An energy storage cluster power margin observation method based on an event triggering mechanism

Abstract

The application discloses a kind of energy storage cluster power margin observation methods based on event triggering mechanism, belong to energy storage system control technical field, the method includes: the error between the total power margin variable of energy storage cluster and the actual value of energy storage cluster power capacity is estimated to obtain the power estimation error variable of each energy storage unit, and then the trigger variable is set using power estimation error variable;Further utilize trigger variable, power estimation error variable and its preset trigger threshold value to represent trigger function;By designing each distributed power source only when trigger function value is greater than zero, the total power margin variable of energy storage cluster in the next step is updated, the communication amount and the amount of calculation of energy storage control of power system can be effectively reduced, so as to solve the technical problem that serious communication resource and computing resource waste caused by frequent information exchange when power system is in steady state operation.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system control technology, and more specifically, relates to a method for observing the power margin of an energy storage cluster based on an event-triggered mechanism. Background Technology

[0002] With the increasing penetration of renewable energy, power regulation is an essential capability for power systems to compensate for power fluctuations and maintain reliable operation. Energy storage, capable of rapidly processing and discharging energy to maintain system power balance, is a crucial means of promoting efficient energy utilization and ensuring the safe and stable operation of the system. For large-scale energy storage systems, the energy storage power station directly influences market operations. However, for small-capacity distributed energy storage systems, aggregation of numerous small-scale energy storage systems is often achieved through energy storage clusters. The flexible deployment of numerous distributed small-capacity energy storage systems, coupled with the continuous increase in grid connection scale in recent years, offers significant regulation potential. For large-capacity energy storage demands, small-capacity energy storage units are often aggregated through energy storage clusters, following collective control objectives, thus appearing as a large-scale aggregated energy storage cluster at the upper level. For the control of geographically dispersed distributed energy storage clusters with varying states and parameters, distributed control, as an alternative to centralized control of energy storage systems, has attracted considerable attention. A limited power range reference is crucial for the reliable operation of distributed power point tracking (DPPT).

[0003] Traditional distributed collaborative control or observation methods based on consensus algorithms mostly require continuous state monitoring and signal transmission. However, in actual engineering, it is difficult to achieve completely continuous monitoring and communication, especially when communication bandwidth and channels are limited. Therefore, in practice, a time-triggered mechanism is usually adopted, that is, each distributed power source transmits information and updates control signals at fixed time intervals. When the time interval is small enough, the communication can be approximated as continuous.

[0004] Since power systems do not require frequent information exchange during steady-state operation, this time-triggered communication mechanism results in a serious waste of communication and computing resources. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a power margin observation method for energy storage clusters based on an event-triggered mechanism. Its purpose is to estimate the error between the total power margin variable of the energy storage cluster and the actual power capacity of the energy storage cluster, and then set trigger variables and their corresponding trigger functions. By designing that each distributed power source only updates the total power margin variable of the energy storage cluster for the next step size when the trigger function value is greater than zero, the communication and computational load of power system energy storage control can be effectively reduced. This solves the technical problem of serious waste of communication and computational resources caused by frequent information exchange during steady-state operation of the power system.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for observing the power margin of an energy storage cluster based on an event-triggered mechanism is provided, comprising:

[0007] S1: For the current step size The error between the total power margin variable of the energy storage cluster and the actual power capacity of the i-th energy storage unit is estimated to obtain the result at the current step size. Power estimation error variable of the i-th energy storage unit ;

[0008] S2: Set the i-th energy storage unit at the current step size. Trigger variable ;

[0009] S3: Set the current step size Power estimation error variable of the i-th energy storage unit With trigger variables The difference is used as the value at the current step size. The relative error variable of the i-th energy storage unit ;

[0010] S4: Utilize the current step size Next The relative error variable of each energy storage unit The relationship between the set trigger threshold and the trigger function characterizes the triggering function in the event triggering mechanism;

[0011] S5: At each step, check the trigger function values ​​of the energy storage cluster aggregator and each energy storage unit in real time; if the trigger function value is greater than zero, then utilize the energy storage cluster aggregator at the current step. Power estimation error variable under For the next step Total power margin variable of the energy storage cluster Update.

[0012] In one embodiment, S1 includes:

[0013] Based on the discrete-time dynamic average consensus algorithm, the energy storage cluster utilizes the current step size... Estimated total power margin , No. Each energy storage unit at the current step size Power margin variable under and the Each energy storage unit and other energy storage units In the previous step Difference in power estimation error , estimate the first Each energy storage unit at the current step size Power estimation error variable under This is used to characterize the total power margin variable of the energy storage cluster at the current step size k and the 1st step size k. The error between the actual power capacity values ​​of each energy storage unit.

[0014] In one embodiment, S1 includes:

[0015] Based on the discrete-time dynamic average consistency algorithm, using the formula Estimate the first Each energy storage unit at the current step size Power estimation error variable under ;

[0016] in, Indicates the first Each energy storage unit at the current step size The intermediate variables under the discrete-time consensus algorithm represent the estimation error intermediate variables. , =0, For discrete sampling time intervals, It is a positive coefficient. This is the communication weighting coefficient. Representing energy storage cluster aggregators, Represents each energy storage unit.

[0017] In one embodiment, S2 includes:

[0018] Using formula Set the first Each energy storage unit at the current step size Trigger variable , It is the time step of the event triggering mechanism. satisfy .

[0019] In one embodiment, S4 includes:

[0020] Using formula Set the first Each energy storage unit at the current step size Triggering function in the event triggering mechanism ,in, The set trigger threshold.

[0021] In one embodiment, S5 includes:

[0022] S51: Within each sampling step, the energy storage cluster aggregator and each energy storage unit check their own trigger function values ​​in real time;

[0023] S52: If If the trigger condition is met, the trigger variable will be triggered. Updated to the latest power estimation error variable And utilize energy storage cluster aggregators at the current pace Power estimation error variable under Update the next step size Estimated power margin of energy storage clusters ;

[0024] S53: If not satisfied When the trigger condition is met, all variables retain their original values.

[0025] In one embodiment, S52 includes:

[0026] like If the triggering condition is met, the trigger variable will be triggered. Updated to the latest power estimation error variable ; and utilize Update the next step size Estimated power margin of energy storage clusters ;in, For discrete sampling time intervals, It is a positive coefficient.

[0027] According to another aspect of the present invention, an energy storage cluster power margin monitoring device based on an event-triggered mechanism is provided, comprising:

[0028] The error variable estimation module is used to estimate the current step size. The total power margin variable of the energy storage cluster under the following conditions and the first The error between the actual power capacity values ​​of each energy storage unit is estimated to obtain the result at the current step size. Next Power estimation error variable for each energy storage unit ;

[0029] The trigger variable setting module is used to set the first... Each energy storage unit at the current step size Trigger variable ;

[0030] The relative variable determination module is used to determine the current step size. Next Power estimation error variable for each energy storage unit With trigger variables The difference is used as the value at the current step size. Next The relative error variable of each energy storage unit ;

[0031] The trigger function setting module is used to utilize the current step size. Next The relative error variable of each energy storage unit The relationship between the set trigger threshold and the trigger function characterizes the triggering function in the event triggering mechanism;

[0032] The detection and update module is used to check the trigger function values ​​of the energy storage cluster aggregator and each energy storage unit in real time at each step. If the trigger function value is greater than zero, the update module utilizes the energy storage cluster aggregator's update function at the current step. Power estimation error variable under For the next step Total power margin variable of the energy storage cluster Update.

[0033] According to another aspect of the present invention, a power storage station control system is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0034] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0035] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0036] (1) This invention provides a power margin observation method for energy storage clusters based on an event triggering mechanism. The error between the total power margin variable of the energy storage cluster and the actual power capacity of the energy storage cluster is estimated to obtain the power estimation error variable of each energy storage unit. Then, the power estimation error variable is used to set the trigger variable. Furthermore, the trigger variable, the power estimation error variable and their preset trigger threshold are used to characterize the trigger function. By designing that each distributed power source only updates the total power margin variable of the energy storage cluster in the next step size when the trigger function value is greater than zero, the communication and computation of power system energy storage control can be effectively reduced, thereby solving the technical problem of serious waste of communication and computation resources caused by frequent information exchange during steady-state operation of the power system.

[0037] (2) This scheme estimates the first time based on the discrete-time dynamic average consensus algorithm. Each energy storage unit at the current step size Power estimation error variable under It has low computational complexity, which can improve control rate and increase response speed.

[0038] (3) This scheme utilizes the formula Estimate the first Each energy storage unit at the current step size Power estimation error variable under It can improve estimation accuracy while maintaining low computational complexity.

[0039] (4) This scheme utilizes the formula Set the first Each energy storage unit at the current step size Trigger variable It can change the update step size from the sampling step size in the traditional time-triggered process. Transform into event-triggered step size This greatly reduces the communication burden during the observation process; in addition, since the proposed observation method is implemented in a discrete time manner, it naturally avoids Zeno behavior.

[0040] (5) This scheme utilizes the formula Set the first Each energy storage unit at the current step size Triggering function in the event triggering mechanism ,in, The trigger threshold is set as the threshold value. The trigger threshold determines the number of event triggers and the estimation accuracy during the observation process. A larger trigger threshold results in fewer event triggers and a lower system communication burden, but also a larger observation estimation bias. Existing event-triggered distributed methods often use dynamic trigger thresholds to eliminate estimation bias, but complex trigger threshold settings can easily lead to system non-convergence, and are not entirely applicable to different observation models. The constant trigger threshold proposed in this scheme is easy to implement, and the convergence condition is the same for observers with and without event triggering mechanisms.

[0041] (6) In this scheme, within each sampling step, the energy storage cluster aggregator and each energy storage unit check the trigger function value in real time. If The trigger condition is met, trigger variable Updated to the latest power estimation error variable Not satisfied When the trigger condition is met, all variables retain their original values. Therefore, the event trigger time step for the entire observation process is determined by the following formula: It can effectively reduce the communication and computational load of energy storage control in power systems. Attached Figure Description

[0042] Figure 1A flowchart of a power margin observation method for energy storage clusters based on an event-triggered mechanism provided in an embodiment of the present invention;

[0043] Figure 2 The simulation example provided in one embodiment of the present invention uses the IEEE standard 33-node system.

[0044] Figure 3 A schematic diagram of a power margin observation method for energy storage clusters based on an event-triggered mechanism provided in an embodiment of the present invention;

[0045] Figure 4a A schematic diagram of the power margin values ​​of each energy storage unit provided in an embodiment of the present invention.

[0046] Figure 4b A schematic diagram of the total power margin value and its estimated value provided in an embodiment of the present invention.

[0047] Figure 4c A schematic diagram of the time triggering sequence of each energy storage unit provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0049] like Figure 1 As shown, this invention provides a method for observing the power margin of energy storage clusters based on an event-triggered mechanism, including:

[0050] S1: For the current step size The total power margin variable of the energy storage cluster under the following conditions and the first The error between the actual power capacity values ​​of each energy storage unit is estimated to obtain the result at the current step size. Next Power estimation error variable for each energy storage unit ;

[0051] S2: Set the first Each energy storage unit at the current step size Trigger variable ;

[0052] S3: Set the current step size Next Power estimation error variable for each energy storage unit With trigger variables The difference is used as the value at the current step size. Next The relative error variable of each energy storage unit ;

[0053] S4: Utilize the current step size Next The relative error variable of each energy storage unit The relationship between the set trigger threshold and the trigger function characterizes the triggering function in the event triggering mechanism;

[0054] S5: At each step, check the trigger function values ​​of the energy storage cluster aggregator and each energy storage unit in real time; if the trigger function value is greater than zero, then utilize the energy storage cluster aggregator at the current step. Power estimation error variable under For the next step Total power margin variable of the energy storage cluster Update.

[0055] In one embodiment, S1 includes:

[0056] Based on the discrete-time dynamic average consensus algorithm, the energy storage cluster utilizes the current step size... Estimated total power margin , No. Each energy storage unit at the current step size Power margin variable under and the Each energy storage unit and other energy storage units In the previous step Difference in power estimation error , estimate the first Each energy storage unit at the current step size Power estimation error variable under This is used to characterize the total power margin variable of the energy storage cluster at the current step size k and the 1st step size k. The error between the actual power capacity values ​​of each energy storage unit.

[0057] In one embodiment, the error estimation is based on a discrete-time dynamic average consensus algorithm. The expression for estimating the error between the total power margin variable of the energy storage cluster and the actual power capacity of the energy storage cluster is as follows: ;

[0058] in, As intermediate variables in the estimation process, , For discrete sampling time intervals, It is a positive coefficient. Let be the power estimation error variable for the i-th energy storage unit.

[0059] Specifically, the power margin variable of each energy storage unit affects the estimation error variable. To update, use the following expression: ; This is an estimate of the total power margin. It is the first The power margin variable of energy storage, subscript This refers to the energy storage unit's identification number. It should be noted that this refers to the energy storage cluster aggregator. The rest of the first Each energy storage unit is as follows: The error variable Substituting these values ​​into the error estimation expression yields the error estimate between the total power margin of the energy storage cluster and the actual power margin of each energy storage unit.

[0060] In one embodiment, to reduce the communication burden on the communication network during the total power margin observation process, S2 introduces an event triggering mechanism and sets a trigger variable. for: ;in It is the time step of the event triggering mechanism, and satisfy Since the observation method proposed in this invention is implemented in a discrete-time manner, Zeno behavior is naturally avoided.

[0061] In one embodiment, a relative error variable is defined before deriving the event triggering conditions. for: Set the trigger function in the event triggering mechanism as follows: .in The trigger threshold is a normal number. It determines the number of event triggers and the estimation accuracy during the observation process. A larger trigger threshold results in fewer event triggers and a lower system communication burden, but also a larger observation estimation bias. Many existing event-triggered distributed methods use dynamic trigger thresholds to eliminate estimation bias; however, complex trigger threshold settings can easily lead to system non-convergence, and are not entirely applicable to different observation models. Therefore, the constant trigger threshold proposed in this embodiment is easy to implement, and the convergence condition is the same for observers with and without event triggering mechanisms.

[0062] In one embodiment, within each sampling step, the energy storage cluster aggregator and each energy storage unit check the trigger function value in real time. The trigger condition is met, trigger variable Updated to the latest power estimation error variable Not satisfied When the trigger condition is met, all variables retain their original values. Therefore, the event trigger time step for the entire observation process is determined by the following formula: .

[0063] In one embodiment, the energy storage cluster aggregator updates the total power margin estimate using the following formula: ;in It is a positive coefficient. Through steps S1 and S2, and using an undirected connection communication network to ensure the convergence requirement of the average consensus algorithm in the observation method is met, the observation of the maximum and minimum allowable power range of the energy storage cluster is realized.

[0064] According to another aspect of the present invention, an energy storage cluster power margin monitoring device based on an event-triggered mechanism is provided, comprising:

[0065] The error variable estimation module is used to estimate the current step size. The total power margin variable of the energy storage cluster under the following conditions and the first The error between the actual power capacity values ​​of each energy storage unit is estimated to obtain the result at the current step size. Next Power estimation error variable for each energy storage unit ;

[0066] The trigger variable setting module is used to set the first... Each energy storage unit at the current step size Trigger variable ;

[0067] The relative variable determination module is used to determine the current step size. Next Power estimation error variable for each energy storage unit With trigger variables The difference is used as the value at the current step size. Next The relative error variable of each energy storage unit ;

[0068] The trigger function setting module is used to utilize the current step size. Next The relative error variable of each energy storage unit The relationship between the set trigger threshold and the trigger function characterizes the triggering function in the event triggering mechanism;

[0069] The detection and update module is used to check the trigger function values ​​of the energy storage cluster aggregator and each energy storage unit in real time at each step. If the trigger function value is greater than zero, the update module utilizes the energy storage cluster aggregator's update function at the current step. Power estimation error variable under For the next step Total power margin variable of the energy storage cluster Update.

[0070] The following description is in Figure 2 The performance of the energy storage cluster power margin observation device based on the event-triggered mechanism proposed in this invention was tested in the standard IEEE 33 bus test system shown.

[0071] Figure 2 The energy storage system shown contains eight Energy Storage Systems (ESS), each mounted on a different bus. These eight ESS units are of two types: Type I with a high power-to-energy ratio and Type II with a low power-to-energy ratio. For example, Type I ESS units have capacities of 100kW / 100kWh and 100kW / 2kWh. A communication link supports information exchange between the energy storage system aggregator and the ESS units. A schematic diagram illustrating the observation process of the energy storage cluster's power margin is shown below. Figure 3 As shown, the input is the power margin of each energy storage unit, and the total power margin of the energy storage cluster is estimated under event-triggered conditions.

[0072] The power margin changes of the eight energy storage units during the test are as follows: Figure 4a As shown, initially, the power margin variable of each of the eight energy storage units is 100kW. The power margin variable of energy storage unit 1 decreases to 0 at t=20s and recovers at t=60s. The power margin variable of energy storage unit 5 decreases to 0 at t=30s and recovers at t=60s. The other energy storage units remain unchanged. The estimated total power margin variable of the energy storage cluster changes as the power margin variables of each energy storage unit change. like Figure 4b As shown. From Figure 4b It can be observed that when the power margin reference value of each energy storage unit in the energy storage cluster changes, the observation results of the observation method proposed in this invention on the total power margin value of the energy storage cluster are... It also changes accordingly, approximating the changed reference total power margin variable. Furthermore, from Figure 4b As can be seen from the magnified image, under non-zero trigger thresholds, there is a small estimation bias between the steady-state observations and the reference values. The time trigger sequences for each energy storage unit are as follows: Figure 4c As shown, according to Figure 4c As can be seen from the moment of triggering, the communication burden of the system is greatly reduced due to the introduction of the event triggering mechanism.

[0073] According to another aspect of the present invention, an energy storage power station control system is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0074] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0075] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for observing the power margin of an energy storage cluster based on an event-triggered mechanism, characterized in that, include: S1: Based on the discrete-time dynamic average consensus algorithm, utilizing the energy storage cluster at the current step size Estimated total power margin , No. Each energy storage unit at the current step size Power margin variable under and the Each energy storage unit and other energy storage units In the previous step Difference in power estimation error , estimate the first Each energy storage unit at the current step size Power estimation error variable under Used to characterize the current step size The total power margin variable corresponding to the energy storage cluster and the first The error between the actual power capacity values ​​of each energy storage unit; S2: Set the first Each energy storage unit at the current step size Trigger variable ; S3: Set the current step size Next Power estimation error variable for each energy storage unit With trigger variables The difference is used as the value at the current step size. Next The relative error variable of each energy storage unit ; S4: Utilize the current step size Next The relative error variable of each energy storage unit The relationship between the set trigger threshold and the trigger function characterizes the triggering function in the event triggering mechanism; S5: At each step, check the trigger function values ​​of the energy storage cluster aggregator and each energy storage unit in real time; If the trigger function value is greater than zero, then the energy storage cluster aggregator will be used at the current step size. Power estimation error variable under For the next step Total power margin variable of the energy storage cluster Updating will trigger the variable Updated to the latest power estimation error variable ; S1 includes: based on the discrete-time dynamic average consistency algorithm, using the formula... Estimate the first Each energy storage unit at the current step size Power estimation error variable under ; Indicates the first Each energy storage unit at the current step size The intermediate variables under the discrete-time consensus algorithm represent the estimation error intermediate variables. , =0, For discrete sampling time intervals, It is a positive coefficient. This is the communication weighting coefficient. Representing energy storage cluster aggregators, Represents each energy storage unit; S2 includes: using the formula Set the first Each energy storage unit at the current step size Trigger variable , It is the time step of the event triggering mechanism. satisfy .

2. The method for observing the power margin of an energy storage cluster based on an event-triggered mechanism as described in claim 1, characterized in that, S4 includes: Using formula Set the first Each energy storage unit at the current step size Triggering function in the event triggering mechanism ,in, The set trigger threshold.

3. The method for observing the power margin of an energy storage cluster based on an event-triggered mechanism as described in claim 1, characterized in that, S5 includes: S51: Within each sampling step, the energy storage cluster aggregator and each energy storage unit check their own trigger function values ​​in real time; S52: If If the triggering condition is met, the trigger variable will be triggered. Updated to the latest power estimation error variable And utilize energy storage cluster aggregators at the current pace Power estimation error variable under Update the next step size Estimated power margin of energy storage clusters ; S53: If not satisfied When the trigger condition is met, all variables retain their original values.

4. The method for observing the power margin of an energy storage cluster based on an event-triggered mechanism as described in claim 3, characterized in that, S52 includes: like If the triggering condition is met, the trigger variable will be triggered. Updated to the latest power estimation error variable ; and utilize Update the next step size Estimated power margin of energy storage clusters ;in, It is a positive coefficient.

5. A power margin monitoring device for an energy storage cluster based on an event-triggered mechanism, used to execute the power margin monitoring method for an energy storage cluster based on an event-triggered mechanism as described in any one of claims 1-4, characterized in that, include: The error variable estimation module is used to estimate the energy storage cluster's performance at the current step size based on the discrete-time dynamic average consensus algorithm. Estimated total power margin , No. Each energy storage unit at the current step size Power margin variable under and the Each energy storage unit and other energy storage units In the previous step Difference in power estimation error , estimate the first Each energy storage unit at the current step size Power estimation error variable under This is used to characterize the total power margin variable of the energy storage cluster at the current step size k and the 1st step size k. The error between the actual power capacity values ​​of each energy storage unit; The trigger variable setting module is used to set the first... Each energy storage unit at the current step size Trigger variable ; The relative variable determination module is used to determine the current step size. Next Power estimation error variable for each energy storage unit With trigger variables The difference is used as the value at the current step size. Next The relative error variable of each energy storage unit ; The trigger function setting module is used to utilize the current step size. Next The relative error variable of each energy storage unit The relationship between the set trigger threshold and the trigger function characterizes the triggering function in the event triggering mechanism; The detection and update module is used to check the trigger function values ​​of the energy storage cluster aggregator and each energy storage unit in real time at each step. If the trigger function value is greater than zero, then the energy storage cluster aggregator will be used at the current step size. Power estimation error variable under For the next step Total power margin variable of the energy storage cluster Updating will trigger the variable Updated to the latest power estimation error variable .

6. A control system for an energy storage power station, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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