Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

Due to the unpredictable output characteristics of distributed photovoltaics,their integration into the grid can lead to voltage fluctuations within the regional power grid.Therefore,the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios.This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic(PV)generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks(TCN)and Long Short-Term Memory(LSTM).To begin with,an analysis of the spatiotemporal distribution patterns of PV generation is conducted,and outlier data is handled using the 3σ rule.Subsequently,a novel approach that combines temporal convolution and LSTM networks is introduced,with TCN extracting spatial features and LSTM capturing temporal features.Finally,a real spatiotemporal dataset from Gansu,China,is established to compare the performance of the proposed network against other models.The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy,with a single-step Mean Absolute Error(MAE)of 1.782 and an average Root Mean Square Error(RMSE)of 3.72 for multi-step predictions.

Analysis and Power Quality Improvement in Hybrid Distributed Generation System with Utilization of Unified Power Quality Conditioner

This paper presents a comprehensive study that includes the sizing and power flow by series and parallel inverters in a distributed generation system(DGs)that integrates the system of hybrid wind photovoltaic with a unified power quality conditioner(UPQC).In addition to supplying active power to the utility grid,the system of hybrid wind photovoltaic functions as a UPQC,compensating reactive power and suppressing the harmonic load currents.Additionally,the load is supplied with harmonic-free,balanced and regulated output voltages.Since PVWind-UPQC is established on a dual compensation scheme,the series inverter works like a sinusoidal current source,while the parallel inverter works like a sinusoidal voltage source.Consequently,a smooth alteration from interconnected operating modes to island operating modes and vice versa can be achieved without load voltage transients.Since PV-Wind-UPQC inverters handle the energy generated through the hybrid wind photovoltaic system and the energy demanded through the load,the converters should be sized cautiously.A detailed study of the flow of power via the PV-Wind-UPQC is imperative to gain a complete understanding of the system operation and the proper design of the converters.Thus,curves that allow the sizing of the power converters according to the power flow via the converters are presented and discussed.Simulation results are presented to assess both steady state and dynamic performances of the grid connected hybrid system of PV-Wind-UPQC.This investigation is verified by simulating and analyzing the results with Matlab/Simulink.

Effect of photovoltaic panel electric field on the wind speed required for dust removal from the panels

Methods to remove dust deposits by high-speed airflow have significant potential applications,with optimal design of flow velocity being the core technology.In this paper,we discuss the wind speed required for particle removal from photovoltaic(PV)panels by compressed air by analyzing the force exerted on the dust deposited on inclined photovoltaic panels,which also included different electrification mechanisms of dust while it is in contact with the PV panel.The results show that the effect of the particle charging mechanism in the electric field generated by the PV panel is greatly smaller than the effect of the Van der Waals force and gravity,but the effect of the particle charged by the contact electrification mechanism in the electrostatic field is very pronounced.The wind speed required for dust removal from the PV panel increases linearly with the PV panel electric field,so we suggest that the nighttime,when the PV electric field is relatively small,would be more appropriate time for dust removal.The above results are of great scientific importance for accurately grasping the dust distribution law and for achieving scientific removal of dust on PV panels.

A Control Strategy for Smoothing Active Power Fluctuation of Wind Farm with Flywheel Energy Storage System Based on Improved Wind Power Prediction Algorithm

The fluctuation of active power output of wind farm has many negative impacts on large-scale wind power integration into power grid. In this paper, flywheel energy storage system (FESS) was connected to AC side of the doubly-fed induction generator (DFIG) wind farm to realize smooth control of wind power output. Based on improved wind power prediction algorithm and wind speed-power curve modeling, a new smooth control strategy with the FESS was proposed. The requirement of power system dispatch for wind power prediction and flywheel rotor speed limit were taken into consideration during the process. While smoothing the wind power fluctuation, FESS can track short-term planned output of wind farm. It was demonstrated by quantitative analysis of simulation results that the proposed control strategy can smooth the active power fluctuation of wind farm effectively and thereby improve power quality of the power grid.

A comprehensive review on the development of data-driven methods for wind power prediction and AGC performance evaluation in wind–thermal bundled power systems

The wind–thermal bundled power system achieves energy complementarity and optimized scheduling, which is an important way to build a new type of energy system. For the safe and stable operation of the wind–thermal bundled power system, accurate data-driven analysis is necessary to maintain real-time balance between electricity supply and demand. By summarizing the development and characteristics of wind–thermal bundled power system in China and different countries, current research in this field can be clearly defined in two aspects: short-term wind power prediction for wind farms and performance evaluation of automatic generation control (AGC) for thermal power generation units. For short-term wind power prediction, it is recommended to focus on historical data preprocessing and artificial intelligence methods. The technical characteristics of different data-driven wind power prediction methods have been compared in detail. For performance evaluation of AGC units, a comprehensive analysis was conducted on current evaluation methods, including the “permitted-band” and “regulation mileage” methods, as well as the issue of evaluation failure in traditional evaluation methods in practical engineering. Finally, the relative optimal dynamic performance of AGC units was discussed and the future trend of data-driven research in wind–thermal bundled power system was summarized.

Application of Power Electronics Converters in Renewable Energy

Against the backdrop of global energy shortages and increasingly severe environmental pollution,renewable energy is gradually becoming a significant direction for future energy development.Power electronics converters,as the core technology for energy conversion and control,play a crucial role in enhancing the efficiency and stability of renewable energy systems.This paper explores the basic principles and functions of power electronics converters and their specific applications in photovoltaic power generation,wind power generation,and energy storage systems.Additionally,it analyzes the current innovations in high-efficiency energy conversion,multilevel conversion technology,and the application of new materials and devices.By studying these technologies,the aim is to promote the widespread application of power electronics converters in renewable energy systems and provide theoretical and technical support for achieving sustainable energy development.

Research on Wind Farm Participation in Power Grid AGC Control

Since wind power has the features of being intermittent and unpredictable, and usually needs transmission over long distances, grid integration of large-scale wind power will exert signif icant influence on power grid planning and construction, and will make a heavy impact on the safe and reliable operation of power systems. To deal with the diff iculties of large scale wind power dispatch, this paper presents a new automatic generation control (AGC) scheme that involves the participation of wind farms. The scheme is based on ultra-short-term wind power forecast. The author establishes a generation output distribution optimization mode for the power system with wind farms and verif ies the feasibility of the scheme by an example.

Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective shortterm wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e.,a wind power prediction model based on multi-class autoregressive moving average(ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method;the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy,but also the parameter estimation efficiency.

A general analytical approach to reach maximum grid support by PMSG-based wind turbines under various grid faults

A novel fault ride-through strategy for wind turbines,based on permanent magnet synchronous generator,has been proposed.The proposed strategy analytically formulates the reference current signals,disregarding grid fault type and utilizes the whole system capacity to inject the reactive current required by grid codes and deliver maximum possible active power to support grid frequency and avoid generation loss.All this has been reached by taking the grid-side converter’s phase current limit into account.The strategy is compatible with different countries’grid codes and prevents pulsating active power injection,in an unbalanced grid condition.Model predictive current controller is applied to handling rapid transients.During faults,the energy storage system maintains DC-link voltage,which causes voltage fluctuations to be eliminated,significantly.A fault ride-through strategy was proposed for PMSG-based wind turbines,neglecting fault characteristics,second,reaching maximum possible grid support in faulty grid conditions,while avoiding over-current and third,considerable reduction in energy storage system size and power rating.Inspiring simulations have been carried out through MATLAB/SIMULINK to validate the feasibility and competency of the proposed fault ride-through method and efficiency of the entire control system.

Coordinated voltage control of renewable energy power plants in weak sending-end power grid

The utilization of renewable energy in sending-end power grids is increasing rapidly,which brings difficulties to voltage control.This paper proposes a coordinated voltage control strategy based on model predictive control(MPC)for the renewable energy power plants of wind and solar power connected to a weak sending-end power grid(WSPG).Wind turbine generators(WTGs),photovoltaic arrays(PVAs),and a static synchronous compensator are coordinated to maintain voltage within a feasible range during operation.This results in the full use of the reactive power capability of WTGs and PVAs.In addition,the impact of the active power outputs of WTGs and PVAs on voltage control are considered because of the high R/X ratio of a collector system.An analytical method is used for calculating sensitivity coefficients to improve computation efficiency.A renewable energy power plant with 80 WTGs and 20 PVAs connected to a WSPG is used to verify the proposed voltage control strategy.Case studies show that the coordinated voltage control strategy can achieve good voltage control performance,which improves the voltage quality of the entire power plant.

基于权重调节模型预测控制的风-光-储-氢耦合系统在线功率调控

该文针对风-光-储-氢系统中风光的波动性、储氢的动态响应特性,实时在线优化匹配储-氢功率与风-光功率问题,提出基于权重调节模型预测控制(MPC)的风-光-储-氢耦合系统在线功率调控方法。文中构建了风-光-储-氢耦合能源系统拓扑结构,建立了耦合系统状态空间模型,以耦合系统功率平衡为目标,以制氢功率、燃料电池功率和电池功率为控制变量,根据氢储能和电池储能的特性及各约束条件将目标函数转换为二次规划问题进行求解,并在自定义s-函数建立的MPC控制器中依据复合储能系统的状态信息对权重因子进行调节,实现了控制器的参数自适应,最后完成了功率控制层与能量管理层的闭环仿真。通过仿真分析,对比三种调控方法,验证了该文所提基于变权重MPC功率调控方法的有效性和优越性。

风-光-抽蓄零碳电力系统多时间尺度协调调度模型

高比例新能源并网带来的波动性影响和新能源消纳水平不足已成为新型电力系统中亟须解决的问题。为此,基于风、光、负荷预测精度随时间尺度缩短而逐级提高的特点和抽蓄机组日内灵活调节特性,建立风-光-抽蓄零碳电力系统多时间尺度协调调度模型。以运行成本最小为目标,建立日前24 h发电计划、日内1 h发电计划和实时15 min发电计划。通过多时间尺度的协调配合,保证风、光、抽蓄出力良好跟踪负荷,逐级修正发电计划。以含6台抽蓄机组的风-光-抽蓄零碳电力系统为例开展仿真分析,结果表明所提多时间尺度协调调度模型有利于减少系统弃风、弃光量,且系统消纳风光的能力与抽蓄电站装机容量有关。

基于CNN-Bi-LSTM功率预测的海岛综合能源系统优化调度

合理构建海岛综合能源系统对沿海能源清洁化转型意义重大,其优化调度更是实现海岛能源供需平衡的有效途径。为此,提出了一种考虑风光功率预测的海岛综合能源系统优化调度方法。首先,搭建包含氢能设备、海水源热泵、海水淡化装置、波浪能发电装置等新型能源转换设备的系统模型。其次,海上气候多变会导致新能源发电不稳定,故采用含环境变量重要性排序的一维卷积神经网络和双向长短时记忆神经网络(convolutional neural network-bi-directional long short-term memory,CNN-Bi-LSTM)联合模型对发电功率进行预测。然后,为维持海岛基本生存条件,以电-冷-淡水-氢平衡为约束,以改善系统运行经济性和可再生能源消纳率为目标函数,建立综合能源系统优化调度模型。对夏冬两个典型日进行仿真分析,结果表明所提出的预测模型具有较高的预测精度,所提优化调度方法可以实现海岛能源供需平衡,同时能够有效降低系统运行成本,提高可再生能源消纳率。

人工智能技术在新能源功率预测的应用及展望

构建新型电力系统是实现我国“双碳”战略目标的主要举措之一,风力发电和光伏发电作为两种最具代表性的新能源,其波动性和随机性给电网安全和新能源消纳带来了重大挑战,新能源功率预测是降低其随机性影响的核心关键技术。近年来,随着大数据技术和以深度学习、强化学习为代表的新一代人工智能(artificial intelligence,AI)技术在诸多领域的成功应用,其在新能源功率预测方面的应用仍有方兴未艾之势。首先该文论述AI技术在新能源功率预测应用的理论基础,并对AI技术在风电和光伏功率预测方面的应用进行系统总结,包括数据增强和特征构建等多种数据处理技术的应用,传统机器学习算法、深度学习算法以及组合算法在模型构建方面的应用,以及进化算法、群智能优化算法、强化学习等多种智能优化算法在模型训练和超参数优化方面的应用。然后,对当前相关文献进行统计分析,并基于新能源预测大赛结果和实际预测系统调研情况,对当前学术界研究热点和趋势、产业界模型应用情况进行对比和分析。最后,对当前新能源功率预测在场景自适应、小样本学习、数值天气预报系统(numerical weather prediction,NWP)数据时空分辨率、分布式新能源预测等方面存在的一些问题进行剖析,并对采用强化学习、元学习、图神经网络(graph neural network,GNN)等多种AI技术解决相关问题的前景进行展望。

Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

Renewable energy has become a solution to the world’s energy concerns in recent years.Photovoltaic(PV)technology is the fastest technique to convert solar radiation into electricity.Solar-powered buses,metros,and cars use PV technology.Such technologies are always evolving.Included in the parameters that need to be analysed and examined include PV capabilities,vehicle power requirements,utility patterns,acceleration and deceleration rates,and storage module type and capacity,among others.PVPG is intermit-tent and weather-dependent.Accurate forecasting and modelling of PV sys-tem output power are key to managing storage,delivery,and smart grids.With unparalleled data granularity,a data-driven system could better anticipate solar generation.Deep learning(DL)models have gained popularity due to their capacity to handle complex datasets and increase computing power.This article introduces the Galactic Swarm Optimization with Deep Belief Network(GSODBN-PPGF)model.The GSODBN-PPGF model predicts PV power production.The GSODBN-PPGF model normalises data using data scaling.DBN is used to forecast PV power output.The GSO algorithm boosts the DBN model’s predicted output.GSODBN-PPGF projected 0.002 after 40 h but observed 0.063.The GSODBN-PPGF model validation is compared to existing approaches.Simulations showed that the GSODBN-PPGF model outperformed recent techniques.It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day.

风光预测后微电网的优化运行

采用自回归滑动平均模型进行风电预测,采用多元线性回归预测算法进行光伏预测,采用分段线性化法处理微型燃气轮机燃料费用与发电功率的关系,建立考虑充放电效率与状态的蓄电池模型。以微电网内燃料费用最低和从外部电网购电费用最低为优化目标,调用蓄电池储放能,优化微电网的运行控制策略。使用CPLEX软件求解优化函数并给出优化运行结果,结果表明所提的运行优化策略发挥了蓄电池逢电价低储能、逢电价高放能的作用,比传统的仅考虑燃料费用的控制策略节省了运行费用。

微电网风/光发电功率预测软件的设计与开发

风力发电和光伏发电功率的预测是进行微电网能量调度计划制定的前提。运用基于时间序列法的风/光发电功率预测模型,引用等效平均风速概念,以提高风功率预测的准确度;采取在线滚动建模的方式修正基于时间序列法的预测模型,最后运用天气预报信息修正风/光发电功率预测的误差。设计了风/光发电功率预测软件的功能组成结构,制定了包括超短期、扩展短期与短期预测模块的程序流程,应用实例验证了所开发软件的实用性与有效性。

风光互补发电系统方案设计及控制策略优化分析

分析了风能、太阳能互补特性、风光互补并网发电的系统结构及各部分的运行原理和工作特性,结合风机功率预测试验与光伏电站功率预测试验,提出风光互补系统的控制策略。

多点数值天气预报风速和辐照度集中式修正方法研究

数值天气预报(numerical weather prediction,NWP)修正是提升风光功率预测精度的关键技术之一,但目前鲜有对NWP辐照度修正的研究,同时现有的NWP风速修正方法大多只考虑单一位置,忽略了风速间的时空耦合特性,影响修正效果。针对这一问题,提出了考虑区域风光资源时空相关性的多点NWP风速和辐照度集中式修正方法。以区域内多个风电场和光伏电站的实测风速、辐照度数据为修正模型的学习目标,建立基于注意力神经网络的多点NWP集中修正模型,同时修正多个具有一定相关性的场站级NWP数据。结合某区域8个风电场和7个光伏电站的NWP数据和历史风速/辐照度数据,对所提方法进行验证,结果表明,相比于传统的单点NWP修正方法,所提方法能够有效提高NWP精度。

基于W-BiLSTM的可再生能源超短期发电功率预测方法

针对现有新能源超短期预测方法难以有效挖掘和分析数据的固有波动规律,且当时序过长时易丢失重要信息等问题,提出了一种基于注意力(Attention)机制的小波分解-双向长短时记忆网络(W-BiLSTM)超短期风、光发电功率预测方法。首先,利用小波分解提取输入时间序列的时域信息和频域信息。随后,考虑双向信息流,采用双向长短时记忆网络(BiLSTM)进行预测,引入注意力机制,通过映射加权和学习参数矩阵赋予BiLSTM隐含状态不同的权重,有选择性地获取更多有效信息。最后,通过实际数据进行仿真验证。仿真结果表明,所提模型与现有模型相比,具有良好的预测性能。