Study on Improvement of MPPT Efficiency in PV Generation System with Partial Shadow

The maximum power point of PV （photovoltaic） generation moves depending on weather conditions and load. Therefore, it is significant to make sure that the panels can work at the maximum power point under MPPT （maximum power point tracking） control. However, it has the problems of low efficiency and unstable operation when panels are covered by the partial shadow. The result is that the output power may be substantially decreased. To overcome this issue, the authors propose a new plug-in operation point correction system. This system is put between PV panels and PCS （power conditioning system） in the existing PV generation system. In this paper, the experimental results describe that the output electric energy increases approximately 1.4 times as compared with the conventional system when the proposed correction system is inserted.

MPPT Control System for PV Generation System with Mismatched Modules

Nowadays, in a household PV （photovoltaic） generation system, it is generally connecting PV modules in series and then output to the power-conditioner. However, when PV modules are mismatched, it will lead to a wrong MPPT （maximum power point tracking） to all modules and a power decreasing of the whole system. Aiming at this problem, this paper presents the idea which improves the MPPT without changing the conventional power-conditioner, by adding a Buck type DC-DC （direct current） converter behind each module. Simulations of PSIM （power simulation） and experiments are taken to prove this theory. The result shows that, by this idea, the generated power of the conventional PV generation system can be greatly increased under the condition of mismatch.

Efficiency Improvement of PV Generation System by Using Improved P ＆ O Method of MPPT

The conventional P ＆ O （perturb-and-observe） method, which is the most widely used as MPPT （maximum power point tracking） control, has the problem of low efficiency and unstable operation when solar radiation changes drastically. Aiming at this problem, this paper improves the conventional P ＆ O method to reduce the bad effect of solar radiation by shortening the sampling interval of PV module＇s output power while keeping the operating period unchanged. Experiments are conducted to study efficiency gains of improved method when solar radiation changes drastically. The result shows that, by this method, the efficiency of MPPT control can be increased 17% in average when PV module simulator is used and 20% at maximum when actual PV module is used, compared with the conventional P ＆ O method.

Hot-Spot Detection System with Correction of Operating Point for PV Generation System

Currently, the production and the number of installations of PV （photovoltaic） modules have been increasing rapidly because of a feed-in tariff in Japan. Accordingly, the number of failures has also increased. Many failures are a result of the Hot-Spot phenomenon in which defective cell becomes hot when shadow occurs on the cell, On the other hand, if shadow occurs on normal cell, there are cases that P＆O method that is MPPT （maximum power point tracking） control method incorporated in conventional PV system cannot track maximum power point and generated power decreases. The correspondence is required rapidly if these trouble occur. However, conventional PV system monitors generated power, correspondence is impossible by monitoring generated power. Previously, the authors developed real time Hot-Spot detection system that incorporates into PCS （power conditioning system）. Thus, the authors developed plug-in type Hot-Spot monitoring system that includes ＂PV peak shift method＂ and confirmed effectiveness of the system in this time. ＂PV peak shift method＂ loads ＂Scan method＂ that is MPPT control method and measures I-V （current-voltage） characteristic by changing voltage of module from open to short by ＂Scan method＂ on a regular basis. The developed Hot-Spot monitoring system uses slope of I-V characteristic of PV module. Inserting developed system into already installed PV system, Hot-Spot can be easily monitored in real time and PV system can be operated at maximum power point.

A Techno-Economical Characterization of Solar PV Power Generation in Rwanda:The Role of Subsidies and Incentives

Standalone Solar PV systems have been vital in the improvement of access to energy in many countries.However,given the large cost of solar PV plants’components,in developing countries,there is a dear need for such components to be subsidised and incentivised for the consumers to afford the produced energy.Moreover,there is a need for optimal sizing of the solar PV plants taking into account the solar information,energy requirement for various activities,and economic conditions in the off-grid regions in Rwanda.This study aims to develop optimally sized solar PV plants suited to rural communities in Rwanda.Likewise,it aims at characterizing the impacts of subsidies and incentives on the profitability and affordability of solar PV plants’energy in Rwanda.In the study,we have developed a model on basis of which the plant power(peak power)and costs of energy can be predicted given the load requirements using PVSyst.The model was validated using data corrected at eight different sites.Our generalized predictive model’s results matched the results obtained using field measurement data as inputs.The models have been able to replicate with a by degree of accuracy the peak powers and the plants’costs for different loads and were used to evaluate the economic viability of solar PV plants in Rwanda.It was found that with incentives and subsidies of 20%,the solar PV systems’costs,the Levelised Cost of Energy would drop from a maximum of 0.098 Euro to a minimum of 0.072 Euro,the payback period was reduced from a maximum of 7.5 years to a minimum of 6.0 years while the return on investments was seen to vary between 425.72 and 615.32 per cent over the plants’lifetime of 25 years.Overall our findings underscore the importance of government subsidies and incentives for solar PV energy generation projects to be significantly profitable.

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.

High Efficiency PCS with Scan-Type MPPT Control for a Grid-Connected PV Power Generation System

For a standalone PV （photovoltaic） power generation system, the author previously proposed a new MPPT （maximum power point tracking） control method in which the I-V characteristics are scanned with a detection interval control that operates at specified intervals and monitors the maximum power point. The author has obtained satisfactory results using this new MPPT control method. This paper investigates the application of the new MPPT control method for a PCS （power conditioning system） in a grid-connected type PV power generation system. The experimental results clearly demonstrate that the developed PCS offers outstanding effectiveness in tracking the maximum power point in partially shaded environments.

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.

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy

As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting.The methods for forecasting wind power and PV production.The physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production.Moreover,the experiments demonstrated that cloud map identification has a significant impact on PV generation.With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

Insolation Forecasting Method Using NN with EPSO in Consideration of Correlation between Other Regions

Recently, there is a movement that aims to realize a distributed energy system in Japan. The PV （photovoltaic） generation is especially expected, and it is anticipated that interconnection number of PVs will increase more and more ha the future. However, an amount of insolation is easily affected by the weather, the output of PV and the supply of electricity becomes unstable. And it causes various problems in a distribution system. Therefore, it is important to forecast the amount of insolation. In previous study, MLPNN （multilayer perceptron neural network） is a general method to forecast the amount of insolation. However, it is in danger of falling into a local solution by only MLPNN. In this study, the authors propose a forecasting method of amount of insolation using MLPNN and EPSO （evolutionary particle swarm optimization）. The authors use EPSO in addition to MLPNN to solve the problem. The authors also propose a forecasting method of amount of insolation using other regions weather data for the accuracy improvement.

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.

LCOE Analysis of Solar Tracker Application in China

In China, systemic techno-economic analysis for solar tracker has been absent. To fill the blank, by taking the economic analysis of solar tracker application as the research object and using the LCOE method widely used internationally, the techno-economic analysis model of solar tracker was established according to conditions in China. Influence factors on LCOE were analyzed by using the established model, and the relationship between each cost factor and the cost component of energy leveling of tracker was further studied. In addition, the calculation method of investment payback period based on energy leveling analysis was established, and the influence of various factors on investment payback period was revealed through an example calculation. The research results will help to measure the economy of tracker application more accurately and comprehensively, and promote the popularization and application of solar tracker. The economic analysis model of solar tracker application was established by using LCOE method. The influence factors and cost component of LCOE were analyzed with the model. The payback period of solar tracker investment was also analyzed based on LCOE method.

Modeling,Oscillation Analysis and Distributed Stabilization Control of Autonomous PV-based Microgrids

Driven by rising energy demand and the goal of carbon neutrality,renewable energy generations(REGs),especially photovoltaic(PV)generations,are widely used in the urban power energy systems.While the intelligent control of microgrids(MG)brings economic and efficient operation,its potential stability problem cannot be ignored.To date,most of the research on modeling,analyzing and enhancing the stability of MG usually assume the DC-link as an ideal voltage source.However,this practice of ignoring the dynamics of DC-link may omit the latent oscillation phenomena of autonomous PV-based MG.First,this paper establishes a complete dynamic model of autonomous PV-based MG including PV panels and DC-link.Different from previous conclusions of idealizing DC-link dynamics,participation factor analysis finds the potential impact of DClink dynamics on system dynamic performance,and different influence factors including critical control parameters and nonlinear V-I output characteristic of PV array are considered to further reveal oscillation mechanisms.Second,based on the average consensus algorithm,a distributed stabilization controller with strong robustness is proposed to enhance stability of the PVbased MG,which does not affect the steady-state performance of the system.Finally,the correctness of all theoretical analysis and the effectiveness of the proposed controller are verified by time domain simulation and hardware-in-loop tests.