Seed bank survival underpins plant population persistence but studies on seed bank trait-environment interactions are few. Changes in environmental conditions relevant to seed banks occur in desert ecosystems owing to solar energy development. We developed a conceptual model of seed bank survival to complement methodologies using in-situ seed bank packets. Using this framework, we quantified the seed bank survival of two closely related annual desert plant species, one rare (Eriophyllum mohavense) and one common (Eriophyllum wallacei) and the seed bank-environment interactions of these two species in the Mojave Desert within a system that emulates microhabitat variation associated with solar energy development. We tracked 4,860 seeds buried across 540 seed packets and found, averaged across both species, that seed bank survival was 21% and 6% for the first and second growing seasons, respectively. After two growing seasons, the rare annual had a significantly greater seed bank survival (10%) than the common annual (2%). Seed bank survival, across both species, was significantly greater in Shade (10%) microhabitats compared to Runoff (5%) microhabitats and Control microhabitats (3%). Our study confers insight into this early life-stage across rare and common congeners and their environmental interactions using a novel conceptual framework for seed bank survival.

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. In many instances, utility companies pay for both energy and peak demand. PV systems can effectively reduce energy consumption during their operational periods, while, their contribution during peak hours, particularly in the early morning and late evening, is often limited. To address this issue, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV-battery systems remains a challenge. This study proposes a novel statistical model to optimize PV-battery system sizing for enhanced peak demand reduction. The model aims to flatten 95% of peak demand days up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV-battery system successfully flattens 95% of daily peak demand up to 2000 kW, yielding a financial benefit of $812,648 over 20 years.

Integrated photovoltaic-fuel cell (IPVFC) systems, amongst other integrated energy generation methodologies are renewable and clean energy technologies that have received diverse research and development attentions over the last few decades due to their potential applications in a hydrogen economy. This article systematically updates the state-of-the-art of IPVFC systems and provides critical insights into the research and development gaps needed to be filled/addressed to advance these systems towards full commercialisation. The design methodologies, renewable energy-based microgrid and off-grid applications, energy management strategies, optimisations and the prospects as self-sustaining power source were covered. IPVFC systems could play an important role in the upcoming hydrogen economy since they depend on solar hydrogen which has almost zero emissions during operation. Highlighted herein are the progresses as well as the technical challenges requiring research efforts to solve to realise numerous potential applications of IPVFC systems such as in unmanned aerial vehicles, hybrid electric vehicles, agricultural applications, telecommunications, desalination, synthesis of ammonia, boats, buildings, and distributed microgrid applications.

This paper aims to present a smart high speed battery charger, powered by a photovoltaic module array, for a LiFePO4 battery as a solar energy storage device. With a battery charging strategy, the presented battery charger involves a Buck converter as the core equipped with a simple maximum power point (MPP) tracker. Considering complexity reduction and easy hardware implementation, a constant voltage MPP tracking approach is adopted such that the maximum amount of output power can be delivered to the load in response to an arbitrary change in the solar radiation. A smart two-stage charging strategy, with a constant current mode followed by a constant voltage mode, is employed in such a way that the battery charge process can be accelerated largely, while the damage caused by overcharging can be prevented. In the end, the performance of this proposal is validated experimentally.

In manual maintenance inspections of large-scaled photovoltaic (PV) or rooftop PV systems, several days are required to survey the entire PV field. To improve reliability and shorten the amount of time involved, this study proposes an electrical examination based method for locating multiple faults in the PV array. The maximum power point tracking (MPPT) algorithm is used to estimate the maximum power of each PV panel; this is then compared with metering the output power of PV array. Power degradation indexes are parameterized to quantify the degradation between maximum power and metered output power. Bidirectional hetero-associative memory (BHAM) networks are then used to locate multiple faults within the entire PV field. For a rooftop PV system with two strings, as seen in Figure 1, experimental results demonstrate that the proposed model has computational efficiency for real-time applications and that its algorithm is easily implemented in a mobile intelligent vehicle.

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to emend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets Luo converter occupied brushless DC motor (BLDC) directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. Luo converter is newly developed dc-dc converter has high power density, better voltage gain transfer and superior output waveform and able to track optimal power from PV modules. For BLDC speed controlling there is no extra circuitry and phase current sensors are enforced for this scheme. The recentness of this attempt is adaptive neuro-fuzzy inference system (ANFIS)-FPA operated BLDC directed PV pump with advanced Luo converter has not been formerly conferred.

The integration of the increasing share of Renewable Energy Sources (RES) requires the availability of suitable energy storage systems to improve the grid flexibility, and Compressed Air Energy Storage (CAES) systems could be a promising option. In this study, a CO2-free Diabatic CAES system is proposed and analysed. The plant configuration is derived from a down-scaled version of the McIntosh diabatic CAES plant, where the natural gas is replaced with green hydrogen, produced on site by a Proton Exchange Membrane electrolyser powered by a Photovoltaic power plant. In this study, the components of the hydrogen production system are sized to maximize the Self-Consumption share of PV energy generation and the effect of the design parameters on the H2-CAES plant performance are analysed on a yearly basis. Moreover, a comparison between the use of natural gas and hydrogen in terms of energy consumption and CO2 emissions is discussed. The results show that the proposed hydrogen fuelled CAES can effectively match the generation profile and the yearly production of the natural gas fuelled plant by using all the PV energy production, while producing zero CO2 emissions.

Free and open source hardware (FOSH) development has been shown to increase innovation and reduce economic costs. This article reviews the opportunity to use FOSH like a sanction to undercut imports and exports from a target criminal country. A formal methodology is presented for selecting strategic national investments in FOSH development to improve both national security and global safety. In this methodology, first the target country that is threatening national security or safety is identified. Next, the top imports from the target country as well as potentially other importing countries (allies) are quantified. Hardware is identified that could undercut imports/exports from the target country. Finally, methods to support the FOSH development are enumerated to support production in a commons-based peer production strategy. To demonstrate how this theoretical method works in practice it is applied as a case study to the current criminal military aggressor nation, who is also a fossil fuel exporter. The results show there are numerous existing FOSH and opportunities to develop new FOSH for energy conservation and renewable energy to reduce fossil fuel energy demand. Widespread deployment would reduce the concomitant pollution, human health impacts, and environmental desecration as well as cut financing of military operations.

The European Union Green Deal aims at curbing planet-warming greenhouse gas emissions and introducing clean energy production. But to achieve energy efficiency, the opportunity cost of different energies must be assessed. In this article, we analyse two different systems for the treatment of wastewater that, at the same time, produce energy for its own operation. On the one hand, high-rate algal ponds system (HRAP) is considered; on the other hand, we study a conventional activated sludge system which uses photovoltaic power (AS+PV). This paper offers a viability analysis of both systems based on the capacity to satisfy their energetic consumption. In order to assess this viability, the probability of not achieving the energy consumption threshold at least one day is studied. The results point that the AS+PV system self-sufficiency is achieved using much lesser surfaces than those of HRAP system (for the former, less than 6.500 m2, for the latter 40.000 m2). However, the important AS capital cost makes still the HRAP system more economic, although storage provides a great advantage for using the AS+PV in locations where we have a lot of irradiance. This viability analysis, along with the opportunity cost analysis, will be used to assess these two projects devoted to the treatment of wastewater.

An off-grid solar energy storage system (ESS) in National Pingtung University of Science and Technology (NPUST) was built and officially operated on Jun. 16th 2022. The system is installed in a 40' general container with PV panels of solar power 8250 W_p on top of the container. The ESS is made by repurposed lithium iron phosphate (LFP) batteries of 20 kWh capacity, where a battery management system (BMS) is adopted to ensured the safety of the battery system. An energy management system (EMS) is built to receive, process, analyse, store, and output the energy information. In this manuscript, a brief engineering report about the system is given.

The article studies and analyzes a hybrid power source system, lithium battery, and fuel cell assisted by flexible photovoltaic panels for hybrid electric vehicles (HEVs). The new configuration operates based on optimizing the performance of the dual power source to maximize the available energy and operating time. The operational mode simulates specific driving conditions in urban, peripheral, and intercity routes under variable driving patterns and traffic conditions. The simulation results show that the new configuration increases the hybrid electric vehicle performance, providing higher reliability despite the more complex design. The proposed system reduces the external power supply dependence and avoids unexpected sudden stops due to energy exhaustion. The new configuration also extends the vehicle driving range depending on driving mode and route conditions. The vehicle driving range extension may reach up to 276.3 km. The hybridization of fuel cells with lithium battery and PV panel assistance enhances battery management, reducing battery servicing and increasing lifespan. The proposed topology is cheaper than a single fuel cell power system by 17.2%, reducing the total cost related to the single battery power system if we consider the battery recharge for its entire lifespan. The cost saving is 2.6% for the American market and 14.8% for the European one.

This paper introduces a structured design for an effective power-sharing technique among converter-interfaced distributed generation (DG) units within a microgrid that operates without a synchronous generator. The proposed power-sharing technique leverages the battery energy storage system (BESS) to promptly respond to network changes. The real power output of each commercial distributed energy resources (CDER) unit is governed by a frequency-droop characteristic and a complementary frequency restoration strategy. Deloading techniques are employed by solar and wind power generators to enhance response during power supply or demand disturbances. To ensure microgrid stability, small-signal analysis is conducted for controller design, including the determination of stability margins. Initial tuning of the power-sharing technique parameters is achieved using the Ziegler-Nichols Method, followed by further optimization through a meta-heuristical algorithm to enhance the response time of energy sources. The proposed power-sharing technique's performance is evaluated on a benchmark medium voltage network using industry-standard commercial software. The test results demonstrate the precise and rapid power-sharing capabilities among DGs facilitated by the proposed technique, highlighting its effectiveness in dynamic microgrid environments.

The work carried out first-principles calculations within the framework of density functional theory to study the structural stability of the CsSnI₃ compound and the influence of phase transitions on their electronic and optical properties. Using the GGA and SCAN functionals, the relaxed structures of the CsSnI₃ phases were obtained and their geometric characteristics were assessed. Using the Phonopy code based on VASP, calculations of phonon and thermodynamic properties were performed, and the temperatures of phase transitions of CsSnI₃ were determined. The temperature dependences of the thermodynamic parameters α-, β-, γ- and δ-phases of CsSnI₃ were analyzed. The trends in free energy, entropy, enthalpy, heat of formation energy and heat capacity were justified in terms of the pattern of changes in the total energy of the four phases of CsSnI₃ from VASP calculations. It is shown that at 0 K the non-perovskite structure of the CsSnI₃ compound (δ-CsSnI₃) is the most stable (followed by γ-CsSnI₃), and the tetragonal phase (β) is quite unstable, having the highest energy among the perovskite phases. It was revealed that at temperatures above 450 K the tetragonal phase becomes stable, and when the temperature drops, it transforms into the cubic phase (α-CsSnI₃). The phase transition between the β and γ phase of perovskite occurs in the range of 300-320 K, and at 320 K a black-yellow transformation of CsSnI₃ occurs in which the cubic phase (black perovskite) undergoes a phase transition to a non-perovskite conformation (yellow phase). The presence of temperature phase transitions between two orthorhombic phases of CsSnI₃ at 360 K was discovered, although direct transitions of the α⟷γ and γ⟷δ types have not yet been reported in any experiment, except for γ→δ transitions under the influence of moisture. Based on well-relaxed structures (from the SCAN calculations), the band gap widths for four CsSnI₃ phases were calculated and compared with experimental measurements. Electronic properties and Fermi level shifts as a result of phase transformations of CsSnI₃ were assessed using the HSE06 functional and machine learning prediction. The values of the complex dielectric constant and the refractive index of all phases of the CsSnI₃ were determined.

This contribution considers an improved control scheme for three-phase two-stage grid-tied photovoltaic (PV) power system based on integral sliding mode control (ISMC) theory. The proposed control scheme consists of maximum power point tracking (MPPT), DC-Link voltage regulation and grid currents synchronization. A modified voltage-oriented maximum power point tracking (VO-MPPT) method based on ISMC theory is proposed for design of an enhanced MPPT under irradiation changes. Moreover, a novel DC-Link voltage control based on ISMC theory is proposed in order to achieve good regulation of DC-Link voltage over its reference. To inject the generated PV power into the grid with high quality, a voltage oriented control based on space vector modulation (SVM) and ISMC (VOC-ISMC-SVM) has been developed to control the grid currents synchronization. Numerical simulations are performed in Matlab/SimulinkTM environment in order to evaluate the proposed control strategy. In comparison with conventional control scheme, the developed control strategy provides an accurate MPP tracking with less power oscillation as well as a fast and an accurate DC-Link regulation under climatic conditions variations. Moreover, the transfer of the extracted power into the grid is achieved with high quality.

The purpose of water treatment through ultraviolet (UV) radiation is to interrupt the development and multiplication of microorganisms by preventing their growth and reproduction.Ultraviolet treatment has become a very affordable solution to produce clean water of optimal quality in regions where access to drinking water is essential. The experimental water treatment unit is powered by a photovoltaic (PV) source. It incorporates a discharge lamp used to generate ultraviolet radiation, which is harnessed to address bacteriological issues related to water quality. This lamp is continuously powered by a photovoltaic (PV) source through an inverter and an electronic ballast. The water treatment system also includes a motor pump designed to draw contaminated water from the input reservoir, a flow control valve to adjust flow rates as needed, and a filter aimed at enhancing the quality of the contaminated water. The water circulates between the input reservoir (contaminated water) and the output reservoir (treated water) through the UV lamp. The relationship between the intensity of the UV lamp and the water flow rate can ensure an adequate water quality.

Of all the renewable energy sources, solar photovoltaic (PV) power is estimated to be a popular source due to several advantages such as its free availability, absence of rotating parts, integration to building such as rooftops, and less maintenance cost. The nonlinear current-voltage (I–V) characteristics and power generated from a PV array primarily depend on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, and partial shading of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is that realized that the RIO algorithm has improved convergence characteristics in terms of finding the optimal solution than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO-based MPPT.

This paper presents a microgrid distributed energy resources (DERs) for a rural standalone system. It is made up of solar photovoltaic (solar PV) system, battery energy storage system (BESS), and wind turbine coupled to permanent magnet synchronous generator (WT-PMSG). The DERs are controlled by maximum power point tracking (MPPT) based proportional intergral (PI) controllers for both maximum power tracking and error feedback compensation. The MPPT uses the perturb and observe (P&O) algorithm for tracking the maximum power point of the DERs. The PI gains are tuned using the Ziegler-Nichol’s method. The developed system was built and simulated in MATLAB/Simulink under two conditions - constant load, and step load changes. The controllers enabled the BESS to charge even during conditions of varying load and other environmental factors such as change of irradiance and wind speed. The reference was tracked very well by the output voltage of the DC grid. This is a useful research for electrifying the rural islanded areas, too far from the grid.

Various renewable energy sources such as wind power and photovoltaic (PV) have been increasingly integrated into the power system through power electronic converters in recent years. However, power electronic converter-driven stability issues under specific circumstances, for instance, modal resonances might deteriorate the dynamic performance of the power systems or even threaten the overall stability. In this paper, the integration impact of a hybrid renewable energy source (HRES) system on modal interaction and converter-driven stability is investigated in an IEEE 16-machine 68-bus power system. Firstly, an HRES system is introduced, which consists of full converter-based wind power generation (FCWG) and full converter-based photovoltaic generation (FCPV). The equivalent dynamic models of FCWG and FCPV are then established, followed by the linearized state-space modeling. On this basis, converter-driven stability analyses are performed to reveal the modal resonance mechanisms of the interconnected power systems and the modal interaction phenomenon. Additionally, time-domain simulations are conducted to verify effectiveness of dynamic models and support the converter-driven stability analysis results. To avoid detrimental modal resonances, an optimization strategy is further proposed by retuning the controller parameters of the HRES system. The overall results demonstrate the modal interaction effect between external AC power system and the HRES system and its various impacts on converter-driven stability.

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

The diverse socio-economic and environmental impacts that the set-up of a new photovoltaic installation has must be weighed carefully in order to reach the best possible solution. Among the different photovoltaic systems there are several classification criteria, depending on the technology, application and the size of the modules that define them. The size (installed nominal capacity) stands out as an impartial and critical measure in the decision making process. In this article we use a multi-criteria decision making method to analyse the responses of five experts to a detailed questionnaire in which several different criteria are correlated with various photovoltaic installation sizes. The limitation associated to the low number of experts is addressed with a robustness and sensitivity analysis. With this study we seek first, to apply and demonstrate the feasibility of a methodology which combines technical information with multi-criteria decision making methods, and second, to obtain a clear result oriented that increases the benefits of a forthcoming photovoltaic installation of modules in distributed generation adding up to 1GW total peak power in standard conditions. We observe a consistent result in which smaller photovoltaic modules are the ideal solution that maximises the socio-economic benefits of any installation. If a decision has to be taken about the type of photovoltaic plant to be installed, the conclusion is clear: given a certain size, small, easily scalable installations are the best solution for stake-holders, for the inhabitants, and for the environment.

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degrada-tion, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at Standard Test Condition (STC) efficiency. After considering losses, the system generates 2815 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27744 BDT from selling energy to the grid and operating costs of 64060.60 BDT/year. Remarkably, this initiative can prevent 33074.061 tCO2 emissions over the project’s 25-year lifespan.

As renewable energy generation prediction system has been introduced into the energy trading market, making a model to accurately predict the quantity of solar photovoltaic (PV) energy generation has become a significant problem. Moreover, to encourage an accurate prediction of the quantity of energy generation, an incentive system has been implemented for those who predict the quantity of solar PV energy under the error rate of 8%. Therefore, it has become more important to investigate and analyze current prediction technology numerically and develop more advanced prediction system. In this study, we tried to develop a better model to improve the accuracy of solar PV energy generation quantity by comparing three models made with gradient boosting machine (GBM), Model 1, Model 2, Model 3 respectively. Model 1 was built with the whole training data set without any additional preprocessing. After conducting some additional preprocessing procedure to predict solar energy generation more accurately, we made Model 2 with the whole training data set and Model 3 with only upper 10% of energy generation capacity. To compare the accuracy of three models, normalized mean absolute error (nMAE) was used as an evaluation index. The nMAE of Model 1 was 9.64% while the Model 2 showed 8.41%. Also, Model 3, which was constructed with the training set of upper 10% energy generation capacity, outperformed with the nMAE of 8.08%. For further study, to check the effectiveness of models constructed with GBM, a time series model, autoregressive integrated moving average (ARIMA), was also built and the nMAE was compared.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.