Search Results (40)

The development of solar technologies and the widespread adoption of photovoltaic (PV) panels have significantly transformed the global energy landscape. PV panels have evolved from niche applications to become a primary source of electricity generation, driven by their environmental benefits and declining costs. However, the performance and operational lifespan of PV systems are often compromised by various faults, which can lead to efficiency losses and increased maintenance costs. Consequently, effective and timely fault detection methods have become a critical focus of current research in the field. This work proposes an innovative video-based method for the dimensional evaluation and detection of malfunctions in solar panels, utilizing processing techniques applied to aerial images captured by unmanned aerial vehicles (drones). The method is based on a novel mini-pattern matching algorithm designed to identify specific defect features despite challenging environmental conditions such as strong gradients of non-uniform lighting, partial shading effects, or the presence of accidental deposits that obscure panel surfaces. The proposed approach aims to enhance the accuracy and reliability of fault detection, enabling more efficient monitoring and maintenance of PV installations. Full article

Renewable energy sources are critical to the global effort to achieve carbon neutrality. Alongside hydropower, wind and nuclear plants, the photovoltaic (PV) systems developed greatly, with new PV technologies emerging in recent years. Although the conversion efficiencies are improving and the materials used have a lower impact on the environment, the feasibility of these technologies is required to be assessed. This paper proposes a levelized cost of energy (LCOE) model to assess the feasibility of five PV technologies: high-efficiency silicon heterojunction cells (HJT), N-type monocrystalline silicon cells (N-type), P-type passivated emitter and rear contact cells (PERC), N-type tunnel oxide passivated contact cells (TOPCon) and bifacial TOPCon. The LCOE considers capital investment, government incentives, operation and maintenance costs, residual value of PV modules and total energy output during the PV system’s life span. To determine the influence of PV system’s capacity over the LCOE values, three systems are analyzed for each technology: 3 kW, 5 kW and 7 kW. The results show that the largest PV systems have the lowest LCOE values, ranging from 2.39 c€/kWh (TOPCon) to 2.92 c€/kWh (HJT) when incentives are accessed, and ranging from 6.05 c€/kWh (TOPCon) to 6.51 c€/kWh (HJT) without subsidies. The 3 kW and 5 kW PV systems have higher LCOE values due to lower energy output during lifetime. Full article

Evaluating the competitiveness of electricity is the most important issue. The main aim of this study was to determine the cost-effectiveness of renewable energy production in the European Union (EU) using the levelized cost competitiveness of renewable energy sources. The weighted average cost of capital (WACC) for onshore wind was calculated for European (EU) countries. The levelized cost of electricity (LCOE) approach was used to evaluate the energy costs of renewable energy sources. Energy production costs were compared across different technologies. The capital expenditures associated with solar PV are expected to decrease from USD 810/kW in 2021 to USD 360/kW in 2050. The power factor will remain stable at 14% during the analyzed period. Fuel, CO₂, and operation and maintenance (O&M) costs will be maintained at USD 10/MWh at all three time points of the analysis (2021, 2030, and 2050), whereas the LCOE will decrease from USD 50/MWh in 2021 to USD 25/MWh in 2050. The capital expenditures associated with onshore wind energy will decrease from USD 1590/kW in 2021 to USD 1410/kW in 2050. The power factor will increase from 29% to 30%, and fuel, CO₂, and O&M costs will reach USD 15/MWh in all three years. The LCOE will decrease from USD 55/MWh in 2021 to USD 45/MWh in 2050. In offshore wind projects, capital expenditures are expected to decrease considerably from USD 3040/kW in 2021 to USD 1320/kW in 2050. Full article

A standalone hybrid renewable energy system (HRES) that combines different types of renewable energy sources and storages offers a sustainable energy solution by reducing reliance on fossil fuels and minimizing greenhouse gas emissions. In this paper, a standalone hybrid renewable energy system (HRES) involving wind turbines, photovoltaic (PV) modules, diesel generators (DG), and battery banks is proposed. For this purpose, it is necessary to size and run the proposed system for feeding a residential load satisfactorily. For two typical winter and summer weeks, weather historical data, including irradiance, temperature, wind speed, and load profiles, are used as input data. The overall optimization framework is formulated as a bi-level mixed-integer nonlinear programming (BMINLP) problem. The upper-level part represents the sizing sub-problem that is solved based on economic and environmental multi-objectives. The lower-level part represents the energy management strategy (EMS) sub-problem. The EMS task utilizes the model predictive control (MPC) approach to achieve optimal technoeconomic operational performance. By the definition of BMINLP, the EMS sub-problem is defined within the constraints of the sizing sub-problem. The MATLAB R2023a environment is employed to execute and extract the results of the entire problem. The global optimization solver “ga” is utilized to implement the upper sub-problem while the “intlinprg” solver solves the lower sub-problem. The evaluation metrics used in this study are the operating, maintenance, and investment costs, storage unit degradation, and the number of CO₂ emissions. Full article

Various studies have explored community-shared solar (CSS) initiatives to help lower energy costs and increase the use of renewable energy sources. Various forms of CSS have been developed worldwide, specifically adapted to meet local economic and environmental conditions as well as technological readiness. This study proposes a variant of CSS that incorporates monthly photovoltaic (PV) rental options and load management functions for households in South Korea, a country characterized by limited land availability, high population density, and extremely high land-use costs. This study evaluates the feasibility of the proposed CSS by assessing the economic benefits for all stakeholders involved, including households, the CSS business (or government), and the grid service provider. It utilizes a mathematical programming model for its formulation and employs an iterative algorithm based on Karush–Kuhn–Tucker conditions for solving it. Additionally, a numerical assessment is conducted with 400 customers classified into three different categories of energy usage. The findings indicate that participating households experienced a reduction in electricity costs ranging from 36.8% to 56.7%, depending on the season and specific scenarios. The CSS business also realized significant profits while the grid service provider benefited from reduced fluctuations in power supply, leading to improved efficiency in grid operations and maintenance. Full article

Over the last decades, environmental awareness has provoked scientific interest in green energy, produced, among others, from solar sources. However, for the efficient operation and longevity of green solar plants, regular inspection and maintenance are required. This work aims to review vision-based monitoring techniques for the fault detection of photovoltaic (PV) plants, i.e., solar panels. Practical implications of such systems include timely fault identification based on data-driven insights and problem resolution, resulting in enhanced energy outputs, extended lifetime spans for PV panels, cost savings, as well as safe and scalable inspections. Details regarding the main components of PV systems, operation principles and key non-destructive fault detection technologies are included. Advancements in unmanned aerial vehicles (UAVs), as well as in artificial intelligence (AI), machine learning (ML) and deep learning (DL) methods, offering enhanced monitoring opportunities, are in focus. A comparative analysis and an overall evaluation of state-of-the-art vision-based methods for detecting specific types of defects on PVs is conducted. The current performance and failures of vision-based algorithms for solar panel fault detection are identified, raising their capabilities, limitations and research gaps, towards effectively guiding future research. The results indicate that shading anomalies significantly impact the performance of PV units, while the top five fault detection methodologies, according to preset evaluation criteria, involve deep learning methods, such as CNNs and YOLO variations. Full article

As the global shift toward sustainable transportation gains momentum, the integration of electric vehicles (EVs) becomes imperative, necessitating a robust and environmentally friendly charging infrastructure. Leveraging the abundant solar potential in the region, this study examines the technical, economic, and environmental feasibility of deploying photovoltaic electric vehicle charging stations (PV-EVCSs) in Hail City, Saudi Arabia, as a case study. This study examines factors such as the energy demand, grid integration, and user accessibility, aiming to address the challenges and opportunities presented by the urban fabric. The proposed solar charging station network seeks to catalyze a paradigm shift toward a cleaner and more sustainable transportation ecosystem, embodying a forward-thinking approach to meeting the evolving needs of urban mobility in the 21st century. The analysis encompasses many scenarios, encompassing a range of car battery sizes, charger powers, and car slots per station. Zone 4 is identified as the most crucial area, where seven charging stations are needed to fulfill the expected demand in the absence of any private charging alternatives. The economic evaluation of the 1047.35 kWp PV system reveals an estimated conventional payback time of 11.69 years, accompanied by a return on assets of 10.17%. The system generates accumulated cash flows amounting to SR 7,169,294.62 over 30 years, while the estimated operational and maintenance expenses are predicted to be SR 50,000 per year. The overall investment cost for the solar PV and EV charging stations is SR 4,487,982. This cost is offset by the yearly electricity savings from solar and grid sources, which can reach up to SR 396,465.26 by year 30. This work presents a detailed plan for the future of sustainable transport. It combines technical, environmental, and economic aspects to promote a cleaner and more sustainable urban mobility system. Full article

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations. Full article

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses’ microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation. Full article

Due to its widespread availability and inexpensive cost of energy conversion, solar power has become a popular option among renewable energy sources. Among the most complete methods of utilizing copious solar energy is the use of photovoltaic (PV) systems. However, one major obstacle to obtaining the optimal performance of PV technology is the need to maintain ideal operating temperature. Maintaining constant surface temperatures is critical to PV systems’ efficacy. This review looks at the latest developments in PV cooling technologies, including passive, active, and combined cooling methods, and methods for their assessment. As advances in research and innovation progress within this domain, it will be crucial to tackle hurdles like affordability, maintenance demands, and performance in extreme conditions, to enhance the efficiency and widespread use of PV cooling methods. In essence, PV cooling stands as a vital element in the ongoing shift towards sustainable and renewable energy sources. Full article

Remotely performing real-time distributed generation control and a demand response is a basic aspect of the grid ancillary services provided by grid operators, both the transmission grid operators (TSOs) and distribution grid operators (DNOs), in order to ensure that voltage, frequency and power loads of the grid remain within safe limits. The stochastic production of electrical power to the grid from the distributed generators (DGs) from renewable energy sources (RES) in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the efforts of the DNOs to keep the grid’s operation within safe limits and prevent cascading blackouts while staying in compliance with the SAIDI and SAIFI indices during repair and maintenance operations. Also taking into consideration the aging of the existing grid infrastructure, and making it more prone to failure year by year, it is yet of great significance for the DNOs to have access to real-time feedback from the grid’s infrastructure—which is fast, has low-cost upgrade interventions, is easily deployed on the field and has a fast response potential—in order to be able to perform real-time grid management (RTGM). In this article, we present the development and deployment of a control system for DG units, with the potential to be installed easily to TSO’s and DNO’s substations, RES plants and consumers (i.e., charging stations of electric vehicles). This system supports a hybrid control mechanism, either via ripple signaling or through a network, with the latter providing real-time communication capabilities. The system can be easily installed on the electric components of the grid and can act as a gateway between the different vendors communication protocols of the installed electrical equipment. More specifically, a commercially available, low-cost board (Raspberry Pi) and a ripple control receiver are installed at the substation of a PV plant. The board communicates in real-time with a remote server (decision center) via a 5G modem and with the PV plants inverters via the Modbus protocol, which acquires energy production data and controls the output power of each inverter, while one of its digital inputs can be triggered by the ripple control receiver. The ripple control receiver receives on-demand signals with the HEDNO, triggering the digital input on the board. When the input is triggered, the board performs a predefined control command (i.e., lower the inverter’s power output to 50%). The board can also receive control commands directly from the remote server. The remote server receives real-time feedback of the acquired inverter data, the control signals from the ripple control receiver and the state and outcome of each performed control command. Full article

In order to optimize the extraction of solar energy, photovoltaic sources are commonly operated under the control of the so-called maximum power point (MPPT) strategy. However, as the rate of PV installations increases explosively, traditional MPPT algorithms may cause problems such as frequency deviation and power fluctuations, making system frequency stability a challenge due to the inherent intermittent and stochastic nature of PVs. Consequently, in order to reduce the investment and maintenance costs of storage systems, innovative control is expected for PV sources to provide ancillary services for the system, especially for weak systems such as microgrids. In this paper, a novel active power control (APC) strategy, based on characteristic curve fitting, is proposed to flexibly regulate the PV output power. The transient process performance and robustness of the system are improved with the proposed APC strategy. In conjunction, an f–P droop mechanism is designed to provide a frequency regulating (FR) service for the AC microgrid. The comprehensive control strategy unifies the FR function with the traditional MPPT function in a single control structure, allowing the PV source to operate either in the MPPT mode when the system frequency is nominal or in FR mode when the frequency exceeds it. The transition between MPPT and FR is autonomous and fully decentralized, which improves the PV generation efficiency as well as ensuring generation fairness among different parallel PV sources. Importantly, the proposed control strategy does not require any internal bundled energy within the PV generation system to achieve FR capability, but it effectively collaborates with the system-level energy storage system, thus reducing the necessary battery capacity. A detailed dynamic model of a PV generation system is constructed to validate the feasibility and effectiveness of the proposed control strategy. Full article

Electric substations (ESS) are important facilities that must operate even under contingency to guarantee the electrical system’s performance. To achieve this goal, the Brazilian national electricity system operator establishes that alternating current (AC) auxiliary systems of ESS must have, at least, two power supplies, and in the case of failure of these sources, an emergency generator (EG) must at least supply energy to the essential loads. In order to improve the availability of auxiliary systems, a microgrid with other sources, such as photovoltaic (PV) systems and Battery Energy Storage Systems (BESS), can be an alternative. In this case, an economical optimization of the PV/BESS system must be addressed considering the costs associated with the installation and maintenance of equipment, and the gains from the credits generated by the photovoltaic system in the net metering scheme. In this paper, the size of the BESS system was determined to supply energy to the load of auxiliary systems of an ESS, as well as a PV system to achieve a null total cost. Furthermore, multi-objective optimization using the genetic algorithm technique was employed to optimize the size of the hybrid PV/BESS to minimize the investment cost and time when the demand was not met. Simulations under different scenarios of contingency were allowed to obtain the Pareto frontier for the optimal sizing of a PV/BESS system to supply energy to AC auxiliary systems in an ESS under contingency. Full article

It is known that the natural convection (NC) solar drying process is a simple and cheap method for drying foodstuffs, but it is not preferable for common users in the case of drying high-moisture content agro-products due to the slow rate of drying. Meanwhile, the forced convection (FC) drying process is most appropriate for such products, but its economic feasibility may be affected due to high initial and maintenance costs. Therefore, the present study proposed a controlled natural convection (CNC) drying mode using a solar greenhouse dryer (SGD) for drying grapes with two types of cover materials, glass and Plexiglas, through intermittent operation with a PV system to save energy as a simple and inexpensive domestic dryer instead of the common forced convection SGD and the conventional natural convection SGD. The obtained results of the new CNC drying mode using a Plexiglas SGD showed a higher drying rate than the NC drying mode and are close to the FC drying mode using the same cover material. The initial moisture content of the grapes was reduced from 5.91 g water/g dry matter to the final moisture content of 0.15 g water/g dry matter within 12 h and 15 h for the CNC and NC drying modes, respectively, using the Plexiglas SGD. Moreover, the thermal drying efficiency for the two mentioned drying modes was 12.5 and 9.7%, respectively. The Page model was found to be the most appropriate model to predict the kinetics of the SGD in all drying modes, regardless of the cover type. The new CNC drying mode using the Plexiglas SGD achieved the lowest cost per kg of dried grapes (1.26 USD/kg), the highest total saved costs over the lifespan of the dryer (USD 245.46) and the shortest payback period (1.08 years) compared to the other two dryers, NC-SGD and FC-SGD. Generally, the CNC-SGD had good performance over the NC-SGD because it is not affected by the fluctuation in the volume, velocity and direction of the inlet ambient air/wind during drying grapes as a high-moisture content product without external heating sources or complicated parts. Thus, the proposed drying system has the advantage in terms of simplicity, cheapness and saving energy compared to FC-SGD. Full article

As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered by foreign objects in the environment such as leaves and bird droppings, resulting in a decrease in photoelectric conversion efficiency, power losses, and even the “hot spot” phenomenon, resulting in damage to the modules. Existing methods mostly inspect foreign objects manually, which not only incurs high labor costs but also hinders real-time monitoring. To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional block attention module, which can identify foreign object shading on the surfaces of PV modules in actual operating environments. This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules. The results show that the advanced model can significantly improve the target detection AP values. Full article