Photovoltaic Distribution Research Articles

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson’s correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.

A Novel Areal Maintenance Strategy for Large-Scale Distributed Photovoltaic Maintenance

A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency.

A Generalized Load Model Considering the Fault Ride-Through Capability of Distributed PV Generation System

Considering the voltage stability problem brought by large-scale distributed PV access to the distribution network, this paper proposes a generalized load model that considers the fault ride-through capability of distributed PV. Firstly, the detailed model of the distribution network is established, and the detailed model is calibrated based on the measured data, the simulation errors are below 1%. And then establish a generalized load model considering distributed PV high and low voltage traversal ability. The sensitivity analysis results are used to rank the parameters to be identified, and the parameters with higher sensitivity are identified. The parameters are obtained from the detailed model and measured data, and four sets of parameters are identified and simulated under different PV penetration rates and fault conditions. The calculated fitting errors are less than 1%. The results show that the generalized load gray box model of the distribution network with distributed PV high and low voltage ride-through capability can reflect the dynamic characteristics of the distribution network well.

Multi-objective optimization strategy for the distribution network with distributed photovoltaic and energy storage

The randomness and fluctuation of large-scale distributed photovoltaic (PV) power will affect the stable operation of the distribution network. The energy storage system (ESS) can effectively suppress the power output fluctuation of the PV system and reduce the PV curtailment rate through charging/discharging states. In order to improve the operation capability of the distribution network and PV consumption rate, an optimal multi-objective strategy is proposed based on PV power prediction. First, the back propagation (BP) neural network with an improved genetic algorithm (GA) is used to predict PV power output. Furthermore, an adaptive variability function is added to the GA to improve the prediction accuracy. Then, the distribution network model containing distributed PV and the ESS is constructed. The optimal object contains network power loss, voltage deviation, and PV consumption. The model is solved based on the improved multi-objective particle swarm optimization (MOPSO) algorithm of Pareto optimality. The probabilistic amplitude is adopted to encode the particles for avoiding local optimal. Finally, the proposed optimal strategy is verified by the IEEE 33-bus distribution network. The results show that the proposed strategy has an obvious effect on reducing the network power loss and voltage deviation, as well as improving the PV consumption rate.

Research on a Distributed Photovoltaic Two-Level Planning Method Based on the SCMPSO Algorithm

In response to challenges such as voltage limit violations, excessive currents, and power imbalances caused by the integration of distributed photovoltaic (distributed PV) systems into the distribution network, this study proposes at two-level optimization configuration method. This method effectively balances the grid capacity and reduces the active power losses, thereby decreasing the operating costs. The upper-level optimization enhances the distribution network’s capacity by determining the siting and sizing of distributed PV devices. The lower-level aims to reduce the active power losses, improve the voltage stability margins, and minimize the voltage deviations. The upper-level planning results, which include the siting and sizing of the distributed PV, are used as initial conditions for the lower level. Subsequently, the lower level feeds back its optimization results to further refine the configuration. The model is solved using an improved second-order oscillating chaotic map particle swarm optimization algorithm (SCMPSO) combined with a second-order relaxation method. The simulation experiments on an improved IEEE 33-bus test system show that the SCMPSO algorithm can effectively reduce the voltage deviations, decrease the voltage fluctuations, lower the active power losses in the distribution network, and significantly enhance the power quality.

Rooftop PV Development Suitability and Carbon Benefits: An Anhui Province Case Study

As one of the most rapidly developing provinces in China in the past two decades, Anhui Province has seen an increasing demand for clean energy in recent years due to industrial transformation and the requirements of dual carbon targets. This paper opts to investigate roof-mounted distributed photovoltaics, which are more suitable for development in densely populated areas. Current research on distributed photovoltaics largely focuses on vague estimations of power generation potential, without adequately considering the specific development conditions of different regions. This paper starts from the actual situation affecting the development of roof-mounted distributed photovoltaics and selects a smaller number of factors that are more in line with reality for hierarchical analysis, constructing a relatively simple but practical evaluation system (“meteorological-geographical-socio-economic”). At the same time, this paper innovatively proposes different schemes for the full lifecycle power generation and emission reduction benefits of roof-mounted distributed photovoltaics and compares them, providing a foundation for subsequent in-depth research. Key findings include the following: The northern regions of Anhui Province exhibit higher suitability for rooftop distributed PV, with residential areas being the primary influencing factor, followed by solar radiation considerations; the annual power generation potential of rooftop distributed PV in Anhui Province constitutes around 80% of the total electricity consumption in 2021, but the potential is predominantly concentrated in rural areas, resulting in spatial disparities in power generation and consumption across the province; developing the rooftop distributed PV industry based on suitability can yield substantial power generation and emission reduction benefits, translating to an estimated reduction of approximately 1.28 × 108 tCO2 annually, representing around one-third of Anhui Province’s carbon emissions in 2021.

Leveraging dynamic power benchmarks and CUSUM charts for enhanced fault detection in distributed PV systems

Faults in photovoltaic (PV) systems are common during their operational lifetime. Existing PV fault detection methods, which are primarily designed for large-scale PV fields, struggle with distributed systems due to their reliance on on-site weather sensors and inability to handle complex local shading effects on PV performance in the built environment. This paper presents a fault detection scheme specifically applicable to distributed PV systems that solely relies on monitored AC output and remote weather sources. Without requiring additional equipment or detailed information on the PV configuration and local shading conditions, the proposed method consists of two steps. First, historical monitoring data is used in a bootstrap fashion to develop machine learning models that establish baselines for normal PV output and corresponding estimation uncertainty. Then, a dynamic PV power benchmark constructed based on the 3-sigma rule, and cumulative sum (CUSUM) control charts are employed simultaneously to detect system malfunctions. Through experimental verification on actual distributed PV systems, the effectiveness of the proposed method is assessed for four categories of frequently observed malfunctions. The comprehensive experimental results indicate the considerable efficiency of the proposed sensorless method in detecting both serious PV malfunctions and minor faults with a severity as small as 4.2%.

Distributed Photovoltaic Generation Aggregation Approach Considering Distribution Network Topology

Distributed photovoltaics (DPVs) are widely distributed and the output is random, which brings challenges to the safe operation of the distribution network, so the construction of photovoltaic aggregations can effectively participate in the flexible regulation of the power system. At present, the extraction of DPV clustering features is not sufficient, only considering the output characteristics of PVs. Certain PVs under some nodes may have a more pronounced regulation effect, but they may be ignored in the clustering process. To address the above problems, this paper proposes a DPV aggregation approach considering the distribution network topology. It combines the voltage sensitivity and the power curve and regards them as clustering features to form the DPV aggregation with the highest average voltage sensitivity participating in voltage regulation. The simulation on the IEEE 33-node system verifies that the proposed aggregation approach can select DPV aggregation more suitable for voltage regulation, and make full use of the aggregation to realize the optimal voltage regulation effect.

Fast-track development of an automated solar photovoltaic module detecting framework utilizing open-access multispectral satellite imagery

The southwest coast of Taiwan is an important habitat for migratory waterbirds. However, the use of fossil fuels has led to climate crises. As society's awareness of environmental issues increases, renewable energy including solar power has become an irreplaceable choice. Monitoring the spread of solar photovoltaic (PV) to mitigate its ecological impact has become an important task. Yet, acquiring information related to the geographic information of this renewable energy is often challenging. Remote sensing techniques for identifying the distribution of solar PV modules have been extensively discussed and published in recent years. However, the use of high-resolution imagery, powerful computational hardware, or high-precision but technically challenging frameworks, particularly in terms of computational costs, hinders the rapid updating and dissemination of information on solar PV distribution. The aim of this study is to rapidly develop a PVs detecting framework. To do so, by utilizing the spectral reflectance characteristics of PV modules, along with open-access Sentinel-2 satellite datasets, we selected false color images derived from Bands 8 (842 nm), 11 (1,610 nm), and 12 (2,190 nm). With the foundation of ArcGIS and the U-Net deep learning architecture, we developed a framework with low training costs, simple data processing, and short training time. Through syntheses of false color with different combinations of spectral bands, we achieved a PVs detecting framework with 94.5% accuracy, 0.94 F1 score, and 0.89 Kappa in aquaculture landscape. In our application in the southwest coast region, the overall accuracy reached 96%, with a 0.92 F1 score and 0.9 Kappa performance. This framework will facilitate the rapid monitoring of the spatial and temporal distribution of PV in Taiwan, accelerate social communication, and alleviate ecological impacts.

Cluster partition-based two-layer expansion planning of grid–resource–storage for distribution networks

In order to realize the optimal planning of grid–resource–storage for distribution networks (DNs) with high penetrated distributed photovoltaics (PVs), a cluster partition-based two-layer expansion planning for DNs is proposed. First, a comprehensive cluster partition index-based cluster partition method is proposed, which involves the indexes such as electrical distance, power balance of the cluster, and cluster size. Second, a cluster partition-based two-layer expansion planning model is proposed. In the upper layer, a line planning model for clusters is established to carry out the planning of cluster connection lines. In the lower layer, a robust source-storage planning model is established with the uncertainty of PVs and loads, and then, the optimal location and capacity of PVs and energy storages (ESs) can be obtained. In addition, the uncertainty regulation parameter is utilized to control the range of uncertainty sets, which can reduce the conservatism of the optimization. Finally, the proposed method is carried out in a real DN in China, which can effectively improve the economy of DN planning.

Integration of multiple distributed solar PV (DSP) into the grid: The case of the distribution network in Freetown, Sierra Leone

The optimal integration of distributed solar photovoltaic (DSP) is a transformative engineering method that is key in contributing to a sustainable and resilient energy future, especially when countries seek to increase the proportion of renewables in their energy mix to reduce carbon emissions. This paper presents a power flow-based approach that makes use of Newton Raphson's method in the ETAP tool to integrate multiple DSPs into both the existing and expanded Freetown distribution networks. The study determined the network's hosting capacities and optimal points of injection for the reduction of active power loss and improvement of bus voltage profiles. The study showed that the existing Freetown distribution network had a hosting capacity of 34.6 MW with an active power loss reduction of 0.967 MW while the expanded Freetown distribution network had a hosting capacity of 59.57 MW with an active power loss reduction of 5.12 MW. Before the injection of the DSPs into both networks, most of the bus voltages were not within acceptable limits. However, with the intervention of the injected DSPs, bus voltages considerably improve. The study showed that the expanded Freetown distribution network is better for DSPs integration compared to the existing Freetown distribution network. To evaluate the impacts of the injected DSPs and to validate the model used, four network scenarios were considered. The study used an analytical approach, considering future load growth and an evolving grid to integrate DSPs for long-term planning. The study will inform policymakers, utilities, etc., about the potential of integrating DSPs.

Centralized and decentralized combined voltage control for distribution network with high penetration PV connected

With the high penetration of distributed photovoltaics (DPVs) integrated into the distribution network, the voltage problem becomes a critical problem. At present, the resources in the current distribution network hardly satisfy the requirements of voltage regulation. However, the huge amount of DPVs in the distribution network could be a potential voltage regulation resource due to the controllable active power and reactive power. In this paper, a day-ahead centralized and intraday decentralized combined voltage control method is proposed for high penetration DPVs connected distribution Networks. In the part of day-ahead control, centralized compensation of the distribution network voltage is achieved by controlling the gear ratio of the capacitor/reactor group on an hourly scale. The intraday control part consists of decentralized local control based on DPVs and centralized control based on energy storage (ES) equipment. The reactive and active power of DPVs is controlled in a decentralized way by using local priority sensitivity control based on the coordination with capacitor optimization. Finally, simulations are performed to verify the performance of the method. The results indicate that this method can effectively solve the voltage problem caused by DPVs.

Macroeconomic, energy, and emission effects of solar PV deployment at utility and distributed scales in Saudi Arabia

For the first time, this paper assesses the macroeconomic, energy, and emission impacts of solar photovoltaic (PV) deployment in Saudi Arabia by linking an energy-and-environment-sector augmented macroeconometric model with a power model and a distributed generation model while distinguishing between utility scale and distributed-generation scale of the PV deployment. We analyze three policy scenarios of deploying 5.5 TWh PV for the period 2021–2030: (S1) fully government-funded utility-scale PV deployment, (S2) half-government-funded utility-scale PV deployment, and (S3) household-funded distributed generation scale PV deployment with little government support. We find that: S1 is best suited if the objective is fiscal expansion, 2 is best suited to improve non-oil fiscal positions, increase non-oil value added and employment, and increase household consumption, and S3 is best suited to reduce energy consumption and associated emissions and enhance international trade. We show that government and foreign investment play an important role, and it would be desirable for the Saudi government to partner in financing renewable energy projects, saving budgetary resources to invest in other projects to support economic growth.

PV Identifier: Extraction of small-scale distributed photovoltaics in complex environments from high spatial resolution remote sensing images

The precise location and size of distributed photovoltaics (PVs) is critical to infer the actual installed capacity and assess the remaining PV generation potential, and is therefore the cornerstone of strategic planning for distributed PV deployment. However, identifying small-scale distributed PVs in complex contexts from high spatial resolution remote sensing (HSRRS) images to obtain their information remains an issue. In this study, we propose an advanced deep learning model, called PV Identifier, to enhance the identification accuracy of small-scale PV systems from HSRRS images. PV Identifier uses a fine-grained feature layer (FFL) compatible with the size of PVs to improve the detection capability of the small-scale distributed PVs. At the same time, it effectively distinguishes between PVs and similar background using a novel semantic constraint module (SCM). We test PV Identifier on a distributed PV dataset in California. Experiments show that the inclusion of the FFL positively affects the model's sensitivity to small distributed PVs. Specifically, the PV Identifier with the FFL increases the Recall of identifying residential rooftop PVs by 1.9% compared to the model without the FFL. In addition, the integration of the SCM effectively improves the model's ability to locate residential rooftop PVs in complex environments, resulting in a 1.8% increase in the corresponding Precision. Compared to the four commonly used segmentation models, PV Identifier exhibits superior identification performance for residential rooftop PVs and commercial and industrial PVs, with an Intersection over Union (IoU) of 74.1% and 89.3%, respectively, which is at least 4.1% and 1.8% higher than other models. Overall, PV Identifier provides a viable solution to the problem of identifying small-scale distributed PV in complex backgrounds from HSRRS images.

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning.

With the fast development of large-scale Photovoltaic (PV) plants, the automatic PV fault identification and positioning have become an important task for the PV intelligent systems, aiming to guarantee the safety, reliability, and productivity of large-scale PV plants. In this paper, we propose a residual learning-based robotic (UAV) image analysis model for low-voltage distributed PV fault identification and positioning. In our target scenario, the unmanned aerial vehicles (UAVs) are deployed to acquire moving images of low-voltage distributed PV power plants. To get desired robustness and accuracy of PV image detection, we integrate residual learning with attention mechanism into the UAV image analysis model based on you only look once v4 (YOLOv4) network. Then, we design the sophisticated multi-scale spatial pyramid fusion and use it to optimize the YOLOv4 network for the nuanced task of fault localization within PV arrays, where the Complete-IOU loss is incorporated in the predictive modeling phase, significantly enhancing the accuracy and efficiency of fault detection. A series of experimental comparisons in terms of the accuracy of fault positioning are conducted, and the experimental results verify the feasibility and effectiveness of the proposed model in dealing with the safety and reliability maintenance of low-voltage distributed PV systems.

Impact of bi-directional electric vehicle and demand response on residential distributed PV capacity planning based on TOU pricing

The widespread deployment of residential distributed photovoltaic (RDPV) remains complex and challenging due to photovoltaic output intermittency, fluctuating electricity demand, and rising electric vehicle (EV) adoption. Simultaneously, the energy storage capabilities of EVs and residential demand response (DR) offer solutions for optimizing RDPV applications. This study proposes an integrated RDPV capacity planning model by encompassing EV charging, vehicle-to-home, and flexible load DR. Five scenarios are established to reveal the impact of various factors on the optimal photovoltaic installation capacity, electricity cost, self-consumption and self-sufficiency rate. A case study of three typical residential electricity demand patterns indicates that DR and vehicle-to-home significantly reduce the optimal photovoltaic installation capacity and total electricity cost. When the feed-in tariff during photovoltaic generation periods is higher than the off-peak pricing, DR results in a reduction in photovoltaic self-sufficiency rate and an increase in photovoltaic self-consumption rate. EV charging and vehicle-to-home have minimal impact on photovoltaic self-consumption rate, while EV charging significantly decreases self-sufficiency rate and vehicle-to-home exacerbates this effect.

NYSolarCast: A solar power forecasting system for New York State

Solar energy generation capacity will need to be greatly increased to meet aggressive clean energy targets by New York State (NYS), which are 70% renewable energy (RE) generation by 2030, and 100% by 2040. Because solar energy is a variable, weather-driven resource, accurate forecasts of solar energy generation both in nowcast (intra-day) and day-ahead time horizons are necessary for electric utilities and independent system operators, as they maintain grid stability and maximize RE use.Meeting this need for NYS, a gridded, open-source solar power forecasting system called NYSolarCast was developed (https://github.com/ncar/NYSolarCast_delivery). NYSolarCast makes 15-min resolution predictions of global horizontal irradiance (GHI) on a 3-km grid covering all of NYS, which are then used to estimate solar power generation both for select utility-scale PV plants (15-min resolution) and for zone-aggregated distributed PV (1-h resolution). The statewide GHI forecasts are made by applying the StatCast statistical forecasting model, which is trained on over two years of GHI observations from pyranometers at all 126 NYS Mesonet stations and gridded numerical weather prediction forecasts from both the Weather Research and Forecasting model tuned for solar applications (WRF-Solar®) and NOAA’s operational High-Resolution Rapid Refresh (HRRR) model. Forecasts are made at each NYS Mesonet site and then blended outward into the rest of the grid. This paper gives an overview of NYSolarCast performance for intra-day (0–6-h) GHI and power forecasts during a one-year period.

Promoting Sustainable Development Goals by Optimizing City-Level Solar Photovoltaic Deployment in China.

Solar photovoltaic (PV) installations, which enable carbon neutrality, are expected to surge in the coming decades. This growth will support sustainable development goals (SDGs) via reductions in power-generation-related environmental emissions and water consumption while generating new jobs. However, where and to what extent PVs should be utilized to support SDGs must be thoroughly addressed. Here, we use multiple PV deployment scenarios to compare the benefits of PVs and related SDGs progress in 366 prefectural-level cities in China. We developed an assessment framework that integrates a PV allocation model, an electricity system optimization model, and a benefit assessment approach. We identify vast differences in PV distribution and electricity transmission and elucidate trade-offs and synergies among the SDGs under various PV implementation scenarios. The water conservation-oriented scenario yields substantial carbon reduction, air pollutant mitigation, and water saving cobenefits, leading to the greatest SDGs improvements. Prioritizing employment creation enhances job-relevant SDGs but inhibits environmental resource benefits. SDGs in less developed cities present greater progress across all scenarios. This study highlights the need to consider spatial heterogeneity and the potential trade-offs between different SDGs and regions when designing energy transition strategies.

Distributed photovoltaic adoption in rural Shandong, China: status, challenges, and influential factors

Distributed photovoltaic systems (distributed PV) enable rural households to replace traditional energy sources, reduce their household carbon footprint, and generate additional income. Due to the multiple benefits, China increasingly prioritizes developing distributed PV in its rural areas. However, the overall status, primary challenges of distributed PV in rural China, and how regional social and economic factors contribute to adoption choices of distributed PV remain largely uninvestigated. Here, we aim to provide insights into the above issues and offer a basis for policy recommendations that can facilitate the adoption of distributed PV, drawing from Shandong Province’s experience. This study is based on a survey conducted in 2023, encompassing a total of 169 households across 36 villages in Shandong Province. Our results show that 43% of the households have embraced distributed PV with various system standards employed. We also find that rural households in Shandong Province encounter challenges engaging in distributed PV systems, such as inadequate policy support, significant heterogeneity of policy promotion among villages, a predominant emphasis on construction rather than management, and an extended payback period. We suggest that future attention should be paid to households that have not experienced extreme weather events and those that have not yet engaged in related low-carbon environmental activities. Local village officials should take the lead in spearheading policy promotion activities to enhance villagers’ awareness and enthusiasm. Besides, efforts should be directed towards guaranteeing the availability of high-quality distributed PV systems with consistent standards.

Impact of High PV Penetration on Transient Stability — a Case Study on the U.S. ERCOT System

This study was performed to assess the impact of high photovoltaic (PV) penetration on transient stability using a series of hypothetical models of the ERCOT system. This was done by calculating the critical clearing time (CCT) when faulting various busses. Varying levels of PV penetration were used to study the system transient stability. For each case 15% wind penetration was also included. It was found that under the PV volt/Var control with plant-level GE SolarControl settings, PV penetration at lower level can slightly improve the transient stability in the ERCOT system model. However, at approximately 54% penetration of renewables (39% PV and 15% wind), transient stability starts to show declining trend. This value has slight variance depending on PV distribution.