We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers...We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers based on the Lambert W function:a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator.Unlike Taylor-linearized explicit models,our proposed formulation retains the exponential nonlinearity of the PV equations.It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding,all while maintaining limited computational costs and a small memory footprint.The R-Lambert model is integrated into a buck-converter emulator equipped with a discrete PI regulator,which generates current references directly from sensed operating points,thus supporting hardware-constrained implementation.Comprehensive numerical experiments conducted on six commercial modules from various technologies(mono,poly,and multicrystalline)demonstrate significant accuracy improvements under the IEC EN 50530 near-MPP criterion:the V-Lambert solver reduces the±10%Vmpp band error by up to 61 times compared to an explicit-model baseline.Dynamic simulations under varying irradiance,temperature,and load conditions achieve millisecond-scale settling with accurate trajectory tracking.Additionally,processor-in-the-loop experimental validation on an embedded microcontroller supports the simulation results.By unifying exact analytical modeling with embedded realization,this work advances computer modeling for PV emulation,MPPT benchmarking,and controller verification in integrated renewable energy systems.展开更多
Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.W...Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.展开更多
In remote areas far from the grid, wind/PV/storage generating system is relatively a good choice, whatever in resource configuration, performance or prices. For the independent hybrid power system, the output models o...In remote areas far from the grid, wind/PV/storage generating system is relatively a good choice, whatever in resource configuration, performance or prices. For the independent hybrid power system, the output models of wind turbines, photovoltaic arrays and batteries are built in this paper, and based on the objectives of the capacity configuration optimal model, constraints used in the process of capacity configuration are analyzed. These provide convenient conditions and theoretical basis for the optimal capacity configuration of independent wind/PV/storage system.展开更多
Residential photovoltaic (PV) systems connected to the grid are used for self-consumption. Any surplus production is fed into the grid and contributes to improving the voltage. Several techniques are developed to mode...Residential photovoltaic (PV) systems connected to the grid are used for self-consumption. Any surplus production is fed into the grid and contributes to improving the voltage. Several techniques are developed to model their connection. However, studies on methods of injecting energy production into the Low Voltage (LV) network are nowadays a problem. This paper proposes a mathematical model to determine the current to be injected and calculate each node’s voltage. The current equation is a recurrence relation with an initial condition. This initial condition is for the case of a single PV system connected to the LV grid. The equation can also be written in matrix form. Similarly, the voltage solution is a recurrence relation. It also has an initial condition for the first node. Both mathematical formulae with the proposed initial conditions are consistent and can be used for the determination of the current and voltage of the different nodes in the grid.展开更多
Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the ...Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.展开更多
Uttarakhand state comes under special category state where approximately 69.45% population lived in rural area under the population density with varied range of 37 to 607 persons per sq.km. Although Uttarakhand is hav...Uttarakhand state comes under special category state where approximately 69.45% population lived in rural area under the population density with varied range of 37 to 607 persons per sq.km. Although Uttarakhand is having per capita consumption of 1112.29 kWh which is higher than national average per capita consumption of 779 kWh as till date, but remote communities, villages are not able to access clean, cheep and good quality of energy due to uneven terrain, lack of proper transmission & distribution lines [1]. 100% villages are electrified under the RGGVY scheme as per the Ministry of Power Government of India, but due to poor loading of transformer, lack of grid infrastructure and natural calamities, remote house owners are not able to get good quality of power thus affect the livelihood and source of income generation in various means [2]. As Uttarakhand state having future plans to be make state energy sufficient and energy access to all by year 2016-2017, so major ground level initiative have been taken by Government of Uttarakhand. The government of Uttarakhand has incorporated innovative business model to provide good quality of power with non-conventional energy source. Under the initiative invlovement of local people and village level, panchayats have ownership and responsibility to operate these clean energy business model to improve livelihood in remote hilly places of Uttarakhand. Under this analysis, five different type of community models are categorized as Community 1, Community 2, Community 3, Standalone 1 & Standalone 2 for rural &remote communities based on number of unclustered households with the distance covered between 200 m to 20 km, and electrical loads i.e. lighting, fan, mobile chargers, television along with time of day energy consumption patterns. These community models are for remote hilly location where grid integration and distribution lines are not feasible to built due to hilly terrain, low soil strength and huge expenses for expanding power cables for supplying good quality power. The preliminary studies and simulations has been done in HOMER tool by considering the various composite source of power, i.e. Solar PV with battery bank, Solar PV with battery Bank & Generator, and Solar PV along with DG. These three hybrid source of power generation with Solar PV as base source under five different community models, the techno-commercial feasibility has been analyzed in terms of load sharing proposition with Solar PV and battery, DG, Energy production through PV, load consumption per year, Excess and unmet energy monitoring, battery sizing to meet the load during nights, DG operation when the solar energy not available due to weather condition and non availability of sunshine in night. Financial feasibility has been examined in terms of levelized cost of energy, cost summary and O&M cost per year of three integrated sources of energy generation with Solar PV under each community model. Solar PV power plant , which is the best renewable source of energy to cater energy access issue in remote hilly places. The Uttarakhand receives good amout of daily average radiation level of 5.14 - 5.50 kWh/m2/day. Financial feasible community models for different hilly region based on their energy consumption need to be implemented with the help of local community by providing ownership to local people, panchayat, for it not only caters energy access issue but also provides clean, cheep, uninterruptable energy and improves livelihood standard to locals by engaging them into operation maintenance and tariff or rent collection. The study shows that Solar PV power plant with battery bank is the optimal solution considering life cycle cost of hybrid system. It is feasible due to low operation and maintenance cost, price declination of battery and Solar PV module, battery prices at time of replacement.展开更多
当前的基于词向量的多文档摘要方法没有考虑句子中词语的顺序,存在异句同向量问题以及在小规模训练数据上生成的摘要冗余度高的问题。针对这些问题,提出基于PV-DM(Distributed Memory Model of Paragraph Vectors)模型的多文档摘要方法...当前的基于词向量的多文档摘要方法没有考虑句子中词语的顺序,存在异句同向量问题以及在小规模训练数据上生成的摘要冗余度高的问题。针对这些问题,提出基于PV-DM(Distributed Memory Model of Paragraph Vectors)模型的多文档摘要方法。该方法首先构建单调亚模(Submodular)目标函数;然后,通过训练PV-DM模型得到句子向量计算句子间的语义相似度,进而求解单调亚模目标函数;最后,利用优化算法抽取句子生成摘要。在标准数据集Opinosis上的实验结果表明该方法优于当前主流的多文档摘要方法。展开更多
由于光伏发电系统在复杂外部环境与气象条件下存在显著的发电波动性与不确定性,传统预测方法难以有效预测特征之间的非线性耦合关系,导致传统的功率预测精度受限。为此,提出了一种基于注意力机制的神经网络方法(Self-attention based ne...由于光伏发电系统在复杂外部环境与气象条件下存在显著的发电波动性与不确定性,传统预测方法难以有效预测特征之间的非线性耦合关系,导致传统的功率预测精度受限。为此,提出了一种基于注意力机制的神经网络方法(Self-attention based neural network,SANN),用于实现不同场景下的高精度光伏发电功率精准预测。首先,构建以气象元素(辐照度、温度、风速、湿度、天气类型等)为输入的特征数据集,并通过标准化处理与时间戳对齐等处理方式构建多变量输入结构数据集。然后,利用特征间多头注意力机制,自动挖掘变量之间的动态依赖关系,增强模型对复杂环境场景下功率变化的敏感度与表征能力。最后,选择在典型场景下实现所提方法的预测效果验证。结果表明,该方法在平均绝对误差MAE、均方根误差RMSE及决定系数R^(2)等指标上均优于其他传统方法,说明其在光伏发电功率预测方面具有较高的预测准确性和可靠性。展开更多
基金funded by Scientific Research Deanship at University of Ha’il-Saudi Arabia through project number(RG-24014).
文摘We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers based on the Lambert W function:a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator.Unlike Taylor-linearized explicit models,our proposed formulation retains the exponential nonlinearity of the PV equations.It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding,all while maintaining limited computational costs and a small memory footprint.The R-Lambert model is integrated into a buck-converter emulator equipped with a discrete PI regulator,which generates current references directly from sensed operating points,thus supporting hardware-constrained implementation.Comprehensive numerical experiments conducted on six commercial modules from various technologies(mono,poly,and multicrystalline)demonstrate significant accuracy improvements under the IEC EN 50530 near-MPP criterion:the V-Lambert solver reduces the±10%Vmpp band error by up to 61 times compared to an explicit-model baseline.Dynamic simulations under varying irradiance,temperature,and load conditions achieve millisecond-scale settling with accurate trajectory tracking.Additionally,processor-in-the-loop experimental validation on an embedded microcontroller supports the simulation results.By unifying exact analytical modeling with embedded realization,this work advances computer modeling for PV emulation,MPPT benchmarking,and controller verification in integrated renewable energy systems.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R442)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.
文摘In remote areas far from the grid, wind/PV/storage generating system is relatively a good choice, whatever in resource configuration, performance or prices. For the independent hybrid power system, the output models of wind turbines, photovoltaic arrays and batteries are built in this paper, and based on the objectives of the capacity configuration optimal model, constraints used in the process of capacity configuration are analyzed. These provide convenient conditions and theoretical basis for the optimal capacity configuration of independent wind/PV/storage system.
文摘Residential photovoltaic (PV) systems connected to the grid are used for self-consumption. Any surplus production is fed into the grid and contributes to improving the voltage. Several techniques are developed to model their connection. However, studies on methods of injecting energy production into the Low Voltage (LV) network are nowadays a problem. This paper proposes a mathematical model to determine the current to be injected and calculate each node’s voltage. The current equation is a recurrence relation with an initial condition. This initial condition is for the case of a single PV system connected to the LV grid. The equation can also be written in matrix form. Similarly, the voltage solution is a recurrence relation. It also has an initial condition for the first node. Both mathematical formulae with the proposed initial conditions are consistent and can be used for the determination of the current and voltage of the different nodes in the grid.
基金This research is funded by Prince Sattam BinAbdulaziz University,Grant Number IF-PSAU-2021/01/18921.
文摘Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.
文摘Uttarakhand state comes under special category state where approximately 69.45% population lived in rural area under the population density with varied range of 37 to 607 persons per sq.km. Although Uttarakhand is having per capita consumption of 1112.29 kWh which is higher than national average per capita consumption of 779 kWh as till date, but remote communities, villages are not able to access clean, cheep and good quality of energy due to uneven terrain, lack of proper transmission & distribution lines [1]. 100% villages are electrified under the RGGVY scheme as per the Ministry of Power Government of India, but due to poor loading of transformer, lack of grid infrastructure and natural calamities, remote house owners are not able to get good quality of power thus affect the livelihood and source of income generation in various means [2]. As Uttarakhand state having future plans to be make state energy sufficient and energy access to all by year 2016-2017, so major ground level initiative have been taken by Government of Uttarakhand. The government of Uttarakhand has incorporated innovative business model to provide good quality of power with non-conventional energy source. Under the initiative invlovement of local people and village level, panchayats have ownership and responsibility to operate these clean energy business model to improve livelihood in remote hilly places of Uttarakhand. Under this analysis, five different type of community models are categorized as Community 1, Community 2, Community 3, Standalone 1 & Standalone 2 for rural &remote communities based on number of unclustered households with the distance covered between 200 m to 20 km, and electrical loads i.e. lighting, fan, mobile chargers, television along with time of day energy consumption patterns. These community models are for remote hilly location where grid integration and distribution lines are not feasible to built due to hilly terrain, low soil strength and huge expenses for expanding power cables for supplying good quality power. The preliminary studies and simulations has been done in HOMER tool by considering the various composite source of power, i.e. Solar PV with battery bank, Solar PV with battery Bank & Generator, and Solar PV along with DG. These three hybrid source of power generation with Solar PV as base source under five different community models, the techno-commercial feasibility has been analyzed in terms of load sharing proposition with Solar PV and battery, DG, Energy production through PV, load consumption per year, Excess and unmet energy monitoring, battery sizing to meet the load during nights, DG operation when the solar energy not available due to weather condition and non availability of sunshine in night. Financial feasibility has been examined in terms of levelized cost of energy, cost summary and O&M cost per year of three integrated sources of energy generation with Solar PV under each community model. Solar PV power plant , which is the best renewable source of energy to cater energy access issue in remote hilly places. The Uttarakhand receives good amout of daily average radiation level of 5.14 - 5.50 kWh/m2/day. Financial feasible community models for different hilly region based on their energy consumption need to be implemented with the help of local community by providing ownership to local people, panchayat, for it not only caters energy access issue but also provides clean, cheep, uninterruptable energy and improves livelihood standard to locals by engaging them into operation maintenance and tariff or rent collection. The study shows that Solar PV power plant with battery bank is the optimal solution considering life cycle cost of hybrid system. It is feasible due to low operation and maintenance cost, price declination of battery and Solar PV module, battery prices at time of replacement.
文摘当前的基于词向量的多文档摘要方法没有考虑句子中词语的顺序,存在异句同向量问题以及在小规模训练数据上生成的摘要冗余度高的问题。针对这些问题,提出基于PV-DM(Distributed Memory Model of Paragraph Vectors)模型的多文档摘要方法。该方法首先构建单调亚模(Submodular)目标函数;然后,通过训练PV-DM模型得到句子向量计算句子间的语义相似度,进而求解单调亚模目标函数;最后,利用优化算法抽取句子生成摘要。在标准数据集Opinosis上的实验结果表明该方法优于当前主流的多文档摘要方法。
文摘由于光伏发电系统在复杂外部环境与气象条件下存在显著的发电波动性与不确定性,传统预测方法难以有效预测特征之间的非线性耦合关系,导致传统的功率预测精度受限。为此,提出了一种基于注意力机制的神经网络方法(Self-attention based neural network,SANN),用于实现不同场景下的高精度光伏发电功率精准预测。首先,构建以气象元素(辐照度、温度、风速、湿度、天气类型等)为输入的特征数据集,并通过标准化处理与时间戳对齐等处理方式构建多变量输入结构数据集。然后,利用特征间多头注意力机制,自动挖掘变量之间的动态依赖关系,增强模型对复杂环境场景下功率变化的敏感度与表征能力。最后,选择在典型场景下实现所提方法的预测效果验证。结果表明,该方法在平均绝对误差MAE、均方根误差RMSE及决定系数R^(2)等指标上均优于其他传统方法,说明其在光伏发电功率预测方面具有较高的预测准确性和可靠性。
