A power distribution network and distributed power supply grid-connected optimization scheduling method

Abstract

The application discloses a kind of optimization scheduling method of distribution network and distributed power grid, specifically related to optimization scheduling technical field, including by the difference of real-time and historical solar photovoltaic output model, determine the performance information of distributed photovoltaic power grid, and based on the state of charge data of distributed photovoltaic power connected energy storage system, determine the performance information of distributed photovoltaic power grid, determine the grid connection strategy of distributed photovoltaic power, based on considering the node system topological connection relationship of distribution network and node, branch related data, simulate the possibility risk that exists in the node of distributed photovoltaic power grid in distribution network, determine the grid connection risk information of distribution network, and consider the matching degree of distributed photovoltaic power output power and demand power, determine the grid connection load matching information of distribution network, the overall grid connection performance of distribution network is quantified, the application helps to ensure the stable operation of distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optimal scheduling, and more particularly, to an optimal scheduling method for grid connection of a power distribution network and a distributed power source. BACKGROUND

[0002] With the rapid development of renewable energy, distributed photovoltaic power sources (including household photovoltaic systems and small photovoltaic power stations) have gradually become an important part of modern power distribution networks. Traditional photovoltaic power source grid connection strategies usually deal with the volatility of photovoltaic output through simple technical standards or excessive reliance on energy storage systems. However, such methods often ignore the complex interactions between photovoltaic power sources, energy storage systems, and power distribution networks, as well as the changing external conditions. Existing technologies are not easy to screen photovoltaic power sources suitable for direct grid connection, and it is difficult to consider the stability of the power distribution network from the perspective of photovoltaic power sources. SUMMARY

[0003] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide an optimal scheduling method for grid connection of a power distribution network and a distributed power source to solve the problems raised in the background art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0005] An optimal scheduling method for grid connection of a power distribution network and a distributed power source, specifically comprising the following steps:

[0006] S1: Based on historical data of the distributed photovoltaic power source, determine the output model of solar photovoltaic. By comparing the differences between the real-time and historical output models of solar photovoltaic, determine the performance information of the distributed photovoltaic power source when it is connected to the grid, and based on the state of charge data of the energy storage system connected to the distributed photovoltaic power source, determine the performance information of the energy storage when the distributed photovoltaic power source is connected to the grid;

[0007] S2: By comprehensively analyzing the performance information of the distributed photovoltaic power source and the performance information of the energy storage when the distributed photovoltaic power source is connected to the grid, evaluate the grid connection performance of the distributed photovoltaic power source, and determine the grid connection strategy of the distributed photovoltaic power source;

[0008] S3: Based on the node-related data of the power distribution network, simulate the possible risks of the distributed photovoltaic power source grid connection nodes in the power distribution network, determine the grid connection risk information of the power distribution network, and consider the matching degree of the output power and the demand power of the distributed photovoltaic power source, determine the grid connection load matching information of the power distribution network;

[0009] S4: When the same type of distributed photovoltaic power source is connected to the power distribution network, the grid connection risk information and the grid connection load matching information of the power distribution network are fused and evaluated to quantify the overall grid connection performance of the power distribution network, and a warning signal is generated to optimize the scheduling of the distributed photovoltaic power source.

[0010] In a preferred embodiment, the labor performance information and the energy storage performance information of the distributed photovoltaic power source when connected to the grid include:

[0011] The labor performance information of the distributed photovoltaic power source when connected to the grid is represented by an output distribution difference coefficient, and the energy storage performance information of the distributed photovoltaic power source when connected to the grid is represented by an energy storage change fluctuation coefficient, wherein, the output distribution difference coefficient is the energy storage change fluctuation coefficient is

[0012] In a preferred embodiment, the output distribution difference coefficient is obtained by:

[0013] Based on the historical data of different distributed photovoltaic power sources, the probability density functions of different distributed photovoltaic power sources under different scenarios are determined, and the probability density functions of the distributed photovoltaic power source in the monitoring interval under different scenarios are calculated, and the calculation formula is: ; wherein, is the probability density function of the distributed photovoltaic power source in the monitoring interval, g is the actual light intensity at a certain time, is the maximum light intensity in the monitoring interval, is the Gamma function, , is the parameter of the Beta distribution, , , is the mean of the sunlight intensity in the monitoring interval, is the variance of the sunlight intensity in the monitoring interval;

[0014] By obtaining the photoelectric conversion efficiency of different distributed photovoltaic power sources and the area of the photovoltaic solar cell panel, the output power of the photovoltaic power generation system of different distributed photovoltaic power sources is determined, and the calculation formula of the output power of the photovoltaic power generation system of different distributed photovoltaic power sources is: ; wherein, P is the output power of the photovoltaic power generation system of the photovoltaic power source, A is the area of the photovoltaic solar cell panel, is the photoelectric conversion efficiency;

[0015] According to the probability distribution model of solar photovoltaic, it can be obtained that the output model of solar photovoltaic approximately obeys Beta distribution, and the probability distribution model of solar photovoltaic is represented as: ; wherein, is the probability distribution model of historical solar photovoltaic, is the maximum output power of solar photovoltaic power generation under the light intensity in the monitoring interval;

[0016] The probability distribution model of different distributed photovoltaic power sources in the monitoring interval is collected in real time, and the probability distribution model of different distributed photovoltaic power sources in the monitoring interval is marked as: ;

[0017] The real-time probability distribution model of different distributed photovoltaic power sources is compared with the historical probability distribution model of solar photovoltaic power by Kullback-Leibler divergence, and the power distribution difference coefficient is calculated, and the calculation formula is: .

[0018] In a preferred embodiment, the acquisition logic of the energy storage variation fluctuation coefficient is:

[0019] Based on the energy storage system connected by the distributed photovoltaic power source, the state of charge variation difference equation of the power distribution network energy storage system is obtained, and the state of charge variation rate of the power distribution network energy storage system is calculated based on the state of charge variation difference equation, and the state of charge variation rate is marked as: , wherein t=1, 2, 3, …, T, T is a positive integer, and t is the number of different time in the monitoring interval;

[0020] The state of charge variation difference equation expression of the power distribution network energy storage system is: ; wherein, represents the state of charge at time t, represents the self-discharge rate, , respectively represent the charging and discharging flag of the energy storage system, represents the actual charging power of the energy storage system, represents the actual discharging power of the energy storage system, and respectively represent the charging and discharging efficiency of the energy storage system, represents the capacity of the energy storage system;

[0021] The average value and the standard deviation of the state of charge variation rate in the monitoring interval are obtained, and the average value and the standard deviation of the state of charge variation rate in the monitoring interval are respectively marked as: and , wherein, , ;

[0022] The energy storage variation fluctuation coefficient is calculated, and the calculation formula is: .

[0023] In a preferred embodiment, the grid-connected performance of the distributed photovoltaic power source is evaluated, including:

[0024] The power supply evaluation model is constructed by weighted calculation on the normalized output distribution difference coefficient and the energy storage change fluctuation coefficient, and the power supply evaluation coefficient is generated, and the calculation formula of the power supply evaluation coefficient is: ; wherein, is the power supply evaluation coefficient, , the output distribution difference coefficient and the energy storage change fluctuation coefficient are respectively proportional coefficients, , all greater than 0.

[0025] In a preferred embodiment, the grid-connected risk information and the grid-connected load matching information of the power distribution network are determined, including:

[0026] The grid-connected risk information of the power distribution network is represented by a distributed photovoltaic risk coefficient, and the grid-connected load matching information of the power distribution network is represented by a load mismatch degree coefficient, wherein, is the distributed photovoltaic risk coefficient, is the load mismatch degree coefficient.

[0027] In a preferred embodiment, the distributed photovoltaic risk coefficient acquisition logic is:

[0028] According to the node position of the power distribution network, the distributed photovoltaic power supply connected in parallel at the node position is accessed, that is, the node topology structure of the power distribution network is modeled, the distributed photovoltaic power supply connected at different nodes is calibrated, the fault set and the fault rate caused by the distributed photovoltaic power supply connected at different node positions are obtained by simulating different fault scenarios, that is, the Monte Carlo simulation is performed on different nodes and the connection of the distributed photovoltaic power supply, different system states and fault conditions are randomly sampled, the fault occurrence probability under each scenario is estimated, and the fault influence score of the node position distributed photovoltaic power supply connected in parallel is determined;

[0029] Based on the current operation of the power distribution network, the distributed photovoltaic power supply node already connected in parallel in the current power distribution network is determined, and the load at the distributed photovoltaic power supply node is obtained, the load at different types of nodes and the fault influence score of the distributed photovoltaic power supply connected at different types of nodes are taken as input features, and the distributed photovoltaic risk coefficient is taken as output features, a regression model is constructed, and the calculation formula of the distributed photovoltaic risk coefficient is:

[0030] ; wherein, 1, 2, 3, …, J are the numbers of different types of nodes, is the load at different types of nodes, The failure influence score is marked as a fixed constant when the node is not connected to the distributed photovoltaic power supply, The weight of the different types of nodes, and e is the base.

[0031] In a preferred embodiment, the load mismatch coefficient acquisition logic is:

[0032] The demand load at the distributed photovoltaic power supply grid-connected node in the unit time period is obtained, and the demand load at the distributed photovoltaic power supply grid-connected node in the unit time period is marked as: ; wherein i=1, 2, 3, …, I, I is a positive integer, i is the number of different time points in the unit time period, n=1, 2, 3, …, N, N is a positive integer, and n is the number of the distributed photovoltaic power supply grid-connected node;

[0033] The output power of the distributed photovoltaic power supply at the distributed photovoltaic power supply grid-connected node in the unit time period is obtained, and the output power of the distributed photovoltaic power supply at the distributed photovoltaic power supply grid-connected node in the unit time period is marked as:

[0034] The load matching degree of a single distributed photovoltaic power supply grid-connected node in the unit time period is determined, and the calculation formula is: ; wherein is the load matching degree of the nth distributed photovoltaic power supply grid-connected node;

[0035] The load mismatch coefficient is calculated, and the calculation formula is: .

[0036] In a preferred embodiment, the overall grid-connected performance of the power distribution network is quantified, and a warning signal is generated, including:

[0037] The grid-connected risk information and the grid-connected load matching information of the power distribution network are comprehensively analyzed, the normalized distributed photovoltaic risk coefficient and the load mismatch coefficient are weighted calculated, the power distribution network optimization evaluation model is constructed, and the power distribution network optimization evaluation coefficient is generated. The calculation formula of the power distribution network optimization evaluation coefficient is: ; wherein is the power distribution network optimization evaluation coefficient, , are the proportional coefficients of the distributed photovoltaic risk coefficient and the load mismatch coefficient respectively, , are all greater than 0.

[0038] The technical effects and advantages of the present application are:

[0039] ​The application combines historical data of photovoltaic power supply, state of charge of energy storage system and topology of power distribution network and other factors to comprehensively evaluate the risk of grid connection of distributed photovoltaic power supply and load matching degree, and then optimize the grid connection strategy of photovoltaic power supply, including establishing a solar photovoltaic output model based on historical data and real-time monitoring data, evaluating the risk that may be brought by grid connection of distributed photovoltaic power supply by simulating different fault scenarios based on node topology structure of power distribution network and related branch data, and the application is helpful to ensure stable operation of power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to facilitate the understanding of those skilled in the art, the application will be further described below in combination with the drawings.

[0041] Figure 1 The application is a flowchart of an optimal scheduling method for grid connection of a power distribution network and a distributed power supply. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the application will be described clearly and completely below in combination with the drawings in the embodiments of the application, and obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0043] Embodiment 1

[0044] Figure 1 The application is a flowchart of an optimal scheduling method for grid connection of a power distribution network and a distributed power supply, specifically including the following steps.

[0045] S1: Based on historical data of distributed photovoltaic power supply, determine the output model of solar photovoltaic, determine the performance information of distributed photovoltaic power supply in grid connection by the difference between the real-time and historical output model of solar photovoltaic, and determine the performance information of energy storage in grid connection of distributed photovoltaic power supply based on the state of charge data of the energy storage system connected to the distributed photovoltaic power supply;

[0046] S2: Through comprehensive analysis of the performance information of distributed photovoltaic power supply in grid connection and the performance information of energy storage, evaluate the grid connection performance of distributed photovoltaic power supply, and determine the grid connection strategy of distributed photovoltaic power supply;

[0047] S3: Based on the node related data of the power distribution network, simulate the possible risk of the grid connection node of the distributed photovoltaic power supply in the power distribution network, determine the grid connection risk information of the power distribution network, and determine the grid connection load matching information of the power distribution network by considering the matching degree of the output power and the demand power of the distributed photovoltaic power supply;

[0048] S4: When the same kind of distributed photovoltaic power supply is connected to the distribution network, the grid-connected risk information and grid-connected load matching information of the distribution network are fused and evaluated to quantify the overall grid-connected performance of the distribution network and generate early warning signals to optimize the scheduling of the distributed photovoltaic power supply.

[0049] The relationship between the distributed photovoltaic power supply, the energy storage system and the distribution network is a complex system that requires coordination and mutual dependence. When the photovoltaic power supply performs well and can generate stable power and meet the grid-connected requirements (such as voltage and current fluctuation range), direct grid connection is the simplest and most economical choice. If the power generation of the photovoltaic power supply fluctuates greatly, especially when the weather changes (such as overcast days, cloud cover, etc.), the energy storage system can smooth the power fluctuation and ensure stable power output, reducing the impact on the grid. Therefore, the system can dynamically choose whether to connect the distributed photovoltaic power supply through the energy storage system or directly to the grid.

[0050] The performance information of the distributed photovoltaic power supply and the energy storage performance information when connected to the grid are collected. The performance information of the distributed photovoltaic power supply when connected to the grid is represented by the output distribution difference coefficient, and the energy storage performance information when connected to the grid is represented by the energy storage change fluctuation coefficient.

[0051] The advantages of the output distribution difference coefficient are:

[0052] The output distribution difference coefficient can provide a quantitative evaluation method by quantifying the difference between the real-time output of the distributed photovoltaic power supply and the historical data. By comparing the output difference in different time periods, it can reveal whether the photovoltaic power supply has abnormal fluctuations or abnormal behavior;

[0053] By comparing with historical data, the output distribution difference coefficient not only identifies abnormal behavior of the photovoltaic power supply in a specific monitoring interval, but also reveals the consistency of its long-term output characteristics. Stable photovoltaic power supply can maintain a relatively consistent output mode under different weather conditions, thereby avoiding unnecessary impact on the grid. This is very important for grid scheduling and stability, especially when considering large-scale grid connection, stable output can reduce grid frequency and voltage;

[0054] The output distribution difference coefficient can help discover potential problems of the photovoltaic power supply during real-time monitoring. If the difference coefficient is large, it can indicate that the output characteristics of the photovoltaic power supply have changed significantly, thereby providing early warning of equipment failure, shading problems, etc. This early warning mechanism helps to avoid problems such as grid frequency fluctuation and voltage instability caused by unstable photovoltaic power supply grid connection, improving the safety of grid connection.

[0055] The modeling method of photovoltaic output in the prior art usually adopts a statistical modeling method, that is, assuming the renewable energy output uncertainty as a statistical model, solving the specific parameters of the model through historical data, and obtaining a specific output scene by combining a sampling method. The distributed power output modeling method based on the statistical model mainly includes a probability modeling method, a Markov chain method, a time series method, and a scene tree generation method.

[0056] The acquisition logic of the output distribution difference coefficient is: based on historical data of different distributed photovoltaic power sources, determining probability density functions of different distributed photovoltaic power sources under different scenes, and calculating the probability density functions of the distributed photovoltaic power sources in the monitoring interval under different scenes, and the calculation formula is: ; wherein, is the probability density function of the distributed photovoltaic power source in the monitoring interval, g is the actual light intensity at a certain moment, is the maximum light intensity in the monitoring interval, is a Gamma function, , is a parameter of the Beta distribution, , , is the mean of the sunlight intensity in the monitoring interval, is the variance of the sunlight intensity in the monitoring interval;

[0057] It should be noted that the output of photovoltaic power sources with the same geographical position will be significantly affected by meteorological conditions. Even if the photovoltaic power stations are of the same type, the power sources located in different positions may exhibit different output characteristics. By analyzing different photovoltaic power sources and their output performances under different scenes, the output fluctuations and characteristics of each photovoltaic power source can be more accurately captured.

[0058] The monitoring interval is set by professional staff, and is usually 60 minutes. By setting a fixed monitoring interval, the performance of the photovoltaic power source can be evaluated in the interval. If the output performance of the photovoltaic system in a certain time period is significantly different from the expected value, and the difference does not conform to the probability distribution of the historical performance (for example, the difference between the predicted and actual light intensity is too large), it may indicate that the photovoltaic power source has an abnormal condition, such as equipment failure, shading, connection problem, etc. The output performance in the fixed monitoring interval can be compared with the historical data. By establishing a long-term data set, the output performance of the photovoltaic system under the same meteorological conditions and the same time period is analyzed, so as to evaluate its consistency.

[0059] By obtaining the photoelectric conversion efficiency of different distributed photovoltaic power sources and the area of the photovoltaic solar panel, the output power of the photovoltaic power generation system of different distributed photovoltaic power sources is determined, and the calculation formula of the output power of the photovoltaic power generation system of different distributed photovoltaic power sources is: ; wherein, P is the output power of the photovoltaic power supply photovoltaic power generation system, A is the area of the photovoltaic solar cell panel, is the photoelectric conversion efficiency;

[0060] It should be noted that the output power of the photovoltaic power generation system is a function model of the solar irradiance intensity perceived by the solar cell panel.

[0061] According to the probability distribution model of solar photovoltaic, it can be concluded that the output model of solar photovoltaic approximately obeys Beta distribution, and the probability distribution model of solar photovoltaic is expressed as: ; wherein, is the probability distribution model of historical solar photovoltaic, is the maximum output power of solar photovoltaic power generation under the irradiance intensity in the monitoring interval;

[0062] By real-time collection of the probability distribution model of different distributed photovoltaic power supplies in the monitoring interval, the real-time collection of the probability distribution model of different distributed photovoltaic power supplies in the monitoring interval is marked as: ;

[0063] By comparing the real-time probability distribution model of different distributed photovoltaic power supplies and the probability distribution model of historical solar photovoltaic through Kullback-Leibler divergence, the output distribution difference coefficient is calculated, and the calculation formula is: ; wherein, is the output distribution difference coefficient.

[0064] As can be seen from the formula, the greater the output distribution difference coefficient, the greater the deviation between the current photovoltaic system performance and the historical data, and there may be abnormalities or faults. For example, system failure, inverter problem, shading, wiring problem, etc. may cause the difference between real-time distribution and historical distribution to increase, indicating that the power output of the distributed photovoltaic power supply is unstable, and the energy storage system may need to be adjusted to reduce the impact on the power grid. On the contrary, the smaller the output distribution difference coefficient, the more consistent the output fluctuation of the current photovoltaic system with the historical data, and the system is running normally.

[0065] The distribution network adjusts the work of the photovoltaic power supply and the energy storage system through the intelligent control system. It should be noted that the system can automatically adjust the charge and discharge strategy of the energy storage system according to real-time power demand, photovoltaic power generation and energy storage state parameters, to ensure the balance of power grid load and the stability of power quality. When the power generated by the photovoltaic power generation system exceeds the local demand, the system can feed back the excess power to the distribution network through the energy storage system or directly in the form of grid-connected, to promote the maximization of power utilization;

[0066] If the fluctuation of the energy storage system is large, it may cause the voltage and frequency of the power grid to be unstable. In particular, the output of the photovoltaic power source itself also has fluctuation, when this fluctuation is superimposed with the fluctuation of the energy storage system, it may cause the load of the power grid to be unbalanced, and even cause the power grid to fail;

[0067] If the energy storage system frequently charges and discharges, the working load of the system will increase, which may cause the battery to age rapidly, thereby affecting the long-term stability of the system. When the photovoltaic power source is connected to the grid, the energy storage system should provide stable power output by smoothing the photovoltaic fluctuation, but if the energy storage system itself is unstable, this regulating effect may be weakened.

[0068] The acquisition logic of the energy storage change fluctuation coefficient is: based on the energy storage system connected by the distributed photovoltaic power source, the state of charge change differential equation of the power grid energy storage system is acquired, and the state of charge change rate of the power grid energy storage system is calculated based on the state of charge change differential equation, and the state of charge change rate is marked as: , wherein t = 1, 2, 3, …, T, T is a positive integer, and t is the number of different time in the monitoring interval;

[0069] The state of charge change differential equation expression of the power grid energy storage system is: ; wherein, represents the state of charge at time t, represents the self-discharge rate, , respectively represent the charging and discharging flag of the energy storage system, represents the actual charging power of the energy storage system, represents the actual discharging power of the energy storage system, and respectively represent the charging and discharging efficiency of the energy storage system, represents the capacity of the energy storage system;

[0070] The average value and the standard deviation of the state of charge change rate in the monitoring interval are acquired, and the average value and the standard deviation of the state of charge change rate in the monitoring interval are respectively marked as: and , wherein, , ;

[0071] The energy storage change fluctuation coefficient is calculated, and the calculation formula is: ; wherein, is the energy storage change fluctuation coefficient.

[0072] It can be seen from the formula that the greater the energy storage change fluctuation coefficient, the greater the state of charge change of the energy storage system, the poorer the stability of the system, and the more frequent the charging and discharging fluctuations, or the instability of the charging and discharging efficiency of the energy storage system, which affects the stability of the power grid. Therefore, the distributed photovoltaic power supply cannot be directly connected to the grid, and the photovoltaic power supply needs to be considered after the stability of the energy storage system is improved.

[0073] The performance information of the distributed photovoltaic power supply and the performance information of the energy storage are comprehensively analyzed when the distributed photovoltaic power supply is connected to the grid. The power supply evaluation model is constructed by weighted calculation of the normalized output distribution difference coefficient and the energy storage change fluctuation coefficient, and the power supply evaluation coefficient is generated. The calculation formula of the power supply evaluation coefficient is: ; wherein, is the power supply evaluation coefficient, , is the proportion coefficient of the output distribution difference coefficient and the energy storage change fluctuation coefficient, respectively, , are all greater than 0.

[0074] The power supply evaluation coefficient threshold is set, and the power supply evaluation coefficient of all distributed photovoltaic power supplies is compared with the power supply evaluation coefficient threshold. If the power supply evaluation coefficient is greater than the power supply evaluation coefficient threshold, a first warning is generated for the distributed photovoltaic power supply, indicating that the distributed photovoltaic power supply cannot be directly connected to the grid, and the photovoltaic power generation is stored in the energy storage system. If the power supply evaluation coefficient is less than the power supply evaluation coefficient threshold, the distributed photovoltaic power supply is not warned, indicating that the distributed photovoltaic power supply can be connected to the grid through the energy storage system or directly connected to the grid.

[0075] When the same kind of distributed photovoltaic power supply is connected to the distribution network, the influence of a single distributed photovoltaic power supply on the overall distribution network is not considered, and the performance of the distribution network when simultaneously connecting multiple distributed photovoltaic power supplies needs to be comprehensively analyzed. The grid connection risk information and grid connection load matching information of the distribution network are collected. The grid connection risk information of the distribution network is represented by the distributed photovoltaic risk coefficient, and the grid connection load matching information of the distribution network is represented by the load mismatch degree coefficient.

[0076] The advantages of the distributed photovoltaic risk coefficient are:

[0077] The distributed photovoltaic risk coefficient can comprehensively consider the number, distribution, capacity and fault impact score of the distributed photovoltaic power supply, so as to comprehensively evaluate the potential risk of the photovoltaic power supply connected to the distribution network. The risk coefficient not only considers the output fluctuation and randomness of the photovoltaic power supply, but also includes factors such as fault propagation path and system recovery difficulty. The specific influence of the operation of the photovoltaic power supply on the stability of the distribution network is quantified, which helps to identify possible grid fault modes and their propagation process;

[0078] The distributed photovoltaic risk coefficient can be flexibly applied to small power distribution networks and large regional power grids. It can be applied to power distribution networks with different topologies and provide accurate risk assessment for different types of photovoltaic power sources (such as centralized, distributed, and small photovoltaic power stations), thereby adapting to the needs of different power grid sizes.

[0079] The acquisition logic of the distributed photovoltaic risk coefficient is as follows: according to the node position of the power distribution network, the distributed photovoltaic power source connected to the grid at the node position is accessed, that is, the node topology of the power distribution network is modeled, the distributed photovoltaic power source connected to different nodes is calibrated, the fault set and fault rate caused by the distributed photovoltaic power source connected to different nodes are obtained by simulating different fault scenarios, that is, Monte Carlo simulation is performed on different nodes and the connection of the distributed photovoltaic power source to estimate the fault probability under each scenario, and the fault impact score of the distributed photovoltaic power source connected to the grid at the node position is determined.

[0080] It should be noted that due to the reliability of the power system, most of the samples are reliable and do not need to be load cut off. Therefore, the number of samples that cannot meet the power supply requirements is small. In order to further improve the training accuracy, a special correction mechanism needs to be introduced to train the unreliable power supply samples: a fault set is artificially set for a specific scenario in the network, which can better meet the engineering practice. Secondly, the fault probability is expanded to generate more fault samples. Through the above two ways, the problem of unbalanced training samples is alleviated.

[0081] The fault impact score is based on the fault probability under each fault scenario, the fault propagation path and the range of influence, the time and difficulty of system recovery, and the capacity, output characteristics, and whether the photovoltaic power source can effectively support the load demand.

[0082] Based on the current operation of the power distribution network, the nodes of the distributed photovoltaic power source already connected to the grid in the current power distribution network are determined, and the load at the distributed photovoltaic power source node is obtained. The load at different types of nodes and the fault impact score of the distributed photovoltaic power source connected to different types of nodes are used as input features, and the distributed photovoltaic risk coefficient is used as output features to build a regression model. The calculation formula of the distributed photovoltaic risk coefficient is:

[0083] ; wherein, is the distributed photovoltaic risk coefficient, 1, 2, 3, …, J is the number of different types of nodes, is the load at different types of nodes, is the fault impact score of the distributed photovoltaic power source connected to different types of nodes, and when the node is not connected to the distributed photovoltaic power source, the fault impact score is marked as a fixed constant, wherein, w is the weight of different types of nodes, e is the base number;

[0084] It should be noted that different types of nodes are determined based on the structure of the power distribution network, such as load nodes, power generation nodes, and balance nodes, etc. The impact of distributed photovoltaic power grid connection on the power grid system is different, and the fault impact score at the node without access to distributed photovoltaic power is obtained by training, which is usually 0. Because at this time, photovoltaic power does not participate in system operation, there is no potential impact of photovoltaic power, and the complexity of the model is reduced.

[0085] As can be seen from the formula, the greater the risk coefficient of distributed photovoltaic power, the higher the impact and risk brought by the distributed photovoltaic power connected at each node of the current power distribution network. If multiple high-risk distributed photovoltaic power is connected for a long time, the power grid may face a greater stability risk, increasing the difficulty of overload, failure, recovery, and light abandonment.

[0086] Among them, the advantage of the load mismatch degree coefficient is:

[0087] The load mismatch degree coefficient can quantify the operational stability of the power grid. A lower load mismatch degree means better coordination between the power grid's load and photovoltaic power output, and the system can operate more stably in the face of load fluctuations, avoiding problems such as overload and frequent start-stop of equipment. When photovoltaic output and load are mismatched, the power grid may need to cut off the load to ensure stability. Through the load mismatch degree coefficient, the matching situation can be identified and optimized in advance, reducing the dependence on load shedding and standby power;

[0088] When the output of photovoltaic power and the demand of load are highly matched, the power grid can more efficiently absorb photovoltaic power and reduce the phenomenon of light abandonment. When the load mismatch degree coefficient is low, photovoltaic power can be maximally absorbed by the power grid, ensuring the full use of clean energy.

[0089] The acquisition logic of the load mismatch degree coefficient is: obtaining the demand load at the distributed photovoltaic power grid connection node within a unit time period, and marking the demand load at the distributed photovoltaic power grid connection node within a unit time period as: ; wherein, i=1, 2, 3, …, I, I is a positive integer, i is the number of different time points within a unit time period, n=1, 2, 3, …, N, N is a positive integer, and n is the number of distributed photovoltaic power grid connection nodes;

[0090] Obtaining the output power of the distributed photovoltaic power at the distributed photovoltaic power grid connection node within a unit time period, and marking the output power of the distributed photovoltaic power at the distributed photovoltaic power grid connection node within a unit time period as: ;

[0091] Determine the load matching degree of the single distributed photovoltaic power grid-connected node in a unit time period, and the calculation formula is: ; wherein, is the load matching degree of the nth distributed photovoltaic power grid-connected node.

[0092] It should be noted that the unit time period is a specific length of time set by professional staff. When the photovoltaic power output is consistent with the load, the power grid does not need additional scheduling or load shedding, which can avoid frequent start-stop equipment or adjustment of other power sources, which can greatly improve the stability of the power grid.

[0093] Calculate the load mismatch coefficient, and the calculation formula is: ; wherein, is the load mismatch coefficient.

[0094] As can be seen from the formula, the smaller the load mismatch coefficient, the better the output power of the distributed photovoltaic power at the grid-connected node can meet the load demand, the higher the matching degree between the photovoltaic power and the load, and the more stable the power grid can operate, and the overall distribution network performs better.

[0095] Comprehensively analyze the grid-connected risk information and grid-connected load matching information of the distribution network, and calculate the normalized distributed photovoltaic risk coefficient and load mismatch coefficient by weighting, construct a distribution network optimization evaluation model, and generate a distribution network optimization evaluation coefficient. The calculation formula of the distribution network optimization evaluation coefficient is: ; wherein, is the distribution network optimization evaluation coefficient, , are the proportional coefficients of the distributed photovoltaic risk coefficient and the load mismatch coefficient, , respectively.

[0096] Set the distribution network optimization evaluation coefficient threshold, obtain the distribution network optimization evaluation coefficient of the distribution network in the running state, and compare the distribution network optimization evaluation coefficient with the distribution network optimization evaluation coefficient threshold. If the distribution network optimization evaluation coefficient is greater than the distribution network optimization evaluation coefficient threshold, a second warning signal is generated, indicating that the current operation of the distribution network may be poor, and measures such as load shedding or standby power supply need to be taken to alleviate the risk of unstable power grid. If the power grid optimization evaluation coefficient is less than the distribution network optimization evaluation coefficient threshold, no second warning signal is generated.

[0097] It should be noted that the weights of the coefficients in the power supply evaluation model and the power distribution network optimization evaluation model are obtained by machine learning algorithm simulation training, and the weights of the coefficients are optimized by using historical data and simulation results. The power supply evaluation coefficient threshold and the power distribution network optimization evaluation coefficient threshold can be set according to historical data, experimental results, or specific requirements and standards of the power grid, and can be statistically analyzed by historical operation data.

[0098] The application combines historical data of photovoltaic power supply, state of charge of energy storage system and topology structure of power distribution network and other factors to comprehensively evaluate the risk of grid connection of distributed photovoltaic power supply and load matching degree, and then optimize the grid connection strategy of photovoltaic power supply, including establishing a solar photovoltaic output model based on historical data and real-time monitoring data, evaluating the risk that may be caused by grid connection of distributed photovoltaic power supply based on node topology structure of power distribution network and related branch data by simulating different fault scenarios. The application helps to ensure stable operation of the power distribution network.

[0099] The above formulas are dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0100] The above embodiments can be realized by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center and the like containing one or more available medium sets. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD) or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0101] It should be understood that the size of the sequence number of the above processes does not mean the order of execution in various embodiments of the present application, and the execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0102] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. A skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0103] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0104] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0105] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An optimal scheduling method for power distribution network and grid-connected distributed power sources, characterized in that, Specifically comprising the following steps: S1: Based on the historical data of distributed photovoltaic power supply, the output model of solar photovoltaic is determined, the performance information of the distributed photovoltaic power supply when connected to the grid is determined by the difference between the real-time and historical output model of solar photovoltaic, and the performance information of the energy storage when the distributed photovoltaic power supply is connected to the grid is determined based on the state of charge data of the energy storage system connected to the distributed photovoltaic power supply; S2: By comprehensively analyzing the performance information of the distributed photovoltaic power supply and the performance information of the energy storage when connected to the grid, the performance of the distributed photovoltaic power supply when connected to the grid is evaluated, and the connection strategy of the distributed photovoltaic power supply is determined; S3: Based on the node related data of the distribution network, the possible risk of the distributed photovoltaic power supply connected to the grid node in the distribution network is simulated, the connection risk information of the distribution network is determined, and the matching degree of the output power and the demand power of the distributed photovoltaic power supply is considered to determine the connection load matching information of the distribution network; S4: When the same kind of distributed photovoltaic power supply is connected to the distribution network, the connection risk information and the connection load matching information of the distribution network are fused and evaluated to quantify the overall connection performance of the distribution network, and a warning signal is generated to optimize the scheduling of the distributed photovoltaic power supply; The performance information of the distributed photovoltaic power supply when connected to the grid and the performance information of the energy storage include: The power performance information of the distributed photovoltaic power supply when connected to the grid is expressed by an output distribution difference coefficient, and the energy storage performance information of the distributed photovoltaic power supply when connected to the grid is expressed by an energy storage change fluctuation coefficient, wherein, the output distribution difference coefficient is the energy storage change fluctuation coefficient is By comprehensively analyzing the power performance information and the energy storage performance information of the distributed photovoltaic power supply when connected to the grid, a power supply evaluation coefficient is generated. By setting a power supply evaluation coefficient threshold, the power supply evaluation coefficients of all distributed photovoltaic power supplies are compared with the power supply evaluation coefficient threshold. If the power supply evaluation coefficient is greater than the power supply evaluation coefficient threshold, the distributed photovoltaic power supply cannot be directly connected to the grid. If the power supply evaluation coefficient is less than the power supply evaluation coefficient threshold, the distributed photovoltaic power supply can be connected to the grid through an energy storage system or directly connected to the grid. The acquisition logic of the output distribution difference coefficient is: Based on the historical data of different distributed photovoltaic power sources, the probability density functions of different distributed photovoltaic power sources in different scenarios are determined, and the probability density functions of the distributed photovoltaic power sources in the monitoring interval in different scenarios are calculated, and the calculation formula is: ; wherein, is the probability density function of the distributed photovoltaic power source in the monitoring interval, g is the actual light intensity at a certain moment, is the maximum light intensity in the monitoring interval, is the Gamma function, , is the parameter of the Beta distribution, , , is the mean of the sunlight intensity in the monitoring interval, is the variance of the sunlight intensity in the monitoring interval; The output power of the photovoltaic power generation system of different distributed photovoltaic power sources is determined by acquiring the photoelectric conversion efficiency of different distributed photovoltaic power sources and the area of the photovoltaic solar cell panel, and the calculation formula of the output power of the photovoltaic power generation system of different distributed photovoltaic power sources is: ; wherein P is the output power of the photovoltaic power generation system of the photovoltaic power source, A is the area of the photovoltaic solar cell panel, is the photoelectric conversion efficiency. According to the probability distribution model of the solar photovoltaic, it can be concluded that the output model of the solar photovoltaic approximately obeys Beta distribution, and the probability distribution model of the solar photovoltaic is expressed as: ; wherein, is a historical probability distribution model of the solar photovoltaic, is a maximum output power of the solar photovoltaic under the light intensity in the monitoring interval. The probability distribution model of different distributed photovoltaic power sources in the monitoring interval is collected in real time, and the probability distribution model of different distributed photovoltaic power sources in the monitoring interval is marked as: ; The difference coefficient of power distribution is calculated by comparing the real-time probability distribution model of different distributed photovoltaic power and the historical probability distribution model of solar photovoltaic through Kullback-Leibler divergence, and the calculation formula is: .

2. The optimal dispatching method of power distribution network and grid-connected distributed power source according to claim 1, characterized in that, The acquisition logic of the energy storage change fluctuation coefficient is: Based on the energy storage system connected with the distributed photovoltaic power supply, a state of charge change difference equation of the energy storage system of the power distribution network is obtained, and a state of charge change rate of the energy storage system of the power distribution network is calculated based on the state of charge change difference equation, and the state of charge change rate is marked as: Wherein, t=1, 2, 3, …, T, T is a positive integer, and t is the number of different time in the monitoring interval. The state of charge variation differential equation expression of the power distribution network energy storage system is: ; wherein, represents the state of charge at time t, represents the self-discharge rate, , respectively represent the energy storage system charging and discharging flag, represents the actual charging power of the energy storage system, represents the actual discharging power of the energy storage system, and respectively represent the charging and discharging efficiency of the energy storage system, represents the capacity of the energy storage system; The average value and the standard deviation of the state of charge change rate in the monitoring interval are obtained, and the average value and the standard deviation of the state of charge change rate in the monitoring interval are marked as: and wherein, , ; The energy storage change fluctuation coefficient is calculated, and the calculation formula is: .

3. The optimal dispatching method of power distribution network and grid-connected distributed power source according to claim 2, characterized in that, The evaluation of the performance of the distributed photovoltaic power supply when connected to the grid includes: The power performance information and the energy storage performance information of the distributed photovoltaic power supply when being connected to the grid are comprehensively analyzed, a power supply evaluation model is constructed by weighted calculation on the normalized output distribution difference coefficient and the energy storage change fluctuation coefficient, a power supply evaluation coefficient is generated, and a calculation formula of the power supply evaluation coefficient is: ; wherein, is the power supply evaluation coefficient, , are proportional coefficients of the output distribution difference coefficient and the energy storage change fluctuation coefficient respectively, , are all greater than 0.

4. The optimal dispatching method of power distribution network and grid-connected distributed power source according to claim 3, characterized in that, The determination of the connection risk information and the connection load matching information of the distribution network includes: The grid-connected risk information of the distribution network is expressed by a distributed photovoltaic risk coefficient, and the grid-connected load matching information of the distribution network is expressed by a load mismatch degree coefficient, wherein, is the distributed photovoltaic risk coefficient, is the load mismatch degree coefficient.

5. The optimal dispatching method of power distribution network and grid-connected distributed power source according to claim 4, characterized in that, The acquisition logic of the distributed photovoltaic risk coefficient is: According to the node position of the distribution network, the distributed photovoltaic power supply connected to the grid at the node position is connected, that is, the node topology structure of the distribution network is modeled, the distributed photovoltaic power supply connected to different nodes is calibrated, different fault scenarios are simulated, the fault set and fault rate caused by the distributed photovoltaic power supply connected to different node positions are obtained, that is, the Monte Carlo simulation is performed on different nodes and the connection of the distributed photovoltaic power supply, different system states and fault conditions are randomly sampled, the fault probability in each scenario is estimated, and the fault influence score of the distributed photovoltaic power supply connected to the grid at the node position is determined; Based on the current operation of the distribution network, the distributed photovoltaic power supply node already connected to the grid in the current distribution network is determined, and the load at the distributed photovoltaic power supply node is obtained. The load at different types of nodes and the fault influence score of the distributed photovoltaic power supply connected to different types of nodes are taken as input features, and the distributed photovoltaic risk coefficient is taken as output feature. A regression model is constructed, and the calculation formula of the distributed photovoltaic risk coefficient is: ; wherein, 1, 2, 3, …, J are the numbers of different types of nodes, is the load at different types of nodes, is the failure impact score of different types of nodes accessing distributed photovoltaic power supply, when the node does not access the distributed photovoltaic power supply, the failure impact score is marked as a fixed constant, is the weight of different types of nodes, e is the base.

6. The optimal dispatching method of power distribution network and grid-connected distributed power source according to claim 5, characterized in that, The acquisition logic of the load mismatch coefficient is: The demand load at the grid-connected node of the distributed photovoltaic power supply in a unit time period is acquired, and the demand load at the grid-connected node of the distributed photovoltaic power supply in a unit time period is marked as: ; wherein, i=1, 2, 3, …, I, I is a positive integer, i is the number of different time points in a unit time period, n=1, 2, 3, …, N, N is a positive integer, and n is the number of the grid-connected node of the distributed photovoltaic power supply. The output power of the distributed photovoltaic power supply at the grid-connected node of the distributed photovoltaic power supply in a unit time period is obtained, and the output power of the distributed photovoltaic power supply at the grid-connected node of the distributed photovoltaic power supply in the unit time period is marked as: ; The load matching degree of a single distributed photovoltaic power grid-connected node in a unit time period is determined, and the calculation formula is: ; wherein, is the load matching degree of the nth distributed photovoltaic power grid-connected node. The load mismatch degree coefficient is calculated by the following formula: .

7. The optimal dispatching method of power distribution network and grid-connected distributed power source according to claim 6, characterized in that, The quantification of the overall connection performance of the distribution network includes: The grid-connected risk information and the grid-connected load matching information of the power distribution network are comprehensively analyzed, a power distribution network optimization evaluation model is constructed by weighted calculation on the normalized distributed photovoltaic risk coefficient and the load mismatch degree coefficient, and a power distribution network optimization evaluation coefficient is generated. The calculation formula of the power distribution network optimization evaluation coefficient is: ; wherein, is the power distribution network optimization evaluation coefficient, , are proportional coefficients of the distributed photovoltaic risk coefficient and the load mismatch degree coefficient respectively, , are all greater than 0.

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