Low-carbon strategy controller adaptive response state coordination method and system

Abstract

The invention belongs to the technical field of intelligent control, and particularly relates to a low-carbon strategy controller adaptive response state coordination method and system. According to the method, firstly, collection parameters are set, operation parameters of each control node are obtained according to the collection parameters, corresponding operation evaluation scores are set for the operation parameters of each control node, and then system evaluation parameters are created based on the operation parameters of all the control nodes and the operation evaluation scores of all the operation parameters. The method comprises the following steps: establishing a system evaluation parameter of each control node, judging whether the system evaluation parameter conforms to a system evaluation index or not, and if the system evaluation parameter does not conform to the system evaluation index, adjusting the system control node until the system evaluation parameter conforms to the system evaluation index. Whether the distributed energy system needs to be adjusted or not is judged according to the system evaluation index, so that adjustment of each control node in the distributed energy system is more efficient.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control technology, and in particular relates to a method and system for coordinating the adaptive response states of a low-carbon strategy controller. Background Art

[0002] Distributed energy systems are small and medium-sized energy conversion and utilization systems that are directly oriented to users, produce and supply energy on-site according to user needs, have multiple functions, and can meet multiple goals. During the use of distributed energy, a low-carbon strategy controller is required to coordinate the various control nodes in the entire system. Existing low-carbon strategy controllers mostly control the entire system through adaptive response. The adaptive response state means that the controller can automatically adjust its parameters according to the dynamic changes of the system to maintain the optimal performance of the system. The core of adaptive control is to automatically adjust the controller parameters according to the real-time performance or characteristics of the system to maintain the stability and performance of the control system.

[0003] Existing low-carbon strategy controllers often fail to take into account the importance of different system control nodes in distributed energy systems when using adaptive response control systems, resulting in inefficient regulation of different system control nodes, which may cause insufficient energy supply to more important system control nodes. Summary of the invention

[0004] The purpose of the present invention is to address the problems existing in the background technology and to propose a low-carbon strategy controller adaptive response state coordination method and system.

[0005] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, a method for coordinating an adaptive response state of a low-carbon policy controller for a distributed energy system is provided, which is applied to a distributed energy system and includes: Set acquisition parameters, obtain the operating parameters of each control node according to the acquisition parameters, and set corresponding operating evaluation scores for the operating parameters of each control node; Creating system evaluation parameters based on the operating parameters of all control nodes and the operating evaluation scores of each operating parameter, and determining whether the system evaluation parameters meet the system evaluation indicators; If the system evaluation parameters do not meet the system evaluation indicators, the system control nodes are adjusted until the system evaluation parameters meet the system evaluation indicators.

[0006] Preferably, setting acquisition parameters, acquiring operating parameters of each control node according to the acquisition parameters, and setting a corresponding operating evaluation score for the operating status of each control node include: Create an operating parameter table; Set the collection frequency and collection period of each system control node respectively; Select a system control node; Collect multiple operating parameters of the system control node within the collection period according to the collection frequency, and put all the collected operating parameters into the operating parameter table; Return to select a system control node until all system control nodes are selected, and obtain multiple operating parameters of each control node.

[0007] Preferably, before setting the acquisition parameters, obtaining the operating parameters of each control node according to the acquisition parameters, and setting the corresponding operating evaluation score for the operating state of each control node, the following steps are included: Create a rating score model; Obtain multiple control nodes and an operation evaluation score of each control node; Divide all control nodes into training sets and test sets according to random proportions; Inputting the test set into the evaluation score model to train the evaluation score model, thereby obtaining a trained evaluation score model; The test set is input into the trained evaluation score model, and whether the trained evaluation score model is qualified is judged according to the predicted evaluation score output by the trained evaluation score model; a qualified trained evaluation score model can automatically output the operation evaluation score of the control node according to the input control node.

[0008] Preferably, the test set is input into the trained evaluation score model, and judging whether the trained evaluation score model is qualified according to the predicted evaluation score output by the trained evaluation score model includes: Set the error threshold; Randomly select a system control node from the test set; Inputting the system control node into the trained evaluation score model and obtaining the predicted evaluation score output by the trained evaluation score model; Obtaining the operation evaluation score of the system control node; Determine whether the thresholds of the prediction evaluation score and the operation evaluation score are greater than the error threshold; If the thresholds of the predicted evaluation score and the running evaluation score are greater than the error threshold, the trained evaluation score model is considered unqualified, and all control nodes are divided into training sets and test sets according to random proportions. If the threshold of the predicted evaluation score and the running evaluation score is less than or equal to the error threshold, the trained evaluation score model is considered qualified.

[0009] Preferably, creating a system evaluation parameter based on the operating parameters of all control nodes and the operating evaluation score of each operating parameter, and judging whether the system evaluation parameter meets the system evaluation index includes: Calculate the system operation evaluation parameters through formula 1; Formula 1; Among them, P is the system operation evaluation parameter, A1 is the operation parameter of the first system control node, A2 is the operation parameter of the second system control node, A3 is the operation parameter of the third system control node, M is the operation evaluation score of the first system control node, N is the operation evaluation score of the second system control node, and Q is the operation evaluation score of the third system control node.

[0010] Preferably, creating a system evaluation parameter based on the operating parameters of all control nodes and the operating evaluation score of each operating parameter, and judging whether the system evaluation parameter meets the system evaluation index, further comprises: Set the qualified threshold of system evaluation indicators; Determine whether the system operation evaluation parameter is greater than or equal to the qualified threshold of the system evaluation index; If the system operation evaluation parameter is greater than or equal to the qualified threshold of the system evaluation index, then the system evaluation parameter is judged to meet the system evaluation index; If the system operation evaluation parameter is less than the system evaluation index qualified threshold, it is judged that the system evaluation parameter does not meet the system evaluation index.

[0011] Preferably, if the system evaluation parameter does not meet the system evaluation index, adjusting the system control node until the system evaluation parameter meets the system evaluation index includes: Set the system control node weight; Combine the system control node weights and system operation evaluation parameters to create system adjustment parameters through formula 2; Formula 2; Among them, Q is the system adjustment parameter, A1 is the operating parameter of the first system control node, A2 is the operating parameter of the second system control node, A3 is the operating parameter of the third system control node, M is the operating evaluation score of the first system control node, N is the evaluation score of the second system control node, Q is the evaluation score of the third system control node, B1 is the weight of the first system control node, B2 is the weight of the second system control node, and B3 is the weight of the third system control node.

[0012] Preferably, if the system evaluation parameter does not meet the system evaluation index, adjusting the system control node until the system evaluation parameter meets the system evaluation index, further comprising: Set the system adjustment qualified threshold; Determine whether the system adjustment parameter is greater than or equal to the system adjustment qualified threshold; If the system adjustment parameter is greater than or equal to the system adjustment qualified threshold, the system control nodes are adjusted in sequence according to the weights of the system control nodes until the system adjustment parameter is less than the system adjustment qualified threshold.

[0013] Preferably, the method for coordinating the adaptive response state of a low-carbon strategy controller for a distributed energy system further includes: Set the detection cycle and the threshold of the number of unqualified times in the cycle; Calculate the number of unqualified times within the test cycle; Determine whether the number of unqualified times within the detection period is greater than or equal to the unqualified times threshold; If the number of unqualified times within the detection cycle is greater than or equal to the unqualified times threshold, the system operation is stopped; If the number of unqualified times within the detection cycle is less than the unqualified number threshold, the system operation is maintained.

[0014] In a second aspect, the present application further provides a low-carbon strategy controller adaptive response state coordination system, comprising: A collection component, the collection component is electrically connected to the distributed energy system, and the operation parameters of each system control node of the distributed energy system are collected through the collection component; A processing component, wherein the acquisition component is communicatively connected with the processing component, and the low-carbon strategy controller adaptive response state coordination method for a distributed energy system as described in any one of the above is executed through the processing component.

[0015] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: First, set the collection parameters, obtain the operating parameters of each control node according to the collection parameters, set the corresponding operating evaluation score for the operating parameters of each control node, and then create system evaluation parameters based on the operating parameters of all control nodes and the operating evaluation scores of each operating parameter, and judge whether the system evaluation parameters meet the system evaluation indicators. If the system evaluation parameters do not meet the system evaluation indicators, adjust the system control nodes until the system evaluation parameters meet the system evaluation indicators. This application creates system evaluation indicators by combining the operating parameters of each control node in the distributed energy system, and judges whether the distributed energy system needs to be adjusted based on the system evaluation indicators, thereby making the application more efficient in adjusting each control node in the distributed energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1A schematic diagram of a flow chart of a low-carbon strategy controller adaptive response state coordination method proposed by the present invention; Figure 2 This is a structural schematic diagram of a low-carbon strategy controller adaptive response state coordination system proposed by the present invention.

[0017] Reference numerals: 100, acquisition component; 200, processing component. DETAILED DESCRIPTION

[0018] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0019] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0020] Embodiment 1, as Figure 1 As shown, the present invention proposes a low-carbon strategy controller adaptive response state coordination method for a distributed energy system, comprising: S100, setting acquisition parameters, acquiring operating parameters of each control node according to the acquisition parameters, and setting corresponding operating evaluation scores for the operating parameters of each control node.

[0021] S200: Create system evaluation parameters based on the operation parameters of all control nodes and the operation evaluation score of each operation parameter, and determine whether the system evaluation parameters meet the system evaluation index.

[0022] S300: If the system evaluation parameter does not meet the system evaluation index, adjust the system control node until the system evaluation parameter meets the system evaluation index.

[0023] Specifically, if the system evaluation parameters meet the system evaluation indicators, the original operating state of the distributed energy system is maintained.

[0024] In the present invention, firstly, the acquisition parameters are set, the operating parameters of each control node are obtained according to the acquisition parameters, and the corresponding operating evaluation scores are set for the operating parameters of each control node. Then, the system evaluation parameters are created based on the operating parameters of all control nodes and the operating evaluation scores of each operating parameter, and it is judged whether the system evaluation parameters meet the system evaluation indicators. If the system evaluation parameters do not meet the system evaluation indicators, the system control nodes are adjusted until the system evaluation parameters meet the system evaluation indicators. The present application creates system evaluation indicators by combining the operating parameters of each control node in the distributed energy system, and judges whether the distributed energy system needs to be adjusted based on the system evaluation indicators, thereby making the present application more efficient in adjusting each control node in the distributed energy system.

[0025] In an optional embodiment, the S100 includes: S110, creating an operation parameter table.

[0026] S120: Set a collection frequency and a collection period for each system control node.

[0027] S130: Select a system control node.

[0028] S140, collecting multiple operating parameters of the system control node within a collection period according to a collection frequency, and putting all collected operating parameters into an operating parameter table.

[0029] S150, return to S130, until all system control nodes are selected, and multiple operating parameters of each control node are obtained.

[0030] It should be noted that, in this embodiment, since the control nodes of the distributed energy system are different, and since different control nodes have different characteristics, corresponding acquisition parameters should be set for each control node separately.

[0031] For example, the entity of the first control node is a residential area, the entity of the second control node is a factory, and the entity of the third control node is a school. Since the residential area has high energy consumption during the day and low energy consumption at night, the factory has high energy consumption all day, and the school has high energy consumption during the day and low energy consumption or even zero energy consumption at night, the collection parameters of these three control nodes should be set accordingly. Specifically, the collection frequency is higher during the high energy consumption period, and slightly lower during the low energy consumption period, and the zero energy consumption period can be used as not setting the collection cycle, thereby reducing the collection of invalid information and reducing interference with system evaluation indicators.

[0032] In an optional embodiment, before S100, the method further includes: K100, create evaluation score model.

[0033] K200, obtains multiple control nodes and the operation evaluation score of each control node.

[0034] K300, divides all control nodes into training and test sets according to random proportions.

[0035] Specifically, the proportion of the training set should be higher than that of the test set.

[0036] K400, input the test set into the evaluation score model to train the evaluation score model, and obtain the trained evaluation score model.

[0037] K500 inputs the test set into the trained evaluation score model, and determines whether the trained evaluation score model is qualified according to the predicted evaluation score output by the trained evaluation score model. A qualified trained evaluation score model can automatically output the operation evaluation score of the control node according to the input control node.

[0038] It should be noted that the running evaluation scores in the data samples of the training set and the data samples of the test set are all from past data.

[0039] Since different control nodes are at different distances from the distributed energy system, an evaluation score model is created and trained to directly output the operation evaluation score of the control node based on the relevant information of the input control node.

[0040] In an optional embodiment, the K500 includes: K510, set the error threshold.

[0041] Specifically, the smaller the error threshold is set, the more accurate the operation evaluation score output by the evaluation score model will be.

[0042] K520, randomly select a system control node from the test set.

[0043] K530, input the system control node into the trained evaluation score model and obtain the predicted evaluation score output by the trained evaluation score model.

[0044] K540, obtain the operation evaluation score of the control node of the system.

[0045] K550, determines whether the thresholds of the prediction evaluation score and the operation evaluation score are greater than the error threshold.

[0046] K560, if the threshold of the predicted evaluation score and the running evaluation score is greater than the error threshold, the trained evaluation score model is considered unqualified, and all control nodes are returned to be divided into training set and test set according to random proportions.

[0047] K570, if the threshold of the predicted evaluation score and the running evaluation score is less than or equal to the error threshold, the trained evaluation score model is considered qualified.

[0048] It should be noted that after the evaluation score model is trained, the data samples in the test set are needed to verify whether the operation evaluation score of the control node output by the trained evaluation score model is correct. In order to improve the training efficiency of the evaluation score model, an error threshold is set to provide the evaluation score model with a certain error range. Correspondingly, the smaller the error threshold is set, the more accurate the operation evaluation score output by the evaluation score model is.

[0049] In an optional embodiment, the S200 includes: S210, calculating the system operation evaluation parameters by formula 1.

[0050] Formula 1.

[0051] Among them, P is the system operation evaluation parameter, A1 is the operation parameter of the first system control node, A2 is the operation parameter of the second system control node, A3 is the operation parameter of the third system control node, M is the operation evaluation score of the first system control node, N is the operation evaluation score of the second system control node, and Q is the operation evaluation score of the third system control node.

[0052] It should be noted that, since the distributed energy system contains multiple control nodes, for each control node in the distributed energy system, an operation evaluation score needs to be set separately and added to Formula 1, that is, Formula 1 is not limited to three control nodes, but is applicable to multiple control nodes. When there are multiple control nodes, Formula 1 can be expressed as Formula 3: Formula 3.

[0053] Among them, P is the system operation evaluation parameter, A i is the operating parameter of the ith system control node, M i is the evaluation score of the ith system control node, and n is the total number of control nodes in the distributed energy system.

[0054] The system operation evaluation parameters are calculated by combining the operation parameters of each system control node in the system and the operation evaluation score of the system control node. Since the operation parameters of each system control node are dynamically collected, the system operation evaluation parameters can change synchronously, thereby completing the adaptive evaluation of the system.

[0055] In an optional embodiment, the S200 further includes: S220, setting a qualified threshold for system evaluation indicators.

[0056] S230, determining whether the system operation evaluation parameter is greater than or equal to the system evaluation index qualification threshold.

[0057] S240: If the system operation evaluation parameter is greater than or equal to the system evaluation index qualification threshold, it is determined that the system evaluation parameter meets the system evaluation index.

[0058] S250: If the system operation evaluation parameter is less than the system evaluation index qualification threshold, it is determined that the system evaluation parameter does not meet the system evaluation index.

[0059] It should be noted that each time the system operation evaluation parameters are calculated by Formula 1 or Formula 3, it is necessary to judge the system operation evaluation parameters, that is, to compare the calculated system operation evaluation parameters with the qualified threshold of the system evaluation indicators, so as to judge whether the system operation evaluation parameters under the current collection frequency are qualified. If the system operation evaluation parameters under the current collection frequency are qualified, this proves that the distributed energy system is operating normally during the current collection time.

[0060] In an optional embodiment, the S300 includes: S310, setting system control node weights.

[0061] S320, combining the system control node weights and the system operation evaluation parameters, and creating system adjustment parameters through Formula 2.

[0062] Formula 2.

[0063] Among them, Q is the system adjustment parameter, A1 is the operating parameter of the first system control node, A2 is the operating parameter of the second system control node, A3 is the operating parameter of the third system control node, M is the operating evaluation score of the first system control node, N is the evaluation score of the second system control node, Q is the evaluation score of the third system control node, B1 is the weight of the first system control node, B2 is the weight of the second system control node, and B3 is the weight of the third system control node.

[0064] Specifically, since the distributed energy system includes multiple control nodes, for each control node in the distributed energy system, the system control node weight needs to be set separately and added to Formula 2, that is, Formula 2 is not limited to 3 control nodes, but is applicable to multiple control nodes. When there are multiple control nodes, Formula 2 can be expressed as Formula 4: Formula 4.

[0065] Among them, Q is the system adjustment parameter, A iis the operating parameter of the ith system control node, M i is the evaluation score of the ith system control node, n is the total number of control nodes in the distributed energy system, N i is the system control node weight of the ith system control node.

[0066] It should be noted that since the importance of different system control nodes is different, the adjustment priority of different control nodes is also different. The more important the system control node, the higher its adjustment priority, and the higher the adjustment priority of the system control node, the greater the corresponding system control node weight.

[0067] As described in the above embodiment, the entity of the first control node is a residential area, the entity of the second control node is a factory, and the entity of the third control node is a school, then the corresponding system control node weights from large to small are factory, school, and residential area. That is, when adjusting the control node, the factory system control node is adjusted first, followed by the school, and finally the residential area.

[0068] In an optional embodiment, the S300 further includes: S330, setting a system adjustment qualified threshold.

[0069] S340, determining whether the system adjustment parameter is greater than or equal to the system adjustment qualified threshold.

[0070] S50: If the system adjustment parameter is greater than or equal to the system adjustment qualified threshold, the system control nodes are adjusted in sequence according to the weights of the system control nodes until the system adjustment parameter is less than the system adjustment qualified threshold.

[0071] It should be noted that after calculating the system adjustment parameters by formula 3 or formula 4, it is necessary to determine whether the system adjustment parameters meet the system adjustment qualified threshold. Only when the system adjustment parameters are greater than or equal to the system adjustment qualified threshold, it is necessary to adjust the system control nodes in order according to the weight of the system control nodes. Otherwise, there is no need to adjust the system control nodes.

[0072] The reason for executing the judgment steps S330 to S350 is to avoid the situation where the control nodes in the distributed energy system need to be adjusted due to changes in system operation evaluation parameters or system adjustment parameters caused by slight fluctuations in the system, and to give each system control node a little redundancy for fluctuations, thereby maintaining the smooth operation of each node in the system.

[0073] When the system adjustment parameter is greater than or equal to the system adjustment qualified threshold, it can be seen that the change of the system adjustment parameter has exceeded the system adjustment qualified threshold, and the degree of change of the system control node is large. It can be ruled out that the change of the system adjustment parameter is caused by the slight fluctuation of the distributed energy system. At this time, it is necessary to adjust each system control node in order from large to small according to the system control node weight until the distributed energy system is stable.

[0074] In an optional embodiment, the low-carbon strategy controller adaptive response state coordination method for a distributed energy system further includes: S400, setting a detection cycle and a threshold value of the number of unqualified times in the cycle.

[0075] S410, calculating the number of unqualified times in the detection cycle.

[0076] S420, determining whether the number of unqualified times within the detection cycle is greater than or equal to a threshold number of unqualified times.

[0077] S430: If the number of unqualified times within the detection cycle is greater than or equal to the unqualified times threshold, the system operation is stopped.

[0078] S440: If the number of unqualified times within the detection cycle is less than the unqualified times threshold, the system operation is maintained.

[0079] It should be noted that, in this embodiment, if the distributed energy system has multiple cases where the system is less than the qualified threshold of the system evaluation index within a single detection cycle, it can be judged that one or more system control nodes within the distributed energy system may have an abnormality. If the system continues to operate, the degree of damage to the abnormal system control node may be increased. Therefore, in order to protect the system control node, when the number of unqualified times within a detection cycle is greater than or equal to the unqualified number threshold, the system operation is stopped.

[0080] By executing steps S400 to S440, the protection of each system control node in the distributed energy system is improved, thereby preventing the system control node that has already experienced an abnormality from being further damaged.

[0081] Embodiment 2, as Figure 2 As shown, the present invention proposes a low-carbon strategy controller adaptive response state coordination system for a distributed energy system, which includes a collection component 100 and a processing component 200.

[0082] The collection component 100 is electrically connected to the distributed energy system, and collects the operating parameters of each system control node of the distributed energy system through the collection component 100. The collection component 100 is in communication connection with the processing component 200, and the low-carbon strategy controller adaptive response state coordination method for distributed energy system in the aforementioned embodiment 1 is executed through the processing component 200.

[0083] It should be noted that, in this embodiment, the operating parameters of each system control node are collected by the collection component 100, and the collected data is transmitted to the processing component 200. The processing component 200 combines the collected data to implement adaptive control of each control node of the distributed energy system through the adaptive response state coordination method of the low-carbon strategy controller for distributed energy systems.

[0084] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. A low-carbon strategy controller adaptive response state coordination method for distributed energy systems, characterized in that: include: Set acquisition parameters, obtain the operating parameters of each control node according to the acquisition parameters, and set corresponding operating evaluation scores for the operating parameters of each control node; Creating system evaluation parameters based on the operating parameters of all control nodes and the operating evaluation scores of each operating parameter, and determining whether the system evaluation parameters meet the system evaluation indicators; If the system evaluation parameters do not meet the system evaluation indicators, the system control nodes are adjusted until the system evaluation parameters meet the system evaluation indicators.

2. The low-carbon strategy controller adaptive response state coordination method according to claim 1 is characterized in that: Set the collection parameters, obtain the operating parameters of each control node according to the collection parameters, and set the corresponding operating evaluation score for the operating status of each control node, including: Create an operating parameter table; Set the collection frequency and collection period of each system control node respectively; Select a system control node; Collect multiple operating parameters of the system control node within the collection period according to the collection frequency, and put all the collected operating parameters into the operating parameter table; Return to select a system control node until all system control nodes are selected, and obtain multiple operating parameters of each control node.

3. The low-carbon strategy controller adaptive response state coordination method according to claim 2 is characterized in that: Before setting the collection parameters, obtaining the operating parameters of each control node according to the collection parameters, and setting the corresponding operating evaluation score for the operating status of each control node, the following steps are included: Create a rating score model; Obtain multiple control nodes and an operation evaluation score of each control node; Divide all control nodes into training sets and test sets according to random proportions; Inputting the test set into the evaluation score model to train the evaluation score model, thereby obtaining a trained evaluation score model; The test set is input into the trained evaluation score model, and whether the trained evaluation score model is qualified is judged according to the predicted evaluation score output by the trained evaluation score model; a qualified trained evaluation score model can automatically output the operation evaluation score of the control node according to the input control node.

4. The low-carbon strategy controller adaptive response state coordination method according to claim 3 is characterized in that: Input the test set into the trained evaluation score model, and judge whether the trained evaluation score model is qualified according to the predicted evaluation score output by the trained evaluation score model, including: Set the error threshold; Randomly select a system control node from the test set; Inputting the system control node into the trained evaluation score model and obtaining the predicted evaluation score output by the trained evaluation score model; Obtaining the operation evaluation score of the system control node; Determine whether the thresholds of the prediction evaluation score and the operation evaluation score are greater than the error threshold; If the thresholds of the predicted evaluation score and the running evaluation score are greater than the error threshold, the trained evaluation score model is considered unqualified, and all control nodes are divided into training sets and test sets according to random proportions. If the threshold of the predicted evaluation score and the running evaluation score is less than or equal to the error threshold, the trained evaluation score model is considered qualified.

5. The low-carbon strategy controller adaptive response state coordination method according to claim 4 is characterized in that: Create system evaluation parameters based on the operating parameters of all control nodes and the operating evaluation scores of each operating parameter, and determine whether the system evaluation parameters meet the system evaluation indicators, including: Calculate the system operation evaluation parameters through formula 1; Formula 1; Among them, P is the system operation evaluation parameter, A1 is the operation parameter of the first system control node, A2 is the operation parameter of the second system control node, A3 is the operation parameter of the third system control node, M is the operation evaluation score of the first system control node, N is the operation evaluation score of the second system control node, and Q is the operation evaluation score of the third system control node.

6. The low-carbon strategy controller adaptive response state coordination method according to claim 5 is characterized in that: Creating system evaluation parameters based on the operating parameters of all control nodes and the operating evaluation scores of each operating parameter, and judging whether the system evaluation parameters meet the system evaluation indicators, also includes: Set the qualified threshold of system evaluation indicators; Determine whether the system operation evaluation parameter is greater than or equal to the qualified threshold of the system evaluation index; If the system operation evaluation parameter is greater than or equal to the qualified threshold of the system evaluation index, then the system evaluation parameter is judged to meet the system evaluation index; If the system operation evaluation parameter is less than the system evaluation index qualified threshold, it is judged that the system evaluation parameter does not meet the system evaluation index.

7. The low-carbon strategy controller adaptive response state coordination method according to claim 6 is characterized in that: If the system evaluation parameters do not meet the system evaluation indicators, the system control nodes are adjusted until the system evaluation parameters meet the system evaluation indicators, including: Set the system control node weight; Combine the system control node weights and system operation evaluation parameters to create system adjustment parameters through formula 2; Formula 2; Among them, Q is the system adjustment parameter, A1 is the operating parameter of the first system control node, A2 is the operating parameter of the second system control node, A3 is the operating parameter of the third system control node, M is the operating evaluation score of the first system control node, N is the evaluation score of the second system control node, Q is the evaluation score of the third system control node, B1 is the weight of the first system control node, B2 is the weight of the second system control node, and B3 is the weight of the third system control node.

8. The low-carbon strategy controller adaptive response state coordination method according to claim 7 is characterized in that: If the system evaluation parameters do not meet the system evaluation indicators, the system control nodes are adjusted until the system evaluation parameters meet the system evaluation indicators, which also includes: Set the system adjustment qualified threshold; Determine whether the system adjustment parameter is greater than or equal to the system adjustment qualified threshold; If the system adjustment parameter is greater than or equal to the system adjustment qualified threshold, the system control nodes are adjusted in sequence according to the weights of the system control nodes until the system adjustment parameter is less than the system adjustment qualified threshold.

9. The low-carbon strategy controller adaptive response state coordination method according to claim 8, characterized in that: The adaptive response state coordination method for a low-carbon strategy controller for a distributed energy system also includes: Set the detection cycle and the threshold of the number of unqualified times in the cycle; Calculate the number of unqualified times within the test cycle; Determine whether the number of unqualified times within the detection period is greater than or equal to the unqualified times threshold; If the number of unqualified times within the detection cycle is greater than or equal to the unqualified times threshold, the system operation is stopped; If the number of unqualified times within the detection cycle is less than the unqualified number threshold, the system operation is maintained.

10. A low-carbon strategy controller adaptive response state coordination system for distributed energy systems, characterized in that: include: A collection component, the collection component is electrically connected to the distributed energy system, and the operation parameters of each system control node of the distributed energy system are collected through the collection component; A processing component, wherein the acquisition component is communicatively connected to the processing component, and the adaptive response state coordination method of a low-carbon strategy controller for a distributed energy system as described in any one of claims 1 to 9 is executed through the processing component.

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