To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and obj...To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and objective combination of assignment and technique for order preference by similarity to an ideal solution(TOPSIS)—rank sum ratio(RSR)(TOPSIS-RSR)method.Based on the traditional distribution network evaluation system,a comprehensive evaluation system has been constructed.It fully considers the new development requirements of distributed PV access on the environmental friendliness and absorptive capacity of the distribution grid and comprehensively reflects the impact of distributed PV grid connection.The analytic hierarchy process(AHP)was used to determine the subjective weights of the primary indicators,and the Spearman consistency test was combined to determine the weights of the secondary indicators based on three objective assignment methods.The subjective and objective combination weights of each assessment indicator were calculated through the principle of minimum entropy.Calculate the distance between the indicators to be evaluated and the positive and negative ideal solutions,the relative closeness ranking,and qualitative binning by TOPSIS-RSR method to obtain the comprehensive evaluation results of different scenarios.By setting up different PV grid-connected scenarios and utilizing the IEEE33 node simulation algorithm,the correctness and effectiveness of the proposed subject-object combination assignment and integrated assessment method are verified.展开更多
Photovoltaic(PV)power generation is undergoing significant growth and serves as a key driver of the global energy transition.However,its intermittent nature,which fluctuates with weather conditions,has raised concerns...Photovoltaic(PV)power generation is undergoing significant growth and serves as a key driver of the global energy transition.However,its intermittent nature,which fluctuates with weather conditions,has raised concerns about grid stability.Accurate PV power prediction has been demonstrated as crucial for power system operation and scheduling,enabling power slope control,fluctuation mitigation,grid stability enhancement,and reliable data support for secure grid operation.However,existing prediction models primarily target centralized PV plants,largely neglecting the spatiotemporal coupling dynamics and output uncertainties inherent to distributed PV systems.This study proposes a novel Spatio-Temporal Graph Neural Network(STGNN)architecture for distributed PV power generation prediction,designed to enhance distributed photovoltaic(PV)power generation forecasting accuracy and support regional grid scheduling.This approach models each PV power plant as a node in an undirected graph,with edges representing correlations between plants to capture spatial dependencies.The model comprises multiple Sparse Attention-based Adaptive Spatio-Temporal(SAAST)blocks.The SAAST blocks include sparse temporal attention,sparse spatial attention,an adaptive Graph Convolutional Network(GCN),and a temporal convolution network(TCN).These components eliminate weak temporal and spatial correlations,better represent dynamic spatial dependencies,and further enhance prediction accuracy.Finally,multi-dimensional comparative experiments between the STGNN and other models on the DKASC PV dataset demonstrate its superior performance in terms of accuracy and goodness-of-fit for distributed PV power generation prediction.展开更多
Concave clouds will cause miscalculation by the power prediction model based on cloud ieatures for distributed photovoltaic （PV） plant. The algorithm for decomposing concave cloud into convex images is proposed. Ado...Concave clouds will cause miscalculation by the power prediction model based on cloud ieatures for distributed photovoltaic （PV） plant. The algorithm for decomposing concave cloud into convex images is proposed. Adopting minimum polygonal approximation （MPP） to demonstrate the contour of concave cloud, cloud features are described and the subdivision lines of convex decomposition for the concave clouds are determined by the centroid point scattering model and centroid angle func- tion, which realizes the convex decomposition of concave cloud. The result of MATLAB simulation indicates that the proposed algorithm can accurately detect cloud contour comers and recognize the concave points. The proposed decomposition algorithm has advantages of less time complexity and decomposition part numbers compared to traditional algorithms. So the established model can make the convex decomposition of complex concave clouds completely and quickly, which is available for the existing prediction algorithm for the ultra-short-term power output of distributed PV system based on the cloud features.展开更多
The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power st...The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power stations,the current situation is that it is dificult to access the current dispatch data network.According to the temporal and spatial characteristics of distributed PV,a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid.The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations,which makes the corresponding output estimation algorithm have higher accuracy.展开更多
Solar energy has been widely used in power generation.With the development of solar energy,the distributed photovoltaic power generation and the distributed grid-connected PV systems become the center of attention.Thi...Solar energy has been widely used in power generation.With the development of solar energy,the distributed photovoltaic power generation and the distributed grid-connected PV systems become the center of attention.This paper provided a brief introduction to distribution-level solar energy.Firstly,the development of solar energy was analyzed,and the distributed photovoltaic power generation was discussed.Secondly,the distributed grid-connected PV systems and basic theory of photovoltaic solar channel were analyzed.In order to ensure PV power is connected to grid stably and reliably,some related aspects such as the establishment of mathematical model for solar photovoltaic cell,the analysis of I-V characteristics of solar photovoltaic cell,and the tracking of its maximum power point(MPPT)to control the behaviour of the DC/DC converter were discussed.Finally,a simulation model was necessary to be established by using PSCAD/EMTDC function module to verify and simulate the mathematical model and control strategies,and some suggestions were put forward for the sustainable development of solar energy.展开更多
Distributed PV networks have become so popular,local grid capacity is often overwhelmed.Now China is transitioning to market pricing to encourage better local use and distribution of renewable energy.
With the increasingly serious climate change and energy crisis,photovoltaic(PV)generation,as one of the most important renewable energy resources,has experienced dramatic growth worldwide due to its environmental frie...With the increasingly serious climate change and energy crisis,photovoltaic(PV)generation,as one of the most important renewable energy resources,has experienced dramatic growth worldwide due to its environmental friendliness.How-ever,the uncertainty and intermittency of PV bring inevitable challenges to power systems.With the rapid development of distributed PV and the continuous evolution of the electricity market,increasingly high penetration levels of distributed PV generation have led to a series of problems in power system operations,such as voltage fluctuation,frequency deviation,etc.The market participation of distributed PV needs to be solved.Reasonable market participation form,market mechanism and bidding strategies are vital to the development of distributed PV in the electricity market.This paper comprehensively reviews the development and impacts of distributed PV in the electricity market and discusses the relevant market modes and bidding strategies in detail.展开更多
Substantial usage of electronic-based renewable energy resources has completely changed the dynamic behaviours and response time of power networks,which are now fundamentally different from traditional power networks ...Substantial usage of electronic-based renewable energy resources has completely changed the dynamic behaviours and response time of power networks,which are now fundamentally different from traditional power networks dominated by Synchronous Generators(SGs).This paper evaluates the dynamic response of small-scale Photovoltaic(PV)inverters,which dominate the distribution networks and influence the dynamics of the entire power grid.Recently,some critical events which occurred in Australia have shown that the dynamic responses of small-scale inverters do not always follow the inverter standards.Subsequently,these uncertainties make PV inverters’response unpredictable and have the potential to threaten the security of power networks.The detailed investigation of the dynamic response characteristics of small-scale PV inverters to grid disturbances is lacking in the current literature.This paper presents new findings from experimental testing under extensive network disturbance scenarios.Furthermore,a datadriven method is proposed to accurately describe the dynamics of solar PV subjected to various frequency disturbances.The results provide beneficial insight to the network operators in predicting power system response to extreme disturbances and avoiding potential grid instability issues,which will assist in achieving 100%penetration of power electronics-based renewable energy resources in the future.展开更多
Distributed photovoltaic(PV)systems play an important role in supplying many recent microgrids.The absence of reactive power support for these small-scale PV plants increases total microgrid losses and voltage-instabi...Distributed photovoltaic(PV)systems play an important role in supplying many recent microgrids.The absence of reactive power support for these small-scale PV plants increases total microgrid losses and voltage-instability threats.Reactive power compensations(RPCs)should be integrated to enhance both microgrid losses and voltage profiles.RPC planning is a non-linear,complicated problem.In this paper,a combined RPC allocation and sizing algorithm is proposed.The RPC-integrating buses are selected using a new adaptive approach of loss sensitivity analysis.In the sizing process,the uncertainties in PV power and load demand are modelled using proper probability density functions.Three simulation techniques for handling uncertainties are compared to define the accurate and fast accurate method as follows:Monte Carlo simulation(MCS),scenario tree construction and reduction method,and point estimation method(PEM).The load flow equations are solved using the forward-backward sweep method.RPCs are optimally sized using the beetle-antenna-based strategy with grey wolf optimization(BGWO)to overcome the local minima problem that appeared in the other pre-proposed methods.Results have been compared using particle swarm optimization and conventional GWO.The proposed model is verified using the IEEE 33 radial bus system.The expected power loss has been reduced by 22% and 31% using compensation of 26% and 44%,respectively.The results obtained prove that the BGWO optimal power flow and PEM to handle the uncertainty can significantly reduce the computation time with sufficient accuracy.Under the study conditions,PEM reduces the computation time to 4 minutes compared with 4 hours for MCS,with only a 3% error compared with MCS as an uncertainty benchmark method.展开更多
To further improve the utilization level of distributed photovoltaics(PV)and realize low-carbon travel,a novel optimization cooperative model that considers multi-stakeholders including integrated prosumers(IP),power ...To further improve the utilization level of distributed photovoltaics(PV)and realize low-carbon travel,a novel optimization cooperative model that considers multi-stakeholders including integrated prosumers(IP),power to hydrogen(P2H),and grid company(GC)is proposed,based on the Nash bargaining theory.Electricity trading among the three and hydrogen trading between IP and P2H is considered in the model to maximize their own interests.Specifically,IP focus on distributed PV integration and low carbon travels,P2H strives to improve cost competitiveness of hydrogen,and electricity-hydrogen trading prices and quantities are optimized between them.Also,GC obtains profits by charging grid fees considering power flow constraints.The cooperation game model is transformed into a mixed-integer linear programming problem through linearization methods.Case studies show that electricity-hydrogen trading among the three has apparent advantages over pure electricity coupling or non-cooperation.With future reduction of investment prices of PV and P2H and maturity of the carbon trading market,the profit margin of this cooperation model can be further enhanced.展开更多
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 increa...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.展开更多
基金support of the project“State Grid Corporation Headquarters Science and Technology Program(5108-202299258A-1-0-ZB)”.
文摘To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and objective combination of assignment and technique for order preference by similarity to an ideal solution(TOPSIS)—rank sum ratio(RSR)(TOPSIS-RSR)method.Based on the traditional distribution network evaluation system,a comprehensive evaluation system has been constructed.It fully considers the new development requirements of distributed PV access on the environmental friendliness and absorptive capacity of the distribution grid and comprehensively reflects the impact of distributed PV grid connection.The analytic hierarchy process(AHP)was used to determine the subjective weights of the primary indicators,and the Spearman consistency test was combined to determine the weights of the secondary indicators based on three objective assignment methods.The subjective and objective combination weights of each assessment indicator were calculated through the principle of minimum entropy.Calculate the distance between the indicators to be evaluated and the positive and negative ideal solutions,the relative closeness ranking,and qualitative binning by TOPSIS-RSR method to obtain the comprehensive evaluation results of different scenarios.By setting up different PV grid-connected scenarios and utilizing the IEEE33 node simulation algorithm,the correctness and effectiveness of the proposed subject-object combination assignment and integrated assessment method are verified.
基金supported by the State Grid Corporation of China Headquarters Science and Technology Project“Research on Key Technologies for Power System Source-Load Forecasting and Regulation Capacity Assessment Oriented towards Major Weather Processes”(4000-202355381A-2-3-XG).
文摘Photovoltaic(PV)power generation is undergoing significant growth and serves as a key driver of the global energy transition.However,its intermittent nature,which fluctuates with weather conditions,has raised concerns about grid stability.Accurate PV power prediction has been demonstrated as crucial for power system operation and scheduling,enabling power slope control,fluctuation mitigation,grid stability enhancement,and reliable data support for secure grid operation.However,existing prediction models primarily target centralized PV plants,largely neglecting the spatiotemporal coupling dynamics and output uncertainties inherent to distributed PV systems.This study proposes a novel Spatio-Temporal Graph Neural Network(STGNN)architecture for distributed PV power generation prediction,designed to enhance distributed photovoltaic(PV)power generation forecasting accuracy and support regional grid scheduling.This approach models each PV power plant as a node in an undirected graph,with edges representing correlations between plants to capture spatial dependencies.The model comprises multiple Sparse Attention-based Adaptive Spatio-Temporal(SAAST)blocks.The SAAST blocks include sparse temporal attention,sparse spatial attention,an adaptive Graph Convolutional Network(GCN),and a temporal convolution network(TCN).These components eliminate weak temporal and spatial correlations,better represent dynamic spatial dependencies,and further enhance prediction accuracy.Finally,multi-dimensional comparative experiments between the STGNN and other models on the DKASC PV dataset demonstrate its superior performance in terms of accuracy and goodness-of-fit for distributed PV power generation prediction.
基金Supported by the National High Technology Research and Development Programme of China(No.2013AA050405)Doctoral Fund of Ministry of Education(No.20123317110004)+1 种基金Foundation of Zhejiang Province Key Science and Technology Innovation Team(No.2011R50011)the Natural Science Foundation of Zhejiang Province(No.LY15E070004)
文摘Concave clouds will cause miscalculation by the power prediction model based on cloud ieatures for distributed photovoltaic （PV） plant. The algorithm for decomposing concave cloud into convex images is proposed. Adopting minimum polygonal approximation （MPP） to demonstrate the contour of concave cloud, cloud features are described and the subdivision lines of convex decomposition for the concave clouds are determined by the centroid point scattering model and centroid angle func- tion, which realizes the convex decomposition of concave cloud. The result of MATLAB simulation indicates that the proposed algorithm can accurately detect cloud contour comers and recognize the concave points. The proposed decomposition algorithm has advantages of less time complexity and decomposition part numbers compared to traditional algorithms. So the established model can make the convex decomposition of complex concave clouds completely and quickly, which is available for the existing prediction algorithm for the ultra-short-term power output of distributed PV system based on the cloud features.
基金the Science and Technology Program of State Grid Corporation of China(No.5211TZ1900S6)。
文摘The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power stations,the current situation is that it is dificult to access the current dispatch data network.According to the temporal and spatial characteristics of distributed PV,a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid.The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations,which makes the corresponding output estimation algorithm have higher accuracy.
文摘Solar energy has been widely used in power generation.With the development of solar energy,the distributed photovoltaic power generation and the distributed grid-connected PV systems become the center of attention.This paper provided a brief introduction to distribution-level solar energy.Firstly,the development of solar energy was analyzed,and the distributed photovoltaic power generation was discussed.Secondly,the distributed grid-connected PV systems and basic theory of photovoltaic solar channel were analyzed.In order to ensure PV power is connected to grid stably and reliably,some related aspects such as the establishment of mathematical model for solar photovoltaic cell,the analysis of I-V characteristics of solar photovoltaic cell,and the tracking of its maximum power point(MPPT)to control the behaviour of the DC/DC converter were discussed.Finally,a simulation model was necessary to be established by using PSCAD/EMTDC function module to verify and simulate the mathematical model and control strategies,and some suggestions were put forward for the sustainable development of solar energy.
文摘Distributed PV networks have become so popular,local grid capacity is often overwhelmed.Now China is transitioning to market pricing to encourage better local use and distribution of renewable energy.
基金This work was partially supported by the National Key R&D Program of China(2018YFB0905000)National Natural Science Foundation of China(51761135015,51877189)+1 种基金the Fundamental Research Funds for the Central Universities(2018QNA4015)The work of C.Wan was supported by the Hundred Talents Program of Zhejiang University.
文摘With the increasingly serious climate change and energy crisis,photovoltaic(PV)generation,as one of the most important renewable energy resources,has experienced dramatic growth worldwide due to its environmental friendliness.How-ever,the uncertainty and intermittency of PV bring inevitable challenges to power systems.With the rapid development of distributed PV and the continuous evolution of the electricity market,increasingly high penetration levels of distributed PV generation have led to a series of problems in power system operations,such as voltage fluctuation,frequency deviation,etc.The market participation of distributed PV needs to be solved.Reasonable market participation form,market mechanism and bidding strategies are vital to the development of distributed PV in the electricity market.This paper comprehensively reviews the development and impacts of distributed PV in the electricity market and discusses the relevant market modes and bidding strategies in detail.
文摘Substantial usage of electronic-based renewable energy resources has completely changed the dynamic behaviours and response time of power networks,which are now fundamentally different from traditional power networks dominated by Synchronous Generators(SGs).This paper evaluates the dynamic response of small-scale Photovoltaic(PV)inverters,which dominate the distribution networks and influence the dynamics of the entire power grid.Recently,some critical events which occurred in Australia have shown that the dynamic responses of small-scale inverters do not always follow the inverter standards.Subsequently,these uncertainties make PV inverters’response unpredictable and have the potential to threaten the security of power networks.The detailed investigation of the dynamic response characteristics of small-scale PV inverters to grid disturbances is lacking in the current literature.This paper presents new findings from experimental testing under extensive network disturbance scenarios.Furthermore,a datadriven method is proposed to accurately describe the dynamics of solar PV subjected to various frequency disturbances.The results provide beneficial insight to the network operators in predicting power system response to extreme disturbances and avoiding potential grid instability issues,which will assist in achieving 100%penetration of power electronics-based renewable energy resources in the future.
文摘Distributed photovoltaic(PV)systems play an important role in supplying many recent microgrids.The absence of reactive power support for these small-scale PV plants increases total microgrid losses and voltage-instability threats.Reactive power compensations(RPCs)should be integrated to enhance both microgrid losses and voltage profiles.RPC planning is a non-linear,complicated problem.In this paper,a combined RPC allocation and sizing algorithm is proposed.The RPC-integrating buses are selected using a new adaptive approach of loss sensitivity analysis.In the sizing process,the uncertainties in PV power and load demand are modelled using proper probability density functions.Three simulation techniques for handling uncertainties are compared to define the accurate and fast accurate method as follows:Monte Carlo simulation(MCS),scenario tree construction and reduction method,and point estimation method(PEM).The load flow equations are solved using the forward-backward sweep method.RPCs are optimally sized using the beetle-antenna-based strategy with grey wolf optimization(BGWO)to overcome the local minima problem that appeared in the other pre-proposed methods.Results have been compared using particle swarm optimization and conventional GWO.The proposed model is verified using the IEEE 33 radial bus system.The expected power loss has been reduced by 22% and 31% using compensation of 26% and 44%,respectively.The results obtained prove that the BGWO optimal power flow and PEM to handle the uncertainty can significantly reduce the computation time with sufficient accuracy.Under the study conditions,PEM reduces the computation time to 4 minutes compared with 4 hours for MCS,with only a 3% error compared with MCS as an uncertainty benchmark method.
基金supported by the Science and Technology Project of State Grid Corporation of China(5400-201999493A-0-0-00).
文摘To further improve the utilization level of distributed photovoltaics(PV)and realize low-carbon travel,a novel optimization cooperative model that considers multi-stakeholders including integrated prosumers(IP),power to hydrogen(P2H),and grid company(GC)is proposed,based on the Nash bargaining theory.Electricity trading among the three and hydrogen trading between IP and P2H is considered in the model to maximize their own interests.Specifically,IP focus on distributed PV integration and low carbon travels,P2H strives to improve cost competitiveness of hydrogen,and electricity-hydrogen trading prices and quantities are optimized between them.Also,GC obtains profits by charging grid fees considering power flow constraints.The cooperation game model is transformed into a mixed-integer linear programming problem through linearization methods.Case studies show that electricity-hydrogen trading among the three has apparent advantages over pure electricity coupling or non-cooperation.With future reduction of investment prices of PV and P2H and maturity of the carbon trading market,the profit margin of this cooperation model can be further enhanced.
文摘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.
