Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.

A PV powered shunt active power filter for power quality improvement

This paper deals with power quality improvement using a three-phase active power filter(APF) connected to a PV power system. A direct power control(DPC) approach is proposed to eliminate harmonic current caused by any nonlinear loads and at the same time guarantees the delivery of a part of the load request from the same PV source. A boost converter is used for maximum power point(MPP) tracking purposes under various climate conditions through a fuzzy logic technique. The suggested study is tested under a MATLAB/Simulink environment. The obtained results depict the efficacy of the proposed procedures to meet the IEEE 519-1992 standard recommendation on harmonic levels.

Investigation on Grid Connections of Wind Power and PV Power in China

In order to fully comprehend the developing status of wind power and photovoltaic (PV) power generation, a special investigation on the integration of wind power and PV power was launched by the agencies of the State Electricity Regulatory Commission (SERC) throughout China during July-October 2010. This report is completed based on the investigation along with routine supervisory and management programs. There are totally 573 wind power projects and 94 PV power projects involved. Existing problems in these projects are pointed out and proposals for regulation are put forward.

Analysis of the Performance Indicators of the PV Power System

The energy assessment of the PV power systems is carried out by using different types of performance indicators that benchmark the output of these systems against the PV panel maximum output at hypothetical operation conditions. In this paper, a comparative analysis of six types of performance indicators is conducted and a new performance indicator which considers PV panel slope and orientation is proposed. The proposed indicator is benchmarking the PV system actual output against the maximum output of the same system if it would operate in two axis tracking mode. The proposed performance indicator is used to develop a friendly user calculator of PV system output that can be used by, energy providers and PV system installers to evaluate the output of the PV grid connect network. The advantage of the developed calculator is high-lighted by a case study that estimates energy capacity of different residential rooftop PV systems installed in a residential suburb in Sydney.

Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

Exploring the merits of geographical diversification of solar PV power plants for a resilient PV-dominated electricity grid in India

India is highly dependent on solar photovoltaics(PV)to harness its vast solar resource potential and combat climate change.However,∼90%of the installed PV capacity in India is concentrated in the top nine states,with the remaining states lagging behind.The research reveals that during monsoons,heavy cloud cover and rain lead to high solar resource variability,intermittency and the risk of very low PV generation,which can result in reliability issues in future PV-dominated electricity grids.Although energy storage can help in overcoming high intermittency,there are multiple challenges associated with it.The novelty of this study lies in demonstrating the benefits of combining multiple PV sites in various regions to mitigate the risks of low PV generation and high variability.The variability of individual sites was found to be up to∼3.5 times higher than the variability of combined generation.During noon,prominent solar park sites like Bhadla and NP Kunta experience a decrease in power generation to values as low as∼10%of the rated PV capacity.However,the minimum generation of the large-scale dispersed PV generation is>30%.Furthermore,the research identifies other benefits of dispersing PV generation across the country,viz.,reduction of seasonal variability by adding PV capacity in the southern region,widening of the PV generation span,more room for PV capacity addition,reduction in storage and ramping needs,utilization of hydroelectric potential of the north-east and PV potential of Ladakh,and creating opportunities for sustainable development in rural agrarian regions through agrivoltaics.

BIPV模式在工业厂房屋顶的应用研究

中国大力推进分布式光伏发电的同时,存在许多工业厂房屋顶无法满足光伏发电系统安装条件的问题,这极大制约了光伏发电技术的推广。由于传统的光伏发电系统是直接附着在建筑物屋顶,即采用的是BAPV模式,这会导致因需增加建筑物的承载力而增加建设成本的情况出现,从而制约了分布式光伏发电的应用,而将光伏发电系统与建筑集成的光伏建筑一体化(BIPV)模式可有效解决此类问题。针对BIPV模式和BAPV模式对工业厂房钢结构的影响进行了研究,并根据实际案例对这两种模式下的光伏发电系统建设、维护成本进行了分析。研究结果表明:1)BIPV模式相较于BAPV模式可以减少工业厂房钢结构屋顶恒荷载,对于轻微超限的项目可以省去加固成本,对于超限明显的项目可以减少加固成本。2)在同等建设条件下,BIPV模式相较于BAPV模式可以减少屋顶建设成本;并且从中长期来看,相较于BAPV模式,BIPV模式平均1万m^(2)的厂房可以减少126万建设维护成本。

“双碳”背景下BIPV的应用与探索

“双碳”目标提出后,中国的绿色经济发展有了更加明确的方向。城镇化率不断增长的同时,中国的建筑业进入了鼎盛发展时期;但与此同时出现的建筑能耗问题也越来越严重,这种发展形式与“双碳”目标背道而驰。而光伏发电技术与建筑相结合成为解决这一问题的有效方式,光伏建筑一体化(BIPV)产业的发展是实现“双碳”目标的重要途径。针对当前BIPV产业的发展状况及发展优势进行了分析,并分析了影响BIPV建筑发电量的因素,然后从技术、法律法规、市场3个方面提出了优化举措。分析结果显示:光伏组件方位角及安装倾角、光伏组件表面灰尘及其安装方式是制约BIPV建筑发电量的主要因素,BIPV后续发展需要从技术方面进行改进以确保年发电量达到预期目标;BIPV产业应从技术、法律法规及市场3个方面进行改进和完善,有助于其更好的发展。该研究结果对BIPV理论研究和工程人员施工设计具有一定的参考价值。

基于PVsyst的单面与双面光伏组件最佳安装倾角对比

随着光伏发电技术不断进步,单面光伏组件与双面光伏组件的价差不断缩小。得益于发电量增益大,双面光伏组件在实际项目中受到愈来愈多的青睐。当光伏发电项目采用单面或双面光伏组件时,光伏组件的最佳安装倾角成为一个重要的考虑因素。选取了3个太阳能资源等级地区,利用PVsyst对在这3个地区建设的光伏电站分别采用单面和双面光伏组件时的安装倾角与光伏电站首年发电量及光伏组件正面接收的太阳辐照量之间的关系进行了仿真模拟。得到以下结论:1)以光伏电站首年发电量最大为衡量标准时,双面光伏组件的最佳安装倾角大于或等于有阴影遮挡的单面光伏组件的最佳安装倾角,但小于无阴影遮挡的单面光伏组件的最佳安装倾角。2)就单面光伏组件而言,无阴影遮挡条件下增加光伏组件安装倾角带来的发电量增益高于有阴影遮挡安装条件时。3)在无阴影遮挡情况下,在一定范围内增大单面光伏组件的安装倾角可以相应提升其发电量;相对于双面光伏组件,无阴影遮挡的单面光伏组件通过适当提高其安装倾角所获得的发电量增益较小,且增大安装倾角,光伏阵列之间的间距也需要相应增加,这会增大用地量,从而增加项目总成本。因此,在实际项目中,尽可能采用双面光伏组件是一个较为理想的选择。

基于PVsyst的倾角可调固定式光伏支架的调节次数及倾角偏差影响分析

以位于内蒙古自治区的采用倾角可调固定式光伏支架的某光伏电站为例,首先利用PVsyst软件对不同光伏支架倾角下的年发电量进行模拟,得到最佳倾角;然后对光伏支架倾角的浮动范围进行分析;最后针对倾角可调固定式光伏支架的调节时间及调节次数进行研究,并分析1年调节2次倾角时倾角偏差对发电量的影响。研究结果显示:1)相对于全年采用最佳倾角固定式光伏支架的光伏电站,光伏支架倾角年调节次数由2次增加到12次,光伏电站年发电量增益可由4.72%增至5.81%。2)光伏支架倾角调节次数与季节无直接相关性;相对于全年采用最佳倾角固定式光伏支架的光伏电站,随着光伏支架倾角调节次数的增加,光伏电站的年发电量增益逐渐增加,但增幅在逐渐减小。3)在确定光伏支架年调节次数时,需要综合考虑发电量的增益和需要投入的成本,选择2~4次较为合适。4)倾角可调固定式光伏支架1年调节2次倾角时,每次倾角可允许存在±5°的偏差范围,且综合考虑现场工况和调节精度等实际情况后,年理论发电量的最大损失率仅约为0.3%。以期研究结果可为光伏电站的光伏支架倾角设计方案比选、运维时的倾角调节优化及技术标准制定等工作提供参考。

基于PVsyst软件模拟发电量数据的光伏电站电能损耗的分析方法研究

电能损耗是光伏电站非常重要的综合性指标,对其进行分析可以为光伏电站能效管理提供数据支持。在电能损耗的计算过程中,最大负荷损耗小时数的选取非常关键。提出了一种基于PVsyst软件模拟所得的发电量数据来分析光伏电站电能损耗的方法,结合工程实际情况给出了精确计算年最大负荷损耗小时数的改进算法数学模型,并对采用不同算法得到的年最大负荷损耗小时数进行了比较分析;以山西省大同市鸦儿崖乡100 MW光伏电站为算例,对其电气设备及线缆的电能损耗进行详细分析,验证了所提出光伏电站电能损耗分析方法的正确性。研究结果表明:1)由于光伏电站容配比的影响,应分别计算光伏电站直流侧和交流侧的年最大负荷损耗小时数,直流侧和交流侧的电能损耗也需要分别计算。2)该方法基于PVsyst软件模拟得到的光伏电站全年各小时的发电量数据,可准确计算出光伏电站的年最大负荷损耗小时数,在数学逻辑上合理,可以提高光伏电站电能损耗计算的准确度。该方法也可应用于其他实际运行的发电项目的电能损耗分析。

Analysis for Effects of Temperature Rise of PV Modules upon Driving Distance of Vehicle Integrated Photovoltaic Electric Vehicles

The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO2 emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.

PV Power Forecasting Using an Integrated GA-PSO-ANFIS Approach and Gaussian Process Regression Based Feature Selection Strategy

This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model(i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach,and long short-term memory(LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error(RMSE),normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE(hybrid model combining similar day approach and Elman), HSL(hybrid model combining similar day approach and LSTM), and HKSE(hybrid model combining K-means++,similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.

Calculation Method and Analysis for the Annual Power Generation of PV Faades in China

The application of PV faades emerges greatly in recent years and however its calculation methods and analysis remains insufficient under the weather conditions of China. In such demand,this paper investigates PV faade in terms of PV electricity generation in different arrangements and weather conditions of four major cities in China. The calculation models for PV faade are developed and validated by comparing the results with the measured data from the field experiments. A parametric study is carried out to provide a reference for the optimal design of the PV faades. The results show that with various cities,building orientations,building forms,materials and arrangements of PV modules,there is a distinct difference in the electrical output energy of PV faades. Weather conditions play a very important role in terms of PV generation performance of PV faades.

太阳能PV/T光储直驱热电联产系统性能

面对居民日益增长的生活热水和电能需求,光伏/光热(photovoltaic/thermal,PV/T)技术的应用可以降低建筑运行时的能源消耗。本文介绍了一种太阳能PV/T光储直驱热电联产(combined heat and power,CHP)系统,为了减少系统运行过程中的能量损失,采用直流压缩机和储能电池,并在兰州地区对系统的运行性能开展了实验测试。研究结果表明,PV/T系统的光伏板温度相比传统PV组件温度平均降低12.26℃,平均发电效率相对提升8.1%。在将24.4~27.2℃的水加热到50.1~50.7℃的过程中,平均性能系数(coefficient of performance,COP)可达到5.48,相比传统空气源热泵热水器提高82.1%~106.8%。平均集热效率和综合效率分别为37.30%和71.24%,PV/T系统的发电量和耗电量分别为3.33kWh和1.69kWh,发电量相比PV系统提高5.7%。太阳能PV/T光储直驱热电联产系统可以减少建筑部门的能源消耗,并提升PV/T系统的发电效率和综合效率,在晴天条件下可以实现离网运行。

基于PVsyst的光伏系统研究

基于PVsyst软件,以吐鲁番地区某民居为模拟对象,对当地的气象数据进行分析,并构建了一套安装在屋顶的光伏并网发电系统。对光伏并网系统的安装倾角进行优化,并对光伏组件进行选型,对系统损失进行分析。最后根据模拟结果,对影响系统发电效率的因素进行分析并提出了一些优化方案,以及得出吐鲁番地区19.1 kW的光伏并网系统全年发电量为27.9 MW·h,系统效率为80.9%,能量回收期为2.98年,每年可减少32790 kg的CO_(2)排放量,具有一定的环境效益;若按新疆光伏上网电价0.262元(/kW·h)进行计算,每年还可带来7309.8元的收入。该研究对实际光伏并网系统的设计施工具有一定的参考意义。

Robust design and optimization for autonomous PV-wind hybrid power systems

This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness.

Overview of Maximum Power Point Tracking Control Methods for PV Systems

Maximum power point tracking (MPPT) controllers play an important role in photovoltaic systems. They maximize the output power of a PV array for a given set of conditions. This paper presents an overview of the different MPPT techniques. Each technique is evaluated on its ability to detect multiple maxima, convergence speed, ease of implementation, efficiency over a wide output power range, and cost of implementation. The perturbation and observation (P & O), and incremental conductance (IC) algorithms are widely used techniques, with many variants and optimization techniques reported. For this reason, this paper evaluates the performance of these two common approaches from a dynamic and steady state perspective.