A method and system for evaluating the performance of photovoltaic storage systems in medium and low voltage distribution networks

Abstract

The present invention relates to the technical field of photovoltaic storage systems, and specifically to a photovoltaic storage performance evaluation method and system for photovoltaic storage systems in medium and low voltage distribution networks. The photovoltaic storage performance evaluation method includes: determining the power generation factor and the energy storage factor according to the output characteristics of each independent subsystem in the photovoltaic storage system of the medium and low voltage distribution network; the independent subsystem includes a photovoltaic power generation subsystem and an energy storage subsystem; according to the characteristics of the current scene, determining the corresponding application scene as the target application scene; calling the corresponding photovoltaic storage performance prediction model according to the target application scene, and setting the corresponding photovoltaic storage performance prediction model as the target prediction model; inputting the power generation factor and the energy storage factor of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the target prediction model, and outputting the evaluation result of the photovoltaic storage performance. When configuring the photovoltaic storage system of the medium and low voltage distribution network, the present invention introduces the output characteristics of the photovoltaic storage subsystem that is strongly correlated with the geographical characteristics, so as to achieve high-precision, comprehensive and dynamic prediction of photovoltaic storage performance.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic storage systems, and in particular to a photovoltaic storage performance evaluation method and system for photovoltaic storage systems in medium and low voltage distribution networks. Background Art

[0002] At present, the performance prediction of photovoltaic and energy storage systems in medium and low voltage distribution networks mainly has the problem of insufficient synergy. Existing methods often separate the performance prediction of photovoltaic power generation and energy storage systems, ignoring the mutual influence between the two, and it is difficult to give full play to the synergistic advantages of photovoltaic and energy storage systems. At the same time, existing methods usually do not consider coordinated scheduling strategies, such as charging and discharging strategies under peak and valley electricity prices, photovoltaic power smoothing control, etc., resulting in a large deviation between the prediction results and the actual operating conditions. Most prediction methods also lack a comprehensive evaluation of the overall performance of the photovoltaic and energy storage system, making it difficult to fully reflect the system's economy, reliability, and grid-friendliness.

[0003] Given the limitations of existing technologies, it is of great significance to develop new methods for predicting the performance of photovoltaic and energy storage systems in medium and low voltage distribution networks. The new methods need to fully consider the synergistic effect of photovoltaics and energy storage to improve prediction accuracy; incorporate collaborative scheduling strategies into the prediction model to optimize system operation; achieve a comprehensive evaluation of the overall performance of the photovoltaic and energy storage system; and have long-term performance prediction capabilities to provide support for system planning and asset management. Through these innovations, it is expected to achieve high-precision, comprehensive, and dynamic prediction of the performance of photovoltaic and energy storage systems in medium and low voltage distribution networks, providing important technical support for the efficient operation and large-scale application of photovoltaic and energy storage systems.

[0004] Therefore, it is necessary to design a photovoltaic storage performance evaluation method for photovoltaic storage systems in medium and low voltage distribution networks. Summary of the invention

[0005] The purpose of the present invention is to solve at least one of the technical problems existing in the prior art and to provide a method and system for evaluating the photovoltaic storage performance of photovoltaic storage systems in medium and low voltage distribution networks.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows: a method for evaluating the photovoltaic storage performance of a photovoltaic storage system in a medium and low voltage distribution network, comprising the following steps:

[0007] Step 1, determining the power generation factor and energy storage factor according to the output characteristics of each independent subsystem in the photovoltaic storage system of the medium and low voltage distribution network; the independent subsystem includes a photovoltaic power generation subsystem and an energy storage subsystem;

[0008] Step 2: According to the characteristics of the current scenario, determine the corresponding application scenario as the target application scenario;

[0009] Step 3, calling the corresponding photovoltaic storage performance prediction model according to the target application scenario, and setting the corresponding photovoltaic storage performance prediction model as the target prediction model;

[0010] Step 4: input the power generation factor and energy storage factor of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the target prediction model, and output the evaluation result of the photovoltaic storage performance.

[0011] Furthermore, in the step 1, it specifically includes:

[0012] The output characteristics of the photovoltaic power generation subsystem include intermittency, volatility and randomness;

[0013] The output characteristics are characterized by using the influencing factors of each output characteristic;

[0014] The intermittent impact factor calculation formula is:

[0015] ;

[0016] in, Indicates the intermittent impact factor; Indicates intermittent time; represents the total observation time;

[0017] The calculation formula of the impact factor of volatility is:

[0018] ;

[0019] in, The impact factor representing volatility; Indicates the monitoring time period; Indicates at time The output power of the photovoltaic system; Indicates the average output power during the monitoring period;

[0020] The calculation formula of the randomness influence factor is:

[0021] ;

[0022] in, Indicates the impact factor of randomness; Indicates the monitoring time period; Indicates at time The output power of the photovoltaic system; Indicates at time Expected power generation.

[0023] Furthermore, in the step 1, it specifically includes:

[0024] Determine the power generation factor according to the output characteristics of the photovoltaic power generation system; the power generation factor includes power generation efficiency, stability, and system matching;

[0025] The calculation formula of the power generation efficiency is:

[0026] ;

[0027] in, It represents the power generation efficiency index; Indicates the actual output power of the photovoltaic system; It represents the theoretical output power of a photovoltaic system under ideal conditions; Indicates the intermittent impact factor;

[0028] The stability calculation formula is:

[0029] ;

[0030] in, It represents the stability index; Indicates the average output power during the monitoring period; The impact factor representing volatility; Indicates the impact factor of randomness;

[0031] The calculation formula of the system matching degree is:

[0032] ;

[0033] in, Indicates the system matching index; represents the load demand at time t; represents the output power of the photovoltaic system at time t; Indicates the impact factor of randomness.

[0034] Furthermore, in the step 1, it specifically includes:

[0035] The output characteristics of the energy storage subsystem include adjustability, response speed, and capacity;

[0036] The adjustability calculation formula is:

[0037] ;

[0038] in, Indicates the adjustability of the energy storage system; Indicates the maximum power change that the energy storage system can adjust within a preset time interval; Indicates the rated power of the energy storage system;

[0039] The response speed calculation formula is:

[0040] ;

[0041] in, Indicates the response speed of the energy storage system; It indicates the time required for the energy storage system to receive the dispatch instruction and start executing the dispatch instruction.

[0042] Furthermore, in the step 1, it specifically includes:

[0043] Determine the energy storage factor according to the output characteristics of the energy storage subsystem; the energy storage factor includes regulation capability, response speed, and economy;

[0044] The calculation formula of the adjustment ability is:

[0045] ;

[0046] in, It represents the regulatory capacity indicator; Indicates the adjustability of the energy storage system; represents the weight of regulatory capacity;

[0047] The calculation formula of the response speed is:

[0048] ;

[0049] in, Indicates the response speed indicator; Indicates the response speed of the energy storage system; represents the weight of the corresponding speed;

[0050] The economic calculation formula is:

[0051]

[0052] in, : Economic indicators; : Initial investment cost of energy storage system; Energy storage system operation and maintenance costs; : weight coefficient of cost part; : Maximum storage capacity of the energy storage system; : Capacity part weight.

[0053] Furthermore, in the step 2, it specifically includes:

[0054] Collect the environmental information values ​​in the current scene, and then compare these environmental information with each environmental information value in the typical scene. According to the difference between each environmental information, determine the typical scene closest to the current scene as the target application scene;

[0055] Assume the current environment information vector is , the set of environmental information vectors for typical scenes is , i represents the i-th typical scene, and n represents the number of n environmental information.

[0056] Compute the weighted Euclidean distance:

[0057] ;

[0058] in, is the weighted Euclidean distance between the current environment information vector and the i-th typical scene environment information vector; is the weight of the jth environmental information; is the jth element in the current environment information vector; is the jth element in the i-th typical scene environment information vector;

[0059] The typical scene with the smallest weighted Euclidean distance is selected as the typical scene closest to the current scene.

[0060] Furthermore, when determining typical scenes, a clustering algorithm is used to classify different scenes according to the environmental information of each scene, and multiple typical scenes are obtained after classification;

[0061] After determining the typical scenarios, training samples are generated based on the scenarios of the area where the photovoltaic storage system has been established and the historical performance data of each subsystem. The photovoltaic storage performance prediction model corresponding to each typical scenario is trained and generated. The input of the photovoltaic storage performance prediction model is the numerical value of the output factor and the energy storage factor, and the output of the photovoltaic storage performance prediction model is the evaluation score.

[0062] Furthermore, in step 4, it specifically includes:

[0063] Input the values ​​of various power generation factors and energy storage factors of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the corresponding photovoltaic storage performance prediction model, the photovoltaic storage performance prediction model outputs an evaluation score, and compares the evaluation score with a threshold value;

[0064] When the evaluation score is greater than the threshold, it means that the performance of the photovoltaic storage system configuration of the medium and low voltage distribution network is qualified in the current scenario, so the photovoltaic storage system of the medium and low voltage distribution network can be implemented; when the evaluation score is not greater than the threshold, it means that the performance of the photovoltaic storage system configuration of the medium and low voltage distribution network is unqualified in the current scenario, and the parameters in the configuration plan of the photovoltaic storage system of the medium and low voltage distribution network need to be adjusted.

[0065] The present invention also provides a photovoltaic storage performance evaluation system for a photovoltaic storage system in a medium and low voltage distribution network, comprising:

[0066] A system factor module, used to determine the power generation factor and energy storage factor according to the output characteristics of each independent subsystem in the photovoltaic storage system of the medium and low voltage distribution network; the independent subsystem includes a photovoltaic power generation subsystem and an energy storage subsystem;

[0067] The application scenario module is used to determine the corresponding application scenario as the target application scenario according to the characteristics of the current scenario;

[0068] A prediction model module, used to call a corresponding photovoltaic storage performance prediction model according to the target application scenario, and set the corresponding photovoltaic storage performance prediction model as a target prediction model;

[0069] The evaluation module is used to input the power generation factor and energy storage factor of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the target prediction model and output the evaluation result of the photovoltaic storage performance.

[0070] It can be seen from the above description of the present invention that, compared with the prior art, the method for evaluating the performance of a photovoltaic storage system in a medium and low voltage distribution network of the present invention includes at least one of the following beneficial effects:

[0071] 1. When configuring the photovoltaic storage system of the medium and low voltage distribution network, the present invention introduces the output characteristics of the photovoltaic storage subsystem that are highly correlated with geographical characteristics to achieve high-precision, comprehensive and dynamic prediction of photovoltaic storage performance;

[0072] 2. The present invention reflects the output characteristics of the photovoltaic subsystem in the photovoltaic storage system performance through the power generation factor, and reflects the output characteristics of the energy storage subsystem in the photovoltaic storage performance through the photovoltaic storage factor, so as to analyze and evaluate the photovoltaic storage performance of the photovoltaic storage system of the medium and low voltage distribution network more comprehensively and accurately;

[0073] 3. The present invention constructs a corresponding photovoltaic storage performance prediction model for each typical scenario, which can improve the accuracy of the scoring results output by the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a flowchart of the steps of a method for evaluating the photovoltaic storage performance of a photovoltaic storage system in a medium and low voltage distribution network in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0075] The technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0076] Reference Figure 1 As shown, a preferred embodiment of the present invention is a method for evaluating the performance of a photovoltaic storage system in a medium and low voltage distribution network, comprising the following steps:

[0077] Step 1, determining the power generation factor and energy storage factor according to the output characteristics of each independent subsystem in the photovoltaic storage system of the medium and low voltage distribution network; the independent subsystem includes a photovoltaic power generation subsystem and an energy storage subsystem;

[0078] Step 2: According to the characteristics of the current scenario, determine the corresponding application scenario as the target application scenario;

[0079] Step 3, calling the corresponding photovoltaic storage performance prediction model according to the target application scenario, and setting the corresponding photovoltaic storage performance prediction model as the target prediction model;

[0080] Step 4: input the power generation factor and energy storage factor of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the target prediction model, and output the evaluation result of the photovoltaic storage performance.

[0081] When configuring the photovoltaic storage system of the medium and low voltage distribution network, the present invention introduces the output characteristics of the photovoltaic storage subsystem that is highly correlated with the geographical characteristics, so as to achieve high-precision, comprehensive and dynamic prediction of the photovoltaic storage performance; the present invention reflects the output characteristics of the photovoltaic subsystem in the photovoltaic storage system performance through the power generation factor, and reflects the output characteristics of the energy storage subsystem in the photovoltaic storage performance through the photovoltaic storage factor, so as to analyze and evaluate the photovoltaic storage performance of the photovoltaic storage system of the medium and low voltage distribution network more comprehensively and accurately; the present invention constructs a corresponding photovoltaic storage performance prediction model for each typical scenario, which can improve the accuracy of the scoring results output by the model.

[0082] As a preferred embodiment of the present invention, it may also have the following additional technical features:

[0083] In this embodiment, step 1 specifically includes:

[0084] The output characteristics of the photovoltaic power generation subsystem include intermittency, volatility and randomness;

[0085] The output characteristics are characterized by using the influencing factors of each output characteristic;

[0086] The intermittent impact factor calculation formula is:

[0087] ;

[0088] in, Indicates the intermittent impact factor; Indicates intermittent time; represents the total observation time;

[0089] The calculation formula of the volatility impact factor is:

[0090] ;

[0091] in, The impact factor representing volatility; Indicates the monitoring time period; Indicates at time The output power of the photovoltaic system; Indicates the average output power during the monitoring period;

[0092] The calculation formula of the randomness influence factor is:

[0093] ;

[0094] in, Indicates the impact factor of randomness; Indicates the monitoring time period; Indicates at time The output power of the photovoltaic system; Indicates at time Expected power generation.

[0095] In this embodiment, step 1 specifically includes:

[0096] Determine the power generation factor according to the output characteristics of the photovoltaic power generation system; the power generation factor includes power generation efficiency, stability, and system matching;

[0097] The calculation formula of the power generation efficiency is:

[0098] ;

[0099] in, It represents the power generation efficiency index; Indicates the actual output power of the photovoltaic system; It represents the theoretical output power of a photovoltaic system under ideal conditions; Indicates the intermittent impact factor;

[0100] The stability calculation formula is:

[0101] ;

[0102] in, It represents the stability index; Indicates the average output power during the monitoring period; The impact factor representing volatility; Indicates the impact factor of randomness;

[0103] The calculation formula of the system matching degree is:

[0104] ;

[0105] in, Indicates the system matching index; represents the load demand at time t; represents the output power of the photovoltaic system at time t; Indicates the impact factor of randomness.

[0106] In this embodiment, step 1 specifically includes:

[0107] The output characteristics of the energy storage subsystem include adjustability, response speed, and capacity;

[0108] The adjustability calculation formula is:

[0109] ;

[0110] in, Indicates the adjustability of the energy storage system; Indicates the maximum power change that the energy storage system can adjust within a preset time interval; Indicates the rated power of the energy storage system;

[0111] The response speed calculation formula is:

[0112] ;

[0113] in, Indicates the response speed of the energy storage system; It indicates the time required for the energy storage system to receive the dispatch instruction and start executing the dispatch instruction.

[0114] In this embodiment, step 1 specifically includes:

[0115] Determine the energy storage factor according to the output characteristics of the energy storage subsystem; the energy storage factor includes regulation capability, response speed, and economy;

[0116] The calculation formula of the adjustment ability is:

[0117] ;

[0118] in, It represents the regulatory capacity indicator; Indicates the adjustability of the energy storage system; represents the weight of regulatory capacity;

[0119] The calculation formula of the response speed is:

[0120] ;

[0121] in, Indicates the response speed indicator; Indicates the response speed of the energy storage system; represents the weight of the corresponding speed;

[0122] The economic calculation formula is:

[0123]

[0124] in, : Economic indicators; : Initial investment cost of energy storage system; : Operation and maintenance cost of energy storage system; Cost component weight coefficient; : Maximum storage capacity of the energy storage system; Capacity part weight.

[0125] In this embodiment, step 2 specifically includes:

[0126] Collect the environmental information values ​​in the current scene, and then compare these environmental information with each environmental information value in the typical scene. According to the difference between each environmental information, determine the typical scene closest to the current scene as the target application scene;

[0127] Assume the current environment information vector is , the set of environmental information vectors for typical scenes is , i represents the i-th typical scene, and n represents the number of n environmental information.

[0128] Compute the weighted Euclidean distance:

[0129] ;

[0130] in, is the weighted Euclidean distance between the current environment information vector and the i-th typical scene environment information vector; is the weight of the jth environmental information; is the jth element in the current environment information vector; is the jth element in the i-th typical scene environment information vector;

[0131] The typical scene with the smallest weighted Euclidean distance is selected as the typical scene closest to the current scene.

[0132] In this embodiment, when determining typical scenes, a clustering algorithm is used to classify different scenes according to the environmental information of each scene, and multiple typical scenes are obtained after classification;

[0133] After determining the typical scenarios, training samples are generated based on the scenarios of the area where the photovoltaic storage system has been established and the historical performance data of each subsystem. The photovoltaic storage performance prediction model corresponding to each typical scenario is trained and generated. The input of the photovoltaic storage performance prediction model is the numerical value of the output factor and the energy storage factor, and the output of the photovoltaic storage performance prediction model is the evaluation score.

[0134] In this embodiment, step 4 specifically includes:

[0135] Input the values ​​of various power generation factors and energy storage factors of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the corresponding photovoltaic storage performance prediction model, the photovoltaic storage performance prediction model outputs an evaluation score, and compares the evaluation score with a threshold value;

[0136] When the evaluation score is greater than the threshold, it means that the performance of the photovoltaic storage system configuration of the medium and low voltage distribution network is qualified in the current scenario, so the photovoltaic storage system of the medium and low voltage distribution network can be implemented; when the evaluation score is not greater than the threshold, it means that the performance of the photovoltaic storage system configuration of the medium and low voltage distribution network is unqualified in the current scenario, and the parameters in the configuration plan of the photovoltaic storage system of the medium and low voltage distribution network need to be adjusted.

[0137] The present invention also provides a photovoltaic storage performance evaluation system for a photovoltaic storage system in a medium and low voltage distribution network, comprising:

[0138] A system factor module, used to determine the power generation factor and energy storage factor according to the output characteristics of each independent subsystem in the photovoltaic storage system of the medium and low voltage distribution network; the independent subsystem includes a photovoltaic power generation subsystem and an energy storage subsystem;

[0139] The application scenario module is used to determine the corresponding application scenario as the target application scenario according to the characteristics of the current scenario;

[0140] A prediction model module, used to call a corresponding photovoltaic storage performance prediction model according to the target application scenario, and set the corresponding photovoltaic storage performance prediction model as a target prediction model;

[0141] The evaluation module is used to input the power generation factor and energy storage factor of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the target prediction model and output the evaluation result of the photovoltaic storage performance.

[0142] The photovoltaic storage performance evaluation system of the present invention is established based on the photovoltaic storage performance evaluation method of the photovoltaic storage system of the medium and low voltage distribution network, and realizes the above functions through the intelligent terminal.

[0143] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and improved concepts of the present invention within the technical scope disclosed by the present invention, and they should be covered by the protection scope of the present invention.

Claims

1. A method for evaluating the performance of a photovoltaic storage system in a medium and low voltage distribution network, characterized in that: The following steps are involved: Step 1, determining the power generation factor and energy storage factor according to the output characteristics of each independent subsystem in the photovoltaic storage system of the medium and low voltage distribution network; the independent subsystem includes a photovoltaic power generation subsystem and an energy storage subsystem; Step 2: According to the characteristics of the current scenario, determine the corresponding application scenario as the target application scenario; Step 3, calling the corresponding photovoltaic storage performance prediction model according to the target application scenario, and setting the corresponding photovoltaic storage performance prediction model as the target prediction model; Step 4, inputting the power generation factor and energy storage factor of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the target prediction model, and outputting the evaluation result of the photovoltaic storage performance; The output characteristics of the photovoltaic power generation subsystem include intermittency, volatility and randomness; the output characteristics are characterized by using the influencing factors of each output characteristic; Determine the power generation factor according to the output characteristics of the photovoltaic power generation system; the power generation factor includes power generation efficiency, stability, and system matching; The output characteristics of the energy storage subsystem include adjustability, response speed, and capacity; Determine the energy storage factor according to the output characteristics of the energy storage subsystem; the energy storage factor includes regulation capability, response speed, and economy; In the step 2, it specifically includes: Collect the environmental information values ​​in the current scene, and then compare these environmental information with each environmental information value in the typical scene. According to the difference between each environmental information, determine the typical scene closest to the current scene as the target application scene; Assume the current environment information vector is , the set of environmental information vectors for typical scenes is , i represents the i-th typical scene, n represents the number of n environmental information; Compute the weighted Euclidean distance: ； in, is the weighted Euclidean distance between the current environment information vector and the i-th typical scene environment information vector; is the weight of the jth environmental information; is the jth element in the current environment information vector; is the jth element in the i-th typical scene environment information vector; Select the typical scene with the smallest weighted Euclidean distance as the typical scene closest to the current scene; When determining typical scenes, a clustering algorithm is used to classify different scenes according to the environmental information of each scene, and multiple typical scenes are obtained after classification; After determining the typical scenarios, training samples are generated based on the scenarios of the regions where the photovoltaic storage system has been established and the historical performance data of each subsystem. The photovoltaic storage performance prediction model corresponding to each typical scenario is trained and generated. The input of the photovoltaic storage performance prediction model is the value of the output factor and the energy storage factor, and the output of the photovoltaic storage performance prediction model is the evaluation score. In the step 4, it specifically includes: Input the values ​​of various power generation factors and energy storage factors of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the corresponding photovoltaic storage performance prediction model, the photovoltaic storage performance prediction model outputs an evaluation score, and compares the evaluation score with a threshold value; When the evaluation score is greater than the threshold, it means that the performance of the photovoltaic storage system configuration of the medium and low voltage distribution network is qualified in the current scenario, so the photovoltaic storage system of the medium and low voltage distribution network can be implemented; when the evaluation score is not greater than the threshold, it means that the performance of the photovoltaic storage system configuration of the medium and low voltage distribution network is unqualified in the current scenario, and the parameters in the configuration plan of the photovoltaic storage system of the medium and low voltage distribution network need to be adjusted.

2. The method for evaluating the photovoltaic storage performance of a photovoltaic storage system in a medium and low voltage distribution network according to claim 1, characterized in that: In the step 1, it specifically includes: The intermittent impact factor calculation formula is: ； in, Indicates the intermittent impact factor; Indicates intermittent time; represents the total observation time; The calculation formula of the impact factor of volatility is: ； in, Indicates the impact factor of volatility; Indicates the monitoring time period; Indicates at time The output power of the photovoltaic system; Indicates the average output power during the monitoring period; The calculation formula of the random influence factor is: ； in, Indicates the impact factor of randomness; Indicates the monitoring time period; Indicates at time The output power of the photovoltaic system; Indicates at time Expected power generation.

3. The method for evaluating the photovoltaic storage performance of a photovoltaic storage system in a medium and low voltage distribution network according to claim 2, characterized in that: In the step 1, it specifically includes: The calculation formula of the power generation efficiency is: ； in, It represents the power generation efficiency index; Indicates the actual output power of the photovoltaic system; It represents the theoretical output power of a photovoltaic system under ideal conditions; Indicates the intermittent impact factor; The stability calculation formula is: ； in, It represents the stability index; Indicates the average output power during the monitoring period; Indicates the impact factor of volatility; Indicates the impact factor of randomness; The calculation formula of the system matching degree is: ； in, Indicates the system matching index; represents the load demand at time t; represents the output power of the photovoltaic system at time t; Indicates the impact factor of randomness.

4. The method for evaluating the photovoltaic storage performance of a photovoltaic storage system in a medium and low voltage distribution network according to claim 1, characterized in that: In the step 1, it specifically includes: The adjustability calculation formula is: ； in, Indicates the adjustability of the energy storage system; Indicates the maximum power change that the energy storage system can adjust within a preset time interval; Indicates the rated power of the energy storage system; The response speed calculation formula is: ； in, Indicates the response speed of the energy storage system; It indicates the time required for the energy storage system to receive the dispatch instruction and start executing the dispatch instruction.

5. The method for evaluating the photovoltaic storage performance of a photovoltaic storage system in a medium and low voltage distribution network according to claim 4, characterized in that: In the step 1, it specifically includes: The calculation formula of the adjustment ability is: ； in, It represents the regulatory capacity indicator; Indicates the adjustability of the energy storage system; represents the weight of regulatory capacity; The calculation formula of the response speed is: ； in, Indicates the response speed indicator; Indicates the response speed of the energy storage system; represents the weight of the corresponding speed; The economic calculation formula is: ； in, : Economic indicators; : Initial investment cost of energy storage system; : Operation and maintenance cost of energy storage system; : weight coefficient of cost part; : Maximum storage capacity of the energy storage system; : Capacity part weight.

6. A photovoltaic storage performance evaluation system for photovoltaic storage systems in medium and low voltage distribution networks, characterized in that: Used to execute the photovoltaic storage performance evaluation method of the photovoltaic storage system of the medium and low voltage distribution network according to claim 1, the photovoltaic storage performance evaluation system comprises: A system factor module, used to determine the power generation factor and energy storage factor according to the output characteristics of each independent subsystem in the photovoltaic storage system of the medium and low voltage distribution network; the independent subsystem includes a photovoltaic power generation subsystem and an energy storage subsystem; The application scenario module is used to determine the corresponding application scenario as the target application scenario according to the characteristics of the current scenario; A prediction model module, used to call a corresponding photovoltaic storage performance prediction model according to the target application scenario, and set the corresponding photovoltaic storage performance prediction model as a target prediction model; The evaluation module is used to input the power generation factor and energy storage factor of the photovoltaic storage system of the medium and low voltage distribution network to be evaluated into the target prediction model and output the evaluation result of the photovoltaic storage performance.

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