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In the current distribution network's energy structure, photovoltaic (PV) occupies a high proportion. However, the access of a high proportion of PV will lead to the phenomenon of reverse power flow in the distribution network, and then the problem of line overvoltage. When the reverse power flow increases, the problem of line overvoltage also worsens, which endangers the normal operation of power system. To solve this problem, this paper starts with the voltage rise theory of distribution network lines. Firstly, through strict mathematical derivation, it compares the influence of main network line parameters and distribution network line parameters on the voltage rise, and summarizes a simple calculation equation for the voltage rise of the distribution network with high proportion PV. Then, according to the mechanism of voltage rise and the principle of inverter control, considering the economy and practicability of the overvoltage suppression strategy, a reverse power flow overvoltage suppression strategy of a system with high proportion PV is proposed. Finally, a PV system model simulating a small village is used to verify the effectiveness of the proposed overvoltage suppression strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In response to the voltage over-limit in distributed photovoltaic （PV） distribution networks， a reactive voltage control strategy considering cluster segmentation of distributed PV is proposed. Firstly， a modularity index based on reactive voltage sensitivity is introduced； taking account of the reactive balance and coupling within clusters， an improved modularity index is obtained and used for cluster segmentation. Then， a strategy for reactive voltage control within over-limit clusters and a coordinated control strategy between clusters are proposed. When overvoltage clusters cannot restore voltage to a safe level internally， the strategy involves selecting lead nodes from safe clusters with adjustable reactive power capacity to allocate the remaining adjustable reactive power capacity to the overvoltage clusters. Finally， simulations are conducted using an IEEE 33-bus distribution network to validate the effectiveness of the proposed strategy.

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the current distribution network’s energy structure, photovoltaic (PV) occupies a high proportion. However, the access of a high proportion of PV will lead to the phenomenon of reverse power flow in the distribution network, and then the problem of line overvoltage. When the reverse power flow increases, the problem of line overvoltage also worsens, which endangers the normal operation of power system. To solve this problem, this paper starts with the voltage rise theory of distribution network lines. Firstly, through strict mathematical derivation, it compares the influence of main network line parameters and distribution network line parameters on the voltage rise, and summarizes a simple calculation equation for the voltage rise of the distribution network with high proportion PV. Then, according to the mechanism of voltage rise and the principle of inverter control, considering the economy and practicability of the overvoltage suppression strategy, a reverse power flow overvoltage suppression strategy of a system with high proportion PV is proposed. Finally, a PV system model simulating a small village is used to verify the effectiveness of the proposed overvoltage suppression strategy.

When multiple distributed PV (photovoltaic) systems are integrated into multiple nodes of the distribution network, this will lead to the significant influence of the grid-tied node voltage of the power distribution network resulting from the uncertainty of PV power. Therefore, this aspect needs to be further studied in terms of how to effectively characterize the uncertainty of the voltage influence in a grid-tied multi-PV system distribution network. Focusing on this problem, a modeling and analysis method for distribution networks with PV cells based on Markov global sensitivity is proposed in this paper. Firstly, a global Markov chain is constructed using the Markov chain and the power flow equation to model the uncertainty of PV power. Furthermore, a Markov global sensitivity function is proposed to characterize the influence degree of the voltage on the distribution network nodes while multi-point PV system are grid-tied to system. The case study results show that the uncertainty model of multi-point PV grid-connection can be effectively constructed using the proposed method in this paper, and the uncertainty influence analysis is accurate. This is of great significance for grid connection planning and the optimization control of new energy systems, as well as for the new energy consumption increase. [ABSTRACT FROM AUTHOR]

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.

Modern smart grids typically combine physical and communication networks for efficient information exchange and innovative applications. Aligned with digitalization and advancements in smart grids, the integration of photovoltaic (PV) systems comprises a variety of regulatory and technological aspects. However, no previous study has conducted an extensive and systematic analysis of the PV-grid integration framework, particularly for one country. To fill this gap, this paper uses Germany as an example to present a comprehensive, state-of-the-art analysis of integrating distributed PV systems into smart grids, focusing on the regulation and technical implementation of the German Smart Meter Infrastructure and PV control interfaces. Starting from a standardization perspective, this analysis utilizes the Smart Grid Architecture Model to identify crucial roles, components and processes specifically in Germany. Furthermore, it outlines the current implementation of PV integration into distribution networks at a national level. The results of this study show the overall complexity of PV integration in the smart grid context, confirm the feasibility of the German integration approach, and highlight the necessity of deploying standardized information models and communication technologies. These key findings can help market participants with different roles to identify potential technical bottlenecks or other critical points in the regulation and technical implementation. For instance, the proposed in-depth analysis framework provides an orientation for characterizing the PV integration or, more generally, the grid integration scenario of renewables in other countries.

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the "teaching" process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity. [ABSTRACT FROM AUTHOR]

When multiple distributed PV (photovoltaic) systems are integrated into multiple nodes of the distribution network, this will lead to the significant influence of the grid-tied node voltage of the power distribution network resulting from the uncertainty of PV power. Therefore, this aspect needs to be further studied in terms of how to effectively characterize the uncertainty of the voltage influence in a grid-tied multi-PV system distribution network. Focusing on this problem, a modeling and analysis method for distribution networks with PV cells based on Markov global sensitivity is proposed in this paper. Firstly, a global Markov chain is constructed using the Markov chain and the power flow equation to model the uncertainty of PV power. Furthermore, a Markov global sensitivity function is proposed to characterize the influence degree of the voltage on the distribution network nodes while multi-point PV system are grid-tied to system. The case study results show that the uncertainty model of multi-point PV grid-connection can be effectively constructed using the proposed method in this paper, and the uncertainty influence analysis is accurate. This is of great significance for grid connection planning and the optimization control of new energy systems, as well as for the new energy consumption increase.

Abstract Non-technical summary DPV systems, typically small to medium-sized solar power installations on buildings, which primarily and directly supply electricity to industrial, commercial, or residential consumers in proximity. DPV is an advocated renewable substation for climate change and energy saving for merits of low installation costs, high energy efficiency, and the ability to provide decentralized power supply. Our research has theoretical significance in explaining and understanding the development and policy evolution of DPV in China and provide valuable suggestions for future industry policies during grid parity. Technical summary Since 2021, China has been phasing out its decade-long feed-in tariff policies, reducing the photovoltaic industry's dependency on subsidies. Despite the challenges posed by declining electricity prices and slowdown in economic growth, the authorities continue to prioritize the development of DPV due to its low investment costs, high energy efficiency, and decentralized power supply, and these technologies have already achieved demand-side parity. Driven by this phenomenon, this study examines the trajectory of DPV diffusion and the evolution of related policies over the last decade. It unravels the dynamic mechanism of DPV investment through theoretical analysis and develops a macro model to identify optimal installation strategies and renewable energy proportions. Our findings highlight the increasing role of green energy and suggest that green finance is crucial for stimulating DPV investment in the era of grid parity. The study concludes with practical recommendations for overcoming DPV challenges in China. Social media summary DPV has become a prominent renewable energy solution in other countries but not in China. We probe the system dynamics modeling to give explanation and solution during grid parity.

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the “teaching” process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity.

The rational configuration of distributed energy storage can not only realize p peak-shaving and valley-filling during distribution network operation， but also play an important role in improving the consumption of renewable energy and ensuring the safe， reliable， and economic operation of the power grid. In view of the user-side friendly interaction of distributed energy storage， this paper studies the hierarchical optimal configuration of distributed energy storage in distribution networks with a high proportion of PV. In the upper layer， multi-objective models of investment and operation cost and maximized consumption of distributed PV are established from the perspective of both economy and environmental protection. In the lower layer， the friendly interaction of distributed energy storage devices on peak shaving and valley filling of distribution networks is considered， and the daily net load deviation per unit tie-line power is established. For the established non-linear， multi-objective two-layer optimization model， a weight method is used to convert the multi-objective model in the upper layer into a single-objective one.Moreover， an improved genetic algorithm is used for the optimal solution. The simulation is validated by an improved IEEE 33-node example. The results show that the established optimization model can reflect the investment benefits of energy storage and the impact on new energy consumption. The users’ friendly interaction impacts both distributed energy storage configuration and PV consumption in varying degrees.

Abstract Voltage imbalance (VI) is caused by the difference in connected single-phase load or generation in a low voltage distribution network (DN).VI increase in a smart distribution grid is due to the current practice of increasing single-phase distributed generators such as photovoltaic (PV) systems. This paper proposes a decentralized control method to mitigate VI using distributed batteries included in smart grid interfaced residential PV systems. To mitigate VI using the batteries in this way, five challenges must be overcome, i.e., equalizing all battery stress currents within the DN, mitigating VI in abnormal conditions such as signal loss among bus controllers, being immune from the distorted feedback measurements, minimizing the steady-state error at different loads, and overcoming the insufficient number or capacity of the distributed batteries at the same bus. Three fuzzy logic controllers (FLC) are proposed at each bus to overcome these five tasks based on a decentralized control scheme. The proposed decentralized control based on FLC is compared with centralized control based on a PI controller. The proposed control method is tested and verified using simulations in the MATLAB/Simulink software, and the results validate the ability of the scheme to alleviate VI on a smart distribution network under both normal and abnormal conditions.

The model of distributed PV is constructed firstly, then the characteristics of distributed PV are studied and the electromechanical transient current equations are given. Using the actual PV grid-connected controller, a simulation system based on PSASP simulation platform is built. Through this system, the transient power characteristics of distributed PV during low voltage ride-through period are studied. Through theoretical analysis and experimental verification, the fault characteristics of distributed photovoltaic power supply are comprehensively expounded.

Kinmen, the famous Cold War island also known as Quemoy, is a typical island with isolated power grids. It considers the promotion of renewable energy and electric charging vehicles to be two essential strategies to achieve the goal of a low-carbon island and smart grid. With this motivation in mind, the main objective of this study is to design and deploy an energy management system for hundreds of current PV sites distributed on the island, energy storage systems, and charging stations on the island. In addition, the real-time acquisition of the data for power generation, power storage, and power consumption systems will be used for future demand and response analysis. Moreover, the accumulated dataset will also be utilized for the forecast or prediction of renewable energy generated by the PV systems or power consumed by the battery units or charging stations. The results of this study are promising since a practical, robust, and workable system and database are developed and implemented with a variety of Internet of Things (IoT), data transmission technologies, and the hybrid of on-premises and cloud servers. Users of the proposed system can remotely access the visualized data through the user-friendly web-based and Line bot interfaces seamlessly. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Voltage imbalance (VI) is caused by the difference in connected single-phase load or generation in a low voltage distribution network (DN).VI increase in a smart distribution grid is due to the current practice of increasing single-phase distributed generators such as photovoltaic (PV) systems. This paper proposes a decentralized control method to mitigate VI using distributed batteries included in smart grid interfaced residential PV systems. To mitigate VI using the batteries in this way, five challenges must be overcome, i.e., equalizing all battery stress currents within the DN, mitigating VI in abnormal conditions such as signal loss among bus controllers, being immune from the distorted feedback measurements, minimizing the steady-state error at different loads, and overcoming the insufficient number or capacity of the distributed batteries at the same bus. Three fuzzy logic controllers (FLC) are proposed at each bus to overcome these five tasks based on a decentralized control scheme. The proposed decentralized control based on FLC is compared with centralized control based on a PI controller. The proposed control method is tested and verified using simulations in the MATLAB/Simulink software, and the results validate the ability of the scheme to alleviate VI on a smart distribution network under both normal and abnormal conditions. [ABSTRACT FROM AUTHOR]

In a power system with highly proportional renewable energy integration, the power generated by photovoltaic (PV) of high permeability and high proportion needs to be connected to the distribution network through the power electronic inverter. The inverters can generate the low-order harmonic and high-order harmonic near the switching frequency. Harmonic power will be generated when the harmonic current flows through the power grid with the harmonic voltage of the same frequency, and the additional harmonic losses caused should not be neglected. To effectively analyze the voltage quality and calculate the harmonic loss of the low-voltage distribution network integrated with distributed PV, based on the harmonic loss factor of resistance proposed, the harmonic impedance modeling and harmonic loss calculation method for the key equipment of the power grid, such as lines and transformers, are introduced in this paper firstly. Next, a decoupling algorithm of harmonic power flow is proposed, and the influence of the access capacity of PV on voltage quality and line loss of the distribution network is analyzed. Finally, a harmonic loss calculation method based on measured harmonic data of the distribution network is proposed. It is found that the harmonic loss of the low-voltage distribution network accounts for about 0.6% of the total network loss. Therefore, voltage quality can be improved and line loss can be reduced effectively by reasonable access to PV and reducing harmonic order and the current harmonic distortion. [ABSTRACT FROM AUTHOR]

Distributed-solar-photovoltaic (PV) generation is a key component of a new energy system aimed at carbon peaking and carbon neutrality. This paper establishes a policy-analysis framework for distributed-solar-PV generation based on a technical- and economic-evaluation model. Given that the resource endowment is becoming lower and the raw material costs are becoming higher, the profitability of the deployment of distributed-solar-PV-generation projects in China is generally becoming much worse. Some distributed-PV-generation projects are even becoming unprofitable. This will not be helpful for the sustainable development of distributed-PV generation, which will play a vital role in attaining the goal of carbon neutrality. Based on the established model for techno-economic evaluation, a systematic policy analysis is performed to identify the effect of possible policy instruments such as financial policies on improving the economic profitability of distributed-PV-development in China. The results indicate that policy instruments related to preferential financing, green certificate, tax incentives and combinations thereof are available for priority measures aimed at optimizing incentive policies for enhancing the economic viability of distributed-PV deployment in China. Based on these findings, recommendations are proposed to optimize the currently available policy instruments for accelerating the sustainable development of the distributed-PV industry towards a carbon-neutral future. [ABSTRACT FROM AUTHOR]

The integration of a high proportion of distributed PV into distribution networks can cause power backfeeding and voltage limit violations. This paper introduces moment difference analysis theory for distribution networks, reframing PV consumption as the balancing moment difference equations. When maximum node voltage approaches the upper limit, the moment difference between PV and load stabilizes to an approximately constant, termed the standard moment difference. The standard moment difference represents the limit of the network's capacity to consume distributed PV. Essentially, the PV moment is the target for integration, while the load moment serves as the resource to consume the PV moment. Based on this theory, a method for energy storage configuration is proposed. Simplifying a complex multi-branch distribution network into single-branch lines and solving linear equations determines the optimal storage configuration. This method was tested on the Jibei Power Grid and the IEEE 33 and 69 bus systems, increasing computational efficiency by 1611.47–4973.82 times compared to the PSO algorithm, while maintaining an error margin of less than 2.6 %. Furthermore, the discharging strategy reduces the ESS capacity to between 12.39 % and 31.69 % of the original estimate, thereby enhancing both the economic and practical appeal of the approach. • Proposes moment difference theory, reframing PV consumption as balance equations. • PV and load moment difference is a constant termed the standard moment difference. • Load moment as the resource to absorb the PV moment targeted for integration. • Establishes energy storage configuration method based on moment difference theory. [ABSTRACT FROM AUTHOR]

Ensuring a functioning critical infrastructure system is crucial for many industries, including energy. Due to the transformation of the energy sector, it is necessary to take into account the impact of the induced changes on critical infrastructure. The presented paper focuses on the assessment of the effects of small dispersed photovoltaic power plants (PV), which are gaining in popularity both in households and in other buildings. Specifically, it is an impact assessment within the emergence of island operation in the selected area with the aim of restoring power to the facilities included in the critical infrastructure system as soon as possible. The specificity of the considered small photovoltaic power plants is their autonomy and impossibility of control from the superior control system. The presented case study compares possible approaches to power recovery on the one hand and the impact of different levels of dispersed PV on the other. The results provide important conclusions for the expected development of the critical infrastructure system in connection with the increase in autonomous distributed electricity sources.

Reliable and resilient grid operations with high penetration levels of distributed energy resources (DERs) can be achieved with improved situational awareness and seamless integration of DERs with utility enterprise controls. This paper presents the details of the development of a data-enhanced hierarchical control (DEHC) architecture and the results of its evaluation. The DEHC is a hybrid control framework that enables the efficient, reliable, and secure operation of distribution grids with extremely high penetrations of solar photovoltaic (PV) generation by seamlessly integrating centralized utility controls, distributed controls for DERs, and autonomous grid-edge controls. In the DEHC architecture, the advanced distribution management system (ADMS) controls the legacy devices (such as load tap changers and capacitor banks), the PV smart inverters are dispatched by real-time optimal power flow, and the grid-edge devices regulate local voltages in coordination with each other. The DEHC is demonstrated using a commercial ADMS platform, real utility distribution feeder models, and grid-edge devices. The performance of the DEHC architecture is evaluated using simulations and hardware-in-the-loop experiments with voltage regulation as the control objective. The results show that the DEHC enables high penetration levels of PV in distribution feeders by effectively managing system voltages through the synergistic operation of ADMS, distributed PV smart inverter controls, and secondary-level grid-edge device control.

The application of distributed photovoltaics is growing in developing countries due to its declining costs and continued government support. This study analyzes the economics of stylized grid-connected distributed photovoltaics to be implemented in the residential, commercial, and industrial sectors in Bangladesh. An economic model has been developed for the analysis. The model uses hourly data for solar irradiation and potential electricity exchanges between the consumers investing in distributed photovoltaics and the electricity utilities. The study finds that the economics of distributed photovoltaics vary across technology investors/owners, electricity utilities, and society. From consumers' perspective, the economics of distributed photovoltaics depends on the difference in electricity production costs between the photovoltaics technologies implemented and electricity generation technologies used by electricity utilities. The costs of distributed photovoltaics are, however, sensitive to their capital cost, discount rate, and the quality of solar resources. It is also influenced by the size of photovoltaics relative to consumers' electricity load and tariffs to exchange electricity between the electric utilities and the consumers owning distributed photovoltaics. From a national or societal perspective, the deployment of distributed photovoltaics is beneficial even if their environmental benefits are ignored. Considering the improving economics of the technology and the environmental benefits, particularly the climate change and local air quality benefits, countries like Bangladesh, which are endowed with rich solar resources, should enhance the deployment of distributed photovoltaics for their electricity supply. • Distributed PV could be an effective technology to exploit solar energy globally. • An economic analysis is crucial for investment decisions on DPV. • The economic viability varies across stakeholders – consumers, utilities, society. • It is sensitive to cost, power tariff and plant size relative to developer's load. • DPVs are found economically attractive for all stakeholders in Bangladesh. [ABSTRACT FROM AUTHOR]

Kinmen, the famous Cold War island also known as Quemoy, is a typical island with isolated power grids. It considers the promotion of renewable energy and electric charging vehicles to be two essential strategies to achieve the goal of a low-carbon island and smart grid. With this motivation in mind, the main objective of this study is to design and deploy an energy management system for hundreds of current PV sites distributed on the island, energy storage systems, and charging stations on the island. In addition, the real-time acquisition of the data for power generation, power storage, and power consumption systems will be used for future demand and response analysis. Moreover, the accumulated dataset will also be utilized for the forecast or prediction of renewable energy generated by the PV systems or power consumed by the battery units or charging stations. The results of this study are promising since a practical, robust, and workable system and database are developed and implemented with a variety of Internet of Things (IoT), data transmission technologies, and the hybrid of on-premises and cloud servers. Users of the proposed system can remotely access the visualized data through the user-friendly web-based and Line bot interfaces seamlessly.

Modern smart grids typically combine physical and communication networks for efficient information exchange and innovative applications. Aligned with digitalization and advancements in smart grids, the integration of photovoltaic (PV) systems comprises a variety of regulatory and technological aspects. However, no previous study has conducted an extensive and systematic analysis of the PV-grid integration framework, particularly for one country. To fill this gap, this paper uses Germany as an example to present a comprehensive, state-of-the-art analysis of integrating distributed PV systems into smart grids, focusing on the regulation and technical implementation of the German Smart Meter Infrastructure and PV control interfaces. Starting from a standardization perspective, this analysis utilizes the Smart Grid Architecture Model to identify crucial roles, components and processes specifically in Germany. Furthermore, it outlines the current implementation of PV integration into distribution networks at a national level. The results of this study show the overall complexity of PV integration in the smart grid context, confirm the feasibility of the German integration approach, and highlight the necessity of deploying standardized information models and communication technologies. These key findings can help market participants with different roles to identify potential technical bottlenecks or other critical points in the regulation and technical implementation. For instance, the proposed in-depth analysis framework provides an orientation for characterizing the PV integration or, more generally, the grid integration scenario of renewables in other countries. • An interdisciplinary, comprehensive case study of Germany. • Elaboration of the German legislative and technical regulation for PV integration. • Extensive technical implementation of the German CLS and Smart Meter Infrastructure for PV monitoring and control. • Visualization of the PV-grid integration utilizing the German role model and the SGAM method. [ABSTRACT FROM AUTHOR]

Advanced Distribution Management Systems (ADMS) are being widely adopted by electric utilities for managing and optimizing the operations of their distribution systems. Distributed photovoltaic (DPV) systems with smart inverters can be controlled to adjust active power and reactive power outputs, and they are envisioned to become a part of (centrally or distributed) controllable assets managed by the ADMS for optimizing grid operations. This paper proposes an optimal energy dispatch strategy controlling DPV systems for regulating distribution voltages and achieving conservation voltage reduction. A convex optimization model is proposed with the use of linearized power flow, and the gradient projection algorithm is used to solve the optimal active power and reactive power outputs of smart inverters. The proposed optimal energy dispatch is implemented using an open-source ADMS platform, and simulation results have demonstrated the effectiveness of the proposed approach on improving distribution grid operations.

This study analyses distributed photovoltaic (D-PV) system curtailment and impacts on consumers in response to high network voltages. Novel analytical techniques are applied to a unique real-world operational dataset of over 1300 D-PV systems in South Australia to identify 'tripping'. Data-driven methods are valuable due to the diverse range of D-PV conditions. South Australia is an insightful case study due to its D-PV rich network, where around one third of standalone housing now has a PV system. Findings suggest that overall curtailment is low, however some sites experience significant impacts of up to 46–95% curtailment per day, particularly during spring. The uneven distribution of impacts raises concerns regarding fairness, however network solutions to increase hosting capacity must be carefully balanced given the potential costs imposed on consumers without D-PV. Upscaling the estimated D-PV generation loss to all of South Australia indicates a total value of $1.2m-$4.5m per year in lost value to consumers with D-PV, considering clear sky days. Implications for policy makers and network operators are discussed in the context of strong projected D-PV uptake in Australia, and around the world. It is proposed that data-driven methods could inform future regulatory assessment processes to improve outcomes for all consumers. [ABSTRACT FROM AUTHOR]

This paper presents a 3 HP solar direct-drive photovoltaic air conditioning system which operates without batteries, ice thermal storage is used to store solar energy. The refrigeration compressor will suffer from loss of power even cannot startup or shut down if the PV power generation suddenly fluctuates. In the case of the solar radiation fluctuations to keep the system running continuously and steadily, that requires a proper system design to match the power consumption of solar air conditioning system with a proper PV capacity, matching an adaptive controller and a suited compressor simultaneously. Solar air conditioners with different capacity of PV panel, with and without MPPT controller and different types compressors were built and tested outdoors to experimentally investigate the matching characteristics of photovoltaic disturbance and refrigeration compressor. The experimental results of the system with a variable speed compressor and a MPPT controller have shown good ice-making performance and reliable operation as well as a great improvement in the available solar energy. Finally, the highest system COP reached 0.289 when the cumulative daily total radiation was 18.2 MJ/m2 in Kunming, China. The research work shows that the direct drive ice storage by solar photovoltaic system has a certain application prospect in the regions where the electricity is tight but requiring cooling. • Design of air conditioner directly driven by distributed solar PV was studied. • Ice thermal storage was used to completely replace batteries. • A MPPT controller was design to effectively utilize solar energy. • Variable speed compressor was suited for PV directly driven cooling. [ABSTRACT FROM AUTHOR]

• NYSolarCast is an open-source solar forecasting system configurable for any region. • NYSolarCast predicts gridded GHI and both utility-scale and distributed PV power. • NYSolarCast integrates NWP models, machine learning, and real-time weather data. • NYSolarCast was validated for 1 year of 0–6-h GHI and power forecasts in New York. • NYSolarCast GHI forecasts performed comparably to a commercial reference forecast. 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%5fdelivery). 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. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic (PV) solar power plants are playing an increasing role as a power generation resource in the modern electricity grid. However, PVs pose significant challenges to grid planners, operators, owners, investors, aggregators, and other stakeholders. This is due to the high uncertainty of the PV output power, which is caused by its entire dependence on intermittent environmental factors. This has brought a serious problem to the power industry to integrate and manage power grids containing significant penetration of PVs. Thus, an enhanced PV power forecast is very important to operate these power grids efficiently and reliably. Most previous methodologies have focused on predicting the aggregate amount of potential solar power generation at the national or regional scale and ignored the distributed PVs that are installed primarily for local electric supply. Furthermore, a few research groups have carried out predictor selection before training predictive models. This paper proposes an adaptive hybrid predictor subset selection (PSS) strategy to obtain the most relevant and nonredundant predictors for enhanced short-term forecasting of the power output of distributed PVs. In the proposed strategy, the binary genetic algorithm (BGA) is applied for the feature selection process and support vector regression (SVR) is used for measuring the fitness score of the predictors. In order to validate the effectiveness of the proposed strategy, it is applied to actual distributed PVs located in the Otaniemi area of Espoo, Finland. The findings are compared with those achieved by other PSS techniques. The proposed strategy enhances the quality and efficiency of the predictor subset selection, with minimal chosen predictors to achieve enhanced prediction accuracy. It outperforms the other prediction selection methods. Besides, a configuration of an adaptive forecasting model is introduced and the performance tests are presented to further validate the impact of the PSS results for the PV power prediction accuracy enhancement.

In this paper, a novel topology of modular dc–dc converter has been proposed for medium voltage dc (MVdc) integration of high-penetration distributed PV. The proposed topology is featured with input-independent and output-series connected configuration and intermodule power balancing units (PBUs). A high voltage gain could be achieved with series-connected submodules (SMs), while each SM's input port is connected with PV array to realize the independent maximum power point tracking (MPPT) control function. PBUs with bidirectional power conversion ability are installed between adjacent SMs for the elimination of intermodule power mismatch. The operation principles of the proposed converter have been explained in detail along with the corresponding control strategies. The design principles of major passive components have been investigated on the basis of boundary operation conditions, while a quantitative analysis of steady-state power losses has been performed. Moreover, a comparative study is conducted from the aspects of operation range, topology complexity, component stress, and power efficiency. The theoretical results have been validated by both simulations and down-scaled experiments. [ABSTRACT FROM AUTHOR]

As subsidies continue to fall, the technology and cost performance of distributed photovoltaic (PV) determines the progress of its grid parity. Based on the discussion of technology and cost, this paper analyzed the economic performance of China's distributed PV industry by utilizing the two indicators of levelized cost of energy (LCOE) and internal rate of return (IRR). The results show that: firstly, the current external environment is generally conducive to the development of distributed PV, but there are still some constraints. Secondly, with the decrease of unit investment cost, distributed PV can achieve the goal of parity before 2025. Thirdly, distributed PV projects in the three types of solar energy resources all have high IRR, and the economic performance is better for the projects with high proportion of spontaneous self-use. At the end of the paper, policy recommendations are offered as references for the government. • We comprehensively analyzed the technology and cost performance of China's distributed PV. • We focused on the trend and decline path of China's distributed PV costs. • The LCOE of distributed PV in China is expected to achieve full parity in 2025. • Except 100% grid-connected mode, the IRR of distributed PV power plants in three areas is higher than 8% which has shown good economic benefits. [ABSTRACT FROM AUTHOR]

Abstract Household distributed photovoltaic (PV) can supply clean power directly to the end users. With the rapid growth of the distributed PV installed capacity, the problems of PV grid connection and PV consumption are becoming more and more serious in China. It is an effective means to improve the efficiency of PV system by promoting the home self-consumption of PV generation. In this study, a residential demand response model considering distributed PV consumption based on dynamic electricity price is proposed. The household users are classified into several types with different power consumption preferences in this model. Both the comfort and economy of power consumption are quantified to describe the users' satisfaction. The model is established with two goals, maximizing the users' satisfaction meanwhile minimizing PV power into the grid. Then a solution algorithm based on NSGA-II is proposed to solve the model. Finally, an example study based on China's PV policy is presented, and the results show that the model has certain advantages in improving the users' satisfaction and increasing the rate of self-consumption of PV generation. In addition, sensitivity analysis of the provincial government's PV subsidies in China is also carried out and the relevant policy recommendations are presented. Highlights • Household users are classified into several types with different preferences. • The UEV with users' power consumption comfort and economy is designed. • Exploiting the interaction between users and the power grid for PV consumption. • Sensitivity analysis of the provincial government's PV subsidies in China. [ABSTRACT FROM AUTHOR]

Abstract In this paper, an integrated control strategy for a solar cooling system directly driven by distributed photovoltaics (PVs) without a battery is proposed; this strategy matches the optimal operating impedance of the PV array by periodically perturbing the running frequency of the compressor. The theoretical working principle of the control strategy is explained, and two experimental processes are analysed under different solar irradiance levels. Furthermore, comparative tests of the control system with and without a controller were conducted to evaluate the performance of the solar cooling system. The results of the experiment under the impedance matching control strategy reveal that the average photoelectric conversion efficiency reached approximately 0.129 and improved by 83.7% relative to the control system without a controller. The coefficient of performance (COP solar) reached 0.263 using an ice thermal storage model, which was an improvement of 60.4%. The compressor ran continually when the instantaneous irradiance exceeded 143 W/m2. The average efficiency of the control system reached 0.96, and the average three-phase power factor was approximately 0.71. The COP of the refrigeration unit was 2.10, and the total cooling capacity was 103.5 MJ, which was sufficient for cooling a 25.5-m2 air-conditioned room for 8.5 h. Highlights • An impedance matching control strategy for solar cooling system was proposed. • The impedance matching relationship was verified with theory and experiments. • Comparative tests with and without controller were conducted for proposed system. • The compressor ran stably when the instantaneous irradiance exceeded 143 W/m2. • The COP solar reached 0.263 in ice-making thermal storage model. [ABSTRACT FROM AUTHOR]