Intelligent control method and equipment for energy storage converter based on multi-target coordination

Abstract

The invention relates to an intelligent control method and equipment for an energy storage converter based on multi-target coordination. According to the method, communication of a state evaluation coefficient is carried out through a central communication node, the data size of the state evaluation coefficient is small, the communication priority of each cooperative converter is updated in time according to the state evaluation coefficient, and state request information sent by a target converter is determined according to the communication priority of each cooperative converter. Therefore, the target converter does not need to completely acquire the cooperative state information of all the cooperative converters, the communication pressure of the central communication node is reduced, simultaneous communication of a plurality of energy storage converters can be supported, network congestion is avoided, and the accuracy of intelligent control of the energy storage converters is improved.

Description

Technical Field

[0001] The present invention is applicable to the technical field of converter control, and in particular relates to an intelligent control method and device for an energy storage converter based on multi-objective coordination. Background Art

[0002] A distributed energy storage system includes several energy storage units. The control of the energy storage units is usually based on instructions from the power grid dispatching system and the status information of the energy storage system itself. Corresponding control strategies are formulated according to the system's operating requirements and target priorities.

[0003] Existing methods usually use centralized control or distributed control to perform intelligent control of energy storage flow devices. Centralized control collects the status information of all energy storage units through a central controller, calculates the power allocation value of each energy storage unit according to the selected power allocation strategy, and then sends instructions to each energy storage unit. The advantage of this method is that it can achieve global optimal power allocation, but the computing power, reliability and communication requirements of the central controller are relatively high. Under the distributed control method, each energy storage unit has its own controller, and they exchange information through a communication network. Each controller determines the power allocation of its own energy storage unit according to a certain distributed algorithm based on local information and information exchanged with associated units. The advantage of this method is that it has high reliability and flexibility. Even if the controller of a certain energy storage unit fails, other energy storage units can continue to work, but it may take a long time to reach the optimal allocation state.

[0004] In order to ensure the reliability of intelligent control of distributed energy storage systems, a distributed control method can be adopted. However, if a ring topology communication method is used for distributed control, when a node or link fails, the communication of the entire ring may not be able to proceed normally, and the reliability is still low. If a mesh topology communication method is used for distributed control, it is necessary to build complex communication lines, which is costly. Therefore, in consideration of cost and reliability, a star topology communication method can be selected. Through the central node, the central node can be a virtual coordination node, and the controllers of other energy storage units communicate with this central node. However, unlike centralized control, the central node can be used only for communication without the need to determine and issue control instructions, which greatly reduces the possibility of central node failure.

[0005] However, the star topology communication mode will face the communication network congestion caused by insufficient transmission bandwidth of the central node, which will lead to increased information transmission delay, making the state information received by the power distribution controller outdated, thus making inaccurate power distribution decisions, and reducing the accuracy of intelligent control of energy storage converters. Therefore, how to improve the accuracy of intelligent control of energy storage converters has become an urgent problem to be solved. Summary of the invention

[0006] In view of this, an embodiment of the present invention provides an energy storage converter intelligent control method and device based on multi-objective coordination to solve the problem.

[0007] In a first aspect, a method for intelligent control of an energy storage converter based on multi-objective coordination is provided, the method comprising: When the current time point is the i-th first preset time point, obtaining local state information of the target converter, where i is a positive integer; Determine a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point according to the local state information corresponding to the target converter at the i-th first preset time point and the trained state prediction model, where m is a positive integer and m is less than i; Sending the first state evaluation coefficient corresponding to the target converter at the i-th first preset time point to the n coordinated converters corresponding to the target converter through the central communication node, where n is a positive integer; Receiving second state evaluation coefficients respectively corresponding to n cooperative converters at an i-th first preset time point; Determining the communication priorities corresponding to the n cooperative converters at the i-th first preset time point according to the received n second state evaluation coefficients at the i-th first preset time point; Determine, according to the communication priorities respectively corresponding to the n cooperative converters at the i-th first preset time point, the status request information respectively sent to the n cooperative converters at the i+1-th first preset time point; When the current time point is the (i+1)th first preset time point, sending corresponding status request information to the n cooperative converters through the central communication node; Receiving cooperative state information of n cooperative converters at an (i+1)th first preset time point; When receiving a second preset time point of the control instruction for the target converter, determining a first preset time point closest to the second preset time point as a reference time point; According to the cooperative state information respectively corresponding to the n cooperative converters at the reference time point and the local state information of the target converter at the reference time point, the state control amount corresponding to the target converter at the second preset time point is determined.

[0008] In a second aspect, an intelligent control device for an energy storage converter based on multi-objective coordination is provided, the device comprising: A state acquisition module, used for acquiring local state information of the target converter when the current time point is the i-th first preset time point, where i is a positive integer; A state evaluation module, used to determine a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point according to the local state information corresponding to the target converter at the im-th first preset time point to the i-th first preset time point and the trained state prediction model, where m is a positive integer and m is less than i; A first communication module, used for sending the first state evaluation coefficient corresponding to the target converter at the i-th first preset time point to the n coordinated converters corresponding to the target converter through the central communication node, where n is a positive integer; A second communication module is used to receive second state evaluation coefficients respectively corresponding to the n cooperative converters at the i-th first preset time point; A priority determination module, used to determine the communication priorities corresponding to the n cooperative converters at the i-th first preset time point according to the received n second state evaluation coefficients at the i-th first preset time point; A status request module, used to determine the status request information to be sent to the n cooperative converters at the (i+1)th first preset time point according to the communication priorities respectively corresponding to the n cooperative converters at the (i)th first preset time point; A third communication module, configured to send corresponding status request information to the n cooperative converters through the central communication node when the current time point is the (i+1)th first preset time point; A fourth communication module, used for receiving the coordination state information of the n coordinated converters at the (i+1)th first preset time point; a time point determination module, configured to determine, when receiving a second preset time point of a control instruction for the target converter, a first preset time point closest to the second preset time point as a reference time point; The state control module is used to determine the state control quantity corresponding to the target converter at the second preset time point according to the collaborative state information corresponding to the n collaborative converters at the reference time point and the local state information of the target converter at the reference time point.

[0009] Compared with the prior art, the present invention has the following beneficial effects: The state assessment coefficient is communicated through the central communication node. The data volume of the state assessment coefficient is small. The communication priority of each collaborative inverter is updated in time according to the state assessment coefficient. According to the communication priority of each collaborative inverter, the state request information sent by the target inverter is determined, so that the target inverter does not need to fully obtain the collaborative state information of all collaborative inverters, which reduces the communication pressure of the central communication node, can support multiple energy storage inverters to communicate at the same time, avoids network congestion, and thus improves the accuracy of intelligent control of energy storage inverters. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] Figure 1 This is a schematic diagram of an application environment of an energy storage converter intelligent control method based on multi-objective coordination provided in Embodiment 1 of the present invention; Figure 2 It is a flow chart of an intelligent control method for an energy storage converter based on multi-objective coordination provided in Embodiment 1 of the present invention; Figure 3 It is a structural schematic diagram of an energy storage converter intelligent control device based on multi-objective coordination provided in Embodiment 2 of the present invention; Figure 4 It is a structural schematic diagram of a computer device for an intelligent control method of an energy storage converter based on multi-objective coordination provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0020] The first embodiment of the present invention provides an intelligent control method for an energy storage converter based on multi-objective coordination, which can be applied in the following aspects: Figure 1 In the application environment, it is specifically applied to the controller of the target converter, wherein the target converter communicates with its corresponding controller, and the controllers can communicate with each other through a central communication node. The controller includes but is not limited to handheld computers, desktop computers, notebook computers, ultra-mobile personal computers (UMPC), netbooks, cloud terminal devices, personal digital assistants (PDA) and other terminal devices with computing functions.

[0021] See also Figure 2, is a flow chart of a method for intelligent control of an energy storage converter based on multi-objective coordination provided by the first embodiment of the present invention. The intelligent control of the energy storage converter can be applied to Figure 1 In the controller of the target converter, the controller can communicate with the target converter to obtain local state information, the controller has a computing function, and the controller stores a trained state prediction model. Figure 2 As shown, the energy storage converter intelligent control method may include the following steps: Step S201: when the current time point is the i-th first preset time point, obtain local state information of the target converter.

[0022] Among them, i is a positive integer. This embodiment adopts a star topology communication method, which includes a central communication node. The central communication node is only used to support communication between energy storage inverters. The star topology includes several energy storage inverters. Any energy storage inverter can be used as a target inverter. Accordingly, other energy storage inverters serve as cooperative inverters of the target inverter.

[0023] The first preset time point may refer to a preset state information collection time point. In this embodiment, the time intervals between adjacent first preset time points are consistent.

[0024] The local status information may include voltage, SOC and other information. In the present embodiment, the local status information may refer to SOC. It should be noted that the present embodiment is described with the controller of the target converter as the object. When the current time point is the i-th first preset time point, the controller of the cooperative converter also obtains the local status information of the cooperative converter at the same time.

[0025] Step S202, determining a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point according to the local state information corresponding to the target converter at the im-th first preset time point to the i-th first preset time point and the trained state prediction model.

[0026] Among them, m is a positive integer and m is less than i. The state prediction model can adopt a recurrent neural network model, a long short-term memory network model, a time domain convolution model, etc. The first state evaluation coefficient can be used to characterize the predictability of the local state information of the target converter.

[0027] Optionally, determining a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point according to the local state information corresponding to the target converter at the im-th first preset time point to the i-th first preset time point and the trained state prediction model includes: Initialize identification parameter k=im; Input the local state information corresponding to the kth first preset time point to the k+qth first preset time point into the trained state prediction model to obtain the predicted state information corresponding to the k+q+1th first preset time point, where q is the preset prediction step length; Compare the predicted state information and the local state information respectively corresponding to the k+q+1th first preset time point to obtain a comparison result corresponding to the k+q+1th first preset time point; Update k=k+1, return to execute the step of inputting the local state information corresponding to the kth first preset time point to the k+qth first preset time point into the trained state prediction model, until k+q+1=i, and obtain mq comparison results; According to the mq comparison results, a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point is determined.

[0028] Among them, the identification parameter can be used to identify the first preset time point corresponding to the first local state information input into the trained state prediction model, q is the preset prediction step size, and q is less than m.

[0029] The comparison result can represent the difference between the measured state information and the local state information at the first preset time point.

[0030] Specifically, this embodiment adopts a time domain convolution model as a state prediction model, and predicts the local state information at the next first preset time point through q local state information that conforms to the time sequence.

[0031] Comparing the predicted state information and local state information corresponding to the k+q+1th first preset time point may refer to performing Euclidean distance calculation on the predicted state information and local state information corresponding to the k+q+1th first preset time point, and the Euclidean distance calculation result is the corresponding comparison result.

[0032] Determining the first state evaluation coefficient corresponding to the target converter at the i-th first preset time point according to the mq comparison results may refer to performing an average calculation based on the mq comparison results, and determining the average calculation result as the first state evaluation coefficient corresponding to the target converter at the i-th first preset time point.

[0033] This embodiment obtains a first state evaluation coefficient by quantifying the prediction accuracy of a trained state prediction model, thereby characterizing the predictability of local state information and providing a basis for determining subsequent state request information.

[0034] Step S203: sending the first state evaluation coefficient corresponding to the target converter at the i-th first preset time point to the n coordinated converters corresponding to the target converter through the central communication node.

[0035] Among them, n is a positive integer. Since each energy storage inverter can be used as a target inverter, accordingly, the target inverter in this embodiment can be used as a cooperative inverter of other energy storage inverters. Therefore, it is necessary to send the first state evaluation coefficient corresponding to the target inverter at the i-th first preset time point to the n cooperative inverters corresponding to the target inverter through the central communication node.

[0036] Step S204: receiving second state evaluation coefficients respectively corresponding to the n cooperative converters at the i-th first preset time point.

[0037] The second state evaluation coefficient may characterize the predictability of the local state information of the corresponding cooperative converter.

[0038] Specifically, the second state evaluation coefficient may be determined by a controller corresponding to the cooperative converter according to local state information corresponding to the cooperative converter at the imth first preset time point to the i-th first preset time point and a trained state prediction model.

[0039] Step S205: determining the communication priorities respectively corresponding to the n cooperative converters at the i-th first preset time point according to the received n second state evaluation coefficients at the i-th first preset time point.

[0040] The communication priority may be used to determine whether to request status information from the corresponding cooperative converter.

[0041] Optionally, the communication priority of each cooperative converter is initialized to a first preset value; Determine the coefficient upper limit value and the coefficient lower limit value according to the received second state evaluation coefficients at the n ith first preset time points, and form a coefficient interval by the coefficient upper limit value and the coefficient lower limit value; Determine a temporary segmentation threshold according to a plurality of second state evaluation coefficients at the i-th first preset time points within the coefficient interval, and identify the coefficient interval as a first execution state; Compare the second state evaluation coefficient corresponding to each cooperative converter at the i-th first preset time point with the temporary segmentation threshold; if the second state evaluation coefficient corresponding to any cooperative converter at the i-th first preset time point is greater than the temporary segmentation threshold, keep the communication priority of the cooperative converter unchanged; otherwise, update the communication priority of the cooperative converter by one; When all coefficient intervals are in the first execution state, all temporary segmentation thresholds are taken as intermediate values, and several coefficient intervals are formed according to the coefficient upper limit value, the coefficient lower limit value and all intermediate values. The steps of determining the temporary segmentation threshold according to the second state evaluation coefficients at several i-th first preset time points in the coefficient interval are returned in sequence until the maximum value of the communication priorities of all cooperative inverters is the same as the preset priority upper limit, and the communication priority corresponding to each cooperative inverter is obtained.

[0042] Among them, the upper limit value of the coefficient is the maximum value among the second state evaluation coefficients at the n i-th first preset time points, and the lower limit value of the coefficient is the minimum value among the second state evaluation coefficients at the n i-th first preset time points. The coefficient interval is determined by using the upper limit value of the coefficient as the right boundary value and the lower limit value of the coefficient as the left boundary value.

[0043] The coefficient interval is initially in the second execution state, and after a temporary segmentation threshold is determined according to the coefficient interval, the coefficient interval is identified as the first execution state.

[0044] Specifically, a number of coefficient intervals are formed according to the coefficient upper limit value, the coefficient lower limit value and all the intermediate values. This may mean that for any coefficient upper limit value, coefficient lower limit value or intermediate value, the coefficient upper limit value, coefficient lower limit value or intermediate value that is closest to it is selected to form a coefficient interval. When there is only one coefficient interval for the first division, when the coefficient interval is in the first execution state, the temporary division threshold is used as the intermediate value, and two coefficient intervals can be formed according to the coefficient upper limit value, the coefficient lower limit value and the intermediate value. According to these two coefficient intervals, the step of determining the temporary division threshold according to the second state evaluation coefficient at several i-th first preset time points within the coefficient interval is returned in sequence. At this time, it is necessary to wait until both coefficient intervals are in the first execution state before redetermining the coefficient interval.

[0045] The preset priority upper limit may refer to a preset priority level. The preset priority upper limit should be 2 to the power of a, where a is a positive integer. In this embodiment, a is set to 3, and accordingly, the preset priority is set to 8.

[0046] Optionally, determining the temporary segmentation threshold according to the second state evaluation coefficients at a plurality of i-th first preset time points within the coefficient interval includes: According to a plurality of second state evaluation coefficients at the i-th first preset time points within the coefficient interval, the temporary segmentation threshold is determined using the Otsu threshold method.

[0047] Among them, the Otsu threshold method is a method for finding the optimal threshold based on maximizing the inter-class variance.

[0048] Step S206: Determine the status request information to be sent to the n cooperative converters at the (i+1)th first preset time point according to the communication priorities respectively corresponding to the n cooperative converters at the i-th first preset time point.

[0049] The status request information may represent the status information content requested by the target converter to the cooperative converter.

[0050] Optionally, determining the status request information to be sent to the n cooperative converters at the (i+1)th first preset time point respectively according to the communication priorities respectively corresponding to the n cooperative converters at the i-th first preset time point includes: Determining a priority threshold according to the communication priorities respectively corresponding to the n cooperative converters at the i-th first preset time point; For any cooperative converter, if the communication priority of the cooperative converter is greater than the priority threshold, determine the first state request information to be sent to the cooperative converter at the (i+1)th first preset time point; Otherwise, second state request information is sent to the cooperative converter at the (i+1)th first preset time point.

[0051] Among them, the priority threshold can be used to determine the content of the status request information sent to the collaborative inverter at the i+1th first preset time point. The first status request information may indicate that the corresponding collaborative inverter does not need to return the collaborative status information at the i+1th first preset time point, and the second status request information may indicate that the corresponding collaborative inverter needs to return the collaborative status information at the i+1th first preset time point.

[0052] Optionally, for any collaborative inverter, if the collaborative inverter receives the second status request information corresponding to the i+1th first preset time point, the local status information of the collaborative inverter at the i+1th first preset time point is sent to the target inverter through the central communication node as the collaborative status information at the i+1th first preset time point.

[0053] Specifically, half of the maximum communication priority may be used as the priority threshold, and implementers may choose other methods of determining the priority threshold according to actual conditions, such as tertiles, median, etc.

[0054] Step S207: when the current time point is the (i+1)th first preset time point, corresponding status request information is sent to the n cooperative converters through the central communication node.

[0055] Step S208: receiving the coordination state information of the n coordinated converters at the (i+1)th first preset time point.

[0056] Among them, when no control instructions are received for the target converter, the target converter selectively requests the collaborative status information of the collaborative converter according to the communication priority, thereby reducing the amount of communication data and reducing the communication pressure of the central communication node. It can support multiple energy storage converters to communicate at the same time, avoiding network congestion, and thus improving the accuracy of intelligent control of the energy storage converter.

[0057] Specifically, for the collaborative inverter that sends the first status request information at the i+1th first preset time point, the controller of the target inverter predicts the collaborative status information at the i+1th first preset time point through several collaborative status information before the i+1th first preset time point, and uses the prediction result as the collaborative status information of the collaborative inverter at the i+1th first preset time point.

[0058] Step S209: upon receiving a second preset time point of the control instruction for the target converter, determining a first preset time point closest to the second preset time point as a reference time point.

[0059] The control instruction may be used to instruct the target converter to make adjustments.

[0060] Step S210, determining a state control amount corresponding to the target converter at a second preset time point according to the cooperative state information corresponding to the n cooperative converters at a reference time point and the local state information of the target converter at the reference time point.

[0061] The state control amount may refer to the adjustment amount of the target converter.

[0062] Optionally, determining the state control amount corresponding to the target converter at the second preset time point according to the cooperative state information respectively corresponding to the n cooperative converters at the reference time point and the local state information of the target converter at the reference time point includes: According to the cooperative state information respectively corresponding to the n cooperative converters at the reference time point and the local state information of the target converter at the reference time point, a consistency algorithm is used to determine the state control quantity corresponding to the target converter at the second preset time point.

[0063] The consistency algorithm may refer to the target converter updating its own local state according to the difference between the cooperative state information of the cooperative converter and its own local state information. The larger the difference is, the larger the update amplitude is.

[0064] In this embodiment, the state assessment coefficient is communicated through the central communication node. The data volume of the state assessment coefficient is small. The communication priority of each collaborative inverter is updated in time according to the state assessment coefficient. According to the communication priority of each collaborative inverter, the state request information sent by the target inverter is determined, so that the target inverter does not need to fully obtain the collaborative state information of all collaborative inverters, which reduces the communication pressure of the central communication node, can support multiple energy storage inverters to communicate at the same time, avoids network congestion, and thus improves the accuracy of intelligent control of the energy storage inverter.

[0065] Corresponding to the method of the above embodiment, Figure 3 The schematic diagram of the structure of an energy storage converter intelligent control device based on multi-objective coordination provided by the second embodiment of the present invention is shown. The energy storage converter intelligent control device is applied to the controller of the target converter. The controller can communicate with the target converter to obtain local state information. The controller has a computing function and stores a trained state prediction model in the controller. For ease of explanation, only the part related to the embodiment of the present invention is shown.

[0066] See also Figure 3 , the energy storage converter intelligent control device includes: The state acquisition module 301 is used to acquire the local state information of the target converter when the current time point is the i-th first preset time point, where i is a positive integer; A state evaluation module 302 is used to determine a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point according to the local state information corresponding to the target converter at the im-th first preset time point to the i-th first preset time point and the trained state prediction model, where m is a positive integer and m is less than i; The first communication module 303 is used to send the first state evaluation coefficient corresponding to the target converter at the i-th first preset time point to the n coordinated converters corresponding to the target converter through the central communication node, where n is a positive integer; The second communication module 304 is used to receive the second state evaluation coefficients respectively corresponding to the n cooperative converters at the i-th first preset time point; The priority determination module 305 is used to determine the communication priorities corresponding to the n cooperative converters at the i-th first preset time point according to the received second state evaluation coefficients at the n-th i-th first preset time point; The state request module 306 is used to determine the state request information to be sent to the n cooperative converters at the (i+1)th first preset time point according to the communication priorities respectively corresponding to the n cooperative converters at the (i)th first preset time point; The third communication module 307 is used to send corresponding status request information to the n cooperative converters through the central communication node when the current time point is the (i+1)th first preset time point; A fourth communication module 308, configured to receive the coordination state information of the n coordinated converters at the (i+1)th first preset time point; A time point determination module 309, configured to determine, when receiving a second preset time point of a control instruction for a target converter, a first preset time point closest to the second preset time point as a reference time point; The state control module 310 is used to determine the state control amount corresponding to the target converter at the second preset time point according to the cooperative state information corresponding to the n cooperative converters at the reference time point and the local state information of the target converter at the reference time point.

[0067] Optionally, the state assessment module 302 includes: A first initialization unit, used for initializing an identification parameter k=im; A state prediction unit, used to input the local state information corresponding to the kth first preset time point to the k+qth first preset time point into the trained state prediction model, and obtain the predicted state information corresponding to the k+q+1th first preset time point, where q is a preset prediction step length; A state comparison unit, used to compare the predicted state information and the local state information corresponding to the k+q+1th first preset time point, respectively, to obtain a comparison result corresponding to the k+q+1th first preset time point; The first iteration unit is used to update k=k+1, and return to execute the step of inputting the local state information corresponding to the kth first preset time point to the k+qth first preset time point into the trained state prediction model until k+q+1=i, and obtain mq comparison results; The coefficient determination unit is used to determine the first state evaluation coefficient corresponding to the target converter at the i-th first preset time point according to the mq comparison results.

[0068] Optionally, the priority determination module 305 includes: A second initialization unit, used to initialize the communication priority of each cooperative converter to a first preset value; An interval determination unit, configured to determine a coefficient upper limit value and a coefficient lower limit value according to the received second state evaluation coefficients at the n ith first preset time points, and form a coefficient interval by the coefficient upper limit value and the coefficient lower limit value; a segmentation unit, configured to determine a temporary segmentation threshold value according to a plurality of second state evaluation coefficients at the i-th first preset time points within the coefficient interval, and mark the coefficient interval as a first execution state; A priority updating unit, used to compare the second state evaluation coefficient corresponding to each cooperative converter at the i-th first preset time point with the temporary segmentation threshold, if the second state evaluation coefficient corresponding to any cooperative converter at the i-th first preset time point is greater than the temporary segmentation threshold, then the communication priority of the cooperative converter is kept unchanged; otherwise, the communication priority of the cooperative converter is updated to increase by one; The second iterative unit is used to, when all coefficient intervals are in the first execution state, take all temporary segmentation thresholds as intermediate values, form several coefficient intervals according to the coefficient upper limit value, the coefficient lower limit value and all intermediate values, and return in sequence to execute the step of determining the temporary segmentation threshold according to the second state evaluation coefficients at several i-th first preset time points in the coefficient interval, until the maximum value of the communication priorities of all cooperative inverters is the same as the preset priority upper limit, thereby obtaining the communication priority corresponding to each cooperative inverter.

[0069] Optionally, the segmentation unit includes: The threshold segmentation subunit is used to determine a temporary segmentation threshold by using the Otsu threshold method according to a number of second state evaluation coefficients at the i-th first preset time point within the coefficient interval.

[0070] Optionally, the status request module 306 includes: A threshold determination unit, configured to determine a priority threshold according to the communication priorities respectively corresponding to the n cooperative converters at the i-th first preset time point; A first request unit is used to determine, for any cooperative converter, if the communication priority of the cooperative converter is greater than a priority threshold, first status request information to be sent to the cooperative converter at an (i+1)th first preset time point; The second requesting unit is configured to send second state request information to the cooperative converter at the (i+1)th first preset time point otherwise.

[0071] Optionally, the status request module 306 further includes: For any collaborative inverter, if the collaborative inverter receives the second status request information corresponding to the i+1th first preset time point, the local status information of the collaborative inverter at the i+1th first preset time point is sent to the target inverter through the central communication node as the collaborative status information at the i+1th first preset time point.

[0072] Optionally, the state control module 310 includes: The control quantity determination unit is used to determine the state control quantity corresponding to the target converter at the second preset time point using a consistency algorithm based on the collaborative state information corresponding to the n collaborative converters at the reference time point and the local state information of the target converter at the reference time point.

[0073] It should be noted that for the information interaction, execution process, etc. among the above-mentioned modules, units, and subunits, since they are based on the same concept as the method embodiments of the present invention, for their specific functions and the technical effects brought about, reference can be specifically made to the method embodiment part, and details will not be elaborated here.

[0074] Figure 4 It is a schematic structural diagram of a computer device for an intelligent control method of an energy storage converter based on multi-objective coordination provided in Embodiment 3 of the present invention. As Figure 4 shown, the computer device of this embodiment includes: a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor is at least one ( Figure 4 only one is shown in the figure), and it also includes a computer program stored in the memory and executable on at least one processor. When the processor executes the computer program, it implements the steps in any of the above-mentioned embodiments of the intelligent control method of the energy storage converter based on multi-objective coordination.

[0075] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 4 this is only an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, it may also include a network interface, a display screen, and an input device, etc.

[0076] The so-called processor may be a CPU, and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0077] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0078] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0079] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.

[0080] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0082] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. An intelligent control method for energy storage converter based on multi-objective coordination, characterized in that: The method comprises: When the current time point is the i-th first preset time point, obtaining local state information of the target converter, where i is a positive integer; Determine a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point according to the local state information corresponding to the target converter at the i-th first preset time point and the trained state prediction model, where m is a positive integer and m is less than i; Sending the first state evaluation coefficient corresponding to the target converter at the i-th first preset time point to the n coordinated converters corresponding to the target converter through the central communication node, where n is a positive integer; Receiving second state evaluation coefficients respectively corresponding to n cooperative converters at an i-th first preset time point; Determining the communication priorities corresponding to the n cooperative converters at the i-th first preset time point according to the received n second state evaluation coefficients at the i-th first preset time point; Determine, according to the communication priorities respectively corresponding to the n cooperative converters at the i-th first preset time point, the status request information respectively sent to the n cooperative converters at the i+1-th first preset time point; When the current time point is the (i+1)th first preset time point, sending corresponding status request information to the n cooperative converters through the central communication node; Receiving cooperative state information of n cooperative converters at an (i+1)th first preset time point; When receiving a second preset time point of the control instruction for the target converter, determining a first preset time point closest to the second preset time point as a reference time point; According to the cooperative state information respectively corresponding to the n cooperative converters at the reference time point and the local state information of the target converter at the reference time point, the state control amount corresponding to the target converter at the second preset time point is determined.

2. The energy storage converter intelligent control method according to claim 1, characterized in that: The determining, according to the local state information corresponding to the target converter at the im-th first preset time point to the i-th first preset time point and the trained state prediction model, a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point includes: Initialize identification parameter k=im; Input the local state information corresponding to the kth first preset time point to the k+qth first preset time point into the trained state prediction model to obtain the predicted state information corresponding to the k+q+1th first preset time point, where q is a preset prediction step length; Compare the predicted state information and the local state information respectively corresponding to the k+q+1th first preset time point to obtain a comparison result corresponding to the k+q+1th first preset time point; Update k=k+1, return to the step of inputting the local state information corresponding to the kth first preset time point to the k+qth first preset time point into the trained state prediction model, until k+q+1=i, and obtain mq comparison results; According to the mq comparison results, a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point is determined.

3. The intelligent control method for energy storage converter according to claim 1, characterized in that: The determining, according to the received n second state evaluation coefficients at the i-th first preset time points, the communication priorities corresponding to the n cooperative converters at the i-th first preset time points respectively includes: Initializing the communication priority of each cooperative converter to a first preset value; Determine a coefficient upper limit value and a coefficient lower limit value according to the received second state evaluation coefficients at the n ith first preset time points, and form a coefficient interval by the coefficient upper limit value and the coefficient lower limit value; Determine a temporary segmentation threshold according to a plurality of second state evaluation coefficients at the i-th first preset time points within the coefficient interval, and identify the coefficient interval as a first execution state; Compare the second state evaluation coefficient corresponding to each cooperative converter at the i-th first preset time point with the temporary segmentation threshold; if the second state evaluation coefficient corresponding to any cooperative converter at the i-th first preset time point is greater than the temporary segmentation threshold, keep the communication priority of the cooperative converter unchanged; otherwise, update the communication priority of the cooperative converter by one; When all coefficient intervals are in the first execution state, all temporary segmentation thresholds are taken as intermediate values, and several coefficient intervals are formed according to the coefficient upper limit value, the coefficient lower limit value and all intermediate values. The steps of determining the temporary segmentation threshold according to the second state evaluation coefficients at several i-th first preset time points in the coefficient interval are returned in sequence until the maximum value of the communication priorities of all cooperative inverters is the same as the preset priority upper limit, so as to obtain the communication priority corresponding to each cooperative inverter.

4. The intelligent control method for energy storage converter according to claim 3 is characterized in that: The determining of the temporary segmentation threshold according to the second state evaluation coefficients at a plurality of i-th first preset time points within the coefficient interval includes: According to the second state evaluation coefficients at a plurality of i-th first preset time points within the coefficient interval, a temporary segmentation threshold is determined using the Otsu threshold method.

5. The intelligent control method for energy storage converter according to claim 1, characterized in that: The determining, according to the communication priorities respectively corresponding to the n cooperative converters at the i-th first preset time point, the status request information respectively sent to the n cooperative converters at the (i+1)th first preset time point includes: Determining a priority threshold according to the communication priorities respectively corresponding to the n cooperative converters at the i-th first preset time point; For any cooperative converter, if the communication priority of the cooperative converter is greater than the priority threshold, determine the first status request information to be sent to the cooperative converter at the (i+1)th first preset time point; Otherwise, second state request information is sent to the cooperative converter at the (i+1)th first preset time point.

6. The intelligent control method for energy storage converter according to claim 5, characterized in that: For any collaborative inverter, if the collaborative inverter receives the second status request information corresponding to the i+1th first preset time point, the local status information of the collaborative inverter at the i+1th first preset time point is sent to the target inverter through the central communication node as the collaborative status information at the i+1th first preset time point.

7. The intelligent control method for energy storage converter according to claim 1, characterized in that: The determining, according to the coordinated state information respectively corresponding to the n coordinated converters at the reference time point and the local state information of the target converter at the reference time point, a state control amount corresponding to the target converter at the second preset time point includes: According to the collaborative state information respectively corresponding to the n collaborative converters at the reference time point and the local state information of the target converter at the reference time point, a consistency algorithm is used to determine the state control quantity corresponding to the target converter at the second preset time point.

8. An intelligent control device for energy storage converter based on multi-objective coordination, characterized in that: The device comprises: A state acquisition module, used for acquiring local state information of the target converter when the current time point is the i-th first preset time point, where i is a positive integer; A state evaluation module, used to determine a first state evaluation coefficient corresponding to the target converter at the i-th first preset time point according to the local state information corresponding to the target converter at the im-th first preset time point to the i-th first preset time point and the trained state prediction model, where m is a positive integer and m is less than i; A first communication module, used for sending the first state evaluation coefficient corresponding to the target converter at the i-th first preset time point to the n coordinated converters corresponding to the target converter through the central communication node, where n is a positive integer; A second communication module is used to receive second state evaluation coefficients respectively corresponding to the n cooperative converters at the i-th first preset time point; A priority determination module, used to determine the communication priorities corresponding to the n cooperative converters at the i-th first preset time point according to the received n second state evaluation coefficients at the i-th first preset time point; A status request module, used to determine the status request information to be sent to the n cooperative converters at the (i+1)th first preset time point according to the communication priorities respectively corresponding to the n cooperative converters at the (i)th first preset time point; A third communication module, configured to send corresponding status request information to the n cooperative converters through the central communication node when the current time point is the (i+1)th first preset time point; A fourth communication module, used for receiving the coordination state information of the n coordinated converters at the (i+1)th first preset time point; a time point determination module, configured to determine, when receiving a second preset time point of a control instruction for the target converter, a first preset time point closest to the second preset time point as a reference time point; The state control module is used to determine the state control quantity corresponding to the target converter at the second preset time point according to the collaborative state information corresponding to the n collaborative converters at the reference time point and the local state information of the target converter at the reference time point.

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