Research on Multi-Scale Modeling of Grid-Connected Distributed Photovoltaic Power Generation

The complexity of distribution network model mainly depends on the model scale of grid-connected distributed photovoltaic (PV) power generation. Therefore, the simulation performance of multi-scale PV model is the key factor of the simulation accuracy in the specific operating scenarios of distribution network. In this paper, a multi-scale model of grid connected PV distributed generation system is proposed based on the mathematical model of grid-connected distributed PV power generation. It is analyzed that differences of simulation performance, such as adaptability of simulation step size, accuracy of output and the effect on voltage profile of distribution network, between PV models with different scales in IEEE 33 node example. Simulation results indicate that the multi-scale model is effective in improving the accuracy and efficiency of simulation under different operating conditions of distribution network.

Availability, Performance and Reliability Evaluation for PV Distributed Generation

Nowadays, renewable energy resources play an important role in replacing conventional energy resources such as fossil fuel by integrating solar, wind and geothermal energy. Photovoltaic energy is one?of?the very promising renewable energy resources which grew rapidly in the past few years, it can be used to produce electric energy through photovoltaic process. The primary objective of the research proposed in this paper is to facilitate the increasing penetration levels of PV systems in the electric distribution networks. In this work, the PV module electrical model is presented based on the mathematical equations and was implemented on MATLAB to simulate the non-linear characteristics I-V and P-V curves with variable input parameters which are irradiance and temperature based on Riyadh region. In addition, the reliability evaluation of distribution networks, including distributed generators of solar photovoltaic (PV) with varying output power capacity is presented also. The Monte Carlo simulation algorithm is applied to test the distribution network which is RBTS Bus 2 and the same has been conducted on the original case of distribution network substation 7029 which is located at KSA Riyadh. The two distribution networks have been modified to include the PV’s distributed generators. The distributed generators contribute to supply a part of the load during normal mode and supply the entire load during component failure or failure of grid operation supply. The PV stochastic models have been used to simulate the randomness of these resources. Moreover, the study shows that the implementation and integration of renewable resources as distributed generations have improved the reliability of the distribution networks.

Impact of PV Distributed Generation on Loop Distribution Network

The rapid spreading of the Photovoltaic (PV) Systems as Distributed Generation (DG) in medium and low voltage networks created many effects and changes on the existing power system networks. In this work, two methods have been used and applied to determine the optimal allocation and sizing of the PV to be installed as DGs (ranging from 250 kW up to 3 MW). The first one is to determine the location according to the maximal power losses reduction over the feeder. The second one is by using the Harmony Search Algorithm which is claimed to be a powerful technique for optimal allocation of PV systems. The results of the two techniques were compared and found to be nearly closed. Furthermore, investigation on the effects on the feeder in terms of voltage levels, power factor readings, and short circuit current levels has been done. All calculations and simulations are conducted by using the MATLAB Simulation Program. Some field calculations and observations have been expended in order to substantiate the research findings and validation.

Solar PV Energy Generation Map of Karnataka, India

A massive plan has been drawn by the Karnataka state of India to initiate several solar power plants at different locations. In view of this, it is of great help to have reliable estimation on solar PV energy generation. Four solar PV power plants in Karnataka state are fully operational installed by Karnataka Power Corporation Limited (KPCL). They are located at Kolar, Belgaum and Raichur with 3 MW capacity each and at Mandya with 5 MW capacity. In the present study, using ground mounted weather station data solar power generation has been estimated and compared with actual generation for two consecutive years of 2012 and 2013 for one location initially, namely 3 MW Kolar Solar PV Plant. The procedure is repeated for rest of the plants. The simulated results have been corrected with ground mounted weather data. After such corrections, the simulated results have been compared with the actual energy generation of the four plants. Results showed a close match with a small deviation of about 5%. The model then applied throughout the state for every 0.25 degree station intervals in a grid manner. The annual energy generation obtained for the state varies from 1.53 to 1.73 MUs/MW. Central and south eastern part of the state are found to yield significantly higher solar power generation as compared to the northern part and south western part of Karnataka. Interestingly, north western part of Kodagu district has shown the least potential of 1.53 MUs/MW as compared to other parts. This can be attributed mainly due to low irradiation and high temperature condition at this location. The energy generated map from our study will be useful and helpful for both solar developers and decision makers of Karnataka state.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

光伏出力序列场景生成新方法

面向更精准的中长期电力电量平衡校验,提出横纵向融合及人为指定持续极端日的光伏出力场景生成新方法。首先,依据日辐照度特征指标及聚类把历史数据划分为多个极端日和多个典型日;然后,利用马氏链生成下一个月横向辐照度日类型序列及特征指标序列的基础上,提出基于日类型出现概率及日类型允许转换矩阵的辐照度日类型序列生成方法,生成下一个月纵向日类型序列及特征指标序列,并将横纵向数据进行融合;最后,基于优化方法及光电转化模型生成月度光伏出力场景,并在此基础上提出人为指定持续极端日光伏出力序列场景生成方法。算例表明所提算法能更好地为运行调度提供参考。

积灰对光伏组件性能影响的试验研究及数值模拟

探究光伏组件表面积灰对其输出特性的影响及污秽颗粒在组件表面的沉积规律,有助于制定清灰方案,提高光电转化效率。以置于华北电力大学风机大厅楼顶的光伏阵列为研究对象,进行人工布灰试验,探究了不同积灰量对组件输出功率、电流和电压的影响;为探究某单一因素对污秽颗粒沉积的影响,利用COMSOL软件建立与光伏组件自然积灰试验条件相同的颗粒沉积数值模型,模拟分析了风速、湿度、颗粒粒径和污秽浓度对光伏组件表面污秽颗粒沉积的影响。试验结果表明:积灰对工作电压影响较小,对输出功率和工作电流影响较大,且当积灰密度为5.07 g/m^(2)时,组件输出功率、电流和电压的变化率分别为8.71%、6.48%和0.40%。模拟结果表明:其他条件相同时,颗粒的沉积量随风速和粒径的增大均先减小后增大,在风速3 m/s和粒径15μm时取得最小值;相同条件下,颗粒沉积量均随湿度和污秽的浓度的增大而增大。

基于时空图网络的分布式光伏发电出力预测

为提高分布式光伏发电出力预测的精度,提出一种针对分布式系统的时空相关性建模方法,并结合深度自注意力网络实现出力预测。首先,分析光伏出力的影响因素及其量化方式,基于注意力机制赋予历史数据不同权重,并针对部分数据缺失及长序列处理等实际应用问题提出相应注意力机制改进策略;然后,计算不同站点间距离及历史出力数据相关性,利用矩阵变换实现特征融合;最后,以某地区分布式光伏系统的实测数据验证了所提方法的有效性,在不同场景、不同天气类型下预测准确率均显著提高。

考虑光伏运行风险的光储充电站双层规划优化模型

光储充电站的容量规划与运行优化是一个多时间尺度的问题,因此提出一种考虑光伏运行风险的光储充电站双层规划优化模型。首先,考虑到双层规划优化中两种时间尺度优化问题特征,建立含上层年时间尺度规划与下层日调度周期优化的双层规划优化模型。其次,根据光伏出力预测误差概率分布,将光伏出力分为3种情况,利用期望值分别计算光伏出力预测误差导致的弃光和切负荷风险费用,同时提出考虑光伏出力预测误差的储能风险运行策略。最后,采用蒙特卡洛方法预测电动汽车充电负荷曲线。调用MATLAB中CPLEX求解器求解模型。仿真结果表明,考虑光伏出力预测误差后,光伏配置容量较为保守,且储能配置容量增加,减少了弃光和切负荷情况的发生。

复杂光照环境下光伏阵列输出特性研究

为解决复杂光照环境下集中式光伏阵列最大功率点跟踪(maximum power point tracking,MPPT)的问题,采用太阳能光伏组件的电流方程以及电路分析的原理,对串、并联光伏组件的输出过程进行理论推导,总结出集中式光伏阵列处于复杂光照环境下的输出特性,建立1种复杂光照环境下集中式光伏阵列输出特性的仿真模型。通过计算机仿真以及系统实验,对集中式光伏阵列的输出特性曲线进行分析,仿真、实验结果与理论分析保持一致。

平抑光伏并网功率波动的混合储能系统优化调度策略

提出了一种光伏(PV)的最大功率跟踪工作点控制和混合储能系统(HESS)协调平抑光伏并网功率波动策略,通过PV和HESS间的密切配合,能有效将PV并网功率波动抑制在电网可接受范围内。采用了多目标非线性约束模型对HESS中电池和超级电容的充放电功率进行优化调度,调度过程中充分考虑了HESS的寿命、偏离校正以及充放电效率;给出了基于滑动平均算法的PV的最大功率跟踪工作点动态控制方法。此外,采用K均值聚类算法构建了3种典型的PV功率波动场景,对所提策略和利用HESS平抑PV功率波动的传统策略进行了对比分析,结果表明:所提策略既能取得良好的平抑PV功率波动效果,还能降低HESS的运行损耗、延长其使用寿命。

多因素耦合对光伏组件表面温度影响的试验研究

低温条件(0℃以下)对光伏发电量影响的相关研究较少,为进一步研究温度对发电量的影响,该文首先研究各个因素对光伏组件表面温度的影响。运用灰色关联分析定量分析影响光伏组件表面温度的多种因素,分析得到各影响因子与光伏组件表面温度的最佳关联顺序为太阳辐照度>环境温度>风速>湿度。并采用多元线性回归方程分析各影响因素与光伏组件表面温度之间的关系,结果为太阳辐照度每升高1 W/m^2,光伏组件正面温度增加0.037℃;环境温度每升高1℃,光伏组件正面温度增加0.851℃;风速每升高1 m/s,光伏组件正面温度降低0.421℃;湿度每升高1%,光伏组件正面温度增加0.248℃。其次,根据太阳电池转换特性还研究了光伏组件表面温度对光伏组件输出电压、电流及发电量的影响,并采用一元线性回归方程分析其关系,结果为光伏组件正面温度每升高1℃,发电量增加0.016 Wh。

常见光伏阵列拓扑结构分析

太阳能光伏发电系统作为一个复杂系统,其中光伏阵列拓扑结构对整个系统的成本和效率有着重要的影响,它也关系着发电系统能否经济可靠地运行。对比分析了几种常见的光伏阵列拓扑结构,并对常见失配条件下阵列输出功率的仿真结果进行分析。分析研究表明阵列拓扑结构对光伏系统输出功率和成本有很大影响,这些研究为光伏阵列拓扑的设计提供了参考。

基于阴影识别的光伏阵列输出特性简化仿真方法

准确绘制光伏(PV)阵列输出特性曲线在光伏发电应用中非常必要。然而,目前的输出特性曲线仿真方法通常将局部遮阴组件等效为整块遮阴并以等效辐照度近似。本文首先分析了辐照度等效对光伏阵列局部遮阴组件输出特性仿真引入的误差,提出了一种考虑局部遮阴情况的光伏阵列输出特性简化仿真方法,然后根据光伏阴影图像特点,详细介绍了阴影识别算法的实现流程。针对实际光伏阵列图像的仿真结果表明,基于阴影识别的光伏阵列输出特性简化仿真方法可以准确且快速地绘制出局部遮阴光伏阵列的输出特性曲线。

基于DDRTS的光伏组件电气特性仿真

依据太阳电池组件电学模型,运用电力系统实时数字仿真系统(DDRTS)软件,搭建了光伏组件模型。结合光伏组件生产厂家给定参数,进行输出特性仿真,验证了光伏发电是与许多参量相关的高度非线性电源;编程实现自定义模型,读入实测数据(光照强度和环境温度),进行光伏组件模型的输出电压、输出电流及出力仿真,通过与实测出力数据比较结果表明,该模型具有较好的精准性,为光电接入电力系统的研究和分析提供了条件。

气象数据弱相关的光伏出力短期预测

光伏出力受气象因素影响,气象数据的有效程度影响着预测结果的准确性。本文提出了气象数据与光伏出力弱相关时短期光伏出力的预测方法。首先采用Pearson关联系数分析法得到影响光伏发电的主要因素,而后采用模糊聚类理论构建相似日,建立了具有优秀小样本学习能力的支持向量回归机预测模型。针对该模型,提出了两阶段确定模型参数的方法,首先采用全局网格搜索确定核参数p和正则化参数C的取值范围,再通过自适应差分进化算法寻找最优核参数p和正则化参数C,以提高参数ε选取范围设置较大时的预测精度。实例测试表明,使用本文提出的SVR方法预测的平均RMSE为5.551%,满足预测要求,比常规BP预测方法提高精度1.238%,在气象数据弱相关时对光伏短期出力有更好的预测能力。

面向光伏建筑的发电量预测方法

发电量是光伏项目重要的性能指标。准确预测发电量对于光伏建筑项目的规划、运营以及经济核算极为重要。面向光伏电站设计开发的发电量预测方法及其软件,难以适应光伏建筑发电量预测的复杂性和特殊性,其预测结果与实际值差距较大。文章提出了一种光伏建筑模型预测法,使用该方法预测光伏建筑发电量的结果较为准确,优于现有方法。

Intelligent windows for electricity generation: A technologies review

Buildings are responsible for over 40% of total primary energy consumption in the US and EU and therefore improving building energy efficiency has significant potential for obtaining net-zero energy buildings reducing energy consumption. The concurrent demands of environmental comfort and the need to improve energy efficiency for both new and existing buildings have motivated research into finding solutions for the regulation of incoming solar radiation, as well as ensuring occupant thermal and visual comfort whilst generating energy onsite. Windows as building components offer the opportunity of addressing these issues in buildings. Building integration of photovoltaics permits building components such as semi-transparent façade, skylights and shading devices to be replaced with PV. Much progress has been made in photovoltaic material science, where smart window development has evolved in areas such as semi-transparent PV, electrochromic and thermochromic materials, luminescent solar concentrator and the integration of each of the latter technologies to buildings, specifically windows. This paper presents a review on intelligent window technologies that integrate renewable energy technologies with energy-saving strategies contributing potential solutions towards sustainable zero-energy buildings. This review is a comprehensive evaluation of intelligent windows focusing on state-of-the-art development in windows that can generate electricity and their electrical, thermal and optical characteristics. This review provides a summary of current work in intelligent window design for energy generation and gives recommendations for further research opportunities.

Unified MPPT Controller for Partially Shaded Panels in a Photovoltaic Array

The power output of the photovoltaic(PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally used may not be competent enough to find the maximum power point(MPP) during partially shaded conditions. The sensible reason for the failure of conventional trackers is during partial shaded conditions the PV arrays exhibit multi peak power curves, thereby making simple maximum power point tracking(MPPT) algorithms like perturb and observe(P&O) to get stuck with local maxima instead of capturing global maxima.Therefore, global search MPPT aided by evolutionary and swarm intelligence algorithms will be conducive to find global power point during partially shaded conditions. This work suggests a unified controller which feeds control signal to its power electronic conditioner placed at each module. The evolutionary algorithm which is taken into consideration in this work is differential evolution(DE).The performance of the proposed method is compared to the classical un-dimensional search controller and it is evident from the Matlab/Simulink results that the unified controller prevails over the distributed counterpart.

含短期预测的光伏配电网智能调压策略

分布式光伏发电在配电网中渗透率不断提高,使馈线中的传输功率减少,导致节点过电压情况严重,网损较大。本文提出一种含光伏输出功率短期预测的光伏配电网智能调压策略。首先,对光伏发电数据进行异常点检测后进行相似日聚类,应用深度神经网络(DNN)进行光伏输出功率预测;其次在大规模光伏发电中,依据改进的电气距离,选择可控光伏的安装并网点;最后,应用新型NSGA-III调控可控光伏设备的有功出力和无功出力,以达到调节配电网节点电压、降低网损的目标。在安徽省某县域配电网系统进行试验分析,证明了所提出的调压策略可以精细化解决电压越限问题,并且降低了总网损。