Multimodel Ensemble Forecast of Global Horizontal Irradiance at PV Power Stations Based on Dynamic Variable Weight

In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.

Comparative Study of Solar PV Power Plant Dimensionnement Connected to Network Based on Monofacial and Bifacial Modules

This article presents the results of comparative study of two PV solar modules technologies,namely monofacial and bifacial.This study main objective is to identify conditions and parameters that make it possible to obtain better energy and economic efficiency from one or other of two technologies.The study reason lies in revival observed on bifacial module in recent years where all the major manufacturers of PV solar panels are developing them where in a few years,this technology risks being at the same price as the monofacial solar panel with better efficiency.Economic indicator used is energy levelized cost(LCOE)which is function technology type,energy productivity,annual investment and operation cost.To achieve this,a 3.685 MWc solar PV power plant was dimensioned and simulated under Matlab for a 3.5 ha site with a 2,320,740,602 FCFA budget for monofacial installation,against 1,925,188,640 FCFA for 2.73 MWc bifacial installation.The LCOE comparative analysis of two technologies calculated over a period of 25 years,showed that plant with bifacial panels is more beneficial if bifacial gain is greater than 9%.It has further been found that it is possible to gain up to 40%of invested cost if bifacial gain reaches 45%.Finally,a loss of about 10%of invested cost could be recorded if bifacial gain is less than 9%.

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.

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.

Maximum Power Point Tracking Using the Incremental Conductance Algorithm for PV Systems Operating in Rapidly Changing Environmental Conditions

Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.

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.

Research and practice of designing hydro/ photovoltaic hybrid power system in microgrid

Small-hydro power station is often used in remote areas beside a river,but it doesn't match electricity demand so well,especially in dry seasons. A photovoltaic (PV) system with battery is a suitable option to complement the electricity gap. In this paper,a new structure of megawatt-class PV system integrating battery at DC-bus (DC: direct current) is proposed to be used in hydro/PV hybrid power system,and 4 main designing considerations and several key equipments are discussed. In 2011,a 2 MWp PV station with the proposed structure was built up in Yushu,China. From stability analysis,the station shows a strong stability under load cut-in/off and solar irradiance's fluctuation.

An optimal strategy for coordinating and dispatching “source-load” in power system based on multiple time scales

Due to the phenomenon of abandoning wind power and photo voltage(PV)power in the“Three Northern Areas”in China,this paper presents an optimal strategy for coordinating and dispatching“source-load”in power system based on multiple time scales.On the basis of the analysis of the uncertainty of wind power and PV power as well as the characteristics of load side resource dispatching,the optimal model of coordinating and dispatching“source-load”in power system based on multiple time scales is established.It can simultaneously and effectively dispatch conventional generators,wind plant,PV power station,pumped-storage power station and load side resources by optimally using three time scales:day-ahead,intra-day and real-time.According to the latest predicted information of wind power,PV power and load,the original generation schedule can be rolled and amended by using the corresponding time scale.The effectiveness of the model can be verified by a real system.The simulation results show that the proposed model can make full use of“source-load”resources to improve the ability to consume wind power and PV power of the grid-connected system.

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.

Design of Real Time Battery Management Unit for PV-Hybrid System by Application of Coulomb Counting Method

This paper presents a real-time battery management unit designed by applying the Coulomb counting method and intended for use in an integrated renewable energy system for PV-Hybrid power supply. Battery management is required to stabilize hybrid systems and extend battery lifetimes. The battery management unit is divided into three main stages. Firstly, analysis of the basic components of the battery type used in the system is considered. Secondly, the state of charge (SOC) estimation method and the deterioration factor of the battery are analyzed. Finally, the overall battery management system, including a computer-based measurement and control unit, is constructed. The control system displays real-time information through LabVIEW 8.5 by estimating the state of charge through various measurements. The system will issue alerts when malfunctions are detected, and the operator can analyze and react to the system in real time to stabilize the system and extend the battery lifetime.

Calculation Method and Analysis for the Annual Power Generation of PV Faades in China

The application of PV facades 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 facade in terms of PV electricity generation in different arrangements and weather conditions of four major cities in China. The calculation models for PV facade 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 facades. 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 facades. Weather conditions nlav a very important role in terms of PV generation nerformance of PV facades.

Modeling and Optimization of Stand-Alone Hybrid Power System : a Case Study for a Household in Urumqi,China

In order to alleviate environmental pollution of Urumqi,China,a stand-alone hybrid wind / PV / battery power system was studied for this region. An accurate structure and model of the standalone hybrid wind / PV / battery power system for a household in that region was presented in this study. On the basis of wind speed,solar radiation,ambient temperature,and load data,the optimal design of the hybrid system is determined using a genetic algorithm( GA). As a result,the optimal hybrid system for a household consists of one wind turbine,21. 407 6 m^2 of PV arrays,and 20. 958 kW ·h of battery bank capacity. The system has a loss of power supply probability( LPSP) of 0. 019 9 and the minimum total annualized cost is $ 35 333.

Decision Technique of Solar Radiation Prediction Applying Recurrent Neural Network for Short-Term Ahead Power Output of Photovoltaic System

In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other

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.

Distributed Solar PV System for Industrial Application

The paper presents the design and field test of a distributed solar PV system for industrial application （DGPVi）. DGPVi utilizes HyPV （hybrid PV） system which generates solar power for self-consumption in lighting and air conditioning in a production line of a factory when solar energy is available. It does not feed the excess PV power to the grid. HyPV will be switched to grid power supply when solar energy is not available. A 3 kWp DGPVi is installed in a factory for field demonstration. The test results show that the solar PV power generated can be utilized immediately. The solar energy generation efficiency （kWh/day per kWp PV installation） of DGPVi is close to that of grid-tied PV system without self-consumption and battery storage. The yearly return on investment of DGPVi is 2.0% at the present installation cost or 3.3% at further cost-down cost. The payback time will be 14.3 years at the present installation cost or 12.1 years at cost-down cost. The present study verifies the economic feasibility of DGPVi.

Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems

The employment of maximum power point tracking techniques in the photovoltaic power systems is well known and even of immense importance. There are various techniques to track the maximum power point reported in several literatures. In such context, there is an increasing interest in developing a more appropriate and effective maximum power point tracking control methodology to ensure that the photovoltaic arrays guarantee as much of their available output power as possible to the load for any temperature and solar radiation levels. In this paper, theoretical details of the work, carried out to develop and implement a maximum power point tracking controller using neural networks for a stand-alone photovoltaic system, are presented. Attention has been also paid to the command of the power converter to achieve maximum power point tracking. Simulations results, using Matlab/Simulink software, presented for this approach under rapid variation of insolation and temperature conditions, confirm the effectiveness of the proposed method both in terms of efficiency and fast response time. Negligible oscillations around the maximum power point and easy implementation are the main advantages of the proposed maximum power point tracking (MPPT) control method.

Influence of Time Complementarity on Energy Storage through Batteries in Hydro PV Hybrid Energy System

The notion of energetic complementarity can be a tool for energy resource managers to prioritize energy generation projects based on renewable resources in both interconnected and independent systems. As a tool in decision-making, it is important to know better the influence of energetic complementarity on the performance of hybrid systems especially with regard to energy shortages but also in relation to other parameters. In recent years, hydro PV hybrid systems have become a growing target of researchers and designers for the idea of installing photovoltaic modules on the water surface of reservoirs. Energetic complementarity has three components: time-complementarity, energy-amplitude and amplitude-complementarity. This paper is dedicated to the study of the influence of time-complementarity on the storage of energy through batteries in hydro PV hybrid systems. The method applied is in the literature and suggests the simulation of the system under study with the idealization of energy availabilities, to remove the effects of climatic variations and the characteristic intermittency of renewable resources. Simulations were performed with the well-known software Homer. The results provided the variations of the states of charge of the batteries as a function of different time-complementarities, indicating as expected better performances associated to higher time-complementarities. The results indicated that the cost of energy for a hybrid system with 28 batteries was equal to US$ 0.502 per kWh and that this cost increased as the time complementarity between energy resources moved away from the situation corresponding to full complementarity. The simulations also showed that the maintenance of the zero failure condition supplying the demands of the consumer loads requires that the load be reduced to 52% if the complementarity is reduced from the full complementarity to zero complementarity, with the cost of energy going from US$ 0.502 per kWh to US$ 0.796 per kWh. The results also allow a better understanding of the influence of time complementarity on the performance of hybrid systems.

Power Electronics and Its Application to Solar Photovoltaic Systems in India

Out of many renewable energy resources, solar energy is one of the conspicuous sources of energy which can supply the increasing demand of energy. As of May 2014, India has an installed PV capacity of 2.5 GW. The solar photovoltaic project includes power electronics with high quality performance devices, incorporated with smart energy management principles. Power electronics is used to improve the energy efficiency of apparatus, and help the generation of environmentally clean energy. In this article the explanation of role of power electronics and the discussion about similar and future concepts in solar photovoltaic systems related to reliability and advancement of each technology in India has been presented.

Economic Analysis and Environmental Impacts of a Hybrid PV System in Arid Climate Considering Different Types of Solar Trackers

This paper presents a study aimed at evaluating and comparing the performance of six different tracking systems for photovoltaic (PV) with diesel-battery hybrid system in arid climate of Kingdom of Saudi Arabia (KSA). The study considered various technical and economic factors including system net present cost (NPC), levelized cost of energy (LCOE), and PV power generation using energy analysis and microgrid design software “HOMER”. It also presents an overview of the current electricity production and demand in the Kingdom. The weather data used in this study have been collected from the new solar atlas launched by King Abdullah City for Atomic and Renewable Energy (KACARE). The selected solar resource monitoring station for this study is located near to Riyadh city and has an annual average daily total irradiation of 6300 W/m2/day. The study shows that, for stand-alone PV system in the vicinity of Riyadh city, tracking system is economically better than fixed angle system. Among the considered tracking systems, VCA system is the most preferable as it has low NPC and LCOE values with a high return on investment (ROI) as well as low carbon dioxide (CO2) emissions due to a high renewable energy penetration.

基于RPC的光储接入牵引供电系统协调控制方法

为将光伏发电接入牵引供电系统进行有效利用,同时兼顾牵引供电系统电能质量改善及列车再生制动能量回收利用,提出了一种基于铁路功率调节器的光储系统接入牵引供电系统的协调控制方法.首先,搭建基于铁路功率调节器的光储系统接入牵引供电拓扑结构,并对其电能质量补偿机理进行理论分析;在此基础上,充分考虑光储系统的运行条件以及列车牵引、制动、空载或惰行工况,提出光储系统接入牵引供电的联合协调控制方法,实现了“光+储+荷”三者间的协调控制、牵引供电系统负序的动态补偿以及谐波的有效抑制;在RT-Lab实时仿真系统中建立了基于铁路功率调节器的光伏接入牵引供电系统仿真模型,并结合实际光照和牵引工况数据验证本文所提出的系统结构以及协调控制方法的有效性.结果表明:本文所提出的系统结构以及协调控制方法能够实现牵引供电系统中光储系统的有效接入,再生制动能量回收率提升至86.7%,功率因数提升6.4%,对谐波特别是高次谐波含有率有较大改善,满足光伏电能高效消纳及综合利用、电能质量动态综合补偿、再生制动能量回收多重需求.