Photovoltaic Units Research Articles

Investigation of photovoltaic solar cell with the complex cooling system

The laminar flow of ZrO2-water nanomaterial through the cooling duct of photovoltaic unit has been analyzed in this article. Two shapes for the ducts (circular and rectangular) were considered. The system with a rectangular duct has been equipped with confined jets with two arrangements. The finite volume method has been utilized for the simulation of the present three-dimensional models and a good agreement has been obtained. The uniformity of isotherms over the silicon layer has been improved by utilizing the new cooling system which leads to a longer lifetime. For the circular tube case, an increase in inlet velocity (V in) makes electrical () and thermal () performances enhance about 1.07% and 1.64%, respectively. With the increase in inlet temperature (T in) in both regions, and reduced by about 3.97% and 28.39%. Incorporating the rectangular duct and jets with proper design leads to increments of and about 58.91% and 3.84% when Ti n = 308.15 K, V in = 0.06 m/s.

A proposed fault identification-based fuzzy approach for active distribution networks with photovoltaic systems

Recently, the issue of protection in active distribution networks has been greater than before due to mushrooming use of photovoltaic (PV) generation units. PV systems coupled to power electronics devices and their control strategies causes significant changes to fault current characteristics leading to maloperation of fault identification schemes. Therefore, this paper proposes a robust self-adaptive fault identification scheme that incorporates the presence of PV systems and considers the effect of uncertainty arising due to their high penetration levels. The real-time voltage and current measurements are captured locally by advanced one-end measuring facilities, considering the uncertain temperature and irradiation conditions, of solar generation units. First, the proposed scheme employs wavelet transform, statistical alienation concept, and fuzzy logic to identify the fault criteria. Then, it uses fuzzy concept-based the system features to obtain the optimal protection scheme setting. For simulation analysis, a 10 MW solar farm-connected to a real-world active distribution system is emulated in PSCAD-EMTDC simulator to create intensive study cases, and the proposed scheme is implemented in MATLAB software. Several fault parameters are examined in the scheme comprising different fault types, fault locations, and transition resistances. Also, the uncertainty arising from PV plants and different loading conditions are taken full consideration in the developed scheme. Simulation results have verified the validity of fault identification scheme; nonetheless, the computational burden is kept reasonable. The proposed scheme is investigated under white gaussian and impulsive noise. Furthermore, sensitivity analysis and comparative study are conducted in terms of accuracy, high decision speed, and sampling frequency variation. The proposed scheme succeeds to detect and classify internal faults for both on/off-grid operation modes and differentiate them from external ones in the case of on-grid mode. The proposed scheme has dominance performance including high speed fault detection within, a half cycle, and provides accuracy more than 98 %.

An Emergency Coordinated Control Strategy to Improve the Transient Stability of a Single-Ended Distribution Network with Flexible Interconnection Channel Blocking.

Based on the scenario of high-penetration distributed photovoltaic connected to an AC/DC distribution network, this paper analyzes the dynamic characteristics of frequency and voltage in a distribution network after the blocking failure of the flexible interconnection channel. In order to enhance the transient stability of the system after the fault, this paper comprehensively considers the active regulation ability of photovoltaic units, and puts forward an emergency coordinated control strategy for a single-ended distribution network with flexible interconnection channel blocking. Firstly, the non-fault channel is overloaded for a short time, then the comprehensive influence of factors such as electrical distance, response time and adjustment cost on the frequency modulation effect of the system is quantitatively evaluated; according to the evaluation results, the photovoltaic and synchronous units are controlled by "control instead of tripping", and finally, the high-frequency tripping is carried out, based on the principle of "photovoltaics first". After the frequency control is completed, the reactive power optimization model of the system is established, and the improved tabu-particle swarm optimization algorithm is used to solve it, so as to optimize the voltage of the distribution network nodes. Finally, an equivalent simulation model is established to verify the coordinated control strategy.

Optimization of Algorithm for Solving Railroad Power Conditioner Compensation Power Reference Value and System Power Quality Analysis Based on Optimal Compensation Model

At present, electrified railroads with complex road conditions are facing the problems of the existence of a single power supply method, the deterioration in power quality, and the difficulty in recycling regenerative braking energy. In order to improve the above problems, this paper establishes a traction power supply system containing photovoltaic units, proposes an optimized compensation model for the RPC (railroad power conditioner), and improves the particle swarm algorithm for solving the reference value of RPC compensation power. First, the structure of the RPC-based traction photovoltaic power generation system and the establishment of the integrated energy management strategy of the system are constructed. Then, the back-to-back converter compensation model with power quality index parameter constraints is established, which establishes the optimization function with the objectives of minimizing the negative sequence current, maximizing the power factor, and minimizing the RPC compensation power, as well as establishes constraints on the active converter capacity and three-phase voltage imbalance. Then, the particle swarm algorithm for solving the RPC compensation power reference value is improved, specifically in the original PSO by introducing dynamically changing inertia weights and learning factors. This not only solves the problem of the single power supply and realizes the nearby consumption of photovoltaic units in the traction system, but also realizes the recycling of regenerative braking energy and the coordinated control of the traction photovoltaic power generation system, improves the power quality of the system, and meets the demand of the RPC for real-time control. In order to verify the effectiveness of the optimized compensation model established in this paper and improve the convergence of the particle swarm algorithm for solving the RPC compensation power reference value, a simulation model of the traction PV power generation system is established in MATLAB/Simulink, and a real-time simulation is carried out to verify it based on the preset working condition data. The results show that the RPC optimization compensation model developed in this paper can coordinate the control system energy flow and improve the system power quality (the power factor increases and the negative sequence current decreases). The improved particle swarm algorithm is more convergent.

On a new method to design solar photovoltaic systems in renewable energy communities: The case of Culatra Island (Ria Formosa, Portugal)

Islands must reach sustainable lifestyles by improving resources management and by getting accustomed to renewable energy sources. Culatra, a small Portuguese island, is actively increasing renewable energy penetration into local processes in order to be the first 100% sustainable Portuguese territory by 2030. Based on the electric consumption of Culatra, the objective of the present study is to project a photovoltaic unit composed of several sub-fields, each with a distinct orientation, in order to increase the self-consumption ratio while at the same time reducing the surplus of energy production, assessed by the self-sufficiency ratio criteria, resulting in a lower levelised cost of energy of the power system. To achieve this, MATLAB’s implementation of a genetic algorithm was used to find the optimised set of orientations for a given load profile. The results indicate that it is possible to optimise the photovoltaic plant to reach a more continuous electricity generation through daytime, reducing the storage needs and increasing the value of photovoltaic systems. The method can be extended to other locations or demand curves, assisting on comparing different energy management strategies, and their respective advantages and disadvantages.

Analyzing the effect of normally distributed cooling channels on a photovoltaic thermal solar unit

Photovoltaic thermal (PVT) systems convert solar radiation into electrical and thermal energy while keeping the PV's temperature within a permissible range. This paper presents a numerical analysis of a novel normal cooling technique for the PV module and a new AI prediction model. It considers a cooling direction normal to the PV panel using rectangular cascade channels with two different orientations. This cascading of the flow inlet channels helps keep the inflow temperature as cold as possible along the PV panel. The numerical simulations are performed based on a flow rate range of 0.00024: 0.0012 LPM, insolation of 400: 1200 W/m2, convection coefficients of 5.48 and 20.7 W/m2.K, and ambient temperatures of 298 and 308 K. The proposed PVT system achieves a decrease in the cell's average temperature by 27K while improving the electrical, thermal, and overall efficiencies by 4.8%,59.5%, and 73.33%, respectively. The numerical database is used for building an ANN prediction model based on a radial base function neural network with Gorilla troops optimizer (RBFNN-GTO). The proposed RBFNN-GTO model is developed to predict the main performance parameters of the PVT which are thermal efficiency, electrical efficiency, overall efficiency, average temperature of the PV panel, and the model temperature un-uniformity. It achieves an overall correlation coefficient of 0.9997 concerning the numerical database and showed an average MSE of 5.4313E-4.

Prospective life cycle environmental impact assessment of renewable energy-based methanol production system: A case study in China

Integrating renewable electricity and green hydrogen with CO2 utilization to produce chemicals has drawn much attention due to its low carbon emission characteristics. The goal of this paper is to investigate the environmental sustainability of producing a steady hourly output of methanol by utilizing electricity and hydrogen produced via renewable energy resources, and CO2 captured from a coal-fired power plant located in Inner Mongolia, China. Six renewable energy-based electricity and hydrogen systems are considered based on photovoltaic, wind turbines and combinations thereof, including battery and grid technologies. The environmental impact indicators of these system are compared comprehensively through life cycle assessment approach, including not only global warming potential but also fossil fuel depletion, water & soil & air pollution indices and human health index. The results indicate that the most environmentally friendly system is Case B2 which uses photovoltaic to produce hydrogen and electricity, with battery for energy storage and excess electricity sold back to grid. It shows apparently the best sustainability performance in global warming potential, which is 0.105 kg CO2-eq per kg methanol. In general, the proposed six systems outperform conventional methanol production systems in terms of global warming potential and abiotic depletion potential, with reductions ranging from 74% to 92% and 51.1%–73.8% respectively, but they have high levels in other environmental sustainability indicators such as human toxicity, eutrophication potential, and ozone layer depletion potential due to the photovoltaic electricity generation unit. The findings highlight that it is essential to consider a comprehensive range of environmental sustainability indicators when developing renewable energy-based methanol systems on a large scale.

Double-layer optimal energy management of smart grid incorporating P2P energy trading with smart traction system

Abstract Moving towards clean and sustainable transportation system, electrification of railway systems along with the use of electric vehicles (EVs) are of great interest. For economic operation of such systems, the peer-to-peer (P2P) energy trading policy became more applicable. Therefore, this paper proposes a double-layer individual-based optimization algorithm for P2P based optimal energy management (EM) of a smart distribution network incorporating flexible smart railway substation taking into consideration multiple energy sources of the traction system and the wayside distribution network with the integration of both EVs and energy hubs (EHs). For efficient operation of the railway substation, captured regenerative braking energy (RBE) are considered. In addition, photovoltaic (PV) units are used on station’s and platforms’ roof-tops for clean energy generation along with a multistory parking garage of electric vehicles (EVs). The wayside distribution network of district distribution area with flexible loads also contains several energy sources including distributed generation and multiple EHs. Optimal energy management is carried out including optimal demand side management of flexible loads and EHs. The optimization study takes into consideration several operational uncertainties arising from several components of the system. The simulation results show the feasibility of applying the proposed EM algorithm with an improvement of the energy economics of the system. The results show a reduction of 6.5 % in energy cost of the distribution network loads, 48 % in net energy cost of EHs, 70.8 % in net energy cost of EVs, and 59.1 % in the cost of energy bought by the traction substation.

Experimental performance of air-type BIPVT systems under different climate conditions

In this study, the thermal/electrical performance of an air-type BIPVT system was investigated under complex environmental conditions by constructing a movable experimental system using copper indium gallium selenide photovoltaic modules as photovoltaic units. Experimental studies were conducted in four regions with different latitudes (Sanya, Hulunbeier, Dongtai, and Beijing) to determine the effect of the quantitative relationships between the ventilation flow rate and climate conditions on the photothermal and photovoltaic performance of the system. Taking Sanya as an example, the results show that the thermal energy and power generation are both positively correlated with irradiation intensity, and that increasing the flow rate can effectively increase the thermal energy. By increasing the ventilation flow rate from 0.4 m/s to 2.2 m/s, the average thermal efficiency throughout the day could be increased from 23.0% to 51.8%, but the electrical efficiency only increased by approximately 3.5%. A comparison of the thermal/electrical performance in different geographic regions revealed that the thermal efficiency varies widely (by as much as 8%) at high flow rates, whereas the variation in relative power generation is small. The results can guide the design of air-type BIPVT systems.

Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

In recent years, there has been a rapid increase in distributed generation (DG) technologies incorporated into distribution networks (DNs) to meet the challenge of load growth. However, the stochastic nature of renewable energy, such as photovoltaic (PV), makes the amount of energy produced uncertain. This uncertainty, along with changes in generation and load demand, can increase energy losses and voltage instability. To address this issue, energy storage systems can be integrated to decrease the effects of the intermittency associated with renewable technologies. This paper proposes a new variant of an equilibrium optimizer (EO) based on reinforced learning, named RLEO, for optimal incorporation of multiple battery energy storage (BES) units integrated synchronously with solar PVs into distribution systems while minimizing energy loss. The RLEO algorithm employs reinforced learning mechanisms to prevent premature convergence of the EO and improve its exploration and exploitation capabilities. The performance of the RLEO algorithm is assessed using standard CEC 2017 benchmark functions and compared with the original EO and other popular algorithms using various statistical criteria. The RLEO algorithm is also applied to determine the optimal size and position of multiple PV units in IEEE 69-bus and 118-bus DNs with single and multi-objective optimization problems. Using the developed algorithm, the optimal arrangement of three non-dispatchable PVs results in a slight increase in the percentage reduction of energy loss across various load profiles: 53.0035 % for commercial, 19.6372 % for industrial, and 28.1783 % for residential. In contrast, by employing the optimal configuration of three PV + BES units, the reduction in energy loss percentage experiences a remarkable surge to 68.3466 % for commercial, 68.0917 % for industrial, and 68.1779 % for residential load scenarios using the proposed algorithm. This clearly indicates that the proposed RLEO algorithm significantly surpasses recent optimization methods documented in the literature when it comes to addressing the challenge of optimal allocation for multiple DGs. Furthermore, its applicability extends to more intricate optimization problems.

A posteriori multiobjective approach for techno-economic allocation of PV and BES units in a distribution system hosting PHEVs

This paper proposes a posteriori multiobjective approach to effectively integrate photovoltaic (PV) units into the distribution network. PV units are inherently non-dispatchable sources due to the intermittent nature of solar irradiance. To address this limitation, battery energy storage (BES) units are employed to support the PV units, thereby transforming them into dispatchable sources. The objective of this study is to optimally allocate dispatchable PV-BES units within a distribution network that accommodates plug-in hybrid electric vehicles (PHEVs). The PHEVs are modelled considering stochastic parameters at three different demand response levels. A multiobjective optimization problem is formulated to address the conflicting technical and economic objectives associated with the allocation of PV-BES units. The multiobjective manta ray foraging optimizer algorithm is utilized for solving the multiobjective optimization problem. Additionally, the technique for order of preference by similarity to ideal solution is used to identify the best trade-off solution, and multiple solutions are presented based on the decision-makers preferences regarding the conflicting objectives under consideration. The proposed approach is validated on 33-bus and 69-bus distribution networks, and extensive case studies are conducted to showcase the advantages of the proposed methodology.

Incorporating photovoltaic inverter capability into stochastic optimal reactive power dispatch through an enhanced artificial gorilla troops optimizer

AbstractThis study addresses the challenging task of solving optimal reactive power dispatch (ORPD) while incorporating renewable energy resources (RERs), considering their stochastic and time‐varying nature. Specifically, the focus is on solving the stochastic optimal reactive power dispatch (SORPD) problem, taking into account uncertainties in load demand and generated power, as well as the reactive power generation capability of photovoltaic (PV) systems. To tackle this problem, an enhanced Artificial Gorilla Troops Optimizer (EGTO) is proposed, which utilizes multiple strategies. The objective is to minimize power loss and improve voltage profile and system stability under uncertain conditions. The algorithm is applied and tested on the IEEE 30‐bus system, both with and without the STATCOM functionality of the PV system. A comparison is conducted against other well‐known optimization algorithms. The results demonstrate the significant improvement achieved by incorporating the PV unit. The inclusion of the PV system leads to reduced expected power losses, voltage deviations, and improved voltage stability. Specifically, without and with the STATCOM functionality, the expected power loss decreases from 5.9218 MW at the base case to 1.1419 MW and 1.1197 MW, respectively. Similarly, the expected voltage deviation decreases from 1.9320 p.u. to 0.0909 p.u. and 0.0893 p.u., respectively, and the expected voltage stability decreases from 0.1336 p.u. to 0.07199 p.u. and 0.07142 p.u., respectively.

An enhanced turbulent flow of water-based optimization for optimal power flow of power system integrated wind turbine and solar photovoltaic generators

This paper uses enhanced turbulent flow in water-based optimization (TFWO), specifically ETFWO, to achieve optimal power flow (OPF) in electrical networks that use both solar photovoltaic (PV) units and wind turbines (WTs). ETFWO is an enhanced TFWO that alters the TFWO structure through the promotion of communication and collaboration. Individuals in the population now interact with each other more often, which makes it possible to search more accurately in the search area while ignoring local optimal solutions. Probabilistic models and real-time data on wind speed and solar irradiance are used to predict the power output of WT and PV producers. The OPF and solution methods are evaluated using the IEEE 30-bus network. By comparing ETFWO to analogical other optimization techniques applied to the same groups of constraints, control variables, and system data, we can gauge the algorithm’s robustness and efficiency in solving OPF. It is shown in this paper that the proposed ETFWO algorithm can provide suitable solutions to OPF problems in electrical networks with integrated PV units and WTs in terms of energy generation costs, improved voltage profiles, emissions, and losses, compared to the traditional TFWO and other proposed algorithms in recent studies.

Fuzzy Logic-based Integrated Cooling System to Improve PV Efficiency

Photovoltaic (PV) temperature is an important parameter that can influence PV performance. The increase in PV temperature can cause a drop in PV output voltage, which indicates that the PV does not operate optimally. In this paper, a design of an integrated cooling system based on fuzzy logic to control the PV temperature is presented. This, in effect, will potentially ensure that the PV performance is at its maximum condition. In this work, the temperature of the PV is kept constant by attaching a cooling system that carries water inside it. The constant temperature of the cooling system is carried out through a radiator and thermoelectric with electronic control. The experiments are carried out using two 320-watt PV systems; one with the cooling mechanism and the latter without the cooling system as a control. Several important data such as temperature, voltage, current, and radiation measurement in each PV were collected for three days from 10:00 to 17:00 with an interval of 10 minutes for each measurement. The results of the integrated cooling system measurement showed an increase in PV power generation and efficiency of 1.32% compared to the control PV unit. From the results of the study, it can be concluded that the fuzzy logic-controlled cooling system contributed to the increase in PV performance.

A study on the placement of photovoltaic units in the North and South of Vietnam for energy loss reduction by using a proposed slime mould algorithm

A study on the placement of photovoltaic units in the North and South of Vietnam for energy loss reduction by using a proposed slime mould algorithm

Electromagnetic Transient Modeling Method of Photovoltaic Power Station Based on FPGA

The rapid development of renewable energy needs the effective support of simulation technology. Aiming at the shortage of both modeling scale and simulation accuracy of the current large-scale photovoltaic power station, an electromagnetic transient modeling and real-time simulation method suitable for large-scale photovoltaic power stations are proposed. The microsecond accurate real-time simulation of 24 photovoltaic power units is implemented on a single FPGA. Through an equivalent modeling method, the electromagnetic transient model of a photovoltaic power unit including a PV array, DC boost circuit, grid-connected inverter, filter, and grid-side transformer is established. Then, based on the FPGA parallel computing and pipeline time division multiplexing technology, the updating framework of the station collecting network and the power unit is constructed, and the real-time simulation model of the station including 24 photovoltaic power units in 4 sub-stations is built. Finally, the microsecond real-time simulation of the photovoltaic power station is realized through RTRES, a self-developed real-time simulator in China’s Southern Power Grid. The comparison with the detailed PSCAD model shows that the simulation error is within ± 2%, which verifies the correctness of the proposed method and model.

Research on Dispatching Coordination Control Method of Distribution Network with Photovoltaic Power Generation

The coordination control of photovoltaic power distribution networks faces challenges such as inaccurate load power prediction and long coordination times. To address these issues, this paper proposes a scheduling coordination control method for photovoltaic power distribution networks. The method involves analyzing the structure of the photovoltaic power generation system, obtaining the operation state of the network using disturbance observation methods, building a dispatching coordination control model, designing constraints for dispatching coordination control, building a power balance function, and solving the total demand power of the load. An algorithm flow is designed to optimize the coordination control of photovoltaic distribution networks. Experimental results demonstrate that the accuracy of photovoltaic unit power prediction using this method reaches 98%, with power grid dispatching and coordination control times always less than 3.8 seconds. This approach effectively improves the effectiveness of distribution network dispatching and coordination control.

Optimal stochastic scheduling in residential micro energy grids considering pumped-storage unit and demand response

Improving energy efficiency, production costs, greenhouse gas emissions, and reliability are some of the most important problems in the energy industry. Energy storage systems and demand-side management (DSM) programs are among the existing solutions for removing such problems. This study presents an improved micro energy grid (MEG) to reduce operating costs as well as greenhouse gas emissions using combined cooling, heating, and power (CCHP) systems, wind turbines (WTs), photovoltaic (PV) units, pumped-storage units, and heating and cooling storage units. In this system, pumped-storage units are employed to store energy. Price- and incentive-based programs are employed to obtain better performance on the demand side. Uncertainties of wind speed, solar power, electricity price, and electrical load are considered to achieve more accurate results. In this paper, the Latin hypercube sampling (LHS) method is applied to generate various scenarios. Then, K-means clustering approach is employed to reduce the number of scenarios. By producing and reducing the proposed scenario, the inherent uncertainty in renewables can be covered, which will result in a more efficient model. Meta-heuristic whale optimization algorithm (WOA) and Pareto model are used to solve single- and multi-objective functions and to optimally design the proposed structure considering contradictory functions, respectively. The features of the proposed developed algorithm are simplicity in execution and absence of derivative operator, increase of local and global search capability and consideration of objective functions with different nature. Moreover, fuzzy decision-making mechanism is proposed to select the optimal solution from the Pareto set of solutions. Finally, the proposed approach in different scenarios on the microgrid with various sources is discussed. The results indicate the proposed method properly obtains the optimal solution.

Seamless Start-Up of a Grid-Connected Photovoltaic System Using Module-Integrated Micro-Converters

In traditional grid-tied photovoltaic (PV) installations, when partial shadowing occurs between different PV modules in a string, bypass diodes short-circuit the output terminals of shadowed modules, and the whole system forgoes their potential energy production. This loss can be recovered if a dc-dc converter (micro-converter) is coupled to every PV module, and operated at the maximum power point (MPP). In this scenario, without communication links between the distributed micro-converter and the grid-tied inverter, a start-up procedure must be carefully designed to seamlessly allow the system to transfer PV power to the grid. During this phase, potentially damaging over-voltages and abrupt transients occurring at the micro-converters/inverter interface must be avoided. In this paper, the control algorithm of each micro-converter is enhanced to provide a smooth start-up operation so that PV units can safely start transferring power to the inverter and the grid. Improving from previous works, the proposed control technique is simple and removes the need for current sensors at the output of each micro-converter and at the inverter dc-link, with an economical advantage. Simulation results demonstrate the successful system start-up behavior, whilst confirming the benefits of the proposed control technique. First, the dc-link is energized from the rectified grid voltage. Then, the micro-converters raise the dc-link voltage so that the available PV power is transferred to the grid, with this sequence of operations not causing any abrupt electrical transient. The results also demonstrate the robust behavior of the PV system under non-uniform solar irradiation conditions.

All-transparent tandem photovoltaic-powered photodetector

Can electronics operate independently and invisibly in designated places? If yes, the user would not recognize the existence of devices and this can be applied to various applications. The device should be optically transparent and simultaneously achieve transparent electric power to satisfy this invisible freedom. Herein, we propose a dual transparent platform consisting of a broad-range translucent photodetector powered by an invisible photovoltaic unit. Transparent photovoltaic (TPV) has a NiO/TiO2 heterojunction for high optical transparency of 79.7% and can produce a maximum voltage of 0.6 V at an efficiency of 13.4%. A transparent broadband photodetector (TBD) that comprises Co3O4/ZnO has high optical transparency (70%) and provides a broad wavelength photodetection. The TBD can be tuned in the reverse bias mode along with the separate power unit (TPV), which significantly enhances the responsivity to 6.8 mA W−1. In contrast to the self-powered operation mode, the dual system (TBD and TPV power units) can actively control the bias voltage to the TBD, supplied from the TPV (0–0.6 V). TPV and TBD have an overall transparency of 56.4% and long-term stability to allow a high degree of freedom in transparent human electronics, ensuring visional transparency and an independent power supply.