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Bridging Photovoltaic (PV) energy generation with Electric Vehicle (EV) technology is an essential development in the modern quest for energy sustainability and advanced transportation methods. Consequently, this research focuses on enhancing efficient power utilization in PV-based EVs through the application of a novel Modified Boost-Luo DC-DC converter and a Chaotic Genetic Algorithm -based Maximum Power Point Tracking system. The Modified Boost-Luo converter provides a high voltage conversion ratio by incorporating a Voltage Multiplier Cell (VMC) and Three-Winding Coupled Inductor (3WCL) into its circuit configuration. The augmented PV output from Modified Boost-Luo converter is directed to the Brushless DC (BLDC) motor of the EV through a three-phase Voltage Source Inverter (3ΦVSI). Moreover, speed regulation in the BLDC motor is achieved using a Proportional–Integral (PI) controller. A distinctive feature of the proposed system involves the storage of surplus PV-generated power in a battery, interconnected to the DC-link via a bidirectional converter. In instances of PV power unavailability, the BLDC motor seamlessly taps into the stored energy in the battery for continued operation. The proposed approach is evaluated through both MATLAB simulation and laboratory prototype implementation, showcasing its potential to advance EV technology. [ABSTRACT FROM AUTHOR]

Cultivating negatively photoblastic plants requires, first of all, the provision of a constant supply of electricity, without which it is impossible to cyclically implement the process of watering the plants. Due to the large amounts of heat generated by some plants during their growth cyclic watering is not only necessary to provide water for their growth, but also to cool them down. Lack of power supply and failure to water within a certain period leads to overheating of the plants and their destruction, thus necessitating the disposal of the production batch. Ensuring stable power supply and energy security of the cultivation is possible using an island PV plant, but only integrated with appropriately sized energy storage. This paper presents the results of an analysis of the feasibility of using PV sources to ensure continuous cultivation of Mung bean sprouts. Determination of the energy demand of the sprout-growing plant was made based on the developed transient simulation model of the production line. The level of energy demand was determined for different production scenarios. The availability of solar energy at the location of the production line was analyzed and the size of the PV system integrated with energy storage was determined. For full-scale production, regardless of the period of the year in which it is carried out, the maximum energy demand was determined based on simulation studies at 36 kWh/day. For full-scale production, the size of the PV system should be 96.5 kWp, and the capacity of the energy storage should provide weekly coverage of the production line and be about 252 kWh. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-output DC–DC power conversion system based on Photovoltaic (PV) technology. PV panels are connected to a series compensated Buck-Boost Converter (SCBBC) to harvest solar energy, while a sliding mode controller (SMC) ensures maximum power point tracking (MPPT). During the intermediate phase, a synchronized Buck-Boost Converter (SBBC) topology is used to ensure effective charging and discharging of batteries. Additionally, a PI-SMC hybrid control strategy is applied at the back end to the Super Lift Luo Converter (SLLC) to maintain the load voltage at a desired value. A Gravitational Search Algorithm (GSA)-based PI controller controls the input current, while the output voltage is controlled by the outer loop (SMC). We use a single-loop SMC approach to validate the performance of the proposed dual-loop control scheme. According to the presented results, the dual-loop control scheme demonstrated higher dynamic performance in controlling input current and output voltage. [ABSTRACT FROM AUTHOR]

Building integrated photovoltaics (BIPVs) consist of PV panels that are integrated into a building as part of its construction. This technology has advantages such as the production of electricity without necessitating additional land area. This paper provides a literature review on recent developments in urban building energy modelling, including tools and methods as well as how they can be used to predict the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The microclimate, namely the urban heat island concept, is introduced and related to the existence of PV systems. It is concluded that urban building energy models (UBEMs) can be effective in studying the performance of PV systems in the urban environment. It allows one to simultaneously predict building energy performance and microclimate effects. However, there is a need to develop new methodologies to overcome the challenges associated with UBEMs, especially those concerning non-geometric data, which lead to a major source of errors, and to find an effective method to predict the effect of PV systems in the urban environment. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive control (MPC)-based approach for optimizing the performance of a photovoltaic (PV) system. The proposed method employs finite voltage-set maximum power point tracking (FVS-MPPT), ensuring precise duty cycle adjustment for a boost converter in the PV system considering the environmental changes in irradiation and temperature. Additionally, MPC is implemented for the grid-side converter to determine the optimal switching vector, ensuring precise control of active power via reference d-axis current and the elimination of reactive power by setting the reference q-axis current to zero. This approach optimizes the converter's performance, maintaining a stable DC-link voltage while ensuring efficient grid integration. To ensure proper synchronization with the grid, a phase-locked loop (PLL) is utilized to provide the necessary grid voltage angle for dq frame transformation. Simulation results highlight the efficiency of the proposed MPC strategy, with the PV-side converter showing a robust response by dynamically adjusting the duty cycle to maintain optimal performance under varying irradiation and temperature conditions. Furthermore, the grid-side converter ensures precise control of active power and eliminates reactive power, enhancing the overall system's stability and efficiency during grid interactions. A functional comparison of simulation results between the conventional P&O algorithm and the FVS-MPPT approach is presented, demonstrating the enhanced performance of the proposed technique over the conventional method including the total harmonic distortion for both techniques. [ABSTRACT FROM AUTHOR]

The deployment of photovoltaic (PV) systems on rooftops in urban environments may encounter shading on the PV collectors from surrounding walls, acting adversely on the generated electricity of the PV systems. Most studies on the shading of PV systems do not deal analytically with the shading losses of PV collectors affected by walls, fences, and obscuring objects. The present article mathematically formulates shadow expressions for wall and inter-row shading on PV collectors and calculates the percentage of annual shading losses affected by wall heights and azimuth angles, the distances of walls to collectors, and the length and azimuth angles of collectors. This study indicates that the shading losses increase for shorter distances from the collector to the walls and for higher walls. Shading losses may reach 7 percent for wall heights of 4 m and at a distance of 2 m from the collectors to a wall. The results indicate that wall shading dominates inter-row shading. [ABSTRACT FROM AUTHOR]

Energy generation and economic development are closely linked, with energy playing a pivotal role in wealth creation. However, the finite nature of fossil fuel resources and associated environmental challenges has emphasized the need for sustainable energy alternatives based on renewable sources. Among these alternatives, photovoltaic (PV) systems hold significant potential for fostering a sustainable energy system. Despite ongoing material research, achieving a major breakthrough in enhancing the conversion efficiency of commercial PV modules remains a challenge. To optimize PV system yield, installation must consider geographic location and design factors. PV modules often yield different results compared to manufacturer specifications due to factors such as high temperatures, dust, and arid to semiarid climates. Therefore, it is crucial to conduct outdoor testing and characterization of PV modules tailored to specific locations, such as Jordan, to maximize system performance. This research aims to examine the impact of ambient temperature, dust accumulation, and solar irradiance intensity on PV system performance in Jordan, providing valuable insights through monitoring power output variations. The findings will contribute to improving PV system efficiency in Jordan's unique climate and aid manufacturers in developing innovative PV applications. The collected data includes solar radiation, temperature, voltage, current, and output power. The results confirm that the Azraq site is a better location when compared to Mafraq based on the possibility of generated more output power using a PV system. [ABSTRACT FROM AUTHOR]

This study examines how photovoltaic (PV) installation parameters—such as tilt angle, azimuth angle, row pitch, height above ground, and albedo impact PV module operating conditions in harsh climates, focusing on irradiance levels and module temperature. It evaluates how these parameters influence degradation rates and the overall lifetime of PV modules. The study correlates variations in module lifetime to lifetime energy generation, economic factors, and environmental impacts. A novel PV optimization strategy is proposed, incorporating lifetime energy yield, levelized cost of electricity, and greenhouse gas emissions, rather than focusing solely on economic metrics. Findings show that installation parameters significantly affect climate stressors and PV module lifetime, making their consideration crucial. For instance, higher tilt angles are recommended to reduce stressor levels and extend the module's lifetime, optimizing energy yield while mitigating losses due to soiling. Height and albedo are identified as particularly sensitive, especially for bifacial modules, where small changes lead to significant differences in lifetime and energy yield. The study highlights an optimal albedo of ≈0.5, aligned with desert sand, suggesting that albedo boosters may not be necessary in desert climates. This approach offers valuable insights for balancing long‐term performance, environmental impact, and economic factors in PV system design. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) energy systems are a leading type of renewable energy systems globally. Predicting PV energy production accurately is crucial for maintaining efficient energy grids, making informed decisions in the energy market, and reducing maintenance costs. To ensure high accuracy and optimal production, it is essential to monitor and analyze these variables regularly. Solar radiation and temperature are two meteorological variables that directly affect the quantity of PV energy generated in PV facilities. The Performance Ratio (PR) is a critical parameter for assessing PV plant performance. A comprehensive model was constructed in this study to forecast solar radiation and temperature using multiple machine learning methods, including Instance-Based K-Nearest Neighbor Algorithm (IBK), Linear Regression, Random Forests, Random Tree, Multilayer Perceptron (MLP), and MLP Regression. Moreover, we used time series approaches, such as Simple Exponential Smoothing (SES), Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt Winter's Seasonal Method (HWES) models for PV systems prediction. Initially, we conducted daily forecasts as well as 1-step ahead forecasts at 5-minute intervals for both solar radiation and temperature. It is crucial to subject both variables to the same methodology in order to construct precise models for forecasting PV. Secondly, we compared the predicted values of solar radiation and temperature with the actual energy yield of the power plant to calculate energy production. Subsequently, a relative analysis of data mining models and time series models have been performed depending on the statistical error criteria like RMSE, MAPE, MABE, MAE, MSE, and direction accuracy (DAC).

This paper presents a novel maximum power point tracking control for a stand-alone photovoltaic (PV) system based on a robust polynomial static output feedback control law subject to input saturation. In detail, a DC/DC boost converter is used to regulate the load and extract the maximum power from the photovoltaic panel. First, a polynomial fuzzy model is used to represent the photovoltaic system. Then, as this control method is based on a reference model, a regression plane is used to generate the desired trajectory representing the optimal dynamics where the PV system supplies maximum power. Then, in order to reduce the number of required sensors, a polynomial output feedback controller was developed, in which the problem of converter performance degradation resulting from duty cycle saturation was avoided by using a saturated control approach. The controller gains have been obtained by solving a sum-of-squares optimization problem, where the H ∞ performance criterion is applied to guarantee the stability of the closed-loop system while achieving an optimal rejection level of external disturbances. To evaluate the performance of the suggested controller, a number of simulations and comparisons were carried out in MATLAB/Simulink environment and under various scenarios of weather conditions. [ABSTRACT FROM AUTHOR]

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The power sector confronts a crucial challenge in identifying sustainable and environmentally friendly energy carriers, with hydrogen emerging as a promising solution. This paper focuses on the modeling, analysis, and techno-economic evaluation of an independent photovoltaic (PV) system. The system is specifically designed to power industrial loads while simultaneously producing green hydrogen through water electrolysis. The emphasis is on utilizing renewable sources to generate hydrogen, particularly for fueling hydrogen-based cars. The study, conducted in Skikda, Algeria, involves a case study with thirty-two cars, each equipped with a 5 kg hydrogen storage tank. Employing an integrated approach that incorporates modeling, simulation, and optimization, the techno-economic analysis indicates that the proposed system provides a competitive, cost-effective, and environmentally friendly solution, with a rate of 0.239 $/kWh. The examined standalone PV system yields 24.5 GWh/year of electrical energy and produces 7584 kg/year of hydrogen. The findings highlight the potential of the proposed system to address the challenges in the power sector, offering a sustainable and efficient solution for both electricity generation and hydrogen production. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) system reliability and durability investigations are essential for industrial maturity and economic success. Recently, PV systems received much interest in Iraq due to many reasons—for instance, power shortage, global warming and pollution. Despite this interest, the precise economic implications of PV system reliability in harsh climates like Iraq remain uncertain. This work assesses the economic implications of PV system component reliability and soiling in Iraq using field experience and historical data. This study identifies the most common failure modes of PV systems installed in Iraq and similar climatic regions, and also ranks their severity. Simulations explore scenarios of PV module degradation rates, inverter lifetimes, soiling rates, and cleaning intervals, revealing that soiling has the most detrimental effect, with cleaning frequency leading to Levelized Cost of Electricity (LCOE) losses of over 30%, depending on the location. Inverter lifetime contributes to LCOE losses between 4 and 6%, depending on the PV system's location. This study also evaluates the impact of tilt angle as a mitigation strategy for reducing soiling loss and its economic implications, finding that installing PV modules at higher tilt angles could reduce the economic impact of soiling by approximately 4.4%. Additionally, the optimal cleaning strategy identified is fully automated dry cleaning with robots, outperforming other strategies economically. Overall, the findings highlight that the LCOE in Iraq is relatively high compared to the global weighted average for utility-scale PV systems, primarily due to high soiling and degradation rates. The LCOE varies within the country, influenced by different degradation rates. This study aims to assist PV stakeholders in Iraq and the Middle East and North Africa (MENA) region in accurately estimating solar bankability; moreover, increasing reliability by minimizing the technical and financial risks by considering key parameters specific to these regions. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Partial shading (PS) conditions pose additional challenges, leading to multiple peaks in the power-voltage (P-V) curve and reduced output power. Therefore, controlling MPPT under partial shading conditions is a complex task. OBJECTIVES: This study aims to introduce a novel MMPT algorithm based on the ant colony incorporated bald eagle search optimization (AC-BESO) method to enhance the efficiency of PV systems. METHODS: The effectiveness of the proposed MPPT algorithm was established through a series of experiments using MATLAB software, tested under various levels of solar irradiance. RESULTS: Compared to existing methods, the proposed AC-BESO algorithm stands out for its simplicity in implementation and reduced computational complexity. Furthermore, its tracking performance surpasses that of conventional methods, as validated through comparative analyses. CONCLUSION: This study confirms the efficacy of the AC-BESO method over traditional strategies. It serves as a framework for selecting an MPPT approach when designing PV systems.

Due to the growing global demand for electricity energy, photovoltaic systems are becoming increasingly important as a continuous and environmentally friendly alternative. They ensure the continuity of electrical production in a healthy and sustainable manner. To ensure the efficiency and optimal performance of these systems, an effective diagnostic model is urgently needed to classify faulty and working solar cells. In recent years, deep learning methods have been used to analyse and process images, providing new insights and guidance in the field of fault diagnosis in PV systems. This research proposes a comparative study of the deep learning models ResNet50, VGG-19, and AlexNet to test their effectiveness in analysing and classifying defective solar cells from non-defective cells using EL images.

Soaring solar cell temperature hindered photovoltaic (PV) efficiency, but a novel radiative cooling (RC) cover developed in this study offered a cost-effective solution. Using a randomly particle-doping structure, the radiative cooling cover achieved a high “sky window” emissivity of 95.3% while maintaining a high solar transmittance of 94.8%. The RC-PV system reached a peak power output of 147.6 W/m2. A field study to explore its potential in various provinces in China revealed significant efficiency improvements, with yearly electricity outputs surpassing those of ordinary PV systems by a relative improvement of 2.78%–3.72%. The largest increases were observed under clear skies and in dry, cool climates, highlighting the potential of RC-PV systems under real weather and environmental conditions. This work provided the theoretical foundation for designing scalable radiative cooling films for PV systems, unlocking the full potential of solar energy.

A numerical modeling methodology based on the finite element method is utilized in this investigation to predict the impact of the PCM characteristics on the system cooling performance and the solar module output power. The thermal analysis considers the transient nature and the nonlinearity of the problem. The boundary conditions include the cyclic variation with time of solar irradiance and ambient temperature in a hot climatic region, with peak temperatures close to 50 °C. Under these circumstances and owing to the various influencing parameters, choosing an appropriate PCM is a challenging task. By allowing the PCM characteristics to vary continuously over the applicable domains and not just by giving specified discrete values, a systematic approach is adopted in this work. This technique is applied to explore the relationships between system input parameters, as defined by the PCM properties, and output variables, as described by cell temperature and module efficiency. Due to the harsh boundary conditions, the obtained results demonstrated that PCM with high thermal conductivity is required to achieve efficient thermal regulation. The enhanced material characteristics can only be provided by composite PCMs with graphite or metal additives. The resulting optimized design variables of the suitable PCM, melting temperature, thermal conductivity, and thickness, can reduce the cell temperature to 57 °C (20% reduction). The corresponding efficiency and power output are raised by around 8%. The power output was found to increase from 125 W (reference case) to 135 W for the optimized case. The results suggest that using a passive cooling system based on PCM is a viable method for enhancing the power output of a solar PV module. The created coupled model predicts the thermal behavior of the PV module and significantly facilitates the PCM selection procedure to ensure its applicability. [ABSTRACT FROM AUTHOR]

Partial shading significantly affects the performance of photovoltaic (PV) power systems, rendering traditional maximum power point tracking (MPPT) methods ineffective. This study proposes a novel hybrid MPPT algorithm integrating voltage scanning and modified Perturb and Observe (P&O) techniques to overcome the limitations posed by partial shading. This algorithm has a simple structure and does not require panel information such as the number of panels or voltage due to its voltage scanning feature. To test the proposed algorithm, a grid-connected PV power system with a power of 252.6 kW was created in the MATLAB/Simulink environment. In this power system, six different PS conditions, containing quite challenging situations, were listed in three different scenarios and simulated. The proposed algorithm was compared with the voltage scanning and P&O and voltage scanning and variable-step P&O methods. It was observed that the proposed algorithm has lower power fluctuations compared to the other two traditional methods. Additionally, this algorithm managed to achieve higher efficiency than the other methods in some cases. [ABSTRACT FROM AUTHOR]

Summary: In this paper, for standalone and grid‐connected PV systems, a three‐phase simplified split‐source inverter (SSI) is proposed and controlled using a model‐predictive control (MPC). The maximum power point tracking (MPPT) approach used is an incremental conductance method based on a PI controller for both systems. The standalone system is composed of PV modules, a three‐phase SSI, and a bidirectional power DC–DC converter that connects a battery bank and a DC‐side capacitor. The output AC voltages of SSI are controlled using MPC. The bidirectional power DC–DC converter regulates the DC‐link voltage (DCLV). The grid‐connected system consists of PV modules, a three‐phase SSI, and an AC‐side L‐filter. The DC‐link PI controller generates reference currents for the MPC algorithm. The MPC uses these reference currents to adjust and deliver the PV power to the grid while regulating the DCLV. The PI controllers' parameters are selected for both systems using the Harris Hawks optimization method. Both PV systems simulation results show that under various operating conditions, they have succeeded in fixing a DCLV and producing a high‐quality AC output voltage and current at low THD. Experimental results for the three‐phase standalone PV system used to verify the system's performance. [ABSTRACT FROM AUTHOR]

This study examines the impact of risk models and investors' risk aversion on the selection of community solar portfolios. Various risk models to account for the volatility in the electrical power output of community solar, namely variance (Var), SemiVariance (SemiVar), mean absolute deviation (MAD), and conditional value at risk (CVaR), were considered. A statistical model based on modern portfolio theory was employed to simulate investors' risk aversion in the context of community solar portfolio selection. The results of this study showed that the choice of risk model that aligns with investors' risk-aversion level plays a key role in realizing more return and safeguarding against volatility in power generation. In particular, the findings of this research revealed that the CVaR model provides higher returns at the cost of greater volatility in power generation compared to other risk models. In contrast, the MAD model offered a better tradeoff between risk and return, which can appeal more to risk-averse investors. Based on the simulation results, a new approach was proposed for optimizing the portfolio selection process for investors with divergent risk-aversion levels by averaging the utility functions of investors and identifying the most probable outcome. [ABSTRACT FROM AUTHOR]

This research delves into a case study of a photovoltaic (PV) energy community, leveraging empirical data to explore the integration of renewable energy sources and storage solutions. By evaluating energy generation and consumption patterns within real-world energy communities (a nominal generation capacity of 33 kWn) in Gipuzkoa, Spain, from May 2022 to May 2023, this study comprehensively examines operational dynamics and performance metrics. This study highlights the critical role of energy consumption patterns in facilitating the integration of renewable energy sources and underscores the importance of proactive strategies to manage demand fluctuations effectively. Against the backdrop of rising energy costs and environmental concerns, renewable energies and storage solutions emerge as compelling alternatives, offering financial feasibility and environmental benefits within energy communities. This study emphasizes the necessity of research and development efforts to develop efficient energy storage technologies and the importance of economic incentives and collaborative initiatives to drive investments in renewable energy infrastructure. The analyzed results provide valuable insights into operational dynamics and performance metrics, further advancing our understanding of their transformative potential in achieving a sustainable energy future. Specifically, our study suggests that storage capacity should ideally support an average annual capacity of 23%, with fluctuations observed where this capacity may double or reduce to a minimum in certain months. Given the current market conditions, our findings indicate the necessity of significant public subsidies, amounting to no less than 67%, to facilitate the installation of storage infrastructure, especially in cases where initial investments are not covered by the energy community. [ABSTRACT FROM AUTHOR]

AbstractPV (photovoltaic) systems, also known as solar panel systems, play an essential role in the mitigation of greenhouse gas emissions and the promotion of renewable energy. Through the conversion of sunlight into usable energy, electricity is generated without emitting greenhouse gases and producing pollutants. Notwithstanding the evolutionary significance of PV systems, the occurrence of defects and anomalies in PV systems may result in diminished power output, consequently impeding the efficiency of the systems and potentially resulting in hazards in certain circumstances. Therefore, early detection of faults and anomalies in PV systems is imperative to guarantee the reliability, efficiency, and safety of the systems. In this paper, we develop a signal decomposition for the purpose of anomaly detection in PV systems. The proposed methodology is grounded on the concept of low-rank and sparse decomposition, with consideration given to the signs of the decomposed low-rank and sparse components, as well as the smooth variations within and between periods in the mean signals. Through the implementation of Monte Carlo simulations, we showcase the efficacy of our proposed methodology in identifying anomalies of varying durations and magnitudes in PV systems. A case study is employed to validate the proposed methodology in detecting anomalies in real PV systems. [ABSTRACT FROM AUTHOR]

In recent years, electricity generation from renewable sources has gained popularity. In particular, electricity generation based on solar energy is expected to be the most suitable solution to meet the energy needs of urban environments in the near future. While providing renewable-based energy to urban areas poses a major challenge, rooftop Photovoltaic (PV) systems that harness solar energy could pave the way to a more sustainable environment for cities. From an environmental perspective, every MWh of electricity generated from renewable sources reduces the introduction of large amounts of greenhouse gases into the atmosphere, thereby promoting a healthy environment and reducing global warming. Small-scale building-connected solar energy systems contribute significantly to the rapid growth of electricity generation potential from solar energy globally. This study aims to investigate the future of PV systems placed on roofs in the central housing sector of Kütahya, focusing especially on building roof areas. In this context, average solar radiation maps are provided to estimate the electricity generation potential of rooftop PV systems and evaluate the production potential. In addition, Geographic Information System (GIS) was used to evaluate the total roof area and solar potential of residential buildings in Kütahya province through spatial analysis. Finally, performance simulations were conducted, allowing solar potential prediction from annual solar irradiance, considering local building construction and PV design requirements. As a result of the analysis, it was determined that a total of 1979.7 MWh of electricity could be produced annually from a total roof area of 35121 m2 from a group of faculty buildings in the university campus located in the Kirazpınar neighborhood of Kütahya center, and a recommendation was made to form a basis for future studies on the energy efficiency of different PV solar cells. [ABSTRACT FROM AUTHOR]

Several countries have encouraged the installation of photovoltaic (PV) systems in urban areas to contribute to the decarbonization goals of the electric power system. At the same time, consumers have adopted PV systems to reduce their electricity bills. While grid-following PV inverters offset active power demand, they can decrease the power factor at the point of interconnection with the grid, subsequently leading to financial penalties imposed by distribution utilities. Additionally, utilities must maintain power factor values above a predefined threshold to maintain acceptable levels of power losses at the transmission level. This paper examines low power factor penalty schemes for distribution utilities and consumers with PV systems. In such an analysis, an optimization approach is used to minimize the costs of penalties associated with low power factor during a consumer’s billing period. This approach makes it possible to reduce the number of low power factor penalties, thus reducing the amount of electricity bills to be paid by consumers. The decision variable in this context is the power factor of the PV inverters. A case study is presented that considers the financial penalties in a city in the metropolitan area of Sao Paulo, Brazil, with various levels of PV penetration in the distribution system. The results show that while the penalties for consumers are low, distribution utilities would incur more significant penalties or require additional investments to maintain the power factor at the values imposed by electric transmission companies. This analysis aims to help regulatory agencies evaluate penalty schemes to reduce electrical losses in the distribution system.

The integration of photovoltaic (PV) into electric power systems has been widely explored and adopted to address the problems associated with the depletion of fossil fuels and the release of greenhouse gases. PV panels convert sunlight into electricity, minimizing the reliance on fossil fuels and mitigating environmental pollution. It is crucial to optimally utilize the PV power in the system; hence maximum power point tracking (MPPT) algorithms have been developed to ensure optimal performance of grid-connected PV systems at the maximum power point (MPP) despite changes in weather conditions. Moreover, deep reinforcement learning (DRL) developments provide a promising approach for optimizing grid-connected PV systems, replacing the conventional proportional-integral-derivative (PID) controllers. However, there is limited research evaluating the efficiency of these systems using DRL techniques. This paper proposes a new dandelion optimizer (DO)-based DRL for MPPT of grid-connected photovoltaic systems and evaluates the proposed method for a 100-MW PV plant connected to a 33-kV distribution system. The proposed DRL technique uses proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG) algorithms for continuous states and discrete or continuous action spaces to adjust the PV-measured voltage based on a reference one produced via DO-PPO and DO-DDPG methods. To test the effectiveness and practicality of the introduced methods, simulations were conducted using actual input data of a 100 MW PV plant connected to a 33-kV distribution system for typical days in summer and winter seasons using MATLAB/Simulink software. The proposed implemented methods were evaluated by comparing their simulation results with other techniques: DO-PID, particle swarm optimization (PSO), and incremental conductance (InC-PI). The findings revealed that the efficiencies of the DC-DC boost and the voltage source converters using the introduced methods were 84.25%- 85.90%, and 78.33%- 81.10% on a summer day while they were 92.77%- 95% and 86.70%- 89.50% on a winter day, respectively, which proves that these methods were efficient and effective, indicating their promising potential for future applications.

This work tackles the scheduling challenge of microgrids for smart homes, aiming to optimize energy management with both renewable and non-renewable sources. A power control center orchestrates the microgrid, coordinating distributed energy resources (DERs) for peak demand fulfillment and excess energy utilization. We propose a proportional-integral control system for efficient demand response, achieving reduced post-scheduling costs and a peak-to-average ratio. Comparative analysis reveals Ant Colony Optimization outperforms Binary Particle Swarm Optimization in cost and peak-to-average ratio reduction. Simulations explore two scenarios: Case 1 integrates with the main grid for reliability, while Case 2 utilizes solely renewable energy sources. Although Case 2 exhibits superior performance, Case 1’s dependence on the main grid offers greater real-world feasibility. Therefore, Case 1 with optimized DER scheduling emerges as the recommended solution for enhancing microgrid efficiency and ensuring reliable power supply in smart homes.

In an archipelago country like Indonesia, maritime transportation is essential. However, the ships, like other modes of transportation, burn fuel, contributing to CO2 emissions and negatively impacting marine life. Alternative renewable energy can help solve this problem while being environmentally friendly and cost-effective. As a result, this research describes installing PV systems to power electronic loads on a pilot ship. The supply-demand scenario was simulated using the System Advisory Model. The experiment lasted 176 days, 32 of which were still hybrid with a diesel generator, and for 144 days, electronics loads were entirely powered by PV systems. The experimental results show that the generated power consistently exceeds what is necessary. MPPT may also control the battery charging to avoid overcharging by ensuring the voltage input is always consistent. The maximum generated power is 2356 W with a peak load of 1669 W, and the average generated power is 1645 W with a load of 720.6 W. According to SIMAPRO's life cycle study results, the most significant environmental impact comes during installation due to using diesel fuel trucks and when the PV system is no longer operational since it will become a landfill, harming the ecosystem. PV systems, on the other hand, have no environmental impact during installation. Economic estimates show significant savings and, of course, profits.

Building integrated photovoltaics (BIPVs) consist of PV panels that are integrated into a building as part of its construction. This technology has advantages such as the production of electricity without necessitating additional land area. This paper provides a literature review on recent developments in urban building energy modelling, including tools and methods as well as how they can be used to predict the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The microclimate, namely the urban heat island concept, is introduced and related to the existence of PV systems. It is concluded that urban building energy models (UBEMs) can be effective in studying the performance of PV systems in the urban environment. It allows one to simultaneously predict building energy performance and microclimate effects. However, there is a need to develop new methodologies to overcome the challenges associated with UBEMs, especially those concerning non-geometric data, which lead to a major source of errors, and to find an effective method to predict the effect of PV systems in the urban environment.

As photovoltaics (PV), also known as solar electricity, has been growing over the years, the energy markets have been gradually moving toward decentralization. However, recent media accusations suggest that decentralized renewable energy is slowly becoming unpopular because of the hidden fees being charged to owners of installed PV systems. In response, this paper investigates the potential for alternative approaches to incentivize owners using peer-to-peer (P2P) sharing. This study provides an analytical comparison between the use of the P2P mechanism, the net-metering mechanism, and a combination of these in the commercial sector. Through the use of a simulation, this case study presents the possible outcomes of the implementation of these models in a microgrid. Using technical and economic indexes the comparison was made by looking at the following indexes: peak power, energy balance, economic benefit, and transaction index. Based on a microgrid of 28 commercial buildings, readings of consumption were taken at intervals of one hour, and a Python model was made to find PV size and compare trading mechanisms. It was found that the combination of P2P and net-metering had the best overall performance, followed by net-metering itself, with the best season being all for both, and summer for net-metering by itself. This shows that a P2P model implemented in a microgrid helps create more energy balance, although the combination would achieve the highest performance. This study can be used by policymakers for proposing renewable energy policies and regulations that are more beneficial to all prosumers and consumers.

The success on forecasting the performance of PV systems, it lies in depicting the non-linear behavior dynamics of its I–V curves, as a direct consequence of the variations in the operational conditions, where the photovoltaic system is set up. Throughout this work, authors address the physical–mathematical basics, and the assumptions made, so as to propose a theoretical approach, on purpose of describing the dynamics concerning to these systems. Findings, as well as the manuscript novelty, they comprise a full model deduced by subsections dealing with the phenomena under analysis; all them derived as function of the irradiance and module temperature.

With rapid technology advancements in renewable energy systems, rooftop photovoltaic (PV) products and systems can be considered a crucial element in the transition toward energy sustainability in residential buildings. Still, residents' initiatives are required to expand the adoption of clean energy-efficient technology to replace conventional energy systems and thereby achieve a sustainable environment. The aim of this study was, therefore, to develop system dynamics models to relate adopters' satisfaction with PV systems through word-of-mouth (WoM), advertising, and competition and then evaluate their impacts on the number of PV installations, generated electric power, and the reduction in CO2 emissions for rooftop buildings in Jordan for the years from 2020 to 2040. Results revealed that the predicted cumulative PV installations will increase to 262 and 558 MW in 2030 and 2040, respectively. Due to this, the cumulative generated power (kWh) (=42.5 GWh) will reach 452 and 964 GWh in 2030 and 2040, respectively. Moreover, the cumulative CO2 emission reductions may reach 262 and 558 million kg CO2 in 2030 and 2040, respectively. In conclusion, continual assessment of the adopters' feedback on installed PV systems, adopting effective advertising, and advancement of PV designs and technology can significantly support achieving energy sustainability in residential buildings and reduce the dependency on traditional, scarce energy resources. [ABSTRACT FROM AUTHOR]

The number of cycles to the end of life for high-power IGBT modules is expressed as a function of the stress parameters in the model. Most of the time, these models are generated on the basis of experimental data from accelerated power-cycling experiments that are done at preset temperatures and stress levels. This paper proposed a systematic Reliability evaluation process for large-scale commercial and utility-level PV power systems. The major contribution of this work is the quantification of the impact of junction temperature on the failure rates of critical components such as PV Inverters and capacitors. Usually, the reliability assessment of the power electronic switch such as IGBT and inverter focused on component level, whereas much fewer cases discussed the Reliability evaluation for the entire PV system. In light of the above concerns, this article discussed the effect of junction temperature on the lifetime of IGBT modules, and the relevant lifetime factor is modelled. This study enables us to include the junction temperature effect on the lifetime model of IGBT modules under the given mission profiles of the converter. [ABSTRACT FROM AUTHOR]