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Renewable energy sources such as solar energy and wind energy are characterized by intermittency and volatility due to their over-dependence on weather conditions. Therefore, it is especially important for the power system containing renewable energy to equip the energy storage system which can not only guarantee the flexibility of renewable energy utilization, but also improve the reliability of system power supply. Because the Battery Energy Storage System (BESS) is suitable for mass production and large-scale applications, it has become the main energy storage system scheme for the power system. Because different BESS have differences in efficiency of storage, storage capacity, discharge ability and maintenance, it is necessary to make a comprehensive evaluation and selection of the type of BESS for power system. In this paper, a comprehensive evaluation index system of BESS is established by taking the photovoltaic power station in Xizang region as an example. However, the existing methods are difficult to accurately measure the attribute values of each indicator and the correlation between experts. Therefore, this paper proposes the intuitionistic uncertain language Choquet ordered weighted aggregation operator (IULCWA) by combining intuitionistic uncertain language with fuzzy measure and Choquet integral, and establishes a fuzzy multi-criteria decision-making (MCDM) method for BESS selection. Comparison and analysis also prove that the modified model has good scientificity and operability. This study can provide a new theoretical basis for the selection of energy storage schemes for new energy batteries, and expand the application scope of fuzzy MCDM method.

Large scale photovoltaic power stations are connected to the power grid system, and their capacity proportion is higher and higher, which brings great challenges to the operation of power grid. It is urgent to present a kind of schedulability of photovoltaic power stations. At the same time, the fast response characteristics of photovoltaic inverter provide conditions for photovoltaic power station to have fast power regulation and support system fast frequency modulation function. This paper presents a coordinated control technology composed of automatic generation control (AGC) system at station level and active power frequency droop control with dead zone control to realize fast frequency regulation of photovoltaic power station in power grid. Finally, the feasibility and effectiveness of fast frequency modulation technology are verified by frequency disturbance and AGC coordination test in a photovoltaic power station in Shaanxi Province.

As an important distributed power generation, solar photovoltaic power generation is gradually being used from independent system to large-scale grid connection. The development of output power prediction of photovoltaic power station is of great significance to maintain the power balance and economic operation of power system. By discussing the influencing factors of photovoltaic power generation power prediction, the effects of various meteorological factors such as solar irradiation, temperature and cloud cover on the output power of photovoltaic power station are analyzed, and the advantages and disadvantages of mathematical statistical methods and artificial intelligence methods are analyzed and compared.

This paper aims at the inspection problems faced by photovoltaic power plants in the long-term operation of photovoltaic power plants in harsh environments such as Qinghai and Tibet plateaus under low air pressure, high cold and hypoxia, large temperature differences, centered on robot technology, fusion sensor application technology, image recognition and processing Technology and artificial intelligence and other advanced technologies, research and development and design of intelligent robot inspection system for photovoltaic power plants. The system is used to monitor the operating status of photovoltaic panels and meteorological parameters in real time to establish a prediction model, estimate the instantaneous power generation of photovoltaic power plants, and establish an image recognition model based on convolutional neural networks, which can accurately detect the failure of photovoltaic power plant equipment. Analysis and discrimination provide innovative technical testing methods for unattended photovoltaic power stations to achieve safe and stable operation of high-altitude photovoltaic power stations and good operational benefits.

For grid connected photovoltaic power station primary frequency regulation method, this paper selected three latest photovoltaic power station frequency regulation methods for the comparative analysis. Firstly, the models of photovoltaic inverter and photovoltaic station were constructed, and the three control methods were simulated and compared. The simulation results show that the active power frequency droop control method has more advantages than the other two control methods. In this method, the response delay time was less than 2s, the regulation time was less than 8s, the response time was less than 4s, and the active power control deviation index was controlled within 1%. Finally, the comparison and analysis of the three latest control methods were completed in the photovoltaic station, which verified the practicability of the three latest control methods and the rationality of the model.

The impact that the photovoltaic (PV) power generation brings to the power system has become noticeable due to its increasing penetration. It is necessary to develop an accurate model of the PV power station for the related studies. However, in the existing electromechanical transient models, the geographic factors, that affect the solar irradiance and thus the output characteristics of the PV power station, have not been fully considered. Therefore, besides the basic factors, this paper takes into account the critical geographic factors to construct the electromechanical transient model of the PV power station. A calculation method of solar irradiance is designed by adding the longitude, latitude, and altitude factors. The PV power generation unit model, collector line and substation transformer models, and the plant-level control system are integrated to achieve the entire model. Finally, the proposed model is compared with the measured data and the existing model, through which the effectiveness is verified.

The integrated energy system is a hot research issue in power system industry. In this paper, an integrated energy system was designed, with data center stations, energy storage station, 5 G and Beidou base stations, photovoltaic power station, gas turbine station, power station, etc., to develop into a comprehensive energy services as a typical application scenario. The power flow chart of energy interaction between stations was discussed, the electrical topology of multiple stations integration was put forward. A brief description of the dynamic cooperative optimal operation strategy of power management, using a typical day of operation control of a typical station as an example was carried out. The results of simulation show that the hybrid integrated energy stations with the proposed power management operate reliable and stable, has high performance and comprehensive energy efficiency.

The development of photovoltaic (PV) technology has led to an increasing share of photovoltaic power stations in the grid. But, due to the nature of photovoltaic technology, it is necessary to use energy storage equipment for better function. Thus, an energy storage configuration plan becomes very important. This paper proposes a method of energy storage configuration based on the characteristics of the battery. Firstly, the reliability measurement index of the output power and capacity of the PV plant is developed according to the power output requirements of the grid. Then an immune algorithm is used to find the economically optimal solution for the energy storage configuration based on the advantages of different batteries in terms of power and capacity. Finally, the feasibility of the scheme is verified using data from a PV plant in Qinghai.

Under the background of sustainable development, more and more countries begin to study and use renewable new energy. Photovoltaic power generation is one of the research hotspots at present. The construction of a safe, reliable and economical photovoltaic power station monitoring system is of great significance to the development of intelligent information management of power system. In order to guarantee the normal operation of distributed photovoltaic power station, it is especially important to design a integrated monitoring system. A distributed photovoltaic power station leakage monitoring system based on power line communication is proposed in this paper, which can effectively monitor the leakage current of distributed photovoltaic power station. This system improves the automation ability and efficiency of the power station. In this paper, the voltage detection technology of power theft and leakage is studied. Through experimental research, the factors causing the instability and low precision of the sensor are found. In view of the shortcomings of Hall current sensor, the corresponding improvement is made through experimental research, so that the temperature drift problem is greatly improved and the measurement accuracy is further improved. The design scheme of the whole circuit system is optimized to best satisfy the requirements of precision and stability of the monitoring system.

Energy stations are generally established in economically developed urban areas, where incremental coal combustion is limited due to a shortage of primary energy. The main ways of renewable energy generation are waste power, biomass power, wind power, photovoltaic, and geothermal. Due to new energy generation has its own fatal disadvantage and the rapid economic development of the city, coupled with the long-established natural gas supply network, gas-based distributed energy stations will become the most important way of integrated energy development. In this paper, an integrated energy station system was designed, using existing substation resources, construction of data center stations, energy storage station, 5 g base stations, photovoltaic power station, gas turbine station etc., realizing the sharing of resource integration, to support smart grid, to develop into a comprehensive energy services as a typical application scenario. Results of the simulation show that the designed integrated energy station system has high performance and high renewable energy penetration.

Considering the time-varying nature of the power system, in order to realize the dynamic modeling of photovoltaic power plants, based on the analysis of the grid-connected structure of PV system, the engineering model of the photovoltaic array and the value of some parameters in the MATLAB calculation example are used. The inverter efficiency was corrected to establish a dynamic model of the output of the photovoltaic inverter. The error analysis was carried out with reference to the actual photovoltaic power station, and the error of the built dynamic model was within the allowable range, which proved the accuracy of the model. The deduction of the output dynamic characteristics of the photovoltaic inverter provides a basis for the subsequent research to model the comprehensive load of photovoltaic access to the distribution network.

The grid connection and operation of photovoltaic power generation in China follows the national standard GB/T 19964 Technical requirements for connecting photovoltaic power station to power system, released in 2012. This paper firstly interprets and analyze the clauses in this standard, including active/reactive power control, frequency control, voltage control and fault ride through. Then these clauses are compared with those in the standard of some countries in Europe and U.S., and the problems in application of these clauses in China are analyzed. Finally, the suggestions to improve the standard are proposed considering the technical developing trend of photovoltaic power generation in China.

In the process of commutation failure and restart of UHVDC system with hierarchical, the reactive power consumption of converter is reduced. The reactive power of system is seriously surplus before AC filter is cut off, and different degrees of overvoltage appear on AC bus of UHVDC and photovoltaic power station, which may cause the disconnection of photovoltaic and other new energy equipment. This paper analyzes the mechanism of over-voltage when the power of UHVDC system is blocked. Based on this mechanism, a coordinated control strategy of transient overvoltage with reactive power surplus of UHVDC system and photovoltaic power station is proposed. The simulation results show that the proposed strategy can reduce the transient overvoltage during commutation failure of UHVDC system with hierarchical.

At present, multi-station fusion forms a variety of modes based on various combinations of substation, data center, energy storage station and charging station. In this paper, an integrated energy system was designed, using existing substation resources, construction of data fusion center stations, energy storage station, 5 g base stations, photovoltaic power station, wind power station, gas turbine station etc., realizing the sharing of resource integration, to support smart grid, to develop into a comprehensive energy services as a typical application scenario. The simulation was created to focus on evaluating the reliability of the system and penetration of the renewable power. Results of the structural optimization model simulation show that the designed integrated control strategy of multi-energy fusion energy station has high performance and comprehensive energy efficiency.

At present, most stochastic production simulation algorithms can not consider large-scale new energy, and there are many deficiencies in the stochastic production simulation algorithm including wind farm and photovoltaic power station. Based on the actual operation data of wind farm and photovoltaic power station, this paper analyzes the power generation characteristics of wind/photovoltaic power station from the Perspective of statistics. The extraction method of wind/photovoltaic power generation output scenario characteristic index, the principle and method of scenario screening and the evaluation index of confidence output capacity are proposed. On this basis, taking the lowest total generation cost or the best energy saving and emission reduction effect of power system as the optimization objective, a practical power system operation simulation model considering three different scenarios of power balance, electricity balance and peak shaving balance is established. The objective of the model is to minimize the total generation cost of the power system or to achieve the best effect of energy conservation and emission reduction. The simulation results show that the proposed system operation simulation model considering large-scale new energy can be effectively used for the research of wind power and photovoltaic power consumption capacity.

It is a commonly used strategy to reduce the levelized cost of energy by improving the ratio of the photovoltaic panel installation DC capacity to inverter AC capacity. However, selection of DC/AC ratio value generally relies on experience, lacking a scientific method to estimate the optimal value under different circumstances. This article analyzes and explores the historical data of photovoltaic power station using data mining technology to establish a photovoltaic power generation prediction model, and then, calculates the return on investment based on the power generation predicted by the model. The calculation result shows that the return on investment generally increases with the increase of $R$ , and then decreases with the increase of $R$ after reaching a maximum value. Therefore, the optimal DC/AC ratio in different regions can be found.

With the increasing proportion of photovoltaic power generation in the power supply structure, in order to realize the photovoltaic power generation on the grid with parity as soon as possible, it is urgent to find the efficiency improvement and cost reduction methods of photovoltaic power generation system. Former researches have shown that the DC cable loss of photovoltaic power station can be reduced by more than half by using 1500V photovoltaic series, which can significantly reduce the system loss of photovoltaic power station and is very important for the early realization of PV grid parity. Traditional two-level and T-type three-level topology has been widely used in series photovoltaic inverter, but for 1500V series inverter application, the voltage stress of switching element of traditional two-level photovoltaic grid connected inverter is greatly increased, which is difficult to meet the efficiency requirements; the vertical bridge switching voltage stress of T-type three-level circuit is equal to DC bus voltage, So as the bus voltage increases to 1500V, the vertical bridge switch needs to use 1700V switch with higher switching loss, which makes it difficult to optimize the efficiency and power density. Therefore, it is necessary to seek a circuit topology with better technical economy. In this paper, an interleaved active neutral point clamped nine level (ANPC-9L) inverter are proposed, the modulation and control strategy are proposed by analyzing the basic principle of the topology. Finally, experimental results obtained with an experimental prototype are presented to verify the effectiveness of the proposed topology, modulation and control strategy.

Suppressing the commutation failure of the DC transmission system is the key to maintaining the stability of the AC/DC hybrid system. Currently, reactive power compensation equipment is generally installed to improve the dynamic reactive power support capability of the system, thereby suppressing the continuous commutation failure of the DC transmission system. The additional installation of reactive power compensation equipment generally has a higher configuration cost. For photovoltaic power stations installed near the DC system, the inverter of the photovoltaic power station can provide a certain amount of dynamic reactive power. A strategy to suppress continuous commutation failure of DC transmission by using the synchronous condenser operation of photovoltaic power plants is proposed in this paper. Firstly, the mechanism of continuous commutation failure in the DC system is analyzed. Secondly, the reactive power support capability of synchronous condenser operation of photovoltaic power station is analyzed. A strategy to suppress DC continuous commutation failure with dynamic reactive power support of photovoltaic inverters to improve the strength of the DC transmission system is proposed. Finally, on the PSCAD/EMTDC simulation platform, a model of the HVDC transmission system connected to the large-scale photovoltaic power station at the receiving end of the power grid is established to verify the effectiveness of the method.

In view of the dispersed distribution of distributed photovoltaic power generation system and the difficulty of centralized meter reading in traditional ways, we use infrared communication, 4G wireless communication and other Internet of things technology, and Netty network communication library as remote communication tools, and design a custom communication protocol that can be used for master station system and meter reading module. Combined with meter reading module and handheld debugger, remote installation and automatic reading of photovoltaic energy meter data can be realized to avoid the trouble of manual copying or building wired communication network in traditional meter reading mode, improve the management level and reduce operation cost of distributed photovoltaic power station. The results show that the photovoltaic infrared meter reading system based on Internet of things is easy to install and stable performance. A large number of energy meters can be read at the same time.

With the high proportion of photovoltaic power generation replacing traditional energy generation, the frequency regulation capability of the power system is weakened. How to improve the frequency regulation capability of the power system where distributed photovoltaic is densely accessed is an important factor to promote the consumption of new energy. To this end, this paper firstly proposes a structure of a photovoltaic combined energy storage unit to form a joint photovoltaic-energy storage system(PV-ES). Under the premise of not increasing the grid-connected inverter, the energy storage unit and the DC side of the photovoltaic inverter are controlled for power exchange, the frequency support is quickly provided when the power grid needs, and the state of charge(SOC) of the energy storage unit is managed. Then, the actual photovoltaic power station is selected and the active frequency control system is established. Finally, the effectiveness of the proposed scheme is verified by monitoring the charge and discharge of the energy storage unit under frequency disturbance.

In the new energy power generation system, the transformer impedance as part of the grid impedance will affect the stability of the system. In order to calculate the transformer impedance, considering that the existing methods rely on the transformer nameplate parameters and require offline detection, this paper proposes an on-line calculation method of transformer impedance based on power loss. This method considers the influence of transformer impedance on system power and establishes a transformer power loss model, based on which a fitting calculation method for transformer impedance is proposed. Finally, the effectiveness of the method is verified by a specific transformer simulation model in photovoltaic power station.

As the installed capacity of photovoltaic systems increases year by year in the world, PV energy has become an important energy source. For the output power of photovoltaic power station, irradiance is the most important factor. In order to solve the problem of lack of irradiance data in some power plants, the data are classified according to weather by decision tree classification method. Under different weather categories, according to current time, power and ambient temperature, different neural network parameters are used to fit irradiance sequence and interpolate the irradiance value. The results show that the average absolute error and root-mean-square error of irradiance data interpolation are 9.12% and 11.57% respectively, which are better than ANN, ARMA, SVM and Weighted KNN.

Since 2013, China’s new photovoltaic installed capacity has been ranked first in the world for six consecutive years. Photovoltaic (PV) modules are key components for the photovoltaic power station, because its performance determines the efficiency of the photovoltaic power station. After photovoltaic modules have been tested and calibrated in laboratories, various problems will still be found during the operation in different stations every year, for example efficiency degradation exceeds design value. First, this paper introduces the outdoor demonstration test technology of photovoltaic modules, including test system architecture, test equipment, and test procedures. Second, a photovoltaic module physical model is established based on the principle of PV generation, and the key parameter extraction methods are introduced. Finally, based on the measured data of the photovoltaic outdoor demonstration, the module performance investigation is performed, including the power generation, energy efficiency ratio, efficiency degradation, voltage and current temperature coefficient.

Photovoltaic power generation is an important part of the global energy system and energy internet. Accurate photovoltaic power forecasting is beneficial to the optimization of power grid scheduling and the promotion of clean energy consumption. This paper analyzes the seasonal and daily characteristics of the photovoltaic power, uses self-organizing maps (SOM) to dig out the inherent disciplines and essential properties of data, clustering sequences with similar characteristics. Then the MEA-BP model is established which applies mind evolutionary algorithm (MEA) to improving BP neural network. MEA coordinates the local and global values by introducing the evolution mechanism of convergence and dissimilation to optimize the weight and threshold of BP. Next, neural network error correction model is proposed to fix forecasting error. Based on the data of a 50MW photovoltaic power station, the numerical results show that the model can improve the accuracy of photovoltaic power forecasting effectively.

Traditional photovoltaic power plant doesn't participate in grid frequency regulation, as the increasing proportion of photovoltaic power in the whole power system, it may have a great impact on the frequency stability. The new standard DL/T 1870-2018 "Technical Specification for Power System Network Source Coordination" issued in 2018 puts forward the requirements for fast frequency response performance of photovoltaic power plant. At some regions with high proportion of photovoltaic power in Northwest China, relevant departments have released documents successively, which claim the photovoltaic power plant should have the ability of fast frequency response. In this paper, according to the requirements of standard and local documents, the key technical indexes of fast frequency response of photovoltaic power plant are analyzed. Several typical schemes of fast frequency response renovation of photovoltaic power plant are listed. A high precision fast frequency response device is developed, the principle and control strategy of the device are introduced, this device is used in laboratory and photovoltaic power plant respectively. As shown in the field test, the fast frequency response performance of photovoltaic power plant is much better with this device, both the frequency regulation response time and power control accuracy are much better than conventional hydro-thermal power plant, this device has a high application value.

Photovoltaic (PV) power prediction is an effective way to ensure the safe operation of the grid connected photovoltaic power station, and the prediction accuracy is very important as well as difficult. In order to improve the prediction accuracy, a combination prediction model based on entropy weight and traditional prediction methods is demonstrated. The combination prediction model used entropy weight to combine three traditional prediction methods which are persistence method, support vector machine (SVM) and prediction method based on similar data. And the entropy weight of each single prediction method is got by evaluating the amount of information of each method objectively. The prediction accuracy of entropy weight combination prediction model and these three single prediction methods are compared by simulation. Case study results show that the combination model based on entropy weight can get proper combination weights for these traditional methods. As a result, the prediction accuracy of entropy weight combination prediction model is much higher than that of single traditional prediction method for all weather types.

The precise minute time scale forecasting of an individual Photovoltaic power station output relies on accurate sky image prediction. To avoid the two deficiencies of traditional digital image processing technology (DIPT) in predicting sky images: relatively limited input spatiotemporal information and linear extrapolation of images, convolutional auto-encoder (CAE) based sky image prediction models are proposed according to the spatiotemporal feature extraction ability of 2D and 3D convolutional layers. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry (PIV) and Fourier phase correlation theory (FPCT) are introduced to build the benchmark models. The results show that the proposed models outperform the benchmark models under different scenarios.

Traditional photovoltaic power generation system has poor control performance and is not suitable for general application. For this, the photovoltaic power station automatic generation control system based on adaptive PID control algorithm is investigated. The precise control of the power generation process is realized from the aspects of power generation control, daily maintenance, power generation process monitoring, luminous energy utilization and so on. The adaptive PID control algorithm is used for comprehensive analysis and evaluation. The algorithm can realize the dynamic record of multiple data input in the power generation flow of photovoltaic power station, and realize diversified analysis and intelligent matching. Through the adaptive PID control algorithm to achieve the fast control of the power generation device, so that the database can improve the working efficiency to a greater extent and reduce the cost of the power station in the control of the power link. The findings show that the automatic power generation control system of photovoltaic power station based on the adaptive PID control algorithm has the advantages of high control accuracy and fast response, which can effectively improve the generation efficiency in the photovoltaic power industry.

Predicting photovoltaic power generation is one of the emphases of energy management system. In recent years, with the increasingly serious environmental pollution, the use of artificial intelligence technology to study the world's largest clean energy solar energy is of great significance. Other than the existing work, this paper proposes a fusion model based on LightGBM and LSTM to forecast short-term photovoltaic power generation. For obtaining the best prediction results, MLP is applied for dynamic weight allocation and combination to generate the prediction results. Based on the real photovoltaic power generation data collected by a photovoltaic power station in China, our model was compared with three traditional models, including XGBoost model, LightGBM model and LSTM model. Besides, the prediction results of four models in a day with enough light and four models in a day with less light are compared. The experiment result indicates that the model proposed in this paper has higher accuracy in short-term photovoltaic power generation forecast and It has good adaptability to the nonlinear feature of illumination.

Photovoltaic power generation not only has the characteristics of randomness and intermittency, but also power electronic equipment such as inverters in the system will generate harmonic components. These will affect the harmonic characteristics of the transformer current, which will affect the second harmonic braking differential protection of the transformer. In this paper, based on the actual control protection model, a detailed model of the 66KV step-up transformer of the photovoltaic power station is built, and the influence of PV on the differential protection of the transformer second harmonic braking ratio differential is analyzed. The research result shows that when the transformer is closed with no load, there is a case where the protection is mistakenly locked; when the transformer has an internal fault, the protection will prolong the action.

It takes a long time for the uninitialized electromagnetic transient simulation to reach the steady-state operation point, and even the divergence of numerical calculation will be caused by the distance between the initial point and the steady-state point. The three-phase imbalance of the AC-DC hybrid power grid cannot be ignored. The three-phase fundamental power flow can effectively improve the electromagnetic transient simulation speed of the AC-DC hybrid power grid. Under the three-phase unbalanced condition, the analytical expression of impedance, harmonic and control characteristics of the active distribution network with new energy resources is very complex, and the feasibility of engineering calculation is low. Therefore, the artificial neural network is used to fit its complex impedance, harmonic and control characteristics, and then it is nested into the three-phase fundamental power flow calculation framework based on the improved node method to modify the power of the new energy resources in the iterative process. In order to adapt to the initialization of large-scale active distribution network, a parallel calculation method based on Multi-Area Thevenin Equivalent (MATE) parallel computing method is proposed to improve the calculation efficiency of three-phase fundamental power flow. The effectiveness of the proposed method is verified by the three-phase unbalanced AC-DC hybrid power grid including photovoltaic power station(PV) with ieee33 node and 330 nodes.

As a kind of energy from the sun, solar energy is recognized as one of the most competitive renewable energy sources due to its inexhaustible and pollution-free nature. IN recent years, photovoltaic grid capacity is gradually increasing. However, the output of photovoltaic power station is affected by various meteorological factors to different degrees, which makes the output of photovoltaic power station show obvious periodicity and uncertainty. Therefore, photovoltaic power prediction is of great significance for the safety and economic operation of photovoltaic power stations. In order to improve the accuracy of the prediction, a climate impact model suitable for the power prediction of photovoltaic power stations was established for short-term power prediction objects. In this paper, strong meteorological factors are selected as input factors of fuzzy c-means clustering (FCM) algorithm. The original photovoltaic output data were clustered according to the weather type, and they were grouped into three differed ° not weather categories: sunny day, cloudy day and rainy day. The influence of different weather conditions on photovoltaic prediction was analyzed based on the algorithm of convolutional neural network to study the effects of different climates on predictions. It provides the data basis for the establishment of short-term prediction model of photovoltaic output.

The ultra-short-term prediction of photovoltaic power system is of great significance to the safe operation of power system. However, the daily and annual periodicity of solar radiation brings strong non-stationary to the photovoltaic power series, which makes it difficult to predict the photovoltaic power series. In order to overcome the problem that the detailed clear sky model relies on a large number of plant parameters, this paper proposes a prediction algorithm based on a modified clear sky model. Firstly, historical data and geographical location of a certain site are utilized to build a modified clear sky model, then the theoretical clear sky power curve fitted by the modified clear sky model are divided by the actual historical power output to get a stabilized time series, finally the method of "online update" is adopted to forecast PV plant power output in the ultra-short-term time horizon (0-4 h). The test results of a photovoltaic power station in Ningxia show that the proposed model can reduce the prediction error of the 4th hour to about 3%.

The power output feature of Distributed photovoltaic power stations (DPV) varies dramatically compared to the traditional photovoltaic power station due to various local weather conditions. Thus it is of great importance to analyze the output features to improve the power forecasting performance. In this paper, weather conditions of DPV are studied via statistical approach to obtain the distinguish forecasting models, which are suitable for sunny day, cloudy day, rainy day and foggy day. Then within each model, the clustering algorithm is applied to select most influencing elements among ambient temperature, radiation intensity, local weather, wind speed and direction. Case study shows that the precision of the proposed algorithm is improved by 0.07% (MAPE) and 31MW (RMSE) compared to the traditional neural network forecasting algorithm.

In photovoltaic power station, the solar cells in the module are exposed to positive or negative bias, which will lead to leakage current between the frame and solar cells. In this paper, the mechanism of leakage current formation is studied by analyzing the distribution of electric fields in the dielectric, and establishing the dielectric leakage model of photovoltaic module. The obtained results indicate that leakage current is not only related with electrical layout of the PV array but also the resistance of EVA and glass.

Solar irradiance is one of the most crucial factors affecting the photovoltaic (PV) power output. Accurate solar irradiance forecasting can help predict future PV power generation for grid managers, thus to mitigate the adverse effect of random PV power on the power grid. However, research on real-time surface irradiance distribution in a certain region still not be presented in previous studies, which can bring further convenience to solar power forecasting and power grid dispatching work. In this paper, a surface irradiance distribution mapping model for PV power station based on ground-based sky images is proposed. First, we process raw sky image with distortion rectification and background message dislodged. Then after cloud motion displacement calculation by hybrid Fourier phase correlation (HFPC) theory, we extract the gray level and position information of pixels in sky images to explore the mapping relationship between sky image and solar irradiance. Subsequently, an adaptive sky image-irradiance mapping model is built, trained, and updated according to real-time sky images and solar irradiance. Simulation results show that the proposed method in relation to forecast can achieve real-time surface irradiance distribution mapping at quite high accuracy.

This paper shows an analysis between the analytical design of photovoltaic power station filter and a real case filter. Indeed, the analytical filter considered in the design phase is an LCL filter, while the real case is an LLCL filter. This difference could impact the current harmonics rejected on the grid and breaks grid codes.

The integration of Distributed Generation (DG) can affect the distribution network in many aspects, such as power flow, short-circuit current, distribution grid protection, etc. In this paper the influence of a MW photovoltaic (PV) station to a distribution network is mainly studied. The mathematical model and equivalent circuit of a photovoltaic cell are presented in the first part of this paper. Then a 1.6MW PV station model is established in the software of DIgSILENT PowerFactory. This model is not a precise one, but it is more convenient for the research of PV station’s affection to a power grid. In the last part of this paper, various simulations are performed to investigate the influence of the PV station to a distribution network. The result shows that the model is capable for our investigation and the influence varies with different operating modes of the PV station.

Grid-connected photovoltaic power generation has become an effective way to utilize solar energy. In order to accurately analyze the dynamic characteristics of a grid-connected photovoltaic power station, an equivalent modeling method based on the Canopy-FCM clustering algorithm is proposed. Considering the lack of theoretical basis for the selection of the traditional clustering index, this paper deduces the transfer function of the grid-connected inverter control system based on the detailed model and proposes a new clustering index, which takes the zero-pole expression of the closed-loop transfer function as the prototype. In order to improve the fuzzy C-means clustering method, which is sensitive to the initial clustering centers and the outliers, a Canopy algorithm with lower computation cost is introduced. Before fuzzy C-means clustering analysis, the Canopy algorithm is used to determine the initial clustering centers and the number of clusters to improve the accuracy and efficiency of the clustering algorithm. The effectiveness of the proposed method is verified by simulation examples.

Ultra-short-term photovoltaic power prediction is one of the important measures to reduce the adverse effects of the safe and stable operation of traditional power systems. First, the periodicity of the PV power is taken into account to extract periodic components. For the remaining components, under different weather types, the local sensitive hashing algorithm algorithm is used to achieve rapid classification of photovoltaic power segments, and the European distance is introduced as a measure of the prediction to predict. Using the data of the photovoltaic power station for verification, the results show that the method has a higher prediction accuracy.

This paper presents a method to research the relationship between the reflection film effect and environmental temperature of photovoltaic power station, and the relationship between the reflection film effect and the environment temperature of photovoltaic power station is carried out. Through the design of experimental model, the reflection film effect of photovoltaic power station was measured, the experimental data were extracted, It is found that the ambient temperature is basically inversely proportional to the increased power generation of the reflective film of the photovoltaic power station. When the ambient temperature is high enough, the addition of the reflective film does not increase the power generation, but consumes electricity.

Cluster wide area controller networking strategy directly affects the efficiency and reliability of operation and control of cascade hydro- optical complementary power generation system. To solve the problems, this paper firstly introduces the communication system architecture of hydropower station and Photovoltaic Station in a cascade hydro- optical complementary power generation system, and further discusses the wide area networking strategy of cluster wide area controller and the intra-station networking strategy. Two wide- area networking strategies, peer- to- peer network and master- slave network, as well as three intra- station strategies, i.e. external control, internal control and direct control, are compared and studied. Finally, the technical routes of peer- to- peer network and internal control are determined, and the cluster wide area controller device is designed accordingly. The feasibility and timeliness of the technical scheme are verified in RTDS laboratory.

In order to prevent large-scale disconnection accidents of photovoltaic power stations caused by voltage fluctuations and faults in power grids, low voltage ride-through characteristics and other detection tests are needed when photovoltaic power stations are connected to power grids. At present, field test and type test are the main methods of detection, but due to the limitation of detection devices and test conditions, the evaluation of low voltage ride-through characteristics of photovoltaic power stations still remains in the single-machine detection mode, which ignores the interaction between photovoltaic inverters and inverters, inverters and static var generator(SVG) devices. The test results cannot truly reflect the low voltage traversing characteristics of the photovoltaic power stations. Using RT-LAB hardware-in-the-loop simulation platform, real photovoltaic inverters and SVG controllers are introduced to build simulation models of photovoltaic power stations. The differences between single-machine detection of photovoltaic inverters and reactive power compensation devices and low-voltage ride-through characteristics detection of the whole station are compared and studied. The simulation results show that, compared with the stand-alone test, the whole station simulation modeling test method can reflect the voltage rise caused by the reactive power characteristics of photovoltaic inverters and SVG devices after the drop, and more truly reflect the low voltage traversing characteristics of photovoltaic power stations.

In view of the strong volatility and randomness of the photovoltaic (PV) power generation, energy management mode of the PV generation station with ESS based on PV power prediction is proposed. Firstly, the circuit model, with the PV power generation unit and the energy storage battery unit, is established inthe PV generation station with ESS(ES). Then, to meet the requirements of power smoothing and tracking planned output, the power control scheme of the energy storage system (ESS) is designed. Finally, in MATLAB/Simulink, the PV generation station with ESS is built in the simulation model. It is shown from the simulation results that the proposed energy management mode can effectively suppress the fluctuation of PV power generation or tracking planned output. The proposed energy management mode can be used to improve the grid-connected stability of photovoltaic power station with energy storage.

A large number of photovoltaic access to the power grid will lead to new characteristics of grid harmonics due to the increase of the scale of converter equipment, and photovoltaic power stations are prone to generate harmonic interaction with the distribution network transmission lines to affect the power quality, so it is of great significance to study the harmonic distribution characteristics and resonance degradation phenomenon of photovoltaic power station. In this paper, the photovoltaic power station model is firstly established to analyze its harmonic output characteristics, and then the photovoltaic power station model is connected to different locations of the IEEE34 node test system to study the impact of different access locations on harmonic distribution. Finally, according to the resonance degradation mechanism of transmission lines, the variation of harmonic amplification coefficient and resonant deterioration degree of transmission lines with different distance and different harmonic frequency are studied. The effectiveness of the model is validated by simulation, and the harmonic distribution law and harmonic interaction factors of photovoltaic power stations are obtained, which provides a theoretical basis for harmonic control of photovoltaic power station.