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This study employs a life cycle assessment (LCA) approach to investigate the environmental burden of photovoltaic power generation systems that use multi-crystalline silicon (multi-Si) modules in Pakistan. This study evaluates the energy payback time (EPBT) of this class of systems, and considers various environmental impacts, including climate change, acidification, and eutrophication. The assessment accounts for upstream, midstream, and downstream processes, including cell as well as module production. The critical stages in the production cycle were identified, including the metallic silicon transformation into solar silicon and the assembly of the panels, which involve energy-intensive materials such as aluminum frames and glass roofing. Despite using the most efficient conversion technology, the former stage consumes a significant amount of electricity. This study reveals that multi-Si PV systems in Pakistan have an EPBT that is considerably less than their lifespan, ranging from 2.5 to 3.5 years. These findings suggest that the development of PV systems in Pakistan is a very interesting option for energy production. Additionally, this study compares solar PV and wind power generation systems in various regions of Pakistan. The study outcomes can facilitate evidence-based decision-making processes in the renewable energy sector and contribute significantly to Pakistan's endeavor to transition toward a sustainable energy system. [ABSTRACT FROM AUTHOR]

An optimal power flow (OPF) model is developed in this paper, which includes three highly volatile energy sources: wind energy, photovoltaic energy, and electric vehicle as a vehicle to grid energy source. An interpretable probabilistic approach is implemented to estimate the power generation from the mentioned energy sources using different probability distribution functions. A single static synchronous series compensator is optimally configured and positioned in the combined grid power system to achieve balanced operation. This significant uncertainty imposed OPF model is solved by blending (a) chaotic mapping technique, and (b) Nelder–mead simplex method with conventional moth swarm algorithm (MSA) and the modified version of MSA is called chaotic simplex MSA (CSMSA). Proposed CSMSA's performance is evaluated analytically using extensive simulation approaches that include six different power‐related simulation instances considering IEEE 30 and 118‐bus test grid systems. The strength of the proposed CSMSA is examined by comparing its performance to five outstanding metaheuristic techniques. [ABSTRACT FROM AUTHOR]

Despite being renewable, photovoltaic energy is not burden‐free, since energy and materials are necessary to manufacture, maintain, dismantle, and recycle photovoltaic systems. Over its life cycle, the assessed carbon footprint of silicon‐based photovoltaic energy published in the literature often ranges from 40 to 110 gCO2eq/kWh. However, most of these estimations rely on life cycle inventory (LCI) data that represent the early‐stage performance of the photovoltaic industry. Indeed, collecting LCI data is time‐consuming and practitioners often reuse existing outdated data, which becomes problematic as the photovoltaic industry has been rapidly and significantly evolving. This analysis relies on the parametrization of existing LCI data to better account for the progress already accomplished by the photovoltaic industry. A Life Cycle Assessment (LCA) model, called PARASOL_LCA, is thus developed. The results of the analysis highlight that the use of outdated LCI data leads to an overestimation of environmental impacts of photovoltaic energy by a factor of 2 or even more for the best current available technologies. The analysis also shows that PARASOL_LCA, with its numerous parameters, can also serve to assess the environmental performance of prospective photovoltaic technologies and to identify impact reduction levers through sensitivity analysis. Highlights: PARASOL_LCA is a model for tailor‐made LCA of silicon‐based PV systems.A generic method to develop parameterized LCA models is presented.A strong reduction trend of the environmental impacts of PV electricity is highlighted.A considerable update for the carbon footprint of PV electricity is observed: from 70 to 15–30 gCO2eq/kWh.A multicriteria sensitivity analysis is performed to identify levers to improve PV environmental performance. [ABSTRACT FROM AUTHOR]

In pursuit of energy self-sufficiency and meeting the growing energy demand, the Philippine government has formulated its Energy Road Map for the year 2040, aiming to strengthen, continue, and accelerate the adoption of renewable energy (RE) across the archipelago. This paper presents a proposed multiple microgrid system integrated into an existing distribution system, utilizing renewable energy sources. The proposed model involves the conversion of a section of the distribution system into a microgrid setup, comprising photovoltaic (PV) energy and fuel cell (FC) technologies connected to a 13.2 kV distribution grid. A modified three-phase three-level voltage-sourced converter (VSC) is employed to control the inverter. The proposed modifications result in improved operational efficiency compared to conventional approaches. Various operating cases are considered, each with a designated power source operating according to a predefined schedule. A unified controller is employed across all operating cases, ensuring system stability. Simulation and experimental results conducted through MATLAB/Simulink demonstrate the impact of VSC in terms of voltage regulation, frequency stability, and accumulated power losses. They revealed that voltage regulation for understudy cases ranged from 0.1 to 4.5%, microgrid frequencies were between 59.1 and 60.08 Hz, and power distribution losses were at 1.2–3.3% of the generated power. [ABSTRACT FROM AUTHOR]

Photovoltaic power forecasting is an important problem for renewable energy integration in the grid. The purpose of this review is to analyze current methods to predict photovoltaic power or solar irradiance, with the aim of summarizing them, identifying gaps and trends, and providing an overview of what has been achieved in recent years. A search on Web of Science was performed, obtaining 60 articles published from 2020 onwards. These articles were analyzed, gathering information about the forecasting methods used, the horizon, time step, and parameters. The most used forecasting methods are machine learning and deep learning based, especially artificial neural networks. Most of the articles make predictions for one hour or less ahead and predict power instead of irradiance, although both parameters are strongly correlated, and output power depends on received irradiance. Finally, they use weather variables as inputs, consisting mainly of irradiance, temperature, wind speed and humidity. Overall, there is a lack of hardware implementations for real-time predictions, being an important line of development in future decades with the use of embedded prediction systems at the photovoltaic installations. [ABSTRACT FROM AUTHOR]

Energy consumption is one of the main costs faced by households, both financially and environmentally. This article analyses the energy consumption of Spanish households and the cost of installing solar panels in order to determine the potential of this form of energy production on a household level. The results show that households with a larger number of members have a higher total consumption but a lower consumption per person. The composition of the household is therefore a key determinant of the potential savings from solar panels. This alternative is financially viable, as the unit price paid for energy in these households is between €0.28 and €0.29 per kilowatt-hour (kWh), while the average cost per kWh obtained from the panels is between €0.15 and €0.21. However, the current public subsidies for the installation of solar panels in Spain are inaccessible to 68% of the country’s households, and there are difficulties in the distribution of energy in neighbouring communities. In other words, although in financial terms home PV self-consumption is a financially viable option, the inability to obtaining subsidies makes the initial investment a major barrier for many households. [ABSTRACT FROM AUTHOR]

Energy consumption is one of the principal components of the operative costs incurred by providers of water services, both financial and environmental. Fortunately, in recent years solar panel technology has improved substantially, and photovoltaic self-consumption has become a tool that can reduce the costs of water reuse and other water services. Regions with a scarcity of water resources make a considerable use of non-conventional sources, consuming a significant amount of energy, which has a high financial and environmental cost and compromises the sustainability of the water supply. This research analyses the possibility of replacing part of this energy with self-consumption through photovoltaic panels based on data obtained for the Region of Valencia in order to analyse the impact of energy substitution on energy costs. Performing a Cost–Benefit Analysis, self-consumption projects require an electricity market price of between 0.14 and 0.18 EUR/kWh, so in financial terms it is not a particularly attractive alternative. However, the avoided greenhouse gas emissions have a high value, and including them in the calculations, the price needed to be in feasible amounts of 0.04–0.10 EUR/kWh for a small installation and 0.02–0.08 EUR/kWh for a large one. In other words, photovoltaic self-consumption is still today an alternative with financial difficulties, but the associated environmental benefit justifies public intervention as it is a beneficial energy alternative in a context of high greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal stochastic allocation of distributed energy resources in distribution networks. Typically, uncertain problems are analyzed in multistage formulations, including case generation routines, resulting in computationally exhaustive programs. In this article, two probabilistic approaches are proposed–range probability optimization (RPO) and value probability optimization (VPO)–resulting in a single-stage, convex, stochastic optimal power flow problem. RPO maximizes probabilities within a range of uncertainty, whilst VPO optimizes the values of random variables and maximizes their probabilities. Random variables were modeled with hourly measurements fitted to the logistic distribution. These formulations were tested on two systems and compared against the deterministic case built from expected values. The results indicate that assuming deterministic conditions ends in highly underestimated losses. RPO showed that by including ±10% uncertainty, losses can be increased up to 40% with up to −72% photovoltaic capacity, depending on the system, whereas VPO resulted in up to 85% increases in power losses despite PV installations, with 20% greater probabilities on average. By implementing any of the proposed approaches, it was possible to obtain more probable upper envelopes in the objective, avoiding case generation stages and heuristic methods. [ABSTRACT FROM AUTHOR]

In this article, forecast models based on a hybrid architecture that combines recurrent neural networks and shallow neural networks are presented. Two types of models were developed to make predictions. The first type consisted of six models that used records of exported active energy and meteorological variables as inputs. The second type consisted of eight models that used meteorological variables. Different metrics were applied to assess the performance of these models. The best model of each type was selected. Finally, a comparison of the performance between the selected models of both types was presented. The models were validated using real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type had a root mean square error (RMSE) of 0.19, a mean square error (MSE) of 0.03, a mean absolute error (MAE) of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower accuracy in the metrics: RMSE = 0.24, MSE = 0.06, MAE = 0.10, correlation coefficient = 0.95, and determination coefficient = 0.90. Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy generation of the solar plant. [ABSTRACT FROM AUTHOR]

Photovoltaic generation is one of the key technologies in the production of electricity from renewable sources. However, the intermittent nature of solar radiation poses a challenge to effectively integrate this renewable resource into the electrical power system. The price reduction of battery storage systems in the coming years presents an opportunity for their practical combination with utility-scale photovoltaic plants. The integration of properly sized photovoltaic and battery energy storage systems (PV-BESS) for the delivery of constant power not only guarantees high energy availability, but also enables a possible increase in the number of PV installations and the PV penetration. A massive data analysis with long-term simulations is carried out and indicators of energy unavailability of the combined system are identified to assess the reliability of power production. The proposed indicators allow to determine the appropriate sizing of the battery energy storage system for a utility-scale photovoltaic plant in a planning stage, as well as suggest the recommended operating points made for each month through a set of graphs and indicators. The presence of an inflection zone has been observed, beyond which any increase in storage does not generate significant reductions in the unavailability of energy. This critical zone is considered the sweet spot for the size of the storage, beyond which it is not sensible to increase its size. Identifying the critical point is crucial to determining the optimal storage size. The system is capable of providing reliable supply of constant power in monthly periods while ensuring capacity credit levels above 95%, which increases the penetration of this renewable resource. Despite the fact that the study focuses exclusively on the analysis from an energy perspective, it is important to consider the constraints associated to real storage systems and limit their oversizing. [ABSTRACT FROM AUTHOR]

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The integration of solar photovoltaic systems into low-voltage distribution networks is witnessing significant global growth. While solar photovoltaic generation offers numerous benefits, exceeding the hosting capacity limits in these networks remains a major technical challenge for network operation, particularly in terms of voltage management. Modern smart inverters are equipped with reactive power, active power, and Power Factor (PF) control features, which can assist in regulating network voltage levels. This paper presents a comparative evaluation of smart inverter control methods (reactive power and PF) to achieve maximum solar PV system penetration without impacting the voltage profile at the Point of Common Coupling (PCC). Additionally, a Battery Energy Storage System (BESS) is employed to enhance the system’s hosting capacity. The active power, reactive power, and bus voltage of the system are analyzed under different control methods using MATLAB/Simulink to determine the most effective approach for achieving maximum hosting capacity without compromising bus voltage. The modeling includes a PV system connected to the grid with various control strategies. The results demonstrate an increase in the Hosting Capacity (HC) of the network, thereby improving grid characteristics. The integration of smart inverter functionalities will greatly facilitate the integration of PV solar installations into electricity grids.

Introduction/purpose: This paper aims to present military green technology through present concepts, solutions and challenges, as well as through future trends, particularly in wireless communication. Methods: Analyses and syntheses were used to consider various aspects, challenges and developments of military green technology. Results: Important aspects of organization, challenges and strategies in military green technologies are presented. Next, the role of a green energy action plan is emphasized. Furthermore, green defence solutions and security challenges are considered. Besides presenting green technologies of today, primarily focused on wireless networks, especially 5G, the paper also considers the impact of biofuels, methanol-based fuel cells, hydrogen fuel cells, hybrid electrical vehicles and photovoltaic energy. Finally, green technologies for the future are presented through some of the most important technologies: next generation wireless networks, pulse detonation engines, piezoelectricity, and biodegradable platforms. Conclusion: Military green technology is now necessary for approaching wireless networking in order to meet challenges of high demands of wireless traffic and energy consumption. Also, military green technology will require further constant considerations in the field of efficiency and sustainability of application as well as research to provide different acceptable solutions, not only in technical, but also in the organizational domain, in order to be applied in military environment.

The dependence on fossil resources remains high, accounting for 80% of the total primary energy production worldwide. In addition, the COVID-19 pandemic has devastated several industries and electric power is no exception. Therefore, renewable energy sources have become an important aspect of the energy sector, contributing to solutions to environmental problems and the development of a sustainable future. Therefore, this study proposes a model to evaluate the energy autonomy of a photovoltaic microgrid (EAPV,MG) using a battery energy storage system (BESS). To carry out the analysis, the energy consumption history of a residence and the solar irradiation data of the location were used. Thus, this study includes the evaluation of the energy compensation of a photovoltaic microgrid that directly injects energy into the residence, and the surplus is stored in a BESS for later use. The results show the relevance of EAPV,MGmodeling for optimal system sizing. The model determines the autonomy of the microgrid without many power variations in the PV system for January, April, and October, corresponding to the summer, autumn, and spring seasons, respectively. However, in July (winter), the photovoltaic MG must increase the installation power considerably and the storage capacity of the BESS. Therefore, an optimal decision evaluating the EAPV,MGwill allow broadening an overview to opt for a PV microgrid that injects the highest amount of energy based on the actual residential load profile and the optimal capacity of the BESS. Consequently, the analysis of the EA of a microgrid based on solar irradiation and actual load data is essential for developing an optimal and stable operation of the residential energy system.

Power generation from Renewable Energy Sources (RESs) is unpredictable due to climate or weather changes. Therefore, more control strategies are required to maintain the proper power supply in the entire microgrid. This paper presents a simulation scheme utilizing a solar system instanced by Photovoltaic (PV) panels coupled to the grid, loads, and an energy storage device. At first, modeling the PV panels cell and their operation were analyzed. The synthesis of the adaptive notch filter was designed to compensate for the input currents. The power converter's/inverter's efficiency and control facility allowed us to link the energy storage system with an electrical grid. Several simulations were accomplished consistently with nonlinear control techniques for the simple inverter, multi-variable filter, notch filter, and without a filter. Finally, the performances of the nonlinear controller with various filters were carried out to regulate the DC bus of the proposed grid. The advantage of these controllers is compensating the reactive power and harmonic currents to obtain a disturbance-free power network. The overall installations and simulations were established using the Matlab/Simulink software. [ABSTRACT FROM AUTHOR]

The need to reduce pollution and the shortage of fossil fuels has led to the increased development of hybrid and full electric vehicles. There is also increased development and an increased use of renewable energy resources such as photovoltaic, wind, tidal, etc. These two trends pose serious challenges to the existing grids: a lack of supply power when the demand is high, deficient management of excess power, an increased number off grid faults, grid instabilities and others. One way to increase the penetration of electric vehicles (EV) into the market and to keep the existing grid infrastructure is to combine renewable energy resources with the grid and local battery packs to make EV charging stations. This paper focuses on developing such an EV charging station. The main advantages of the proposed EV charger include the fact that it uses only off-the-shelf inverters, and it is intended to be used in households where the maximum installed power is 3.6 kW to enable fast-charging operation modes or to reduce the costs of energy while charging the EV battery; it can reduce the energy demand from the grid during peak power consumption; it has the potential to lower electrical energy costs; it offers the possibility of vehicle-to-home (V2H) implementation; it is modular (if other technologies become available and more affordable, the consumers can easily update the system, adding more power or adding other types of renewable resources). [ABSTRACT FROM AUTHOR]

The concurrent worldwide energy crisis has become a strong incentive for researchers, governments, and industry professionals to focus on sustainable energy solutions. Consequently, pavement photovoltaic energy harvesting technologies, as one of the most common sustainable energy solutions, have recently seen a significant improvement, especially in the new innovative designs of pavement solar panels. In this study, an innovative design for a prototype energy harvesting system was proposed based on thin-film photovoltaic solar panels. In addition, the feasibility of utilizing the generated power of the proposed system to illuminate a pedestrian crosswalk to enhance the safety of an at-grade intersection was also analyzed. The designed prototype consists of a thin-film solar panel, a transparent cover to protect the solar panel, and a wooden frame to support the panel and distribute the load. Different materials for the transparent covering plates were investigated, including polycarbonate with varying thicknesses, textured GlassGrit, and textured float glass with corundum skid-resistant coating on the surface. Finite element analysis was also conducted to analyze the behavior of solar panel-incorporated layered asphalt pavement subjected to dynamic wheel loading. The results showed that the suggested model could sustain the structural loads of a moving wheel without failure. Experimental results of the study showed that considering the seven hours of operation on a typical sunny day, the proposed system could generate approximately 699 Watt-hour of power during 7 h of operation (9 a.m.–4 p.m.) from the 304.8 mm (12 inches) × 304.8 mm (12 inches) pavement solar panel. [ABSTRACT FROM AUTHOR]

The investigation of fuel cell vehicles (FCVs) connected to grid (FCV2G) technology regarding the reduction of consumer costs is proposed. Herein, the key contribution is to investigate the energy and profit analysis aspects of an FCV2G system supported by photovoltaic (PV) panels for a 24 h operation. For this, an FCV in the rating of 100 kW is connected to the grid/homes, and a detailed analysis is performed for the FCV2G system supported by PV energy in the rating of 40 kW. The designed system is primarily tested for energy transfer from FCV/PV to grid/homes, and the results are verified for dynamic loading situations. It is observed that FCV + PV supply excess power to the grid during flexible hours of the day, and consumer homes use additional energy from the grid during peak hours. The profit analysis of the established FCV2G system is conducted using the load profile data for the 24 h situation. FCV2G supported by a PV system verifies that the homes' consumption cost is reduced significantly, and the remaining energy is utilized by selling the energy to the grid. [ABSTRACT FROM AUTHOR]

In developing nations like Ethiopia, especially in rural and semi-urban regions, reliable access to electricity through grid-based systems is often limited or unavailable due to inadequate energy infrastructure. To tackle this issue, photovoltaic (PV) technology plays a crucial role in converting solar energy into electricity, especially in remote regions. This study focuses on assessing the daily and monthly energy exposure of PV arrays located near Fiche town, Ethiopia. The study utilizes four network types - Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg-Marquardt (LR) - with eight input variables and a training-testing split of 70%-30% to estimate global solar radiation (GSR). Hidden and output layers are employed with a sigmoid and linear function as activation functions, respectively. The results reveal that the expected mean daily and monthly horizontal global solar radiation (HGSR) ranges between 3.282 kWh/m2/day ≤ HGSR ≤ 6.967 kWh/m2/day and 4.628 kWh/m2 ≤ HGSR ≤ 6.613 kWh/m2, respectively. Furthermore, the study determines that the daily energy delivered to the PV system and available from the PV array to load falls within the range of 0.656 kWh/m2 ≤ Ed ≤ 1.394 kWh/m2 and 0.567 kWh/m2 ≤ El ≤ 1.205 kWh/m2, respectively. The average results for the study period from 2016 to 2021 were 1.175 kWh/m2 and 1.015 kWh/m2 for the Ed and El, respectively. These results demonstrate that PV arrays near Fiche town have abundant solar energy resources, making them highly attractive for electricity generation. Furthermore, utilizing solar energy from PV arrays can help mitigate indoor pollution in the rural communities surrounding Fiche town.

Attaining zero emissions stands as a key aspect of sustainable development. The conventional pathway, known as the “Zero Emissions Society (ZES)”, involves the use of renewable energy generation as an alternative to traditional fossil fuels and promotes the total de-carbonisation of the grid, ultimately leading to net-zero emissions. This requires the establishment of large-scale renewable energy power stations, increased transmission efficiency and energy storage facilities. In addition to this conventional pathway, this review introduces an innovative concept, termed “Zero Emission Utopia (ZEU)”, which originates from an individual-centric perspective. This novel approach enables the achievement of zero carbon emissions in human daily life activities and subsequent propagation of this practice extensively, actualising a zero-emissions scenario. An analysis of the available utopian sources of energy and distributed energy storage methods for this proposed pathway is conducted and presented. In addition, an evaluation case study, quantifying the renewable energy production capacity available to an individual, shows with the help of distributed energy storage methods, the ZEU pathway is feasible for normal families. Moreover, the constructive collaboration between these two pathways is explored, highlighting their potential for mutual enrichment and complementary advantages.

A challenge that consistently arises when reviewing and justifying novel energy models and theorems is the accuracy of the electrical data used. Therefore, this paper presents a dataset representing a complete European residential community based on real-life data. In this case, a community of 250 residential households was constructed with actual energy consumption and photovoltaic generation profiles collected by smart meters in households in different European locations. Additionally, 200 members of the community were ascribed with their photovoltaic generation, while 150 were owners of a battery storage system. New profiles were generated from the sample collected and were randomly given to each end-user according to their previously defined characteristics. Furthermore, one regular and one premium vehicle were allocated to each household – a total of 500 electric vehicles – with information on their capacity, state of charge, and usage. Moreover, data on the location, type, and prices of public electric vehicle chargers were specified.

This study presents the results of research on energy self-sufficiency, safety and production reliability in a salmonids fish farm. For the needs of the breeding facility under construction in the Recirculating Aquaculture System, energy requirement analyses were carried out and guidelines collected for the implementation of innovative solutions, which ensure the security and continuity of energy supplies and the plant's energy self-sufficiency. This research included the development of solutions for the reliability of power supply and ensuring the continuity of operation of devices that are key to the fish farming process, i.e., lighting and pumps. In terms of ensuring the necessary amounts of energy, the possibility to diversify electricity sources was analysed, taking into account the possibility of self-production, including primarily from a photovoltaic power plant and from average power wind farms. The profitability of using energy storage was also examined. Recommendations and a list of energy balances for selected cases of potential power sources are presented. [ABSTRACT FROM AUTHOR]

The use of hydrogen produced from renewable energy enables the reduction of greenhouse gas (GHG) emissions pursued in different international strategies. The use of power-purchase agreements (PPAs) to supply renewable electricity to hydrogen production plants is an approach that can improve the feasibility of projects. This paper presents a model applicable to hydrogen projects regarding the technical and economic perspective and applies it to the Spanish case, where pioneering projects are taking place via photovoltaic PPAs. The results show that PPAs are an enabling mechanism for sustaining green hydrogen projects. • Technical and economic feasibility assessment of green hydrogen projects. • An operational multi-state modeling of electrolyzers is proposed. • PPAs facilitate the decarbonization of hydrogen production facilities. • Sensitivity analysis to PPA prices, network tariffs exemption and public funding rates. [ABSTRACT FROM AUTHOR]

This article outlines how to optimize the parameters of proportional integral (PI) and proportional resonant (PR) controllers of a grid connected three-phase inverter system using Particle Swarm Optimization (PSO). The optimization of the control parameters is constrained to a low total harmonic distortion (THD) in the injected currents to the grid. In contrast to conventional PI and PR tuning methods, the PSO optimized controller inherits a wide-ranging operating condition, having better performance in steady-state and rapid dynamic response under abnormal grid conditions. This approach is used to provide the simulation results under both abnormal and normal grid conditions. Some important characteristics of this technique are that its complexity is not altered, no additional hardware is required, and no additional cost is added. From the presented results, it is observed that the proposed control optimization method ensured a low THD of 0.59% in the injected grid currents. Moreover, the control parameter optimization error converges to zero in finite time. [ABSTRACT FROM AUTHOR]

Green hydrogen production is essential to meeting the conference of the parties' (COP) decarbonization goals; however, this method of producing hydrogen is not as cost-effective as hydrogen production from fossil fuels. This study analyses an off-grid photovoltaic energy system designed to feed a proton-exchange membrane water electrolyzer for hydrogen production to evaluate the optimal electrolyzer size. The system has been analyzed in Baghdad, the capital of Iraq, using experimental meteorological data. The 12 kWp photovoltaic array is positioned at the optimal annual tilt angle for the selected site. The temperature effect on photovoltaic modules is taken into consideration. Several electrolyzers with capacities in the range of 2–14 kW were investigated to assess the efficiency and effectiveness of the system. The simulation process was conducted using MATLAB and considering the project life span from 2021 to 2035. The results indicate that various potentially cost-competitive alternatives exist for systems with market combinations resembling renewable hydrogen wholesale. It has been found that the annual energy generated by the analyzed photovoltaic system is 18,892 kWh at 4313 operating hours, and the obtained hydrogen production cost ranges from USD 5.39/kg to USD 3.23/kg. The optimal electrolyzer capacity matches a 12 kWp PV system equal to 8 kW, producing 37.5 kg/year/kWp of hydrogen for USD 3.23/kg. [ABSTRACT FROM AUTHOR]

In this study, in order to obtain the highest performance from a photovoltaic energy system, a wide frame of what can be done in the cell and panel section has been drawn and especially the maximum power point tracking has been examined. The energy conversion principle of the photovoltaic cell is explained, and the factors that affect the electrical energy generation characteristics of a photovoltaic cell and the energy conversion efficiency of a photovoltaic panel are presented. In addition to managing all these factors to achieve maximum efficiency, it has been shown that the maximum power point is another issue to be considered in order to maximize the potential of the photovoltaic panel. The photovoltaic cell is a semiconductor material and the output characteristic of both the photovoltaic cell and the photovoltaic panel is nonlinear as in other semiconductor materials. The power to be obtainedfrom the photovoltaic panel varies depending on the connected load, temperature and radiation parameters. The maximum power point along the output characteristic of the photovoltaic panel is called the "maximum power point". It has been shown that obtaining the highest performance from a photovoltaic panel depends not only on controlling the efficiency parameters but also on continuously operating at this maximum power point. The most commonly used maximum power point tracking methods and their algorithms are examined and their weaknesses and strengths are interpreted. [ABSTRACT FROM AUTHOR]

Due to geopolitical risks and the consequent rising fossil fuel costs, the interest in electric vehicles is increasing. The widespread use of electric vehicles causes an increase in charging stations and an increase in the load on the electricity grid. For this reason, it becomes necessary to design new charging systems. In this study, the charging unit in an electric vehicle charging system supported by solar energy was designed in Matlab/Simulink environment. Instead of insulated (transformer) DC-DC converters used in charging units, non-isolated (power electronic circuit) multi-stage boost converters are used in the designed charging unit. Thus, if the transformers in not of use; transformer losses (heat losses that will occur in transformer coils) are zeroed, it is aimed to reduce the size of the circuits used and to reduce the system cost. The graphics of the data of the designed system were given and comments were made. [ABSTRACT FROM AUTHOR]

Introduction/purpose: This paper aims to present military green technology through present concepts, solutions and challenges, as well as through future trends, particularly in wireless communication. Methods: Analyses and syntheses were used to consider various aspects, challenges and developments of military green technology. Results: Important aspects of organization, challenges and strategies in military green technologies are presented. Next, the role of a green energy action plan is emphasized. Furthermore, green defence solutions and security challenges are considered. Besides presenting green technologies of today, primarily focused on wireless networks, especially 5G, the paper also considers the impact of biofuels, methanol-based fuel cells, hydrogen fuel cells, hybrid electrical vehicles and photovoltaic energy. Finally, green technologies for the future are presented through some of the most important technologies: next generation wireless networks, pulse detonation engines, piezoelectricity, and biodegradable platforms. Conclusion: Military green technology is now necessary for approaching wireless networking in order to meet challenges of high demands of wireless traffic and energy consumption. Also, military green technology will require further constant considerations in the field of efficiency and sustainability of application as well as research to provide different acceptable solutions, not only in technical, but also in the organizational domain, in order to be applied in military environment. [ABSTRACT FROM AUTHOR]

• A novel multiobjective optimization model is introduced for renewable ROD systems. • Hydrogen is assessed as a feasible storage solution for remote water supply. • The model optimizes the sizing and operation of a hybrid energy management system. • The model adapts to site-specific parameters tailoring solutions for remote communities. • The MILP-solution provides a balance between the exergy destruction and annual cycle costs. Remote communities often face challenges in accessing clean water, crucial for improving their quality of life and health. To address this issue, this work focuses on optimizing the design of a hybrid renewable energy system, integrating photovoltaic (PV) and hydrogen storage to power a reverse osmosis desalination (ROD) system. A novel multiobjective optimization model, implemented as a mixed-integer linear program, is proposed to minimize exergy losses and annual cycle costs, ensuring optimal system performance. The optimized decision variables include the sizing and power allocation of the hybrid energy system, and the operation of the ROD system based on water demand. Operational characteristics such as energy balance, system capacity, and daily water demand are incorporated into the model as constraints. The flexibility of the model allows for site-specific parameters, tailoring solutions to meet the needs of remote communities. The optimization model is tested in this work for two case studies, revealing significant cost-effectiveness disparities. In the first community, the system achieves a levelized cost of water (LCW) and exergy efficiency of 2.119 USD/m3 and 11.48 % for an 18 m3 daily water demand, compared to 3.757 USD/m3 and 8.79 % for a 3 m3 daily demand in the second community. This highlights the economic viability and higher efficiency of such a system for large-scale applications, achieving up to a 16.11 % lower LCW than other studies. Additionally, it demonstrates the flexibility of the proposed optimization model and provides a comprehensive evaluation of hybrid energy systems for remote communities. [ABSTRACT FROM AUTHOR]

This paper investigates the integration of photovoltaic (PV) energy systems with a DC power converter based on a boost converter designed to optimize the power output for resistive loads such as heat elements for heat generation applications. Emphasizing the role of boost converters in increasing the output voltage of PV systems to efficiently supply resistive loads, the performance and efficiency of this integration is evaluated. The work also addresses the basic principles, control strategies and efficiency considerations associated with the fusion of solar PV systems with synchronous boost converters for resistive load applications. The results demonstrate a peak efficiency of 97%, which decreases to 90.5%, 87.5%, and 84% for resistive loads of 10Ω, 15Ω, and 20Ω, respectively at 80W and for a switching frequency of 60KHz. This indicates that efficiency declines as the value of the resistive load increases. Additionally, the results exhibit a notable efficiency increase of 4.6% by simply raising the switching frequency from 20KHz to 100KHz. Through extensive testing, we have substantiated the effectiveness of employing synchronous boost converters to optimize power output and enhance the overall performance of PV systems when supplying resistive heat elements. [ABSTRACT FROM AUTHOR]

Existing buildings provide high surface potential for photovoltaic (PV) installations. When deciding whether a building is suitable for solar energy harvesting, social acceptance needs to be considered. While PV is accepted in general, research regarding the acceptance of PV for specific types of buildings is sparse. In two explorative studies, we investigated the building-specific acceptance of PV installations, taking new PV module designs into account. The aim of the first study (N = 76, passersby in the Botanical Garden, Freiburg i. Br. Germany who volunteered to participate) was to investigate which PV module designs are accepted for different buildings. Six different PV crystalline silicon module prototypes varying in color and surface structure were presented on an exhibition table. Using a paper-pencil-survey, participants rated the modules and combined them with presented buildings. Results show that depending on the building, different modules were favored whereby PV modules were chosen mostly due to color integration. In a second study (N = 109, recruited from the participant pool of the institute of psychology, University of Freiburg, Germany, mostly students, and in social media), participants indicated their acceptance of a PV installation for 24 different buildings in an online survey. Results revealed that social acceptance for PV installations on buildings was generally high and was even higher if the PV module was aesthetically integrated or invisibly. PV modules on historical buildings (including a church) were less accepted than on modern buildings. Yet for invisible PV modules, there were no acceptance differences between buildings. Building variables were found to be more important to predict building-specific acceptance than person-related variables such as environmental concerns, values or political attitude. Taken together, the study gives a first insight into the subject of the social acceptance of urban PV installations. Results underline the importance of aesthetic integration and (in)visibility for PV installation on buildings. • First insights: Two studies on social acceptance of PV installations on buildings. • Social acceptance for PV installation dependent on building type and visual impact. • Acceptance determined stronger by building rather than judging person. • PV modules selected to match existing color schemes, not for artistic additions. • Variability in module appearances important for different building contexts. [ABSTRACT FROM AUTHOR]

The ambition to achieve carbon neutrality by 2050, reinforced by international agreements, requires both the reduction of gas emissions in the transport sector and the acceleration of the realization of the energy transition. There is therefore growth potential for electric mobility and photovoltaic technology. In this context, this study explores the integration of electric vehicles into the Portuguese car fleet using photovoltaic solar energy for battery charging, with the aim of reducing greenhouse gas (GHG) emissions and reducing dependence on fossil fuels for mobility. A multi-phase approach was adopted: i) a sample of electric vehicles representative of the Portuguese car fleet and their energy needs was sized; ii) the equipment to be incorporated into the Self-Consumption Production Units was selected and sized, using PVsyst software version 7.3.4, and based on the energy needs resulting from three scenarios of weekly use of the electric vehicle [50 km (scenario 1), 300 km (scenario 2) and 750 km (scenario 3)]; iii) the reductions in GHG emissions were quantified by analysing: emissions from the Public Service Electricity Grid (RESP) and the Photovoltaic Integration System for Self-Consumption and Grid (SIFAR), and also emissions associated with the fossil fuels under analysis (petrol and diesel). In scenario 1, the difference was 57.5%. In scenario 2, it was −24.3%, and in scenario 3, it was −55.9%. These results demonstrate that SIFAR is an attractive and more sustainable alternative to RESP in the most energy-demanding scenarios, in this case, scenarios 2 and 3. By contributing to a considerable reduction in GHG emissions into the atmosphere, SIFAR stands out for its role in building sustainability. Presenting a new methodological perspective and vision on the integration of renewable energies in the transport sector in Portugal, this study closes a knowledge gap and provides relevant information for planning and public policy. [Display omitted] [ABSTRACT FROM AUTHOR]

In the current move towards a decarbonising energy generation, cities and residential users can play a significant role. Solar photovoltaic has emerged as one of the main clean energy sources in this transition. Therefore, this work investigates the large electricity generation capacity for collective photovoltaic self-consumption in blocks of flats in a densely populated city in southern Spain. Three neighbourhoods were studied according to their income levels (low, medium, and high), by modelling their different energy demand profiles. The demand cover factor and the energy exchanged with the electricity grid were calculated. Subsequently, seven regulatory frameworks for self-consumption and surplus photovoltaic injection were compiled and analysed for each income level, focusing on the economic profitability of these renewable installations for different power ratings (270 and 570 W) and different domestic electricity tariffs. Although some regulations provide more economic benefits, with the constant growth in the nominal power of photovoltaic modules and rising electricity prices, the profitability of self-consumption photovoltaic installations is practically guaranteed regardless of the regulatory framework. Therefore, subsidies should target low-income buildings, where self-consumption installations could be the solution to energy poverty problems. [Display omitted] • The photovoltaic electricity production capacity in blocks of flats was studied. • Poverty energy solutions were proposed for low-income households. • The electricity demand was simulated for one year in 1-min intervals. • Self-consumption rates in different income-level dwellings were determined. • The economic profitability was studied in different regulatory contexts. [ABSTRACT FROM AUTHOR]

Rural farms constitute a vital component of a country’s agricultural landscape, traditionally reliant on energy installations known for their reliability yet notorious for their energy-intensive and inefficient characteristics. While the smart farm concept, integrating renewable energy sources and resource management technologies, has seen widespread adoption in domestic and industrial sectors, rural farms have been slower to embrace these innovations. This study presents a groundbreaking solution, deployed on a rural farm in Portugal, resulting in an impressive 83.24% reduction in energy consumption sourced from the grid. Notably, this achievement translates to a substantial reduction in CO2 emissions, aligning with the growing need for environmentally sustainable farming practices. The technical intricacies of this pioneering solution are comprehensively described and juxtaposed with other scientific case studies, offering valuable insights for replication. This initiative represents a vital first step towards the integration or combination of conventional farming with photovoltaic energy production, exemplified by agrivoltaic systems. In conclusion, this research showcases the potential for rural farms to significantly enhance energy efficiency and financial viability, thereby contributing to a more sustainable and cost-effective agricultural sector. These findings serve as a model for similar endeavors, paving the way for a greener and more economically viable future for rural farming practices.

This paper proposes a novel short-term photovoltaic voltage (PV) prediction scheme using IoT sensor data with the two-stage neural network model. It is efficient to use environmental data provided by the meteorological agency to predict future PV generation. However, such environmental data represent the average value of the wide area, and there is a limitation in detecting environmental changes in the specific area where the solar panel is installed. In order to solve such issues, it is essential to establish IoT sensor data to detect environmental changes in the specific area. However, most conventional research focuses only on the efficiency of IoT sensor data without taking into account the timing of data acquisition from the sensors. In real-world scenarios, IoT sensor data is not available precisely when needed for predictions. Therefore, it is necessary to predict the IoT data first and then use it to forecast PV generation. In this paper, we propose a two-stage model to achieve high-accuracy prediction results. In the first stage, we use predicted environmental data to access IoT sensor data in the desired future time point. In the second stage, the predicted IoT sensors and environmental data are used to predict PV generation. Here, we determine the appropriate prediction scheme at each stage by analyzing the model characteristics to increase prediction accuracy. In addition, we show that the proposed prediction scheme could increase prediction accuracy by more than 12% compared to the baseline scheme that only uses a meteorological agency to predict PV generation.

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation.

The presence of shadows is one of the main disadvantages of photovoltaic solar panel installations in urban environments. This article analyses their effect on solar installations performed on urban elements where the use of solar energy can be considered novel: bus shelters. For this purpose, the PVGIS tool is used in combination with a new methodology for the extraction of the shadow horizon profile from LiDAR point clouds. The results show a 29.90% and 37% reduction of calculated solar radiation using horizon profiles derived from LiDAR point clouds of 0.5 pts/m2 and 1 pt/m2, respectively, versus no horizon profile. By taking shading into account, thanks to this study it is possible to make a more realistic prediction of the use of the electrical energy generated by the sun to cover urban energy consumption in bus shelters. Results show that the energy surplus produced overall allows these elements to be converted into charging points for light electric vehicles, allowing up to 35 units per day/bus shelter to be charged. The use of bus shelters as a place to generate clean energy through solar energy and charge light vehicles makes it possible to move towards sustainable and environmentally friendly cities.

The conservation of the environment and the use of clean energy is one of the main concerns of international governments. In this sense, encouraging the use of clean energy, both locally, nationally, and internationally, contributes to the development of countries. In this order of ideas, this work was motivated with the purpose of contributing to the scope of the SDGs at the national level, specifically, in the city of Barranquilla (Colombia). The thermal sensation inside the public transport in Barranquilla limits the quality of the service and, consequently, the satisfaction of the users due to the high temperatures (T > 30 °C). To address this problem, it is necessary to implement air conditioning systems that improve comfort conditions and are friendly to the environment. In this study, the feasibility of implementing photovoltaic panels as an alternative energy source for air conditioning units in public transport was examined. The study began with the dimensioning of the photovoltaic system and the accumulation of energy. A stress analysis was then performed on the chassis and bodywork of a selected bus. Finally, the work was evaluated based on the results obtained from the dimensioning of the mentioned systems. The results confirmed that a photovoltaic generator with a power of 35.88 kW and an energy storage capacity of 481,204 Ah is required. Additionally, the results also confirmed that the chassis and bodywork are structurally sound. The economic analysis carried out confirmed a Net Present Value and an Internal Rate of Return of 22,936 USD and 11.05%, respectively. The energy alternative proposed for the transport sector, based on the application of photovoltaic energy, constitutes an advance to meet international objectives in relation to the use of clean energy.

A simple, efficient simulator has been developed to predict the generation of photovoltaic energy and its storage in Li-ion batteries, for an autonomous drone with four wings covered by solar panels based on thin-film gallium arsenide photovoltaic cells (III–V). This simulator allows prediction of the effective photovoltaic power produced by the solar panels as well as the battery pack voltage when the drone is flying. Flight parameters such as irradiance, sun inclination angles, and drone Euler angles are considered as input parameters. The measured photovoltaic power and battery pack voltage are in good agreement with the simulated values, making practical use by the XSun company possible. This parametric study shows the effects of climatic and geographic conditions on drone autonomy. In optimal weather conditions on a sunny day, drone flight can last 12 ​h.

The use of photovoltaic energy (PV) and the involvement of residents within energy communities are becoming increasingly important elements of decentralized energy systems. However, ownership structures are still too complex to empower electricity consumers to become prosumers. We developed a token-based system of the gradual transfer of PV ownership rights, from the initial investor to residential and small-scale commercial consumers. To demonstrate the system, we set up a simulation of a 27-party mixed usage building with different load profiles, ranging from single student apartments to office units with battery electric vehicles, in a German energy community. As a result, we show that the proposed system design is economically viable for all involved stakeholders over the simulation horizon from 2022 to 2036, with a payback time of

Different tracking algorithms have been developed to obtain maximum efficiency from the PV power systems under changing atmospheric conditions. Some of these algorithms are effective under uniform irradiance, while the others are effective under partial shading conditions. In this study, a new MPPT method designed with System Identification-SI and named SI-based polynomial ARV is proposed for a PV system under uniform irradiance. The recommended method is also an adaptive reference voltage-ARV-based method. In this method, the model in which the reference voltage is obtained has a simple polynomial structure. This polynomial model has been obtained by assuming that the PV system is a nonlinear black-box type system. The input-output data, which are required for modeling, were created in MATLAB/Simulink environment. Then, by using these data with SI-Toolbox, the mathematical model of the PV system in polynomial structure giving the input-output relationship was obtained. T temperature information was used as model input, which was the generated reference voltage. Simulation studies were carried out under two different changing atmospheric scenarios; and the performance of the proposed method was analyzed. The obtained results were compared with perturb & observe-PO, incremental conductance-IC, constant voltage reference-CVR, artificial neural network-ANN-based ARV, and SI-based nonlinear ARV methods. All simulation results showed that the recommended method is simple structured, applicable, and has high performance.

This paper seeks to advance the discussions involving the articulation between the theory of Sociotechnical Transitions and Practice Theory. The text tries to develop this articulation considering that it is possible to find in the literature notes that the Theory of Sociotechnical Transitions is a suitable partner to be articulated with the Theory of Practice. The proposition was tested in empirical research in which we sought to understand how consumption practices change from adopting photovoltaic energy consumption. The research is categorized as qualitative research. The study offers an interpretation of the object from the theoretical lens provided by Social Practice Theory and Sociotechnical Transitions, also known as Multilevel Perspective or "MLP." In total, 24 interviews were executed, 7 with integrators, 7 with quasi-prosumers, and 10 with prosumers. To analyze the data, content analysis was used. The study's findings demonstrate that sociotechnical systems are reconfigured from the introduction of a new practice to accommodate this newly incorporated practice. Additionally, some of the pre-existing methods are modified during this accommodation process. It is also possible to conclude that the sociotechnical dimensions can be helpful in studies of phenomena that involve the transition of habits, routines, behavior patterns, and practices, as they provide a model of assumptions for sociotechnical change. [ABSTRACT FROM AUTHOR]