Photovoltaic Equipment Research Articles

Influence of Residential Photovoltaic Promotion Policy on Installation Intention in Typical Regions of China

Increasing the popularity of distributed photovoltaic technology among Chinese residents is of great significance to achieve the dual carbon goal (emission peak and carbon neutrality). In this study, we collected 1424 questionnaire samples and used PLS-SEM for group modeling and comparative analysis of bungalow and building residents. The results show that living conditions, costs, risks of installation, maintenance, and economic efficiency are the five significant influencing factors for residents to decide whether to install a distributed residential photovoltaic equipment. Compared with building residents, bungalow residents tend to feel more concerned about the cost and risk of residential photovoltaic equipment during installation, maintenance, and use. On the other hand, bungalow residents show greater sensitivity to the corresponding photovoltaic promotion policies. By contrast, building residents pay more attention to the direct benefit created by the installation of residential photovoltaic equipment. Therefore, adopting the strategy from bungalow residents to building residents could help promote the distributed photovoltaic system progressively. As for the subsidy decline, more attention shall be paid to the progress of a gradual reduction of the subsidies. It is advisable to pay attention to integrating the upstream and downstream industry chains of the distributed photovoltaic systems, thus reducing the concern of residents about the difficulty in installing, maintaining, and protecting the distributed photovoltaic equipment. By clarifying the different impacts of promotion policies on the demand side, this study provides a practical reference for the further adjustment made to distributed photovoltaic promotion policies.

Non-intrusive modeling for integrated energy system based on two-stage GAN

Generally, an accurate model can describe the operating states of a system more effectively and provide a more reliable theoretical basis for the system optimization and control. Different from the traditional intrusive modeling, a non-intrusive modeling method based on two-stage generative adversarial network (TS-GAN) is proposed for integrated energy system (IES). By using this method, non-intrusive modeling for the IES including photovoltaic, wind power, energy storage, and energy coupling equipment can be carried out. First, the characteristics of IES are analyzed and extracted based on the meteorological data, energy output, and energy price, and then the characteristic database is established. Meanwhile, the loads are classified as uncontrollable loads and schedulable loads based on frequency domain decomposition to facilitate energy management. Furthermore, TS-GAN algorithm based on the Stackelberg game is designed. In the TS-GAN, the first-stage GAN is used to generate the operating data of each equipment identified by non-invasive monitoring, and the second-stage GAN distinguishes the accumulated data generated by first-stage GAN and further modifies the generator models of the first-stage GAN. Finally, the effectiveness and accuracy of the proposed method are verified by the simulation of an energy region.

Optimization planning method of distributed generation based on steady-state security region of distribution network

Distributed wind and photovoltaic (PV) power generation are boosting in China. The wide integration of these generators into the distribution network leads to the risk of branch overload and bus voltage violation. To cope with the above risk, some peak output of wind turbines and PV equipment may be curtailed. Although curtailment measure leads to the increase of wind and PV power generation cost, more wind and PV electricity can be integrated into distribution network. In order to balance the power supply cost and the penetration rate of wind and PV electricity, the principle of economic consumption is introduced. Then an optimization planning method considering wind and PV electricity curtailment measure is proposed, and a fast model solving method based on distribution network steady-state security region is proposed. The main work is as follows. Firstly, a maximum and minimum optimization planning model is established. The maximum optimization refers to the maximum consumption of renewable energy electricity during the operation scheme formulating. The minimum optimization refers to choosing the planning scheme with the lowest power supply cost. Secondly, steady-state security region of distribution network is used to solve the optimization model quickly. To the maximum optimization, the model is transformed into a linear programming model with the help of the security region linear expression in power injection space. To the minimum optimization, an initial solution formulation method based on the redundancy information of the system operation state is given, by which the initial solution is close to the final solution. Finally, a case study of the IEEE 33-bus system is carried out and proves the effectiveness of the method. It is found that under the principle of economic consumption, the penetration rate of renewable energy electricity can be effectively improved when suitable renewable electricity curtailment measure is taken.

Application of Particle Swarm Optimization Algorithm in Photovoltaic Constant Pressure Water Supply Electrical Control System

The traditional constant water supply system has problems, low operation efficiency, and the strong nonlinear and large time delay of the control system. It did not meet the needs of water supply in remote and desert areas. To solve these problems, the PV constant voltage water supply system is designed to optimize the Proportion Integration Differentiation (PID) controller method based on the particle swarm optimization (PSO) algorithm. The programmable logic controller (PLC) and frequency converter are used as the core control mechanism of the system, the water pump motor as an actuator, the pressure sensor and the level sensor be used as a feedback mechanism, Photovoltaic power generation equipment is used in power supply agencies. In this paper, the objective utilizes the PSO algorithm to optimize the parameters of the PID controller, and the principle and control process of the PSO-PID algorithm are analyzed. Simulation results by MATLAB verify that the PSO-PID algorithm has better dynamic and steady-state performance than the traditional PID algorithm. Finally, the practicability and effectiveness of the water supply system are proved by the PLC of the OMRON-CP1H series and the touch screen of the WEINVIEW series.

Operation Mode of Integrated Energy System with Liquid Air Energy Storage

This paper takes the energy supply in the park as the research background, the integrated energy system as the research object and establishes the integrated energy system model including liquid air energy storage, distributed photovoltaic, gas turbines and other equipment. When the integrated energy system operates the mode of “ordering heat by power”, the heat wasted as high as 14.647MWh and the cold wasted as high as 24.13MWh. When the system is not equipped with LAES, the output power of the CCHP unit increases by 21MWh, the electricity purchase in power grid increases by 8.123MWh, the heat waste increases by 21.696MWh and the cold waste increases by 12.421MWh. When the integrated energy system operates the mode of [[CHECK_DOUBLEQUOT_ENT]] ordering power by heat ", heat energy of the system has been reasonably utilized. When the system is not equipped with LAES, the power output and heat of the CCHP unit in the system are the same, the thermal energy output results of the system are the same and the electricity purchased by the power grid increases by 32.14MWh.

Research on Distributed PV Monitoring System Based on Ubiquitous Power IOT Architecture

There are problems of low intelligent management and automatic maintenance of traditional distributed PV power plants. This paper designs a distributed PV monitoring system based on ubiquitous power IoT architecture. LoRa edge computing terminal, Storm data stream processing strategy and Spark in-memory programming model are introduced to meet the needs of distributed PV power plants for fast access and storage, analysis and mining of big data. This system improves the real-time and security of power plant data cloud-edge collaboration, and can efficiently perform status monitoring, maintenance management and data analysis of distributed PV power plants. It is of practical significance to improve the whole life cycle management of distributed photovoltaic power generation equipment and promote the transformation of operation and maintenance mode to automation, intelligence and intensification.

МОДЕЛЮВАННЯ САМОПІДТРИМУВАНИХ СТРАТЕГІЙ ДЛЯ УРЯДОВОЇ ФІНАНСОВОЇ СИСТЕМИ, ДЕРЖАВНОЇ БАНКІВСЬКОЇ СИСТЕМИ ТА ІНВЕСТИЦІЙНОЇ СИСТЕМИ ВІДНОВЛЮВАНОЇ ЕНЕРГОГЕНЕРАЦІЇ

The interaction of the government financial system, the state banking system, and the investment system of renewable photo-voltaic (PV) power generation equipment can lead to sustainable strategies of these three parties (including government subsi-dies and bank loans) in the distributed state PV-market depending on its level of development. However, the instability of power output, caused by the variability and changing nature of renewable energy sources, poses challenges for large-scale power dispatch. In addition, the development of the PV-industry has been constrained by a long period of return on investment in solar photovoltaics and the need for large initial investments. With the rapid development of the sharing economy, the provision of financial support and the sharing of investment risks among investors in the PV-energy have become key means of promoting the PV-industry. State incentive policy was considered an effective approach to significant promotion of PV-systems. Government subsidies reduce the need for large initial investments, and market mechanisms, such as feed-in tariffs and tax rebates, increase return on investment and reduce payback periods. In addition, bank loans are considered another major source of external financing for the development of the PV-industry. Third-party financing with appropriate risk-sharing is considered an effective approach to promote the use of photovoltaic technologies. As government subsidies put pressure on the state budget and bank loans require banks to take significant credit risks, there are clear barriers to governments and banks supporting the develop-ment of the PV-industry. By 2022, the issues of computing such targeted government subsidies and bank loans with limited credit risks, which maximize incentives for the diffusion of PV-technologies, remain underdeveloped. The current important issues for suggested numerical studying and modeling are: can government subsidies and bank loans significantly contribute to the diffusion of PV-installations at various levels of the PV-market development; what evolutionarily stable states will be formed at different levels of the PV-market development; how the volume of government subsidies, the share of bank loans, the capacity of PV-installations by investors will affect the evolutionary trajectories of the all PV-market parameters and the transformation of various evolutionarily stable states. To do this, numerical modeling is performed to study the dynamic evolutionary trajectories at different levels of the PV-market development.

A population diversity-controlled differential evolution for parameter estimation of solar photovoltaic models

The cleanliness and renewability of photovoltaic (PV) system make it stand out as a promising energy source. However, limited by the developments of PV equipment, the conversion efficiency of solar energy is still very low. In order to further improve the conversion efficiency, an accurate model with well-estimated parameters is much more important for PV systems. Motivated by this demand, we propose a new differential evolution variant (PDcDE) to tackle the parameter estimation of several kinds of solar PV models. Innovations in this paper include an auto-controlled population strategy to adjust the population size during a search process, a diversity-controlled parameter setting method to decide the scale factor, and a backward search to avoid local optima. The performance of PDcDE is verified on six PV modules against eleven state-of-the-art meta-heuristic algorithms. Extensive comparative results reveal the excellent performance of PDcDE. Moreover, the results analyzed by two statistical methods report its superior efficiency and effectiveness for the parameter estimation of PV systems.

Estimating the Photovoltaic Potential of Building Facades and Roofs Using the Industry Foundation Classes

Photovoltaic energy generation has gained wide attention owing to its efficiency and environmental benefits. Therefore, it has become important to accurately evaluate the photovoltaic energy generation potential of building surfaces. As the number of building floors increases, the area of the facades becomes much larger than that of the roof, providing improved potential for photovoltaic equipment installation. Conventional urban solar potential evaluation methods are usually based on light detection and ranging (LiDAR). However, LiDAR can only be used in existing buildings, and the lack of semantic information in the point cloud data generated by LiDAR makes it impossible to evaluate the photovoltaic potential of facades (including details such as windows) in detail and with accuracy. In this study, we developed a method to accurately extract facades and roofs in order to evaluate photovoltaic potential based on the Industry Foundation Classes. To verify the feasibility of this approach, we used a building from Xuzhou city, Jiangsu province, China. The simulation results indicate that, out of the total building photovoltaic installable area (8995 m2), that of the facade is 8240 m2. The photovoltaic potential of the simulated building could reach 1054.69 MWh/year. The sensitivity studies of the grid resolution, the time interval and the computation time confirmed the reasonability of the determined conditions. The method proposed offers great potential for energy planning departments and the improved utilization of buildings.

Autonomous Photovoltaic LED Urban Street Lighting: Technical, Economic, and Social Viability Analysis Based on a Case Study

This paper analyzes the technical and economic viability and sustainability of urban street lighting installation projects using equipment powered by photovoltaic (PV) energy. First, a description of the state-of-the-art of the technology is performed, studying the components involved in solar LED luminaires for street lighting application and examples of autonomous PV systems installed in different countries. Later, a case study a based on a renovation project of the street lighting installation at a 5000-inhabitant municipality in Lanzarote (Spain) is presented. Two alternatives are analyzed: underground channeling of the previous aerial electrical grid and the installation of LED luminaires, and, on the other hand, the installation of autonomous LED solar luminaires. Simulations concluded that a PV lighting installation proposal guarantees the existing M3 lighting requirements (EN 13201-2:2015) and represents a saving in the material execution budget of 43.78% with respect to the channeled power grid option. Finally, a statistical study has been carried out to assess the social acceptance of Spanish citizens of this autonomous PV technology in urban environments. This considers strengths and weakness of the technology: sustainability, robustness, visual impact, or risk of vandalism. In general, most subjects of all age segments are aware of the problem that means having aerial wiring running at facades (95%) and considers the use of PV in urban lighting sustainable (88%). However, 47% of those surveyed consider that shutdowns due to lack of energy harvesting is problematic and 17% consider this very problematic. This major drawback (visual impact of PV equipment is mostly evaluated as neutral) gives rise to social reluctance, especially in people younger than 50 who remarked this as more problematic than senior segments. Thus, guaranteed operational service is fundamental to have social agreement for PV technology implementation.

The total social cost evaluation of two wind and PV energy development modes: A study on Henan of China

In Henan province of China, the power demand is vigorous, and the existing provincial generators are mostly high-carbon. Henan faces greater challenges in electricity revolution than other provinces. Currently, there are two development modes for generating low-carbon electricity in Henan: the “large-scale and long-distance transmission” (LSLDT) mode and the “local development and consumption” (LDC) mode. To make a better choice on development mode, following works have been done. Firstly, as there is still no macro evaluation on the self-sufficiency ratio by photovoltaic (PV) power in each region of Henan, a rooftop PV installation potential evaluation method based on the roof covering rate is proposed. And two development scenes for wind and PV power generation are set to fit the development trend of Henan in the near future. Secondly, as the load can only be stably supplied by the cooperation of wind turbine, PV equipment and other controllable generation, the real cost of wind turbine and PV power generation is never only the equipment investment. Different from the commonly used levelized cost of electricity method, a bi-level generation planning model based on the total social cost is established. Finally, generation planning schemes are made and the power supply cost per kWh electricity is evaluated under the two development scenes mentioned above. It is found that there is a significant cost advantage when wind and PV electricity are generated with the LDC mode in both scenes. What is more, the existence of reliability cost, wheeling cost and grid-loss cost makes the LSLDT mode difficult to cut down the wind and PV power supply cost. Wind and PV power generation of the LDC mode should be focused on in Henan.

Cost-Benefit Analysis of Small-Scale Rooftop PV Systems: The Case of Dragotin, Croatia

A large drop in prices of photovoltaic (PV) equipment, an increase in electricity prices, and increasing environmental pressure to use renewable energy sources that pollute the environment significantly less than the use of fossil fuels have led to a large increase in installed roof PV capacity in many parts of the world. In this context, this paper aims to analyze the cost-effectiveness of installing PV systems in the rural continental part of Croatia on existing family houses. A typical example is a house in Dragotin, Croatia with an annual consumption of 4211.70 kWh of electricity on which PV panels are placed facing south under the optimal slope. The calculation of the optimal size of a PV power plant with a capacity of 3.6 kW, without battery energy storage, was performed by the Homer program. The daily load curve was obtained by measuring the electricity consumption at the facility every hour during a characteristic day in the month of June. As most of the activities are related to electricity consumption, repeating during most days of the year, and taking into account seasonal activities, daily load curves were made for a characteristic day in each month of the year. Taking into account the insolation for the specified location, using the Internet platform Solargis Prospect, hourly data on the electricity production of selected PV modules for a characteristic day in each month were obtained. Based on the previous data, the electricity injected into the grid and taken from the grid was calculated. Taking into account the current tariffs for the sale and purchase of electricity, investment prices, and maintenance of equipment, the analysis shows that such a PV system can pay off in 10.5 years without government incentives.

Design and Implementation of Photovoltaic Power Station Equipment Operation and Maintenance Platform

Design and Implementation of Photovoltaic Power Station Equipment Operation and Maintenance Platform

An optimal incentive policy for residential prosumers in Chinese distributed photovoltaic market: A Stackelberg game approach

An appropriate policy tool adopted by the government can induce small-scale photovoltaic (PV) installations to alleviate current environmental problems. In this paper, a Stackelberg game model is applied to explore the government's optimal incentive policy and residential prosumers' strategic choices in China's distributed PV market. Regional solar radiation intensity (SRI), Levelized cost of electricity (LCOE), and feed-in tariffs (FIT) are regarded as influencing factors that determine whether the PV investment is inducible in a specific region. The results show that government subsidy is gradually no longer required for smaller-capacity PV investment when the individual characteristics of residents are ignored but only economic benefits are considered. The contribution of government subsidy in the PV market has gradually changed from inducing residents to start investing in solar PV equipment to inducing investment in larger-capacity PV equipment. An appropriate government subsidy can reduce the restrictions of SRI, LCOE, FIT, and other factors on the residential installation. Meanwhile, the results demonstrate the optimal subsidy established according to specific regions can accurately induce residents and maximize the profits of government and residents, while the effectiveness of a unified subsidy policy is very limited. These conclusions can provide valuable insights for policymakers to develop effective and targeted policies.

Optimal allocation of microgrid using a differential multi-agent multi-objective evolution algorithm

Abstract The optimal configuration and allocation of a microgrid are one of the key issues to guarantee the economic and reliable working of a microgrid. This is a multi-objective optimisation problem within which the economic index and the load power shortage rate index should be considered when optimising the configuration. In this article, a differential multi-agent multi-objective evolutionary algorithm (DMAMOEA) was designed to optimise the capacity configuration of a microgrid system, which includes three kinds of equipment: wind turbine, photovoltaic equipment and battery. The final optimisation results were compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm. Simulation results showed the effectiveness of the algorithm. At the end of this article, the representative solutions in the calculation results are compared and explained and the environmental benefits are analysed, which show the effectiveness of the implementation of the microgrid system.

Silver Nanoparticles Formation by Nanosecond Pulsed Laser Irradiation in an Aqueous Solution of Silver Nitrate; Effect of Sodium bis (2-ethyl hexyl) Sulfosuccinate

A photochemical reduction of a silver salt precursor using near-ultra-violet (UV) pulsed laser (355 nm) irradiation into aqueous surfactant sodium-bis (2–ethylhexyl) sulfosuccinate (AOT) solution has succeeded in synthesizing homogenous speculative silver nanoparticles (Ag NPs). Without using any additive, the irradiation from ns laser pulses to aqueous silver nitrate solution was observed to create nanocubes (NCs). The photoproduct was transformed into a nanosphere when irradiated with a particular AOT concentration. The photoproduct concentration of NCs to NSs was approximately ten times lower than the critical concentration of micellar (CMC) in AOT, which means that the growth of NSs was aided in a single layer of AOT adsorbed on silver surfaces. A UV / Visible Spectrophotometer and Transmission/Scanning electron microscopy (TEM/SEM) were used to characterize the photochemically synthesized sample thoroughly. The mean size of AgNSs, analyzed by TEM, was 8 nm. These parameters have shown the growth of AgNSs and discussed in the paper. These nanoparticles are potential candidates for catalyst, semiconductor, photovoltaic equipment, medical diagnostics applications than bulk materials.

Characterization of Technological Innovations in Photovoltaic Rural Electrification, Based on the Experiences of Bolivia, Peru, and Argentina: Third Generation Solar Home Systems

The electrification of isolated homes in rural areas without access to the electric grid has been achieved in part using solar energy transformed into electricity through Photovoltaic (PV) equipment known as Solar Home Systems (SHSs), which has been widely disseminated throughout the world. The use of SHSs in rural electrification projects has been studied from 1980 to date, they have been incorporating advances and technological innovations successively. Through the review of documents on PV projects, articles, and technical reports, it has been possible to identify the changes in the main components of the SHS and the typical configurations, systematizing them in a discrete timeline. Thereby, this article characterizes three generations of SHSs and highlights their differences. The first generation is fully valid between 1980–1995, the second generation in 1995–2010, and the third generation from 2005 to date, which overlaps the beginning. In any case, the time limits in each period are only referential. The later generation, Third Generation Solar Home Systems (3G-SHSs), is highly efficient, uses LED lamps, lithium batteries, microelectronic control, and plug and play connections. Indeed, this equipment can be self-managed by the user and reflects the technology’s high reliability by a minimum maintenance service in situ. Furthermore, their lower costs make access to electricity more affordable for the last mile population. The present research offers a detailed technological and operational characterization of the 3G-SHSs to show the most relevant aspects of support to project developers, planners, and decision-makers to achieve the Sustainable Development Goal (SDG) 7.

Research on the development of distributed power generation strategy through differential scheduling requirements

The development of high-penetration photovoltaic power generation is an important measure to realize the energy revolution. With the continuous increase of photovoltaic capacity, the electricity generated by distributed photovoltaic can’t be absolutely integrated to power grid in the future. It is necessary to improve the scheduling requirements for photovoltaic equipment so that the system can be in safe operation. First of all, four scheduling requirements are set for the grid integration of photovoltaic equipment from the perspective of grid scheduling, which is characterized by differentiated scheduling of power and electricity between the micro-grid composed by photovoltaic equipment, energy storage and utility. Then, the capacity optimization planning model of the photovoltaic and energy storage system is established with the minimum power supply cost of the microgrid as the objective function. Finally, through the case study, it is found that industrial and commercial enterprises have the economic motivation to invest in the construction of the photovoltaic and energy storage microgrid, which will strengthen confidence in promoting the rapid development of photovoltaic power generation.

Optimal Configuration of Wind Turbine and Photovoltaic Based on Day-ahead Economic Dispatch

With the gradual increase of the penetration rate of wind and photovoltaic (PV) electricity, the power system is facing great challenges in integration and peak-shaving capacity. As flexibility resources of the system is limited, wind and PV power generation will not be consumed completely. Allowing the curtailment of the peak output of wind turbine and PV can reduce the cost of auxiliary services such as peak-shaving, relieve the huge pressure in the operation and planning of the system, and improve the overall power supply economy of the system. Firstly, the reason and optimization principle of the curtailment of the peak output of wind turbine and PV are explained. Then, a capacity optimal configuration method of power supply based on day-ahead economic dispatch is proposed. On the premise of allowing the curtailment of the peak output of wind turbine and PV, the corresponding annual power supply cost of wind turbine and PV capacity combination is calculated based on day-ahead unit commitment. The function fitting between power supply cost and wind turbine capacity is carried out. The best wind turbine and PV capacity combination is the value of the extreme point. Finally, the sensitivity analysis of example system is carried out. It is found that with the decrease of unit investment cost of wind turbine and PV equipment, developing wind and PV power generation will be a more economical choice in the future.

Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM

For lifelong and reliable operation, advanced solar photovoltaic (PV) equipment is designed to minimize the faults. Irrespectively, the panel degradation makes the fault inevitable. Thus, the quick detection and classification of panel degradation is pivotal. Among various problems that promote panel degradation, hot spots and micro-cracks are the prominent reliability problems which affect the PV performance. When these types of faults occur in a solar cell, the panel gets heated up and it reduces the power generation hence its efficiency considerably. In this study, the effect of the hotspot is studied and a comparative fault detection method is proposed to detect different PV modules affected by micro-cracks and hotspots. The classification process is accomplished by utilizing Feed Forward Back Propagation Neural Network technique and Support Vector Machine (SVM) techniques. The investigation of both the techniques permits a complete analysis of choosing an effective technique in terms of accuracy outcome. Six input parameters like percentage of power loss (PPL), Open-circuit voltage (VOC), Short circuit current (ISC), Irradiance (IRR), Panel temperature and Internal impedance (Z) are accounted to detect the faults. Experimental investigation and simulations using MATLAB are carried out to detect five categories of faulty and healthy panels. Both methods exhibited a promising result with an average accuracy of 87% for feed-forward back propagation neural network and 99% SVM technique which exposes the potential of this proposed technique.