Reliability-BasedModel for Incomplete Preventive ReplacementMaintenance of Photovoltaic Power Systems

At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.

Research on the MPPT of Photovoltaic Power Generation Based on Improved WOA and P&O under Partial Shading Conditions

Partial shading conditions(PSCs)caused by uneven illumination become one of the most common problems in photovoltaic(PV)systems,which can make the PV power-voltage(P-V)characteristics curve show multi-peaks.Traditional maximum power point tracking(MPPT)methods have shortcomings in tracking to the global maximum power point(GMPP),resulting in a dramatic decrease in output power.In order to solve the above problems,intelligent algorithms are used in MPPT.However,the existing intelligent algorithms have some disadvantages,such as slow convergence speed and large search oscillation.Therefore,an improved whale algorithm(IWOA)combined with the P&O(IWOA-P&O)is proposed for the MPPT of PV power generation in this paper.Firstly,IWOA is used to track the range interval of the GMPP,and then P&O is used to accurately find the MPP in that interval.Compared with other algorithms,simulation results show that this method has an average tracking efficiency of 99.79%and an average tracking time of 0.16 s when tracking GMPP.Finally,experimental verification is conducted,and the results show that the proposed algorithm has better MPPT performance compared to popular particle swarm optimization(PSO)and PSO-P&O algorithms.

Comprehensive Benefit Evaluation of SZ Distributed Photovoltaic Power Generation Project Based on AHP-Matter-Element Extension Model

With the introduction of the“dual carbon goals,”there has been a robust development of distributed photovoltaic power generation projects in the promotion of their construction.As part of this initiative,a comprehensive and systematic analysis has been conducted to study the overall benefits of photovoltaic power generation projects.The evaluation process encompasses economic,technical,environmental,and social aspects,providing corresponding analysis methods and data references.Furthermore,targeted countermeasures and suggestions are proposed,signifying the research’s importance for the construction and development of subsequent distributed photovoltaic power generation projects.

Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids.Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources.However,there are challenges in building models through centralized shared data due to data privacy concerns and industry competition.Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally.In this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model.We design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach.Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

Effect of Photovoltaic Power Generation on Carbon Dioxide Emission Reduction under Double Carbon Background

Increasing the efficiency and proportion of photovoltaic power generation installations is one of the best ways to reduce both CO_(2) emissions and reliance on fossil-fuel-based power supplies.Solar energy is a clean and renewable power source with excellent potential for further development and utilization.In 2021,the global solar installed capacity was about 749.7 GW.Establishing correlations between solar power generation,standard coal equivalent,carbon sinks,and green sinks is crucial.However,there have been few reports about correlations between the efficiency of tracking solar photovoltaic panels and the above parameters.This paper calculates the increased power generation achievable through the use of tracking photovoltaic panels compared with traditional fixed panels and establishes relationships between power generation,standard coal equivalent,and carbon sinks,providing a basis for attempts to reduce reliance on carbon-based fuels.The calculations show that power generation efficiency can be improved by about 26.12%by enabling solar panels to track the sun's rays during the day and from season to season.Through the use of this improved technology,global CO_(2) emissions can be reduced by 183.63 Mt,and the standard coal equivalent can be reduced by 73.67 Mt yearly.Carbon capture is worth approximately EUR 15.48 billion,and carbon accounting analysis plays a vital role in carbon trading.

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

PSO-BP-Based Optimal Allocation Model for Complementary Generation Capacity of the Photovoltaic Power Station

To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.

Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN

Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy problems.To address this research objective,this paper proposes a prediction model based on kernel principal component analysis(KPCA),modified cuckoo search algorithm(MCS)and deep convolutional neural networks(DCNN).Firstly,KPCA is utilized to reduce the dimension of the feature,which aims to reduce the redundant input vectors.Then using MCS to optimize the parameters of DCNN.Finally,the photovoltaic power forecasting method of KPCA-MCS-DCNN is established.In order to verify the prediction performance of the proposed model,this paper selects a photovoltaic power station in China for example analysis.The results show that the new hybrid KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.

Solar Shutters based on Photovoltaic Power Generation

This paper introduces a set of electrical energy-saving system for commercial office buildings,aiming at making better use of solar energy and photovoltaic power generation.Solar energy is a renewable energy source,which is inexhaustible clean energy and has great commercial application value.Based on this fact,we plan to design a unique and novel solar shutter in combination with the daily observation and the shape of solar panels.The shutter blades are equipped with an automatic light tracking system,and the angle of the blades can be adjusted in time through photoresistor induction,that is,as much solar energy as possible can be converted into electric energy for load use,and at the same time,comfortable light can be provided for the house.In essence,the system is a small photovoltaic power generation system,which runs all day with high-efficiency based on automatic sun tracking.Among them,the basic operation route includes:solar position detection,computer data processing,photovoltaic and electric volt energy conversion,circuit connection,etc.From the current debugging results,the shutter has the characteristics of humanization,high efficiency,cleanliness and so on.Through this energy-saving system,we hope to maximize the use of solar energy in the premise of low cost,so as to achieve the purpose of energy saving.

A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

In order to ensure that the large-scale application of photovoltaic power generation does not affect the stability of the grid, accurate photovoltaic (PV) power generation forecast is essential. A short-term PV power generation forecast method using the combination of K-means++, grey relational analysis (GRA) and support vector regression (SVR) based on feature selection (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed. The historical power data were clustered through the multi-index K-means++ algorithm and divided into ideal and non-ideal weather. The GRA algorithm was used to match the similar day and the nearest neighbor similar day of the prediction day. And selected appropriate input features for different weather types to train the SVR model. Under ideal weather, the average values of MAE, RMSE and R2 were 0.8101, 0.9608 kW and 99.66%, respectively. And this method reduced the average training time by 77.27% compared with the standard SVR model. Under non-ideal weather conditions, the average values of MAE, RMSE and R2 were 1.8337, 2.1379 kW and 98.47%, respectively. And this method reduced the average training time of the standard SVR model by 98.07%. The experimental results show that the prediction accuracy of the proposed model is significantly improved compared to the other five models, which verify the effectiveness of the method.

A portable,auxiliary photovoltaic power system for electric vehicles based on a foldable scissors mechanism

In recent years,countries worldwide have actively advocated electric vehicles for environmental protection.How-ever,restrictions on the driving range and charging have hampered the promotion of electric vehicles.This study proposes a portable,auxiliary photovoltaic power system based on a foldable scissors mechanism for electric vehicles.The system includes a photovoltaic power generation module and an electricity transfer module.The photovoltaic power generation module built based on a foldable scissors mechanism is five times smaller than in its unfolded state,improving its portability in its folded state.The electricity transfer module transfers electricity into the cabin via wireless power transfer units and stores electricity in supercapacitors.Solar simulation exper-iments were conducted to evaluate the system’s performance:maximum output power of 1.736 W is measured when the load is 5Ω,while maximum wireless power transfer efficiency is up to 57.7% with 10Ω load.An elec-tric vehicle in Chengdu city was simulated for a case study.The results show that the annual output of a single photovoltaic power system can drive the MINIEV for 423.625 km,indicating that the proposed system would be able to supply power for electric vehicles as an auxiliary power supply system.

Jointly improving energy efficiency and smoothing power oscillations of integrated offshore wind and photovoltaic power: a deep reinforcement learning approach

This paper proposes a novel deep reinforcement learning(DRL)control strategy for an integrated offshore wind and photovoltaic(PV)power system for improving power generation efficiency while simultaneously damping oscilla-tions.A variable-speed offshore wind turbine(OWT)with electrical torque control is used in the integrated offshore power system whose dynamic models are detailed.By considering the control system as a partially-observable Markov decision process,an actor-critic architecture model-free DRL algorithm,namely,deep deterministic policy gradient,is adopted and implemented to explore and learn the optimal multi-objective control policy.The potential and effectiveness of the integrated power system are evaluated.The results imply that an OWT can respond quickly to sudden changes of the inflow wind conditions to maximize total power generation.Significant oscillations in the overall power output can also be well suppressed by regulating the generator torque,which further indicates that complementary operation of offshore wind and PV power can be achieved.

Short-term Photovoltaic Power Forecasting Using SOM-based Regional Modelling Methods

The inherent intermittency and uncertainty of photovoltaic(PV)power generation impede the development of grid-connected PV systems.Accurately forecasting PV output power is an effective way to address this problem.A hybrid forecasting model that combines the clustering of a trained self-organizing map(SOM)network and an optimized kernel extreme learning machine(KELM)method to improve the accuracy of short-term PV power generation forecasting are proposed.First,pure SOM is employed to complete the initial partitions of the training dataset;then the fuzzy c-means(FCM)algorithm is used to cluster the trained SOM network and the Davies-Bouldin index(DBI)is utilized to determine the optimal size of clusters,simultaneously.Finally,in each data partition,the clusters are combined with the KELM method optimized by differential evolution algorithm to establish a regional KELM model or combined with multiple linear regression(MR)using least squares to complete coefficient evaluation to establish a regional MR model.The proposed models are applied to one-hour-ahead PV power forecasting instances in three different solar power plants provided by GEFCom2014.Compared with other single global models,the root mean square errors(RMSEs)of the proposed regional KELM model are reduced by 52.06%in plant 1,54.56%in plant 2,and 51.43%in plant 3 on average.Such results demonstrate that the forecasting accuracy has been significantly improved using the proposed models.In addition,the comparisons between the proposed and existing state-of-the-art forecasting methods presented have demonstrated the superiority of the proposed methods.The forecasts of different methods in different seasons revealed the strong robustness of the proposed method.In four seasons,the MAEs and RMSEs of the proposed SF-KELM are generally the smallest.Moreover,the R2 value exceeds 0.9,which is the closest to 1.

Study of the Impact of Grid Disconnections on the Production of a Photovoltaic Solar Power Plant: Case of Diamniadio Power Plant

Today, renewable energy projects connected to the interconnected network, with powers of the order of tens of megawatts, are more and more numerous in sub-Saharan Africa. And financing these investments requires a reliable amortization schedule. In the context of photovoltaic systems connected to the interconnected electricity grid, the quintessence of damping is the amount of energy injected into the grid. Thus it is fundamental to know the parameters of this network and their variation. This paper presents an evaluation of the impact of power grid disturbances on the performance of a solar PV plant under real conditions. The CICAD photovoltaic solar plant, connected to the Senelec distribution network, with an installed capacity of 2 MWp is the study setting. An energy audit of the plant is carried out. Then the percentage of each loss is determined: voltage drops, module degradation, inverter efficiency. The duration of each disconnection is measured and recorded daily. The corresponding quantity of lost energy is thus calculated from meteorological data (irradiation, temperature, wind speed, illumination) recorded by the measurement unit in one-minute steps. The observation period is three months. The total duration of disconnections related to the instability of the electrical network during the study period is 46.7 hours. The amount of energy lost is estimated at 22.6 MWh. This represents 2.4% of the actual calculated production.

Modelselection,adaptation,and combination for transfer learning in wind and photovoltaic power forecasts

There is recent interest in using model hubs–a collection of pre-trained models–in computer vision tasks.To employ a model hub,we first select a source model and then adapt the model for the target to compensate for differences.There still needs to be more research on model selection and adaption for renewable power forecasts.In particular,none of the related work examines different model selection and adaptation strategies for neural network architectures.Also,none of the current studies investigates the influence of available training samples and considers seasonality in the evaluation.We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast,adopting recent developments from the field of computer vision on 667 wind and photovoltaic parks from six datasets.We simulate different amounts of training samples for each season to calculate informative forecast errors.We examine the marginal likelihood and forecast error for model selection for those amounts.Furthermore,we study four adaption strategies.As an extension of the current state of the art,we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network.This approach outperforms the baseline with only seven days of training data and shows that fine-tuning is not beneficial with less than three months of data.We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach such that we have a similar mean error with only 30 days of training data which is otherwise only possible with an entire year of training data.We achieve a mean error of 9.8 and 14 percent for the most realistic dataset for PV and wind with only seven days of training data.

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.

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.

Seasonal Performance of Solar Power Plants in the Sahel Region: A Study in Senegal, West Africa

The main objective of this study is to evaluate the seasonal performance of 20 MW solar power plants in Senegal. The analysis revealed notable seasonal variations in the performance of all stations. The most significant yields are recorded in spring, autumn and winter, with values ranging from 5 to 7.51 kWh/kWp/day for the reference yield and 4.02 to 7.58 kWh/kWp/day for the final yield. These fluctuations are associated with intense solar activity during the dry season and clear skies, indicating peak production. Conversely, minimum values are recorded during the rainy season from June to September, with a final yield of 3.86 kWh/kW/day due to dust, clouds and high temperatures. The performance ratio analysis shows seasonal dynamics throughout the year with rates ranging from 77.40% to 95.79%, reinforcing reliability and optimal utilization of installed capacity. The results of the capacity factor vary significantly, with March, April, May, and sometimes October standing out as periods of optimal performance, with 16% for Kahone, 16% for Bokhol, 18% for Malicounda and 23% for Sakal. Total losses from solar power plants show similar seasonal trends standing out for high loss levels from June to July, reaching up to 3.35 kWh/kWp/day in June. However, using solar trackers at Sakal has increased production by up to 25%, demonstrating the operational stability of this innovative technology compared with the plants fixed panel. Finally, comparing these results with international studies confirms the outstanding efficiency of Senegalese solar power plants, other installations around the world.

An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II

The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.

Optimization of Distributed Solar Photovoltaic Power Generation in Day-ahead Electricity Market Incorporating Irradiance Uncertainty

This paper proposes a simple and practical approach to model the uncertainty of solar irradiance and determines the optimized day-ahead(DA)schedule of electricity mar-ket.The problem formulation incorporates the power output of distributed solar photovoltaic generator(DSPVG)and forecasted load demands with a specified level of certainty.The proposed approach determines the certainty levels of the random variables(solar irradiance and forecasted load demand)from their probability density function curves.In this process of optimization,the energy storage system(ESS)has also been mod-eled based on the fact that the energy stored during low locational marginal price(LMP)periods and dispatched during high LMP periods would strengthen the economy of DA schedule.The objective of the formulated non-linear optimization problem is to maximize the social welfare of market participants,which incorporates the assured generation outputs of DSPVG,subject to real and reactive power balance and transmission capability constraints of the system and charging/dis-charging and energy storage constraints of ESS.The simulation has been performed on the Indian utility 62-bus system.The results are presented with a large number of cases to demonstrate the effectiveness of the proposed approach for the efficient,economic and reliable operation of DA electricity markets.