of active buildings, an active building energy management system (EMS) control strategy based on multi-energy load collaborative optimization is proposed. Firstly, based on the thermal dynamic characteristics and building performance parameters of active buildings, the overall refined energy consumption model of active buildings is constructed. Secondly, based on the construction of BIPV, the ice storage air conditioning system, the ground source heat pump system, and the integrated demand response (IDR) model, a tiered carbon transaction cost model is introduced, and an energy management strategy that leverages the synergistic application of renewable and active technologies is proposed. This strategy aims to meet the comprehensive needs of active buildings in terms of economic benefits, comfort, and environmental protection. Finally, the strategy’s effectiveness is demonstrated through a practical example.

Energy production through non-conventional renewable sources allows progress towards meeting the Sustainable Development Objectives and constitutes abundant and reliable sources when combined with storage systems. From a financial viewpoint, renewable energy production projects withstand significant challenges such as competition, irreversibility of investments, high uncertainty levels, and considerable investment amounts. These facts make their financial valuation fundamental for all the agents involved. Using the Web of Science (WoS) and Scopus databases, a scientometric analysis was carried out to understand the methods that have been used in the financial appraisal of photovoltaic energy generation projects with storage systems. The present research project was developed from 268 studies published between 2013 and 2023; tools such as Bibliometrix 4.1.3, VOSViewer 1.6.19, and Tree of Science 0.0.1a9 were used. Two main findings stand out: (i) the most used methods in the literature are the traditional ones, and within them, the levelized cost of energy has been used with greater frequency; and (ii) there is an interest in analyzing the investments of these systems for residences within the framework of distributed energy generation. Two gaps were found in the literature: (i) the studies that were carried out have not comprehensively incorporated the financial challenges faced by these investments; and (ii) the evaluation of these projects has not been addressed from the perspective of a utility-based power generator.

This research paper presents a comprehensive study on the implementation of photovoltaic (PV) energy systems at Al-Abrar Mosque in Saudi Arabia. The primary objective was to explore optimal regional solar power strategies. By synergistically integrating technical evaluations of the PV system with economic analyses, including the payback period and levelized cost of energy (LCOE), alongside an investigation of net metering and net billing scenarios, we delineated a pathway toward achieving net zero billing for the mosque’s energy requirements. This study examined two scenarios: Scenario I involved net metering, while Scenario II explored net billing. Our theoretical and simulation results, derived from detailed analyses conducted using PVsyst software, unequivocally demonstrated the superiority of net metering for this specific application. With net metering, the mosque’s energy needs can be efficiently met using minimal infrastructure—comprising only 34 photovoltaic modules and a single inverter. In contrast, net billing requires significantly higher resource demands, underscoring the economic and spatial advantages of net metering. Additionally, the payback period for Scenario I is 7.9 years, while for Scenario II, it extends to 87 years. Through rigorous simulations, this study reaffirmed the practicality and feasibility of the net metering approach within the context of Saudi Arabia. Furthermore, our research provides actionable insights for implementing sustainable solutions at specific sites, such as the Al-Abrar Mosque, and contributes to advancing renewable energy knowledge in the region.

This paper addresses the issue of the abolition of annual net metering in Slovenia and compares the electric energy costs for the studied active user after the abolition. The article also provides an exploration of the role played by an aggregator, which serves as a central entity that enables individuals to participate in the electric energy market. An analysis of the case study of an active user was made, where an analysis was made of the measurements of household consumption and photovoltaic plant production for the year 2022. This article presents an economic analysis with and without net metering and an analysis of the aggregator involvement strategies. In addition, a battery energy storage system was also considered in the analysis. An important part of the article is the identification of the flexibility potential for shiftable loads, which enable an aggregator to acquire insight into the energy consumption profile and energy production profile of active users. The following indicators were used to compare the strategies: annual electric energy cost and the indicators including self-sufficiency, self-consumption, and grid dependency. The findings indicate that, even in the absence of annual net metering, the active user can lower their costs for electric energy with the help of an aggregator.

Amid the ongoing climate crisis, the international community is enacting policies to promote low-carbon energy-sharing communities. The primary objective of such communities is to enhance community-level energy self-sufficiency. Accurate energy self-sufficiency assessments are paramount in planning energy-efficient architectural designs, urban landscapes, and communal environments. In this study, the energy self-sufficiency rate of an energy-sharing community was estimated at the design stage and compared with the actual energy self-sufficiency rate calculated based on data collected over the following year (April 2022 to March 2023). The outcomes reveal that the estimated energy self-sufficiency rate is 171%, whereas the realized rate is 133%, underscoring the disparity between the projections and outcomes. An analysis of the seasonal variations in these discrepancies elucidated a correlation between the differences in the insolation levels between standard typical meteorological year (TMY) data that are conventionally used for energy generation projections and the actual meteorological conditions. Moreover, a notable incongruity surface exists between the monthly average electricity consumption of a standard four-person household, as stipulated by the Korean Electric Power Corporation (KEPCO) at 273 kWh, and the empirical power consumption at 430 kWh, resulting in a variance of approximately 157 kWh. This study illuminates the complex relationship between variables affecting energy self-sufficiency in energy-sharing communities. It serves as a crucial step towards informed decision making and precision in sustainable urban energy solutions.

The DC arc is the main cause of fire in photovoltaic (PV) systems. This is due to the fact that the DC arc has no zero-crossing point and is prone to stable combustion. Failure to detect it in a timely manner can seriously endanger the PV system. This study analyzes the influences of the series arc and the maximum power point tracking (MPPT) algorithm on the PV output characteristics based on the PV equivalent circuit module. The PV voltage and current variation characteristics are obtained when the series arc occurs. The findings indicate that the input voltage of the converter remains unchanged due to the MPPT algorithm before and after the series arc occurs. Furthermore, the PV faulty string output current will drastically decrease when the series arc fault occurs. On this basis, a series arc detection method based on the current change is proposed, suppressing the combustion of the series arc by increasing the target voltage of the MPPT algorithm. The experimental results show that the proposed method can effectively detect and extinguish the series arc in the PV system within 0.6 s. Compared to the other methods, the proposed method can be integrated into the PV system without additional hardware.

To propel us toward a greener and more resilient future, it is imperative that we adopt renewable sources and implement innovative sustainable solutions in response to the escalating energy crisis. Thus, renewable energies have emerged as a viable solution to the global energy crisis, with photovoltaic energy being one of the prominent sources in this regard. This paper represents a significant step in the desired direction by focusing on detailed, comprehensive dynamic modeling and efficient control of photovoltaic (PV) systems as grid-connected energy sources. The ultimate goal is to enhance system reliability and ensure high power quality. The behavior of the suggested photovoltaic system is tested under varying sun radiation conditions. The PV system is complemented by a boost converter and a three-phase pulse width modulation (PWM) inverter, with MATLAB software employed for system investigation. This research paper enhances photovoltaic (PV) system performance through the integration of model-predictive control (MPC) with a high-gain DC–DC converter. It improves maximum power point tracking (MPPT) efficiency in response to the variability of solar energy by combining MPC with the traditional incremental conductance (IN-C) method. Additionally, the system incorporates a DC–AC converter for three-phase pulse width modulation, which is also controlled by predictive control technology supported by Particle Swarm Optimization (PSO) to further enhance performance. PSO was selected due to its capability to optimize complex systems and its proficiency in handling nonlinear functions and multiple variables, making it an ideal choice for improving MPC control performance. The simulation results demonstrate the system’s ability to maintain stable energy production despite variations in solar irradiation levels, thus highlighting its effectiveness.

The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector. The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide.

The proliferation of renewable energy sources to achieve carbon neutrality has rapidly increased the adoption of photovoltaic (PV) systems. Consequently, specialized solar PV systems have emerged for various installation purposes. This study focuses on grid connecting vertically installed bifacial PV modules facing east and west by establishing a test bed within Republic of Korea. Based on weather and generation data collected in Republic of Korea, located in the middle of latitude 34.98° N, from January to July 2023, we analyzed and compared the generation patterns, peak generation, peak hours, and total generation of conventional and vertical PV systems. Moreover, PVsyst was used to model the solar PV generation and analyze the consistency and viability of vertical PV generation by comparing actual operational data with simulation results. The vertical PV system demonstrated a peak power generation of 89.1% compared with the conventional PV system with bifacial modules. Based on operational data from January to July, the power generation output of the vertical PV system decreased to 65.7% compared with that of the conventional system with bifacial modules. This corresponded to 78.8% to 80.2% based on the PVsyst simulation results. In particular, the investigations related to the peak generation levels and occurrence times of vertical PV systems provide insights into the practicality of vertical solar PV systems and their potential for improving the PV hosting capacity.

The increased use of fossil fuels in transportation is considered a major cause of environmental pollution and climate change on a global scale. In international shipping, regulations and strict measures have been introduced by the International Maritime Organization to achieve the goal of a 40% reduction in greenhouse gas (GHG) emissions by 2030, with the envisage to reach net-zero GHG emissions close to 2050. Renewable energy sources, such as solar photovoltaic (PV) systems, can be implemented on new-build or existing marine vessels as an effective alternative source for auxiliary power generation, reducing the dependency on fossil fuels and contributing to decarbonization. In the present paper, a sustainable retrofit design using PV panels on an existing Ro-Ro vessel is analyzed for its feasibility. The proposed system is used for energy production during ship cargo operations and takes advantage of the large space area on the upper deck and its continuous exposure to sunlight during its voyage. To investigate the effectiveness of the PV system as an alternative to fossil fuel consumption, an environmental and economic evaluation is performed. According to the results obtained, the solar PV system can provide approximately 88% of the required energy annually for lighting during ship cargo operations, with the corresponding fuel savings and emission reductions, making the investment economically feasible, with a high potential to contribute to environmental sustainability.

This study implements techno-economic evaluations of different photovoltaic (PV) systems in the East Midlands of the UK. Three application case studies, including an office building, a domestic building, and a poultry shed, are achieved. The building electricity consumption is obtained according to hourly automatic meter readings, and the PV electricity production is predicted based on the Engineering Equation Solver 8.4 software. Meanwhile, the 25-years’ complete economic profitability investigations of the three PV systems are conducted on the basis of the Monte Carlo method; the sensitivity analyses of payback period and net present value are also carried out by using the @RISK 8 software. Furthermore, the payback period and yearly savings are investigated and compared between the Smart Export Guarantee (SEG) and feed-in tariff (FiT) schemes. Technical investigation outcomes conclude that the three PV systems are able to satisfy electrical energy requirements in summer, and the additional electricity could be exported to the grid in this period. In winter, however, the systems have less electricity output resulting in power shortage and input from the grid. Economic study results exhibit that the net present values of the office building, domestic building, and poultry shed are £9108.4, £1717.91, and £7275.86, respectively, corresponding to the payback periods of 6.15 years, 9.12 years, and 9.41 years. This implies that there is an acceptable payback period (

Rapid growth in the integration of new consumers into the electricity sector, particularly in the industrial sector, has necessitated better control of the electricity supply and of the users’ op-erating conditions to guarantee an adequate quality of service as well as the unregulated dis-turbances that have been generated in the electrical network that can cause significant failures, breakdowns and interruptions, causing considerable expenses and economic losses. This research examines the characteristics of electrical variations in equipment within a company in the industrial sector, analyzes the impact generated within the electrical system according to the need for operation in manufacturing systems, and proposes a new solution through automation of the regulation elements to maintain an optimal system quality and prevent damage and equipment failures while offering a cost-effective model. The proposed solution is evaluated through a reliable simulation in ETAP (Energy Systems Modeling, Analysis and Optimization) software, which emulates the interaction of control elements and simulates the design of electric flow equipment operation. The results demonstrate an improvement in system performance in the presence of disturbances when two automation schemes are applied as well as the exclusive operation of the capacitor bank, which improves the total system current fluctuations and improves the power factor from 85.83% to 93.42%. Such a scheme also improves the waveform in the main power system; another improvement result is when simultaneously operating the voltage and current filter together with the PV system, further improving the current fluctuations, improving the power factor from 85.83% to 94.81%, achieving better stability and improving the quality of the waveform in the main power grid.

In this work, a flower-shaped shading system with integrated tracking photovoltaic, suitable for sustainable extensive urban coverages, is designed. Detailed photovoltaic energy yield simulations with a single-diode model approach are performed to disclose the potential of the proposed tracking photovoltaic shading device (PVSD). Simulations are performed with reference to a case study. A double-layer space truss is used to house the innovative modular photovoltaic tracking system, and the first application is envisaged for the coverage of a public market area of a sunny municipality in Southern Italy. By comparing it with the traditional photovoltaic fixed system, the results of the simulations show a steadier energy generation of the new PVSD, and it also provides better coverage with renewable energy during the hours of the day when the traditional system produces low electric energy. Lastly, an early interactive prototype of the PVSD system is presented. The tracking mechanism is carefully designed, 3D-printed at a small scale and tested with a motorized dynamic system controlled by a microcontroller board. The realization of the physical prototype and the engineering of the movement mechanism confirmed the feasibility and the correct functioning of the conceived system opening to real-scale applications.

Solar photovoltaic (PV) systems often encounter a problem called partial shading condition (PSC), which causes a significant decrease in the system’s output power. To address this issue, meta-heuristic algorithms (MHAs) can be used to perform maximum power point tracking (MPPT) on the system’s multiple-peak P-V curves due to PSCs. Particle swarm optimization was one of the first MHA methods to be implemented for MPPT. However, PSO has some drawbacks, including long settling time and sustained PV output power oscillations during tracking. Hence, some improved MHA methods have been proposed. One approach is to combine a MHA with a deterministic approach (DA) such as P & O method. However, such a hybrid method is more complex to implement. Also, the transition criteria from a DA to a MHA and vice versa is sometimes difficult to define. Another approach, as adapted in this paper is to modify the existing MHAs. This includes modifying the search operators or the parameter settings, to enhance exploration or exploitation capabilities of MHAs. This paper proposed to incorporate the stooping behaviour in the golden eagle optimization (GEO) algorithm. Stooping is in fact a hunting technique frequently employed by golden eagles. Inclusion of stooping in the GEO algorithm not only truly model golden eagles’ hunting behaviour but also yields great performance. Stooping behavior only requires one extra parameter. Nevertheless, on average, the proposed method can reduce tracking time by 42.41% and improve dynamic tracking accuracy by 1.95%, compared to GEO. Moreover, compared to PSO, GWO, and BA, the proposed method achieves an improvement of 2.66%, 3.56%, and 4.24% in dynamic tracking accuracy, respectively.

Solar photovoltaic (PV) connection with the grid becomes more prevalent in distributed generation, and the DC grid contributes a significantly to the distributing system. The current study focuses on combining rooftop solar with the DC microgrid. A high-gain DC–DC converter for photovoltaic systems (HGBC-PVS) is proposed in this article to link two lower-voltage photovoltaic panels to a higher-voltage network. The designed converter with low-rating switches generates improved values of voltage gain and efficiency. To gain the most power out of solar modules, Maximum Power Point Tracking (MPPT) is used, which employs the Adaptive Incremental Conductance approach. It extracts the maximum power, thereby facilitating efficient converter operation and generating optimal outputs. The simulation outcomes in terms of voltage, inductor current, output efficiency, and voltage gain are computed, and the outcomes show the significance of the introduced topology. The efficacy of the introduced work is proved by the comparison of measured outputs with the computed outputs for various parameters.

Atmospheric water generation (AWG) is a technological innovation that facilitates the extraction of water from the atmosphere using various techniques. In response to mounting concerns regarding water scarcity in multiple regions globally, AWG has emerged as a promising solution for providing potable water in areas where conventional water sources are limited or contaminated. AWG systems can be implemented across diverse settings, ranging from individual households to large-scale industrial operations, and can be powered by renewable energy sources. Despite the inherent challenges associated with upscaling AWG technology to ensure its affordability and reliability, it possesses the potential to make a significant contribution towards meeting the water requirements of communities in both developed and developing nations. This study aimed to investigate the performance and limitations of a commercially available dehumidifier, namely, the Trotec TTK140S (Heinsberg, Germany), through experimental analysis. Additionally, the feasibility of integrating this dehumidifier with a photovoltaic energy source was explored. Initially, the dehumidifier’s water production and energy consumption were assessed under specific conditions. Subsequently, a comparison was conducted across three different locations (Lisbon, Pretoria, and Riyadh) to evaluate the dehumidifier’s operation and ascertain the photovoltaic module area necessary for it to function independently. This approach effectively addresses one of the main drawbacks of the technology, namely, its substantial energy consumption.

The Photovoltaic (PV) system is an eco-friendly renewable energy system that is integrated with a DC-DC buck-boost converter to generate electrical energy as per the variations in solar irradiance and outdoor temperature. This article proposes a novel Adaptive Fractional Order PID (A-FOPID) compensator with self-adjusting fractional orders to extract maximum power from a stand-alone PV system as ambient conditions change. The reference voltage is generated using a feed-forward neural network. The conventional FOPID compensator, which operates on the output voltage error of the interleaved buck-boost converter, is employed as the baseline maximum-power-point-tracking (MPPT) controller. The baseline controller is retrofitted with an online state-error-driven adaptation law that dynamically modifies the fractional orders of the controller’s integral and differential operators. The adaptation law is formulated as a nonlinear hyperbolic scaling function of the system’s state error and error-derivative variables. This augmentation supplements the controller’s adaptability, enabling it to manipulate flexibly the tightness of the applied control effort as the operating conditions change. The efficacy of the proposed control law is analyzed by carrying out customized simulations in the MATLAB Simulink environment. The simulation results show that the proposed MPPT control scheme yields a mean improvement of 25.4% in tracking accuracy and 11.3% in transient response speed under varying environmental conditions.

There are some significant nonlinearity and delay issues in photovoltaic (PV) system circuits. Therefore, it is very difficult for the existing classic linear control theories to be used in PV systems; this hinders the design of the optimal energy dispatch by considering real-time generation power forecasting methods. To solve this problem, an overall linearized model with variable weather parameters (OLM-VWP) of the PV system is proposed on the basis of small-signal modeling. Meanwhile, a corresponding simplified overall linearized model with variable weather parameters (SOLM-VWP) is presented. The SOLM-VWP avoids analyzing delay characteristics of the complex high-order PV system. Moreover, it can reduce hardware cost and computation time, which makes analysis of the transient performance index of the PV system more convenient. In addition, on the basis of the OLM-VWP and SOLM-VWP, a delay-time model with variable weather parameters (DTM-VWP) of the PV system is also proposed. The delay time of the system can be accurately calculated using the DTM-VWP, and it provides a preliminary theoretical basis for carrying out real-time energy scheduling of the PV system. Finally, simulations are implemented using the MATLAB tool, and experiments are conducted. The results verify that the proposed linearization model of the PV system is accurate and reasonable under varying irradiance and temperature conditions. Meanwhile, the results also verify that the proposed SOLM-VWP and DTM-VWP of the PV system are feasible. Additionally, the results show that some transient performance indexes (delay time, rise time, settling time, and peak time) can be solved by means of equations when the circuit parameters and real-time weather parameters are given.

There are a variety of maximum power point tracking (MPPT) algorithms for improving the energy efficiency of solar photovoltaic (PV) systems. The mode of implementation (digital or analog), design simplicity, sensor requirements, convergence speed, range of efficacy, and hardware costs are the primary distinctions between these algorithms. Selecting an appropriate algorithm is critical for users, as it influences the electrical efficiency of PV systems and lowers costs by reducing the number of solar panels required to achieve the desired output. This research is relevant since PV systems are an alternative and sustainable solution for energy production. The main aim of this paper is to review the current advances in MPPT algorithms. This paper first undertakes a systematic literature review (SLR) of various MPPT algorithms, highlighting their strengths and weaknesses; a detailed summary of the related reviews on this topic is then presented. Next, quantitative and qualitative comparisons of the most popular and efficient MPPT methods are performed. This comparison is based on simulation results to provide efficient benchmarking of MPPT algorithms. This benchmarking validates that intelligent MPPTs, such as artificial neural network (ANN), fuzzy logic control (FLC), and adaptive neuro-fuzzy inference system (ANFIS), outperform other approaches in tracking the MPPT of PV systems. Specifically, the ANN technique had the highest efficiency of 98.6%, while the ANFIS and FLC methods were close behind with efficiencies of 98.34% and 98.29%, respectively. Therefore, it is recommended that these intelligent MPPT techniques be considered for use in future photovoltaic systems to achieve optimal power output and maximize energy production.

In the face of the increasing depletion of non-renewable energy sources and increasingly serious environmental problems, the development of green and environmentally friendly renewable energy sources cannot be delayed. Because of the far-reaching development potential of solar energy, solar power has become an important research object for power development. The available solar energy in space is several times greater than that on Earth. Solar energy from space can be collected by a space solar power station (SSPS) and transmitted to the ground by wireless power transfer. In the full-chain ground-based validation system of SSPS-OMEGA, the spherical concentrator is used, and the light intensity distribution on the solar receiver is non-uniform. The non-uniform light intensity makes the output current of each photovoltaic (PV) cell on the solar receiver greatly different, and causes power losses, known as the mismatch problem. This paper proposes a simple, efficient and easy-to-implement method to optimize the structure of PV arrays to reduce the effect of non-uniform light on the output performance of each PV cell, which is beneficial to the topology of PV arrays and also effectively improves the layout rate. Then, a differential power processing (DPP) converter with a simple structure and easy control is designed to further deal with the power mismatch problem between series-connected PV modules. Finally, a simulation circuit model and a physical hardware model of the differential power processing PV system are built and used in the full-chain ground-based validation system of SSPS-OMEGA. The results demonstrate that the influence of non-uniform lighting on PV cells is effectively reduced, the output power of PV modules connected in series under non-uniform light distribution is substantially increased, and the photoelectric conversion efficiency is significantly improved.

The anaerobic digestion of the organic fraction of municipal solid waste and the biogas production obtained from its stabilization are becoming an increasingly attractive solution, due to their beneficial effects on the environment. In this way, the waste is considered a resource allowing a reduction in the quantity of it going to landfills and the derived greenhouse gas emissions. Simultaneously, the upgrading process of biogas into biomethane can address the issues dealing with decarbonization of the transport. In this work, the production of biogas obtained from the organic fraction of municipal solid wastes in a plug flow reactor is analyzed. In order to steer the chemical reactions, the temperature of the process must be kept under control. A new simulation model, implemented in the MatLab® environment, is developed to predict the temperature field within the reactor, in order to assess how the temperature affects the growth and the decay of the main microbial species. A thermal model, based on two equilibrium equations, is implemented to describe the heat transfer between the digester and the environment and between the digester and the internal heat exchanger. A biological model, based on suitable differential equations, is also included for the calculation of the biological processes occurring in the reactor. The proposed anaerobic digestion model is derived by the combination of these two models, and it is able to simultaneously simulate both thermal and biological processes occurring within the reactor. In addition to the thermal energy demand, the plant requires huge amounts of electricity due to the presence of a biogas upgrading process, converting biogas into biomethane. Therefore, the in-house developed model is integrated into a TRNSYS environment, to perform a yearly dynamic simulation of the reactor in combination with other renewable technologies. In the developed system layout, the thermal energy required to control the temperature of the reactor is matched by a solar thermal source. The electrical demand is met by the means of a photovoltaic field. In this work, a detailed thermoeconomic analysis is also proposed to compare the environmental impact and economic feasibility of a biomethane production plant based on a plug flow reactor and fed by renewables. Several economic incentives are considered and compared to determine the optimal solution, both in terms of energy and economic savings. The plant is designed for the treatment of a waste flow rate equal to 626.4 kg/h, and the biomethane produced, approximately 850 tons/years, is injected into the national gas grid or supplied to gas stations. In the proposed plant, a solar field of an evacuated tube collector having a surface of approximately 200 m2 is able to satisfy 35% of the thermal energy demand while over 50% of the electric demand is met with a photovoltaic field of 400 m2. A promising payback time of approximately 5 years was estimated.

The management of energy in distribution networks has been gathering attention in recent years. The simultaneous control of generation and demand is crucial for achieving energy savings and can further lower energy pricing. The work aims to develop a control scheme for a hybrid microgrid that can provide stability to the bus voltage and effectively manage the power flow. Solar energy is the current trend in renewable energy sources (RES). There is a surge in the installation of solar PV systems both on a large scale and on a small scale, such as rooftop PV systems. Installation of RES at residential premises has to be conducted with a proper power management scheme. The hybrid microgrid for this work consists of a PV system with a boost converter to extract maximum power, a DC-DC bi-directional converter to charge or discharge the hybrid energy-storing devices, and a three-phase AC-DC interlinking converter for exchange of energy with the utility grid. The control and power management scheme checks the voltage of each unit and maintains the power flow according to operating conditions. Disturbances are introduced in the form of load switching and irradiance variation to check the system performance. The system is tested on the MATLAB (R2021a) Simulink platform for varying its different modes of operations. An experimental set-up has been developed with hardware-in-the-loop to validate the simulation results.

Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and ontologies explicitly describe metadata as well as application data objects and domains. The present paper proposes a novel approach to integrate FAIR digital objects and ontologies as metadata models in order to support data access for energy researchers, energy research applications, operational applications and energy information systems. As the first example domain to be modeled using an ontology and to get integrated with FAIR digital objects, a photovoltaic (PV) system model is selected. For the given purpose, a discussion of existing energy ontologies shows the necessity to develop a new PV ontology. By integration of FDOs, this new PV ontology is introduced in the present paper. Furthermore, the concept of FDOs is integrated with the PV ontology in such a way that it allows for generalization. By this, the present paper contributes to a sustainable data management for smart grid operation, especially for interoperability, by using ontologies and, hence, unambiguous semantics. An information system application that visualizes the PV system, its describing data and collected sensor data, is proposed. As a proof of concept the details of the use case implementation are presented.

Photovoltaic (PV) systems are the leading solutions for reducing carbon dioxide (CO2) emissions in Iran’s energy system. However, there are some challenges to investing in PV systems in Iran, such as the low energy market price and the high investment cost of PV systems. Although the flat feed-in tariff (FiT) is defined to help purchase energy from the PV systems, it is not attractive to investors. In this paper, a mathematical formulation is developed for the planning problem of the PV systems with battery energy storages (BESs) considering two incentive policies: (1) Designing time-of-use FiT to encourage the PV systems to sell energy to the grid at peak hours (2) Participating in the carbon trading energy market. The insolation in Iran is calculated regarding mathematical formulations which divide Iran into eight zones. The results of the base case show high payback periods for all zones. In the presence of the incentive policies, the payback period decreases considerably from 5.46 yrs. to 3.75 yrs. for the best zone. Also, the net present value increases more than 170 percent in some zones compared to the base case.

A solution to increase the electrical output of the photovoltaic systems relies on solar tracking mechanisms that increase the amount of received solar energy. The experimental results obtained during a monitoring period of one year are comparatively presented in the paper for five types of photovoltaic modules installed on a fixed platform (as reference) and on a dual-axis solar tracking platform in the Renewable Energy Systems and Recycling R&D Centre of the Transilvania University of Brasov, Romania. The influence of the solar-tracking mechanism and the meteorological conditions specific to the four seasons during the monitoring period on the output of the analysed photovoltaic technologies are discussed in the paper. The solar tracking increases by 28% the amount of the yearly received solar energy and by 29.6% the electrical energy output of the entire PV platform. The solar conversion efficiency of the tracked PV platform is slightly increased (14.34%) when compared with the fixed one (14.17%). When assessing the influence of solar tracking on each type of PV, the results show that the CIGS PV module has the highest relative energy gain (34%) followed by CIS (30.8%), m-Si (30.6%), p-Si (27.3%) and CdTe (23.4%) PV modules.

Worldwide energy shortages and the green energy revolution have triggered an increase in the penetration of standalone microgrids. However, they have limited generation capacity and are wasteful when excess generated energy is curtailed. This presents an opportunity for the coordinated operation of multiple prosumer microgrids that absorb this waste to enhance their resiliency and reliability. This paper proposes a reliable prosumer model with an inbuilt energy management system (EMS) simulator that considers the mentioned deficiencies for constructing resilient interconnected prosumer microgrids. The EMS simulator operates in a real-time dynamic environment to coordinate the prosumer components and performs flexible switching for (1) prioritizing critical load by shedding the non-critical load and (2) meeting load demand locally or from other interconnected prosumers. The EMS simulator maintains energy balance by setting limits for the battery energy storage system (BESS) to preserve energy during low generation and performs real-time monitoring. The novelty of this model lies in its simplicity and flexibility, which allows interconnected prosumer microgrids to operate in cooperation without the need for communication. The proposed model is evaluated in a post-disaster off-grid scenario using a single-phase average value model that considers reasonable computation time. The simulation results of the proposed system indicate the preservation of stored energy while maintaining critical resources beyond three days of poor weather conditions.

Conventional power systems have been heavily dependent on fossil fuel to meet the increasing energy demand due to exponential population growth and diverse technological advancements. This paper presents an optimal energy model and power management of an off-grid distributed energy system (DES) capable of providing sustainable and economic power supply to electrical loads. The paper models and co-optimizes multi-energy generations as a central objective for reliable and economic power supply to electrical loads while simultaneously satisfying a set of system and operational parameters. In addition, mixed integer nonlinear programing (MINLP) optimization technique is exploited to maximize power system generation between interconnected energy sources and dynamic electrical load with highest reliability and minimum operational and emission costs. Due to frequent battery cycling operation in the DES, rainflow algorithm is applied to the optimization result to estimate the depth of discharge (DOD) and subsequently count the number of cycles. The validity and performance of the power management strategy is evaluated with an aggregate load demand scenario of sixty households as a benchmark in a MATLAB program. The simulation results indicate the capability and effectiveness of optimal DES model through an enhanced MINLP optimization program in terms of significant operational costs and emission reduction of the diesel generator (DG). Specifically, the deployment of DES minimizes the daily operational cost by 71.53%. The results further indicate a drastic reduction in CO2 emissions, with 22.76% reduction for the residential community load scenario in contrast to the exclusive DG system. This study provides a framework on the economic feasibility and effective planning of green energy systems (GESs) with efficient optimization techniques with capability for further development.

The thermal regulation of a silicon photovoltaic (PV) solar system is essential as the module surface temperature beyond 25 °C deteriorates its Power Conversion Efficiency (PCE). The intensity of solar radiation seems to have a crucial impact on the PCE of a PV solar system. The present study aims to assess the effect of solar radiation variations on the PCEs of PV modules integrated with and without passive cooling systems. The used passive cooling systems are (a) multi-pipe copper frame filled with Phase Change Material (PCM) and (b) multi-pipe copper frame filled with ZnO-doped PCM. The tests were conducted at the University of Jeddah in the month of March at various local times. The results show that the ambient and module surface temperatures are directly dominated by solar radiations. The conventional PV solar system presents a higher module surface temperature as compared to that of a PV system integrated with ZnO/PCM. The enhanced module surface temperature decreases the open circuit voltage (Voc) and slightly increases the short circuit current (Isc) indicating its reduced electric efficiency.

Within the past decade, since impediments in nonrenewable fuel sources and the contamination they cause, utilizing green energies, such as those that are sun-oriented, in tandem with electric vehicles, is a developing slant. Coordinating electric vehicle (EV) charging stations with sun-powered boards (PV) reduces the burden of EV charging on the control framework. This paper presents a state-of-the-art literature review on remote control transmission frameworks for charging the batteries of electric vehicles utilizing sun-based boards as a source of power generation. The goal of this research is to advance knowledge in the wireless power transfer (WPT) framework and explore more about solar-powered electric vehicle charging stations. To do this, a variety of solar-powered electric vehicle charging station types are thoroughly studied. Following a study of many framework elements, the types of WPT components are explored in a different section. Within the wireless power transmission framework for solar-powered electric vehicle charging, compensators and various coil structures are also investigated, along with the advantages of each coil over the others. This study also discusses the use of artificial intelligence (AI) in WPT frameworks and highlights the important aspects of developing an AI model.

Energy security and the resilience of electricity networks have recently gained critical momentum as subjects of research. The challenges of meeting the increasing electrical energy demands and the decarbonisation efforts necessary to mitigate the effects of climate change have highlighted the importance of microgrids for the effective integration of renewable energy sources. Microgrids have been the focus of research for several years; however, there are still many unresolved challenges that need to be addressed. Energy storage systems are essential elements that provide reliability and stability in microgrids with high penetrations of renewable energy sources. This study provides a systematic review of the recent developments in the control and management of energy storage systems for microgrid applications. In the early sections, a summary of the microgrid topologies and architectures found in the recent literature is given. The main contributions and targeted applications by the energy storage systems in the microgrid applications is defined for each scenario. As various types of energy storage systems are currently being integrated for the reliable operation of the microgrids, the paper analyses the properties and limitations of the solutions proposed in the recent literature. The review that was carried out shows that a hybrid energy storage system performs better in terms of microgrid stability and reliability when compared to applications that use a simple battery energy storage system. Therefore, a case study for a DC microgrid with a hybrid energy storage system was modelled in MATLAB/Simulink. The presented results show the advantages of hybrid energy storage systems in DC microgrids.

In recent years, important regulatory changes have been introduced in Spain in the fields of self-consumption and energy tariffs. In addition, electricity prices have risen sharply, reaching record highs in the last year. This evidences the need to conduct new research studies in order to provide an accurate picture of the profitability of battery energy storage systems and photovoltaic systems. This paper proposes a complex simulation tool developed to assist in the optimal design of these kinds of facilities. The tool is used in this study to analyze the benefits of including batteries in PV systems under different self-consumption models, different consumer profiles and different locations across the country. The research results indicate that at current electricity prices, the use of batteries is less profitable than selling excess energy to the grid, unless the price of batteries drops drastically by more than 50% in all the cases analyzed. However, at current battery prices, they become a valuable resource in facilities that do not feed energy surplus into the grid.

The objective of the proposed work is to develop a Maximum Power Point Tracking (MPPT) controller and inverter controller by applying the adaptive least mean square (LMS) algorithm to control the total harmonics distortion of a solar photovoltaic system. The advantage of the adaptive LMS algorithm is given by its simplicity and reduced required computational time. The adaptive LMS algorithm is applied to modify the Perturb and Observe (P&O), MPPT controller. In this controller, the adaptive LMS algorithm is used to predict solar photovoltaic power. The adaptive LMS maximum power point tracking controller gives better optimal solutions with less steady error 0.7% (6 watts) and 0% peak overshot in power with the tradeoff being more settling time at 0.33 s. The development of the inverter control law is performed using the d-q frame theory. This helps to reduce the number of equations to build a control law. The load current, grid current and grid voltage are sensed and transformed into d and q components. This adaptive LMS control law is used to extract the reference grid currents and, later, to compare them with the actual grid currents. The result of this comparison is used to generate the switching gate pulses for the inverter switches. The proposed controllers are developed and implemented with a solar PV system in MATLAB Simulink. The total harmonics distortion in grid and load current (3.25% and 7%) and voltage (0%) is investigated under linear and non-linear load conditions with changes in solar irradiations. The analysis is performed by selecting step incremental values and sampling time.

Photovoltaic system condition monitoring can be performed via single-diode model fitting to measured current–voltage curves. Model parameters can reveal cell aging and degradation. Conventional parameter identification methods require the measurement of entire current–voltage curves, causing interruptions in energy production. Instead, partial curves measured near the maximum power point offer a promising option for online condition monitoring. Unfortunately, measurement data reduction affects fitting and diagnosis accuracy. Thus, the optimal selection of maximum power point neighbourhoods used for fitting requires a systematic analysis of the effect of data selection on the fitted parameters. To date, only one published article has addressed this issue with a small number of measured curves using symmetrically chosen neighbourhoods with respect to the maximum power. Moreover, no study has determined single-diode fit quality to partial curves constructed via other principles, e.g., as a percentage of the maximum power point voltage. Such investigation is justified since the voltage is typically the inverter reference quantity. Our work takes the study of this topic to a whole new scientific level by systematically examining how limiting the current–voltage curve measuring range to maximum power point proximity based on both power and voltage affects single-diode model parameters. An extensive dataset with 2400 measured curves was analysed, and statistically credible results were obtained for the first time. We fitted the single-diode model directly to experimental curves without measuring outdoor conditions or using approximations. Our results provide clear guidance on how the choices of partial curves affect the fitting accuracy. A significant finding is that the correct selection of maximum power point neighbourhoods provides promising real-case online aging detection opportunities.

To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS.

This article presents a new method based on meta-heuristic algorithm for maximum power point tracking (MPPT) in photovoltaic systems. In this new method, the SALP Swarm Algorithm (SSA) is used instead of classic methods such as the Perturb and Observe (P&O) method. In this method, the value of the duty cycle is optimally determined in an optimization problem by SSA in order to track the maximum power. The objective function in this problem is maximizing the output power of the photovoltaic system. The proposed method has been applied on a photovoltaic system connected to the load, taking into account the effect of partial shade and different atmospheric conditions. The SSA method is compared with the Particle Swarm Optimization (PSO) algorithm and P&O methods. Additionally, we evaluated the effect of changes in temperature and radiation on solving the problem. The results of the simulation in the MATLAB/Simulink environment show the optimal performance of the proposed method in tracking the maximum power in different atmospheric conditions compared to other methods. To validate the proposed algorithm, it is compared with four important indexes: ISE, ITSE, IAE, and ITAE.

Currently, the industrial sector consumes more than 60% of the energy produced in Mexico, mainly from fossil fuels, causing negative impacts on the environment and human beings. Solar energy helps companies diversify their energy sources, generate savings, and reduce dependence on fossil fuels. Moreover, the environmental impact can be reduced when CO2 emissions are reduced. Nevertheless, in Mexico, less than 3.5% of the electricity comes from solar energy, and along with a lack of information about the technical and social aspects involved in photovoltaic (PV) systems, it is difficult for companies to analyze and evaluate relevant data, and thus make effective decisions based on their needs. As such, companies cannot understand the complete lifecycle of PV systems, and, usually, the economic, environmental, and technical decisions are made only using the installation analysis, which is only one stage in the lifespan of PV systems. This paper proposes an S4 framework with the sensing, smart, sustainable, and social features that small and medium-sized companies must consider to install, operate, and dispose of PV systems, considering the Mexican context. The current literature does not show a complete classification to cover the essential S4 features to describe PV systems, so companies only have partial information when deciding about the installation of PV systems. This framework considers all the needs that may exist during the PV systems’ lifecycle, making a detailed evaluation of each of its elements in each lifecycle stage. Consequently, this S4 framework gives a complete guideline allowing companies to decide on PV systems. Finally, this paper presents a case study about a Mexican company that uses the proposed S4 framework to analyze the PV’s lifespan.

The limited availability of fossil fuel and the growing energy demand in the world creates global energy challenges. These challenges have driven the electric power system to adopt the renewable source-based power production system to get green and clean energy. However, the trend of the introduction of renewable power sources increases the uncertainty in the production, control, and operation of power systems due to the erratic nature of the environment. To overcome these meteorological conditions, some support systems, such as storage devices, are integrated with renewable energy sources (RES). A number of storage devices are hybridized to get the hybrid energy storage system (HESS) to get a potential solution for these microgrid problems. For maintaining the robustness and reliability of the power system, proper control, and management of power in the microgrid is very important. In this paper, an analytical study related to power management strategies is given along with different interconnection topologies for the HESS. Analysis and control of storage devices are necessary to avoid the premature degradation of the devices and to get their optimal utilization. Therefore, this article attempts to include different power management schemes used in AC/DC microgrids. Furthermore, various control techniques specific to different energy storage devices are reviewed extensively, which would serve as a complete guide for the design and implementation of a hybrid AC/DC microgrid.

The integration of solar photovoltaic (PV) systems and utility grids has gradually gained significant interest in improving the sustainability of clean power supply for society. However, power quality remains a challenge due to partial shading conditions and harmonics. To overcome these drawbacks, a flexible radial movement optimization based on a dynamic safety perimeter maximum power point tracking algorithm is employed to track maximum power out of a PV system and to ensure that the optimum voltage level at the common DC bus is obtained under partial shading conditions using fixed-tilt installation configuration. Furthermore, a novel inverter control loop system with a double second order generalized integrator phase-locked loop (DSOGI-PLL) is also proposed to mitigate harmonics and improve the power quality of the grid interfacing PV system using MATLAB SIMULINK software. The proposed system has several merits, such as better harmonic suppression capability, control adaptivity, rapid tracking speed, low computational burden and phase and grid synchronization.

In this paper, an algorithm for the maximum extraction of energy generated by photovoltaic (PV) systems was presented. The tracking of the global maximum point of the system is complex due to the non-linearity of the current-voltage (I-V) characteristic curve of the photovoltaic modules, as they vary according to the temperature and solar irradiation in the module. To obtain the best energy efficiency in these systems, it is important that the generation is delivering the maximum power available through the arrangement. In order to solve such problems, in this work an efficient MPPT-FA method was proposed, which showed good traceability when compared to traditional methods. Most traditional MPPT techniques are not able to find the global maximum point to extract the maximum power provided by the PV system. Finally, the Firefly Metaheuristic MPPT method proved to be robust against several partial shading scenarios. Simulations were presented to demonstrate the effectiveness of the proposal when compared to the traditional MPPT-PO method.

Three-phase inverters for photovoltaic grid-connected applications typically require some form of grid voltage phase-angle detection in order to properly synchronize to the grid and control real and reactive power generation. Typically, a phase-locked loop scheme is used to determine this real-time phase angle information. However, in the present work, a novel method is proposed whereby the phase angle of the grid can be accurately identified solely via the grid current feedback. This phase-angle observer is incorporated into a current controller which can manage the real and reactive power of the grid-connected PV inverter system. Moreover, the maximum power point of the photovoltaic arrays is achieved without using a DC–DC converter. The proposed method achieves the grid current and DC-link voltage control objectives without the knowledge of the grid information and without the need for a cascaded control scheme. The design of this combined observer/controller scheme is motivated and validated via a Lyapunov stability analysis. The experimental setup is prototyped utilizing a real-time Typhoon HIL 603 and National Instrument cRIO embedded controller in order to validate the proposed observer/controller scheme under different operation scenarios such as irradiation changes, frequency changes, reactive power injection, and operation with a distorted grid. The results show that the DC-link voltage and the active and reactive powers are well regulated from the proposed control scheme without the measurement of the grid phase and frequency.

In the course of the switch to renewable energy sources, there is a shift from a few large energy sources (power plants) to a large number of small, distributed energy sources (e.g., photovoltaic systems) and energy storage devices (e.g., electric vehicles). This results in the need to know and identify these energy sources and sinks as soon as new devices are installed, in order to ensure grid stability. This paper presents an approach to identify energy sources and energy storage in smart meter data, using photovoltaic systems and electric vehicles as examples. For this purpose, the Pecan Street dataset is used, which has been extended by charging processes from the ACN dataset. The presented approach comprises a combination of a Convolutional Neural Network and a Multilayer Perceptron, which decides separately, on the basis of the smart meter data of a household, whether an electric vehicle and a photovoltaic system are present. It is shown that the combination of both classifiers achieves accuracy of 90.50% in the case of electric vehicle detection and 96.37% in the case of photovoltaic systems. It is also shown that the power levels lower than 0 kW in the case of the photovoltaic system and higher than 5 kW in the case of the electric vehicles have the largest influence on the output of the Multilayer Perceptron branch, which uses the power balance distribution as input.

This paper presents a novel three-port high-step-up/step-down bidirectional DC/DC converter with a coupled inductor for photovoltaic (PV) systems. The proposed converter combines a high-step-up converter, which is used to step-up the PV module to DC bus 200 V, and a battery charge/discharge bidirectional converter to form a three-port bidirectional converter. When sufficient energy is supplied from the PV modules, the converter can step-up the output of the PV modules and provide energy to the DC bus while charging the battery simultaneously. However, when no energy is supplied from the PV modules, the DC bus voltage is provided by the battery. Moreover, the energy stored in the leakage inductor is recycled to the DC-blocking capacitor, and synchronous rectification is conducted in the switch during the step-up mode to reduce switch loss and thereby increase the system’s overall efficiency. Finally, a 500 W three-port bidirectional converter is implemented to verify the feasibility and practicability of the proposed converter. The maximum efficiency of the proposed converter is 95.4%, 94.3%, and 94.7% when operated in the high-step-up mode, battery step-up mode, and step-down mode for the PV modules, respectively.