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У магістерській дисертації розглянуто алгоритми функціонування теплових насосів, які отримують енергію від малої дахової фотоелектростанції та мережі. Визначено характеристики різних алгоритмів функціонування системи. Актуальність теми: перехід на енергоефективні технології є вагомим внеском для досягнення вуглецевого нейтралітету. Поширеною практикою стала заміна газових котлів на комплексні системи на основі ВДЕ. Мета магістерської дисертації: визначення енергетичного співвідношення складових частин комплексної системи опалення приватного будинку на основі теплового насоса та фотоелектростанції. Об’єкт дослідження: процеси перетворення енергії сонячної радіації і теплових потоків довкілля. Предмет дослідження: режимні параметри складових комплексної системи опалення приватного будинку. Методи дослідження: математичне моделювання алгоритмів функціонування опалювальних систем за допомогою програмного пакету MATLAB/SIMULINK. Для реалізації поставленої мети сформульовані наступні завдання: - вдосконалення математичної моделі системи опалення приватного будинку на основі теплового насоса та фотоелектростанції; - визначення основних параметрів, які впливають на функціонування даної системи опалення, а саме: температури навколишнього середовища; інтенсивності сонячної радіації; потужності фотоелектростанції, величини теплової потужності та коефіцієнта перетворення теплового насоса, теплофізичних параметрів будинку, вартості електричної енергії, алгоритмів функціонування системи опалення; - аналіз результатів роботи математичної моделі при різних алгоритмах функціонування, а саме: графіків зміни температури в будинку і температури води в баку-акумуляторі, вартості електроенергії для забезпечення опалення та гарячого водопостачання, рівня забезпеченості споживача електроенергією, визначення необхідних параметрів складових системи опалення для забезпечення конкретних параметрів функціонування, таких як ємність бака-акумулятора та потужність ТЕНа. Наукова новизна роботи полягає у вдосконаленні імітаційної математичної моделі комплексної системи опалення приватного будинку, яка враховує особливості графіку теплового навантаження споживача, фактори впливу навколишнього середовища та показники вартості електроенергії, що дозволило визначити енергетичні показники складових частин системи опалення. The master's dissertation considers operating algorithms of heat pump systems that are powered by small roof-based photovoltaic power plants and power grid. The operating parameters of different algorithms were established. Relevance of the topic: transfer to the energy-efficient technologies is beneficial contribution for the carbon-neutral future. Changing gas boilers on the complex renewable-based heating solution has become the common practice. The purpose of the master's dissertation: is to model different operational algorithms of the complex heating system that includes roof-based photovoltaic power plant and the heat pump that provide maximum self-sufficiency and minimum operational cost. Object of research: the process of converting solar energy and environmental heat flows. Subject of research: operational parameters of the residential heat pump base heating system components. Research methods: mathematical modelling of the heating systems operational algorithms with the MATLAB/SIMULINK software package. The following tasks have been formulated to achieve the goal: - Creating mathematical model of the residential heating system with the heat pump and photovoltaic station; - Determining of the main parameters that influence on the heating system operating: ambient temperature, solar irradiation, photovoltaic station power output, heat pump thermal power and coefficient of performance, thermal parameters of the house, electricity tariff and operational algorithms of the heating system; - Analyze the results of mathematical model of different operational algorithms: plots of the house internal temperature and water storage temperature, total cost of electricity for the space heating and domestic water heating, electricity supply coefficient, determine parameters of the heating system components for providing a particular operational algorithm, such as storage tank volume and electric heater power. The scientific novelty of the work is in improving the mathematical model of the complex residential heat pump base heating system that includes heat load graph of the consumer, external factors of the environment, and electrical energy tariffs that enabled to establish operational parameters of the components of the heating system.

The solar radiation near the surface is the main reason that affects photovoltaic power generation. Accurate ultra‐short‐term solar radiation prediction is the premise of photovoltaic power generation prediction. Here the cloud movement prediction method based on the ground‐based cloud images is presented. The cloud recognition, cloud matching, cloud area correction and cloud movement prediction are performed to predict the drift trajectory of the clouds that will block the sun. Then, using digital image technology, 13 kinds of feature information are extracted from the ground‐based cloud images. Then, these feature information are input into BP neural network, and the parameters of BP neural network are optimized by genetic algorithm. Through a large number of data training, a new ultra‐short‐term prediction model of solar radiation is established. Finally, through experimental comparison, the results show that the prediction accuracy of the model with the feature information of ground‐based cloud images can reach 96%, compared with the model without the feature information of ground‐based cloud images, the accuracy is improved by 5%. The proposed ultra‐short‐term solar radiation prediction model can effectively predict the radiation jumping process caused by cloud occlusion, and greatly improve the prediction accuracy, especially in cloudy weather.

Under the China–Pakistan Economic Corridor, renewable energy projects gradually receive due attention, among which the photovoltaic power stations in Quaid-e-Azam Solar Park represents the most typical power stations in Pakistan. The construction and development processes of the photovoltaic power stations are divided into three stages, with enterprises involved including TBEA Xinjiang Sunoasis Co., Ltd., Urumqi, China and Zonergy Solar Technology Co., Ltd., Zigong, China. The development model of the photovoltaic power stations changed from engineering, procurement, and construction (EPC) plus operation and maintenance (O&M) mode to the Build–Own–Operate (BOO) mode. Through solar power generation and marginal emission factors of photovoltaic power stations, the cumulative electricity generation during the operation period can reach nearly 40.09 billion kWh, and the cumulative emission reduction potential of photovoltaic power stations can reach 23.82 Mt CO2-eq. Based on the multi-stage construction of photovoltaic power stations in the solar park, the land, infrastructure, and transmission facilities could be arranged to simplify the procedures and attract overseas investment. The construction and development model of Quaid-e-Azam Solar Park could provide a reference for the promotion of low-carbon transition, the adjustment of traditional energy structures, the fulfillment of carbon reduction commitments, and the mitigation of climate change, which could provide policy implications for renewable energy development and low-carbon transition in Pakistan.

Based on the meteorological observation data of air temperature, surface temperature and albedo data retrieved from remote sensing images inside and outside the photovoltaic station, as well as the measured soil moisture content and bulk density at different locations of the photovoltaic power station in 2019, the impact of large-scale desert photovoltaic power plants on climate and environment was studied. The results show that air temperature, surface temperature and albedo inside the photovoltaic power station are lower than those outside the station, which are obvious in winter and not obvious in summer. Therefore, the photovoltaic power station is a cold source and an energy sink. The soil moisture content under and between the photovoltaic arrays is larger than other sampling points, and the soil bulk density gradually decreases with the distance from the center of the photovoltaic power station. Therefore, future plans for desert photovoltaic power station construction should take into account the impacts on local climate and environment.

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.

The purpose of this study is to improve control performances of a real case photovoltaic (PV) power station. By considering the cable’s inductance, the LCL filter becomes an LLCL filter with a resonant behavior. To avoid this resonance from contaminating the system and to ensure that the studied case could be realized in the worldwide, a control design is highly required. This is achieved through two PWM strategies: the selective harmonic elimination (SHE) and the selective harmonic modulation (SHM). A brief comparison is highlighted in this paper. The obtained results confirm that SHE strategy presents really a harmful disadvantage compared to SHM (Franquelo et al. in IEEE Transactions on Industrial Electronics 54(6):3022–3029, 2007, Napoles et al. in IEEE Transactions on Industrial Electronics 57(7):2315–2323, 2010), which could impact the current harmonics rejected on the grid and breaks grid codes. An attention has to be paid to the unwanted effect of the SHE strategy as well as the LLCL poor quality attenuation. An efficient combined SHE-LLCL strategy is proposed to improve the performance of a real case grid-connected PV inverter thanks to the flexibility provided by the LLCL filter and the SHE strategy’s simplicity. Obtained results show better performances over traditional researches.

Today, photovoltaic (PV) power generation accounts for a relatively small proportion of total power generation in China. If photovoltaic power can achieve grid parity, it can replace the original traditional thermal power generation, which has positive significance on the environment. The Levelized Cost of Energy (LCOE) is the main general economic indicator for the evaluation of power generation technology in the world. Based on the traditional LCOE evaluation model and considering the shortcomings of the previous LCOE evaluation models, the cost of photovoltaic power generation is refined in this paper. The cost of the taxation with calculating the depreciation, the loss, and the income of the Clean Development Mechanism (CDM) are taken into consideration. Hence, a more accurate and more comprehensive LCOE model is presented. Moreover, combined with other investment indicators, a photovoltaic power generation benefit model corresponding to the current development status is proposed. Taking a 3-MW distributed photovoltaic power station project in Nanjing as a case study, the sensitivity analysis of factors that affect the benefit of photovoltaic power generation is carried out to further explore the feasibility of photovoltaic power to grid parity.

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.

[ES] El presente Trabajo de Fin de Máster tiene como objetivo el diseño de una instalación fotovoltaica de autoconsumo de 55 kWp situada en el Colegio IVAF Luís Fortich, así como la justificación técnica y económica de la instalación. En el proyecto se definirán y seleccionarán de manera justificada todos los elementos necesarios para garantizar el buen funcionamiento de la instalación fotovoltaica. Los principales elementos que se han elegido son los paneles fotovoltaicos, inversor, el cableado, protecciones y aparatos de medida. Además, se completa mediante un plan de mantenimiento de la instalación, así como la fase de desmantelamiento, ya que ambos forman parte del ciclo de vida de la fotovoltaica. El proyecto cuenta también con un segundo objetivo que es el dimensionamiento y diseño de un punto de recarga de vehículos eléctricos de 22kW de potencia, permitiendo así la carga simultánea de dos vehículos. Este segundo objetivo está en línea con el primero, puesto que ambos convergen a un mismo fin, el de promover la transición energética., [EN] The Master¿s Thesis objective is to design a 55 kWp self-consumption photovoltaic installation located at CEIP IVAF Luís Fortich, as well as the technical and economic justification of the installation. In the project, will be defined and selected all the elements necessary to guarantee the proper operation in a justified manner. The main elements that have been chosen are the photovoltaic panels, inverter, wiring, protections and measuring devices. In addition, it is completed by a maintenance plan for the installation, as well as the dismantling phase, since both are part of the photovoltaic life cycle. The project also has a second objective, which is the dimensioning and design of a charging point for electric vehicles with 22kW power. This second objective is in line with the first, since both converge towards the same goal, that of promoting the energy transition., [CA] Les capacitats dels dispositius mòbils (telèfons intel·ligents i tables) per a la visualització de continguts 3D i aplicacions de realitat augmentada van millorant de forma constants en els últims anys. Aquestes capacitats poden ser suficients per a poder realitzar avaluacions perceptives del producte per part de futurs usuaris, així com donar suport a llocs de compra en línia que faciliten una visualització avançada del producte abans de la seua compra. En aquest context, el TFM pretendre realitzar una anàlisi de la influència de la manera de presentació del producte sobre la seua percepció per part de l'usuari mitjançant l'ús d'una sèrie d'escales semàntiques de valoració. Per a això s'utilitzaran una selecció de cadires, de les quals s'haurà d'obtindre un modelatge 3D amb un *texturizado realista que permeta la seua visualització interactiva a través de *WebGL i realitat augmentada en un dispositiu mòbil. A més, s'utilitzarà com a tercer mitjà de presentació, representacions fotogràfiques de les cadires. Es desenvoluparà un treball de camp per a obtindre la valoració per diversos subjectes de les escales semàntiques que permetran valorar diversos atributs del producte després d'haver visualitzat el producte en cadascuna de les tres formes de presentació abans descrites. Finalment, a través de la corresponent anàlisi estadística s'analitzarà com interaccionen les escales semàntiques i els diferents sistemes de visualització.

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.

The photovoltaic power station in Qinghai has been built for 8 years, however, its impact on the regional soil ecological environment has not been studied in depth. To reveal the structure and distribution pattern of archaeal communities in desert soil under the influence of a large photovoltaic power station, a comparative study was carried out between the soil affected by photovoltaic panels and the bare land samples outside the photovoltaic station in Gonghe, Qinghai Province. The abundance, community structure, diversity, and distribution characteristics of archaea were analyzed by quantitative PCR and Illumina-MiSeq high-throughput sequencing, and the main environmental factors affecting the variation of soil archaeal community were identified by RDA. The contribution rate of environmental factors and human factors to microbial community diversity was quantitatively evaluated by VPA. The results showed that there was no significant difference in soil nutrients and other physicochemical factors between the photovoltaic power station and bare land. Thaumarchaeota was the dominant archaeal phylum in the area, accounting for more than 99% of archaeal phylum, while at the level of genus, Nitrososphaera was the dominant archaeal genera. There was no significant difference in archaeal community structure between and under different types of PV panels. The analysis has shown that the construction of a photovoltaic station has little effect on the community structure of soil archaea in a desert area, and it was speculated that the selection of niche played a leading role in the distribution pattern of soil archaeal community. This study provides the basis for a scientific understanding of the characteristics and distribution patterns of soil archaeal communities affected by the construction of a photovoltaic power station.

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.

Owing to the significantly installed capacity of solar energy during the past decades, the operation and maintenance of the large photovoltaic power station is a big challenge, as the manual inspection is labour-intensive and expensive. This Letter presents a solution for the intelligent inspection of the hotspot with the unmanned aerial vehicle in the large-scale photovoltaic plant. First, a traditional image processing method is presented to eliminate the noise and crop the infrared image, which can make the defected feature more obvious. Then, the module is extracted by the line segments detection. Finally, considering the great advance in the realm of the computer vision, a deep-learning-based method for automatic hotspot detection is proposed for locating the hotspot with the data augmentation. The deep-learning-based model can extract the hotspot feature by the training process in the data set. In the end, the performance of the method proposed is extensively evaluated and the numerical results prove the accuracy and precision of the model.

The exploitation of transmission or distribution network of substations requires auxiliary electrical sources with low continuous and alternative voltage. Actually, DC and AC power supplies of auxiliary services are insured by batteries, generator, rectifiers and inverters. In this work, we want to add another source based on renewable energies to obtain a hybrid system and reinforce the main service of transformer substations. The addition of this source will influence operating mode of the existing systems, upgrade the quality of supply and reduce the losses of the transport network. The work presented in this article is a contribution to a good modeling of such system and especially different components of a photovoltaic power station connected to the grid of transmission substations. The main components of a connected PV plant are modeled and simulated in Matlab / Simulink and ETAP.

Accurate modeling is an important method for dynamic response analysis and control strategy verification of high photovoltaic (PV) penetration distribution networks. This paper proposes a precise dynamic modeling framework for the two-staged PV station cluster, namely as deep learning clustering hybrid modeling framework. It includes clustering-based equivalent model and error correction model (ECM). A long short-term memory network is used to form the ECM, which models the dynamic response error between the existing equivalent model and the detailed model. The competence of this framework is validated by numerous case studies based on a practical PV cluster construction. The simulation results reveal that the proposed method is featured of low complexity and fast response speed as the equivalent model but has much higher accuracy.

The distributed photovoltaic power station installed on roof has been widely adopted in city which provides electricity and cuts the energy consumption of building. However, the station also changes the original condition of roof, affects the heat transfer between roof and outside and the conductive heat flux through roof, then the cooling load induced by conductive heat flux. Experiments were carried out on high bracket installation and low bracket installation on flat roof in July and August. The temperatures of photovoltaic module, original roof and shadowed roof were measured to calculate the conductive heat flux through roof. Modification was made for radiation time series method to calculate the cooling load induced by calculated conductive heat flux through roof. For high bracket installation, the cooling load of shadowed roof reduces about 27.5% in whole day, and reduces about 37.4% between 08.00 and 17.00 compared to original roof for sunny day. For low bracket installation, the cooling load of shadowed roof reduces about 18.8% in whole day, and reduces about 27.2% between 08.00 and 17.00 compared to original roof for sunny day. The cooling load of whole roof with distributed PV station also relates to roof utility. Small cooling load can be obtained with big roof utility under same installation method. And the cooling load of roof is lower with high bracket PV station than low bracket PV station with same roof utility in sunny day.

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.

In this paper, a feature extraction method for evaluating the complexity of the Electromagnetic Environment (EME) of the photovoltaic power station is presented by using logarithmic morphological gradient spectrum (LMGS) based on the mathematical morphological theory. We use LMGS to evaluate electromagnetic environment signals. We also explored the impact of structure element (SE) on the MS, MGS, and LMGS. Three types of SE, mean the line SE, square SE and diamond SE, are utilized and compared for computing the LMGS. EME signals with four complexity degrees are simulated to evaluate the effectiveness of the presented method. The experimental results have shown that the feature extraction scheme proposed in this paper is a reasonable method to classify the complexity of EME.

We develop a stochastic optimization model for scheduling a hybrid solar-battery storage system. Solar power in excess of the promise can be used to charge the battery, while power short of the promise is met by discharging the battery. We ensure reliable operations by using a joint chance constraint. Models with a few hundred scenarios are relatively tractable; for larger models, we demonstrate how a Lagrangian relaxation scheme provides improved results. To further accelerate the Lagrangian scheme, we embed the progressive hedging algorithm within the subgradient iterations of the Lagrangian relaxation. We investigate several enhancements of the progressive hedging algorithm, and find bundling of scenarios results in the best bounds. Finally, we provide a generalization for how our analysis extends to a microgrid with multiple batteries and photovoltaic generators.

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.

As legal amendments regarding the stimulation of renewable generation development in Ukraine came into force shifting the main measure from the use of fixed feed-in “green” tariff to the implementation of renewable auctions the requirements for the design of the renewable generation equipment structure changed too. This based on the change of actual criteria used for a renewable generation project. The main indicator became a minimum production cost of electricity which allows for the project to compete with other ones participated in the renewable generation auction. To assess the minimum production cost of electricity generated by solar photovoltaic station a simple non-linear mathematical model of optimal solar photovoltaic station design is proposed. The examples of calculations made with the model demonstrate that essential changes in the production cost of electricity could be achieved depending only on the equipment structure of the solar photovoltaic station which depends on its geographical location.

With the development of national energy strategy, the construction of the Energy Internet has become an important direction for the development of energy systems. The emerging field of multi-station integration has laid a good foundation for the construction of the Energy Internet. Based on the analysis of multi-station integration architecture, a mathematical model for location planning of multi-station integration was proposed. The model is based on the substation and integrates data center, charging station and photovoltaic power station. The particle swarm optimization (PSO) in the artificial intelligence algorithm was used to solve the model and determine the optimal site. Finally, a case was used to verify the correctness and effectiveness of the proposed model and method.

Aiming to solve the problem of low precision of traditional photovoltaic power forecast method under abrupt weather conditions. In this paper, a high-precision photovoltaic power prediction method based on similarity time and LM-BP neural network is proposed. Firstly, the factors affecting the output power of photovoltaic power station are analyzed, and the short-term output power model of photovoltaic power station is established based on similar day and LM-BP neural network. Then, from the perspective of model training efficiency and prediction accuracy, the deficiencies in the short-term power prediction of photovoltaic power stations based on similar days and LM-BP algorithm are analyzed. Secondly, the prediction model of LM-BP neural network based on similar hours is established. Finally, Jiaxing photovoltaic power station is taken as an example for simulation verification. The simulation results show that the proposed method has high accuracy in predicting photovoltaic power under abrupt weather conditions.

We used the data of observational site in photovoltaic power plant (PV site) and reference site in summer 2020 to compare the characteristics of surface energy flux of PV site and Gobi underlying surface. We defined the photovoltaic virtual flux and calculated the proportion of photovoltaic power generation in the net radiation by using daily power generation and generating hours. The results show that the net radiation of PV site was larger than that of reference site throughout the day. Compared with the reference site, the sensible heat flux and latent heat flux of PV site increased by 6% and 53.5%, respectively. Sensible heat flux, soil heat flux, latent heat flux and photovoltaic virtual flux accounted for 43.2%, 14.5%, 8.6% and 6.1% of the net radiation, respectively. The sensible heat accounted for the largest proportion, the energy balance closure increased from 0.6 to 0.67 in the daytime.

This paper shows a comparison between the analytical design of a 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 main objective of this study is to maximize the power injected into the network while respecting the harmonic rejection standards in force, namely, G5/4, French decree of 2008, and IEEE 519 standards, by adopting a current control design to improve the performance of a grid-connected three-phase inverter, which is regarded as the central component in a photovoltaic production system. The selective harmonic modulation strategy (SHM) is a common technique to achieve this goal. For that, a frequency and a time-domain comparison for a grid-connected inverter using both filters have been highlighted. Simulation results confirm the excellent transient behavior of both filter topologies and the advantage to consider the flexibility of LLCL filter when combined with SHM strategy. This manuscript is an extension of an earlier version of “Comparison between LCL and LLCL Filters for a Grid Connected Inverter Using Selective Harmonic Modulation.”

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.