Distributed PV Power Research Articles

Assessment of New Energy Consumption Capacity of Grid Based on Edge Computing

Distributed photovoltaic grid integration brings many negative effects to the distribution network, and carrying out the research on its consumption capacity (CC) in the distribution network is the key to guarantee the safe and stable operation of the distribution network. This paper takes the maximum power access mode and minimum network loss as the optimization objectives, and takes voltage deviation, voltage fluctuation and short circuit capacity as the constraints to construct the evaluation model of distributed PV power generation CC. In view of the above problems, this paper uses edge computing to construct the evaluation of new energy (NE) CC of the grid, and applies it to the IEEE33 node network and the actual network. Finally, the established mathematical model and algorithm are validated (relative to Monte Carlo, the evaluation time of edge computing and time series production simulation is shorter, 13.11s and 23s, respectively).

Preface

It is a great pleasure for us to welcome you here to Changsha for 2023 2nd International Conference on Power System and Energy Technology (ICPSET 2023), which was held in Changsha, China from 18th to 20th August 2023 in virtual form. About 80 delegates took part in the Conference.ICPSET 2023 provided an opportunity to present and discuss the latest news accumulated during last year. Another important feature of this series of meeting has always been the participation of a large number of related scientists and researchers from all over the world. It thus represented an important addition to the series of large international conference on power system and energy technology held every year in different regions of China.The ICPSET meeting brought together experts and scholars interested in studying power system and energy technology to an international platform for presenting and discussing recent results and achievements, including new experimental and theoretical methods in the fields. The meeting’s topics can be characterized as follows: Power System Real-Time Simulation System, Power Transmission, Energy Efficiency, Energy Saving Technology, New Energy Vehicle, etc.Since 2022, the series meeting of ICPSET has been held in China and has become a regular academic meeting investigating latest achievements on power system and energy technology. Recent years have seen a lot of new research and experimental results in related fields. Many excellent papers of plenary lectures and contributions presented at ICPSET 2023 were published in the conference proceedings by Journal of Physics: Conference Series.We listened to the reports of experts from leading universities and research organizations in related fields. Among the many invited speakers, Prof. Yicheng Zhou from Waseda University, Japan delivered a keynote speech on Grid Forming Inverters: Current Status and Some Challenges Issues for the Future, Prof. Denis Sidorov from Siberian Branch of Russian Academy of Sciences and The Skolkovo Institute of Science and Technology, Russia on Crisp and Fuzzy Models for Net-zero Communities’ Development, Prof. Jianbo Bai from Hohai University, China on Development and Application of Digital Technology for Distributed PV Power Stations Under Full Life Cycle, and Prof. Yong Li from Hunan University, China on Autonomous Operation and Energy Management of Multi-Energy Microgrid.We are grateful to all the people who were involved in the whole process of the Conference. We gratefully acknowledge the support from Hunan University (China), as well as the contributions from each author. We also appreciate the publishing support from relevant staff of Journal of Physics: Conference Series.The Committee of ICPSET 2023List of Committee Member is available in this Pdf.

Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations

Accurate regional distributed PV power forecasting provides data support for power grid management and optimal operation. Distributed PV has the characteristics of large quantity, small capacity and difficulty in obtaining meteorological data. Statistical upscaling method is commonly used to forecast regional power. However, the current research ignores how to reasonably divide the sub-regions with similar output characteristics and mine the spatial and temporal correlation between different sub-regions. Therefore, this paper proposes a short-term regional distributed PV power forecasting method based on sub-region division considering spatio-temporal correlation. Firstly, the representative power plant is selected after dividing the sub-region by the AP clustering algorithm. Then, the GCN is used to extract spatial correlation features, and the LSTM is used to extract the evolution features of dynamic spatial correlation features, and the power forecasting models of representative plants in different weather types are established. Finally, the data integrity and similarity of the sub-region are scored, and the upscaling weight is determined to realize the power forecasting of the whole region. The distributed PV power generation data of Pingshan County, Hebei Province, China is used for simulation test. The results show that the forecasting method proposed has higher forecasting accuracy than the traditional model.

Ultra-short-term PV power prediction based on Informer with multi-head probability sparse self-attentiveness mechanism

As a clean energy source, solar power plays an important role in reducing the high carbon emissions of China’s electricity system. However, the intermittent nature of the system limits the effective use of photovoltaic power generation. This paper addresses the problem of low accuracy of ultra-short-term prediction of distributed PV power, compares various deep learning models, and innovatively selects the Informer model with multi-head probability sparse self-attention mechanism for prediction. The results show that the CEEMDAN-Informer model proposed in this paper has better prediction accuracy, and the error index is improved by 30.88% on average compared with the single Informer model; the Informer model is superior to other deep learning models LSTM and RNN models in medium series prediction, and its prediction accuracy is significantly better than the two. The power prediction model proposed in this study improves the accuracy of PV ultra-short-term power prediction and proves the feasibility and superiority of the deep learning model in PV power prediction. Meanwhile, the results of this study can provide some reference for the power prediction of other renewable energy sources, such as wind power.

The Use of Big Data Technology in Distributed Photovoltaic Power Generation Operation and Management--Take China as an Example

Given the lack of distributed PV power generation operation and management capability, this paper profoundly analyzes the current situation of the application of big data technology in the process and management of PV power generation in China and proves the necessity and importance of promoting new equipment, new technology, and new mode based on traditional power system operation and management, using the extensive data information formed in the process and management of power plants, meteorology, environment, and power grid, and launching ample data analysis research. In addition, the typical application scenarios of big data technology in the operation and management of distributed PV power generation are explored, and a new mode of operation and management of distributed PV power generation at the power plant, grid, and government levels is explored and constructed. The study shows that with the dramatic increase in the number of distributed PV power generation, the use of big data technology in scenarios such as the power generation side and grid side has excellent potential to provide adequate support for PV power generation operation, grid scheduling, market-based transaction settlement, and government decision-making.

Short-term prediction for distributed photovoltaic power based on improved similar time period

A short-term prediction method for distributed PV power based on an improved selection of similar time periods (ISTP) is proposed, to address the problem of low output power prediction accuracy due to a large number of influencing factors and the large difference in the degree of influence of various factors. First, the simple correlation coefficient (SCC) based on path analysis is used to screen the main influencing factors with stronger correlation with PV output power, and these factors are classified into three categories. Second, correlations of the three dimensions are calculated, respectively: (i) TOPSIS (with weights optimized by the SCC) determines meteorological correlation, (ii) linear weighting (based on the fuzzy ranking) obtains time correlation, and (iii) load correlation is quantified with existing current parameters. Third, the combined impact correlation (CIC) is obtained by weighting the three correlations above to establish criteria for the selection of similar periods, and a short-term PV power prediction model is established. Finally, experimental results based on real data of Australian Yulara Solar System PV plant demonstrate that errors of proposed ISTP method are respectively improved by 47.06% and 46.09% compared with the traditional ELMAN and similar day method.

Net Power Prediction for High Permeability Distributed Photovoltaic Integration System

The large-scale grid-connected access to distributed PV power generation has posed a great challenge to the new power system. Distributed PV power output and power load have strong uncertainty and volatility, which increases the difficulty of distribution network net power prediction to a certain extent. To improve the prediction accuracy of distribution grid net power, the paper proposes a combined distribution grid net power prediction method based on XGBoost and RBF neural networks. The combination of the two neural network algorithms into the power prediction model makes up for the lack of learning ability of the single neural network model for the input features of net load prediction, and can greatly improve the generalization ability and prediction accuracy of the model. The experimental results show that the described method improves the net power prediction accuracy of the distribution network and outperforms the comparison model.

Impact Analysis of Distributed Solar Photovoltaic Power Generation System Integration on Power Grid Voltage Stability Using Static Techniques

Recently, the rise in consumption of electrical energy and power transmission between various utilities has resulted in the emergence of anxieties about the power system's network voltage stability. Additionally, the necessity for power systems to perform in a secure condition has increased due to an increase in load demand globally. Therefore, the use of renewable energy-based distributed generation is growing quickly to help meet electrical demand and disrupt environmental issues caused by the use of fossil fuels. This type of generation can have a positive or negative influence on the stability of the power system. This paper analyzes the effects of distributed PV power integration on the power grid's voltage stability using static techniques. In order to improve efficacy and accuracy, give a complete and in-depth understanding of the problem of voltage stability, and identify the causes of instability, a combination of four different techniques is employed for analysis. Analysis methodologies include modal analysis, sensitivity analysis, PV curve, and QV curve. The analysis of voltage stability is carried out on an IEEE 14-bus system by using NEPLAN software. The simulation results showed that the integration of a renewable energy resource-based distributed solar PV power system into the test system led to a noticeable improvement in stability degree, a decrease in sensitivity of buses, a significant improvement in system MW loading, an enhancement in voltage profile, and a perceptible increase in reactive power margin

Series DC Arc Characteristic and Diagnosis Strategy for Distributed PV Power Generation

In order to realize the nation’s goal of achieving carbon peaking before year 2030 and the carbon neutrality by year 2060, the installed capacity of distributed PV power generation system has increased year by year in China, which increases the probability of DC arc fault. The features of DC arc may be very weak under some conditions. When the current of DC arc is large and the voltage level of photovoltaic array is high, DC arc will be very stable, and it is hard to be detected. Besides, if there are many high-power devices around DC arc detector, the output signal of current sensor will be seriously interfered, and it will be more difficult to distinguish between normal state and arc fault state. DC arc can be divided into unstable combustion stage and stable combustion stage. When in stable combustion stage, DC arc is also hard to be detected. Fortunately, the features of DC arc are obvious when in unstable combustion stage and the value of current spectrum energy will significantly increase. When the spectrum energy exceeds the set threshold in five consecutive time windows, it is preliminarily determined that there is a suspected arc. If the spectrum energy still exceeds the set threshold within five consecutive time windows, it is finally determined that there is an arc fault, otherwise, short PV array for further diagnosis. If there is an arc fault, the fluctuation characteristics of DC arc will be enhanced and the loop current will go fluctuating intensely again, then spectrum energy calculated will exceed the set threshold and an arc fault alarm can be sent out. Taking into consideration the different features of DC arc’s unstable combustion stage and stable combustion stage, a Series DC arc diagnosis method based on current spectrum energy is proposed, which amplifies the fluctuation characteristics of arc current and keeps arc current fluctuating strongly for a long time by shorting the PV array. The spectrum energy is calculated through FFT analysis of each PV string’s current, compared to the calculated spectrum energy with the set threshold, and then decided whether DC arc fault exists. The results to an experimental platform verify the effectiveness of the arc detection method, which has advantages of low calculation cost and high accuracy.

Research on reactive power optimization of distributed photovoltaic power distribution based on improved PSO algorithm

When photovoltaic power is connected to the distribution network, its power status will change due to its instability. However, some reactive power optimization methods only optimize a single index, ignoring the overall complexity of the power grid and under the influence of the limitation of the optimization algorithm, resulting in a poor reactive power optimization effect. The improved PSO algorithm is used to study the reactive power optimization of distributed PV power distribution. The power flow of PV access is calculated by analyzing the influence mechanism of PV access. After improving the PSO algorithm by niche, Tabu search algorithm, and trigonometric function, the multi-objective reactive power optimization model was solved to realize the distribution reactive power optimization under the influence of photovoltaic access. In the experimental study of the reactive power optimization effect of the IEE333 network, the total voltage deviation of network nodes optimized by the improved PSO algorithm is reduced by about 59.06%, which effectively reduces the power grid energy damage and improves the optimization efficiency.

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.

A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting

In recent years, the integration of distributed photovoltaic has witnessed explosive growth because of its great economic and environmental benefits. However, the intermittence and randomicity of PV power generation are challenging the stable and economic operation of active distribution system, especially when the local historical and forecasting meteorological data are not available. Thus, this paper presents a novel Gated Recurrent Unit based hybrid model for forecasting distributed PV power generations. The proposed method uses locally historical PV power generation data to forecast one-day-ahead solar power time series with five-minute intervals. First, a linear components extraction method is proposed to identify the daily fluctuating patterns of the solar power series. Then, temporal distributed Gated Recurrent Unit models are developed based on the detected daily fluctuating patterns of the PV power generation. A scenarios generation algorithm is proposed to provide forecasting linear trend data for each Gated Recurrent Unit model. Finally, the daily linear series scenarios, the linear and nonlinear parts of the historical data are fed into the Gated Recurrent Unit to forecast the PV generation. Data collected from an actual distributed PV farm in southeastern China are utilized to validate the effectiveness of the proposed model. The results show that the proposed model can provide a nominalized root mean square error of 6.83% and a nominalized mean absolute error of 4.12%. Compared to a single Gated Recurrent Unit forecasting model, the developed model has improved the forecasting accuracy by 62.7%.

A multi-criteria decision-making framework for site selection of distributed PV power stations along high-speed railway

The construction of distributed photovoltaic power stations (DPVPS) along high-speed railway can supply power for the traction power supply system (TPSS) of high-speed railway. The DPVPS site selection is a natural call from the practice with the consideration of full use of solar PV. This paper addresses a multi-criteria decision-making (MCDM) framework for DPVPS site selection along high-speed railway for the first time. In this framework, an evaluation criteria system is established. Considering the particularity of DPVPS site selection problem along high-speed railway, two new criteria namely power consumption capacity of high-speed railway TPSS and impact on high-speed railway TPSS are presented innovatively. In view of the strong grey correlations among criteria, the fuzzy grey relational projection (FGRP) method is applied to select the optimal site which is greatly suitable to deal with the grey correlations. A case in Hebei Province with sensitivity analysis also illustrates the robustness and effectiveness of the framework. The calculation results of the case indicate that the sub-criteria of impact on high-speed railway TPSS (C26) has the largest comprehensive weight, which is 0.129. Among the four candidate sites, G1 gets the highest final score of 0.653. This paper puts forward an effective tool for railway companies to choose the most suitable site to build DPVPS along high-speed railway, which has the certain practical significance.

Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Distributed PV power generation necessitates both intra-hour and day-ahead forecasting of solar irradiance. The UTSA SkyImager is an inexpensive all-sky imaging system built using a Raspberry Pi computer with camera. Reconfigurable for different operational environments, it has been deployed at the National Renewable Energy Laboratory (NREL), Joint Base San Antonio, and two locations in the Canary Islands. The original design used optical flow to extrapolate cloud positions, followed by ray-tracing to predict shadow locations on solar panels. The latter problem is mathematically ill-posed. This paper details an alternative strategy that uses artificial intelligence (AI) to forecast irradiance directly from an extracted subimage surrounding the sun. Several different AI models are compared including Deep Learning and Gradient Boosted Trees. Results and error metrics are presented for a total of 147 days of NREL data collected during the period from October 2015 to May 2016.

Three-phase Unbalanced Interval Power Flow Calculation of Low-voltage Distribution Network with Distributed PV Power Generation

Low-voltage distribution network is a three-phase unbalanced system due to the integration of single-phase loads and single-phase distributed PV arrays. In this paper, three-phase unbalanced interval power flow calculation model of three-phase four-wire low voltage distribution network with distributed PV power generation is established. In the model, intensity of illumination and battery temperature which influence the power output of distributed PV power generation is described as intervals. Then, through the affine interval algorithm, the interval power flow problem is transformed into a deterministic power flow problem and two linear optimization problems. By solving the above problems, the interval power flow solution can be obtained. Finally, the proposed algorithm is applied to an actual 22-bus low-voltage distribution network, and the solution of the affine interval algorithm is compared to the solution of the Monte Carlo sampling method, which verifies the correctness and effectiveness of the proposed algorithm.

QCPV: A quality control algorithm for distributed photovoltaic array power output

Distributed PV power output measurements and their metadata are subject to significant errors and uncertainty. However, no standard mechanism for quality controlling these data is currently available within the literature. For this purpose, we present a two part approach to parameterize PV system metadata and quality control their power output measurements. These methods are based on open-source solver routines and require only one exogenous input (ambient temperature), and therefore are widely applicable. The method allows for PV array orientation to be determined to within ∼4°. Furthermore a loss factor LF is derived which captures overall PV system losses and correctly detects a mean degradation of the modules of ∼0.5% per annum over time. The central routine, entitled QCPV, imposes system specific limits on the measured data mainly through the use of the extraterrestrial irradiance and the clear sky index for photovoltaics kpv. It also enables, amongst other things, the detection of cloud enhancement events and spurious power output reporting through the application of across systems statistics.

Study on Access System of Low Voltage Distributed Photovoltaic Power Station

Due to the actual situation, when carrying out research on the principle of low-voltage distributed PV power station, we should make research on the process of survey and the determination of measurement program. And the working-out process of the connection scheme to the first distributed PV power station in Liaoning region is stated. After the discussing the difficult problem on the development of the connection scheme, the next phase of research direction is proposed.

Research on Grid-Connected and Off-Grid Switching Distributed Photovoltaic Power System

Photovoltaic power system (PV system) includes grid-connected and off-grid (islanding) types. With the improvement of power supply reliability, it is necessary to provide distributed PV power system with functions of grid-connection and off-grid operation, which as well as being easily switched. This research comparative analyzes on model of AC bus and model of DC bus of grid-connected and off-grid PVsystem and presents the control strategy. In order to safely operate and seamlessly switch grid-connected and off-grid, PV system is furnised constant-power control and constant-voltage frequency control with switching operation. Additionally, the switch junction for off-grid and the public supply system must be connected to the synchronization device for controling system of PV system therefore the PV system can be safely operated and switch from off-grid to grid-connected can be smoothly and rapidly.

Empowering Distributed Solar PV Energy for Malaysian Rural Housing: Towards Energy Security and Equitability of Rural Communities

This paper illustrates on how Malaysia’s development landscapes has been poweredby cheap oil and gas making it dependent and addicted on using large amounts of fossil fuels. As acountry that is primarily depended on fossil fuels for generating power supply, Malaysia needs tocogitate of long-term energy security due to fossil fuel depletion and peak oil issues. Loss of theseresources could leadto thereduction of power generation capacitywhich will threaten the stabilityof the electricity supply in Malaysia. This could potentially influence in an increase in electricitycosts which lead to a phase of power scarcity and load shedding for the country. With the risk ofinterrupted power supplies, rural households, especially those of low-income groups areparticularly vulnerable to the post-effects of a power outage and an inequitable distribution to thepeople. Distributed generation of electricity by solar PVs diminishes the vulnerability of thesehouseholds and can also offer an income to them by feeding the power supply to the national gridthrough Feed-in Tariff scheme. At the moment, the deployment of solar PV installations is still inthe introductory stage in Malaysia, where roof-mounted PV panels are only available to commercialand urban residential buildings. This is due to the lack of a suitable renewable energy policy forrural households and the high cost of the solar PV technology. This paper will put forward ananalysis for incorporating solar photovoltaic on roofs of rural houses by identifying the energyconsumption of these households and the extent to which PVs can alleviate electricity insecurity.The results present significant potential for distributed PV power generation in rural areas inMalaysia which shown a considerable amount of electricity needed to be harvested from roofmountedsolar PV for rural people in Malaysia.