Energy storage device constant volume processing method and device and electronic device

Abstract

This application provides a method for determining the capacity of an energy storage device. The method includes: acquiring the operating parameters and operating cost parameters of the energy storage device, and determining sampling points within a sampling period. Clustering the sampling points within the sampling period to obtain clustering results. Determining the electricity load type and electricity resource information based on the clustering results, performing capacity analysis, and determining the charge / discharge capacity. Determining the number of charge / discharge cycles and the charge / discharge cycle interval matching the number of charge / discharge cycles within the sampling period based on the electricity resource information. Calculating the feasible capacity based on the charge / discharge capacity and the number of charge / discharge cycles. Calculating the corresponding resource information coefficient based on the feasible capacity, and selecting the feasible capacity corresponding to the largest resource information coefficient as the set capacity of the energy storage device. This application selects a suitable energy storage device capacity by calculating multiple feasible capacities and the resource information coefficients corresponding to different feasible capacities, thereby achieving peak shaving and valley filling of electricity demand.

Description

Technical Field

[0001] This invention relates to the field of energy storage equipment technology, and more specifically, to a method, apparatus, and electronic device for determining the capacity of an energy storage device and considering the rate of return on investment. Background Technology

[0002] Energy storage devices can be used as power sources for outputting electrical energy or as loads for storing electrical energy. The construction cost of energy storage systems is relatively high. If the capacity of the energy storage device is too large, it will increase additional operating costs. If the capacity is too small, demand control may not be possible. Therefore, determining the appropriate capacity and maximizing benefits when constructing energy storage devices is a key challenge. Summary of the Invention

[0003] To address the existing technical problems, embodiments of the present invention provide a method, apparatus, and electronic device for calibrating energy storage equipment.

[0004] In a first aspect, embodiments of the present invention provide a method for calibrating an energy storage device, the method comprising:

[0005] The operating parameters and operating cost parameters of the energy storage device are obtained, and the sampling points within the sampling period are determined based on the operating parameters of the energy storage device; wherein, the sampling points include: the correspondence between sampling time and load data;

[0006] Clustering is performed on the sampling points within the sampling period to obtain clustering results; wherein, the clustering results are used to indicate the type of electricity load at different times within the sampling period, and to determine the type of electricity load at different times within the sampling period.

[0007] Based on the clustering results, the types of electricity loads in different time periods within the sampling period are determined, and the electricity resource information matching the types of electricity loads in different time periods within the sampling period is determined. Based on the electricity resource information in different time periods within the sampling period and the load data of the sampling points within the sampling period, capacity analysis is performed on different time periods within the sampling period to determine the charging and discharging capacity in different time periods within the sampling period.

[0008] Based on the power consumption information at different times within the sampling period, determine the number of charge-discharge cycles within the sampling period and the charge-discharge cycle interval that matches the number of charge-discharge cycles;

[0009] Based on the charge and discharge capacity at different times within the sampling period and the number of charge and discharge cycles within the sampling period, the feasible capacity in the charge and discharge cycle interval within the sampling period is calculated.

[0010] Based on the feasible capacity in the charge-discharge cycle interval within the calculated sampling period, multiple resource information corresponding to multiple feasible capacities are calculated, and the feasible capacity corresponding to the largest resource information is selected from the multiple resource information as the set capacity of the energy storage device.

[0011] Secondly, embodiments of the present invention also provide a capacity-regulating device for energy storage equipment, the device comprising:

[0012] The acquisition module acquires the operating parameters and operating cost parameters of the energy storage device, and determines the sampling points within the sampling period based on the operating parameters of the energy storage device; wherein, the sampling points include: the correspondence between sampling time and load data;

[0013] The clustering module clusters the sampling points within the sampling period to obtain clustering results; wherein, the clustering results are used to indicate the power load type at different times within the sampling period, and to determine the power load type at different times within the sampling period.

[0014] The feature analysis module determines the power load type for different time periods within the sampling period based on the clustering results, obtains power resource information matching the power load type for different time periods within the sampling period, and performs capacity analysis on different time periods within the sampling period based on the power resource information for different time periods within the sampling period and the load data of the sampling points within the sampling period, to determine the charging and discharging capacity for different time periods within the sampling period.

[0015] The cycle module determines the number of charge-discharge cycles within the sampling period and the charge-discharge cycle interval that matches the number of charge-discharge cycles based on the power consumption information at different times within the sampling period.

[0016] The capacity calculation module calculates the feasible capacity in the charge-discharge cycle interval within the sampling period based on the charge-discharge capacity at different time periods within the sampling period and the number of charge-discharge cycles within the sampling period.

[0017] The determination module calculates multiple resource information corresponding to multiple feasible capacities based on the calculated feasible capacity in the charge-discharge cycle interval within the sampling period, and selects the feasible capacity corresponding to the largest resource information from the multiple resource information as the set capacity of the energy storage device.

[0018] Thirdly, embodiments of the present invention also provide an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected via the bus. When the computer program is executed by the processor, it implements the steps in the energy storage device calibration processing method as described in any one of the first aspects.

[0019] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the energy storage device calibration processing method as described in any one of the first aspects.

[0020] In the solutions provided by the first to fourth aspects of the present invention, sampling points within a sampling period are determined by pre-determining the sampling time. The types of electricity loads at different times are determined by clustering the sampling points within the sampling period. Then, the charging and discharging capacities at different times within the sampling period are determined by combining the electricity load types at different times within the sampling period. Finally, the feasible capacity range within the charging and discharging cycle interval is calculated. Each feasible capacity has multiple corresponding resource information. Appropriate resource information is selected, and the corresponding feasible capacity is determined as the set capacity of the energy storage device. Compared with related technologies where the capacity of energy storage devices is determined solely by human experience, this method, by obtaining sampling points and basic operating parameters, calculating the charging and discharging capacities at different times and the feasible capacity within the charging and discharging cycle interval, and then correlating the feasible capacity with its resource information, accurately determines the reasonable capacity of the energy storage device to be built. This not only greatly saves the project from needing to extend the construction period and expand capacity later due to inaccurate estimation of the energy storage device capacity, but also avoids increased costs and resource waste due to excessively large energy storage device capacity. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the accompanying drawings used in the embodiments of the present invention or the background art will be described below.

[0022] Figure 1 A schematic diagram of the energy storage device capacity setting process provided in an embodiment of the present invention is shown;

[0023] Figure 2 This diagram illustrates the connection of each module in the energy storage device constant-capacity processing apparatus provided in an embodiment of the present invention.

[0024] Figure 3 A schematic diagram of the electronic device for the energy storage device calibration processing method provided in an embodiment of the present invention is shown. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In order to enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Example 1

[0027] The execution entity of the energy storage device calibration processing method proposed in this embodiment is a server.

[0028] This embodiment provides a method for calibrating an energy storage device; see [link to relevant documentation]. Figure 1 The diagram shows a flow chart of a capacity stabilization process for an energy storage device. This capacity stabilization process includes:

[0029] Step 100: Obtain the operating parameters and operating cost parameters of the energy storage device, and determine the sampling points within the sampling period based on the operating parameters of the energy storage device; wherein, the sampling points include: the correspondence between sampling time and load data.

[0030] In step 100 above, the operating parameters include, but are not limited to: the current state of battery health (SOH), the depth of charge and discharge (DOD) of the battery, the charge and discharge efficiency (also known as coulombic efficiency), the number of operating days, and the battery rate.

[0031] Operating cost parameters include, but are not limited to: initial system construction resources, subsequent operation and maintenance costs, and battery recycling unit resource information. It should be noted that the sampling period is one day. The statistics of sampling points require first determining the number of points within a single day, then multiplying by the determined number of days to finally determine the total number of sampling points.

[0032] Step 101: Cluster the sampling points within the sampling period to obtain the clustering results; wherein, the clustering results are used to indicate the power load type at different times within the sampling period, and to determine the power load type at different times within the sampling period.

[0033] The clustering process is as follows: First, the rate of change parameter is obtained, and the X-axis coordinates corresponding to the rate of change parameter represent the number of cluster centers. Then, a weighted graph is constructed based on the KNN algorithm for the non-cluster centers (a weighted graph refers to a data structure expressed using linear algebra, not the illustration in the manual). Each row in the weighted graph represents the distance between a sampling point and each cluster center. The shortest path matrix is ​​derived from this and compared row by row to find the minimum value. Finally, based on the shortest path matrix and the clustering formula... Clustering is performed by assigning all non-cluster center samples to the cluster center of the path closest to the selected sampling point. Where C... k For clustering values, x i For any sampling point in the sampling period.

[0034] The specific clustering process is as follows: When determining the cluster center distance corresponding to a sampling point, a minimum sampling distance is set, and the process iterates incrementally from this minimum until the reverse nearest neighbor (RNN) set of each sampling point is not empty. In other words, an initial value for the sampling distance is set, and under this initial value, it is determined whether the RNN set of each sampling point is empty. If the RNN set of each sampling point is empty, the initial value of the sampling distance is incremented, and the RNN set of each sampling point is re-determined to be empty. This process continues until the RNN set of each sampling point is not empty; the value of the sampling distance in this iteration is then the sampling distance corresponding to each sampling point.

[0035] In step 101 above, the local density and minimum distance corresponding to each sampling point are calculated using the following formulas, including:

[0036] Step A: Obtain the local density:

[0037]

[0038] Where, ρ i The local density corresponding to sampling point xi; KNN xi Let d be the set of the K nearest sampling points to sampling point xi; j∈[1,K]; ij d is the Euclidean distance between sampling points xi and xj; c To truncate the distance, the largest 2% of all sampled distances are typically used.

[0039] Step B: Find the minimum distance:

[0040]

[0041] Where, δ i ρ is the minimum distance corresponding to the sampling point xi; k For a density sequence; for sampling points that are not at the highest density: minimum distance δ i By calculating sampling point X i And any other sampling point X with a density higher than that point j The minimum distance between them; and for the sampling points with the highest density: the minimum distance δ i Choose the maximum distance from other sampling points.

[0042] Step C: After determining the local density and minimum distance corresponding to each sampling point, the number of clusters and cluster centers in the clustering process can be determined based on the local density and minimum distance corresponding to each sampling point, including:

[0043] The decision value is calculated based on the local density and minimum distance corresponding to each sampling point. The decision value is the product of the local density and the minimum distance, i.e., γ. i =ρ i *δ i .

[0044] The calculated decision value γ i After sorting from largest to smallest, calculate each decision value γ. i scalar slope i The calculation formula is as follows:

[0045]

[0046] Where, γ max and γ min These are the maximum and minimum values ​​of the scalar, γ. i and γ i+1 These are the i-th scalar value and the (i+1)-th scalar value, respectively.

[0047] Step D: Calculate the classification parameters θ using scalars and preset weight values. i The specific formula is as follows:

[0048] θi i = (α-i)*slope i ;

[0049] Where α is the weighting coefficient and i is the index of the scalar value arranged in descending order.

[0050] The sort number corresponding to the largest classification parameter is the final number of cluster centers, and the cluster center is the descending decision value γ. i Sampling points for the number of cluster centers in the middle.

[0051] Finally, clustering is performed on multiple sampling points in the sampling space based on the number of clusters and cluster centers to obtain various clustering types, including: constructing a weighted graph G based on the k-nearest neighbor method based on the number of clusters and cluster centers;

[0052] G = (V, E, W);

[0053] Where V is the sampling node, E is the edge, and W is the weight.

[0054] Based on the weighted graph, multiple non-cluster-center sampling points in the sampling space are assigned to the nearest cluster center. In other words, the shortest path matrix D is determined in the weighted graph G, and based on the shortest path matrix, all non-cluster-center sampling points are assigned to the cluster center with the closest path to them. This completes the clustering processing of historical load data.

[0055] Step 102: Based on the electricity load types determined by the clustering results for different time periods within the sampling period, obtain the electricity resource information matching the electricity load types for different time periods within the sampling period, and perform capacity analysis on different time periods within the sampling period based on the electricity resource information for different time periods within the sampling period and the load data of the sampling points within the sampling period to determine the charging and discharging capacity for different time periods within the sampling period.

[0056] In step 102 above, the types of electrical load include: peak load, peak load, average load, and valley load.

[0057] To determine the charge / discharge capacity at different times within the sampling period, the following steps (1) to (3) can be performed:

[0058] (1) Based on the clustering results, determine the sampling time sets corresponding to the load peak, load spike and load trough within the sampling period, and then determine the power load type for different time periods within the sampling period based on the clustering results;

[0059] (2) Based on the power consumption information corresponding to the load peak, load peak and load valley respectively, the power consumption information matched by the sampling time set corresponding to the load peak, load peak and load valley respectively is obtained;

[0060] (3) Obtain the discharge threshold of the energy storage device, and perform capacity analysis on the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period using the following formula to determine the charge and discharge capacity of the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period:

[0061]

[0062]

[0063] Among them, S c Indicates charging capacity; S d Indicates discharge capacity; P i L This represents the load data corresponding to sampling time i in the sampling time set; T∈{T v T s T p};T v T represents the set of sampling times corresponding to the load trough;s T represents the set of sampling times corresponding to the peak load values; p This represents the set of sampling times corresponding to the load peak; MD represents the discharge threshold of the energy storage device.

[0064] In step (2) above, the correspondence between load peak, load average, load peak and load valley and different power resource information is stored in the server in advance.

[0065] In step (3) above, the discharge threshold of the energy storage device is pre-stored in the server.

[0066]

[0067] Step 103: Based on the power consumption information at different times within the sampling period, determine the number of charge-discharge cycles within the sampling period and the charge-discharge cycle interval that matches the number of charge-discharge cycles.

[0068] For example, step 103 will be explained in detail with reference to the following specific example.

[0069] If the power load types corresponding to the sampling time set in a sampling period are as follows: T v1 T f1 T p1 T v2 T p2 T f2 T v3 Among them, T v1 The load trough value of the first cycle interval, T f1 The average load value of the first cycle interval, T p1 The peak load of the first cycle interval, T v2 The load trough value of the second cycle interval, T p2 For the peak load of the second cycle interval, T f2 The average load value of the second cycle interval, T v3 This represents the load trough value of the third cycle interval.

[0070] The number of charge / discharge cycles within the sampling period and the charge / discharge cycle interval matching the number of charge / discharge cycles are determined to be 3, respectively:

[0071] Charge / discharge cycle interval 1: T v1 T f1 T p1 ;

[0072] Charge / discharge cycle interval 2: T v2 T p2 T f2 ;

[0073] Charge / discharge cycle interval 3: T v3 .

[0074] Dividing the charge / discharge cycle intervals maximizes peak-shaving and valley-filling of energy. The first cycle, represented by cycle interval 1, consists of the sampling time sets for load valley values, load average values, and load peak values. Discharge occurs within the peak load sampling time set, while charging / discharging within the load average sampling time set can be determined automatically based on the capacity of the energy storage device; if the device has a large capacity, it can be charged. The process of discharging from the load peak and charging from the load average value allows for the realization of resource information difference benefits. Determining the maximum number of cycle intervals in each sampling period ensures full utilization of battery capacity.

[0075] Step 104: Based on the charge and discharge capacity at different times within the sampling period and the number of charge and discharge cycles within the sampling period, calculate the feasible capacity range for different times within the sampling period.

[0076] In order to calculate the feasible capacity range for different time periods within the sampling period, step 104 above can be performed by steps (1) to (3):

[0077] The feasible capacity range for different time periods within the sampling period is calculated based on the charge / discharge capacity at different times within the sampling period and the number of charge / discharge cycles within the sampling period, including:

[0078] (1) Obtain the charge and discharge capacity in the sampling time set corresponding to the load peak value, load spike value, load trough value and / or load flat value in each charge and discharge cycle interval;

[0079] (2) The minimum value of the charging and discharging capacity in the sampling time set corresponding to the load peak, load peak, load valley and / or load flat value in each charging and discharging cycle interval is determined as the feasible capacity in each charging and discharging cycle interval, and the power load type corresponding to the feasible capacity in each charging and discharging cycle interval is obtained.

[0080] (3) Based on the type of electrical load corresponding to the feasible capacity in each charge-discharge cycle interval, determine the range of feasible capacity corresponding to each type of electrical load in the sampling period, and then calculate the range of feasible capacity in different time periods in the sampling period.

[0081] In step (2) above,

[0082] E n =min(S) c ,S d ), n∈1...N;

[0083]

[0084]

[0085] in, α represents the feasible capacity range for each clustered electrical load type. n This represents the weight for each type of electrical load. For example, the weights are pre-stored on a server or system.

[0086] The following example combines the cyclic interval N in step 103 with the formula in step (3) above, and provides an illustration:

[0087] In step 103, the charge-discharge cycle interval was given as 3. Therefore, the 3 charge-discharge cycle intervals are substituted sequentially into formula E. n =min(S) c ,S d From the given information, we obtain three results: E1, E2, and E3. We then select the maximum and minimum values ​​from E1, E2, and E3; the maximum value is max(E). n The minimum value is min(E). n ).

[0088] In step (3) above,

[0089] E n =min(S) c ,S d ), n∈1...N;

[0090] E BESS =α1E1+α2E2+Step 105: Based on the feasible capacity range of different time periods within the calculated sampling period, calculate multiple resource information corresponding to multiple feasible capacities, and select the feasible capacity corresponding to the largest resource information from the multiple resource information as the set capacity of the energy storage device.

[0091] In step 105 above, multiple resource information corresponding to multiple feasible capacities is calculated using the following formula:

[0092]

[0093] Where n represents the planned operating cycle after the energy storage device is built, and C t C represents the net cash value in year t. t In this context, t represents a year within the planned operating period n, and the value of t must be less than or equal to the planned operating period n. IRR represents resource information, and C0 represents the initial investment cost.

[0094] In summary, this invention provides a method for determining the capacity of an energy storage device. It determines the total number of sampling points by pre-determining the sampling time, clusters the total number of sampling points to determine the types of electricity loads at different times, combines the sampling period with the electricity load type to determine the charging and discharging capacity at different times, and then calculates the feasible capacity within the charging and discharging cycle interval. Each feasible capacity has corresponding resource information. Appropriate resource information is selected, and the corresponding feasible capacity is determined as the set capacity of the energy storage device. Compared to related technologies where the construction of energy storage devices relies solely on human experience to determine the device capacity, this method, by obtaining sampling points and basic operating parameters, calculates the charging and discharging capacity at different times and the feasible capacity within the charging and discharging cycle interval. By matching the feasible capacity with its corresponding resource information, the reasonable capacity of the energy storage device to be built can be intuitively viewed. This not only greatly saves time and avoids the need for later capacity expansion due to inaccurate estimation of the energy storage device capacity, but also avoids increased costs and resource waste caused by constructing excessively large energy storage devices.

[0095] Example 2

[0096] See Figure 2 The diagram shown illustrates the connection of various modules in the energy storage device capacity control processing apparatus. This embodiment discloses an energy storage device capacity control processing apparatus, which includes:

[0097] The acquisition module 10 acquires the operating parameters and operating cost parameters of the energy storage device, and determines the sampling points within the sampling period based on the operating parameters of the energy storage device; wherein, the sampling points include the correspondence between sampling time and load data.

[0098] Clustering module 20 clusters the sampling points within the sampling period to obtain clustering results; wherein, the clustering results are used to indicate the power load type at different times within the sampling period and to determine the power load type at different times within the sampling period.

[0099] The characteristic analysis module 30 obtains the power load type matching the power load type in different time periods within the sampling period based on the clustering results, and performs capacity analysis on different time periods within the sampling period based on the power resource information and the load data of the sampling points within the sampling period to determine the charging and discharging capacity in different time periods within the sampling period.

[0100] The loop module 40 determines the number of charge-discharge cycles within the sampling period and the charge-discharge cycle interval that matches the number of charge-discharge cycles based on the power consumption information at different times within the sampling period.

[0101] The capacity calculation module 50 calculates the feasible capacity range for different time periods within the sampling period based on the charge and discharge capacity at different times within the sampling period and the number of charge and discharge cycles within the sampling period.

[0102] The determination module 60 calculates multiple resource information corresponding to multiple feasible capacities based on the calculated feasible capacity range for different time periods within the sampling period, and selects the feasible capacity corresponding to the largest resource information from the multiple resource information as the set capacity of the energy storage device.

[0103] Furthermore, the types of electrical loads include: peak load, peak load, and valley load.

[0104] The types of electrical loads at different times during the sampling period include:

[0105] The set of sampling times corresponding to the load peak, load spike, and load trough within the sampling period.

[0106] Based on the characteristic analysis module 30, the charge / discharge capacity at different time periods within the sampling period is determined, including:

[0107] Based on the clustering results, the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period are determined, thereby determining the electricity load type for different time periods within the sampling period based on the clustering results.

[0108] Based on the electricity resource information corresponding to the load peak, load spike, and load trough, the electricity resource information matched by the sampling time sets corresponding to the load peak, load spike, and load trough is obtained.

[0109] The discharge threshold of the energy storage device is obtained, and the capacity of the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period is analyzed using the following formula to determine the charge and discharge capacity of the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period:

[0110]

[0111]

[0112] Among them, S c Indicates charging capacity; S d Indicates discharge capacity; P i L This represents the load data corresponding to sampling time i in the sampling time set; T∈{T v T s T p};T v T represents the set of sampling times corresponding to the load trough;s T represents the set of sampling times corresponding to the peak load values; p This represents the set of sampling times corresponding to the load peak; MD represents the discharge threshold of the energy storage device.

[0113] Furthermore, the type of electrical load also includes: load parity.

[0114] The clustering module also includes the types of electricity loads at different times within the sampling period, as well as:

[0115] The set of sampling times corresponding to the average load value within the sampling period.

[0116] The capacity calculation module includes:

[0117] Obtain the charge and discharge capacity from the sampling time set corresponding to the load peak, load spike, load trough and / or load flat value in each charge and discharge cycle interval.

[0118] The minimum value of the charging and discharging capacity in the sampling time set corresponding to the load peak, load spike, load trough and / or load flat value in each charging and discharging cycle interval is determined as the feasible capacity in each charging and discharging cycle interval, and the power load type corresponding to the feasible capacity in each charging and discharging cycle interval is obtained.

[0119] Based on the power load type corresponding to the feasible capacity within each charge-discharge cycle interval, the feasible capacity range corresponding to each power load type within the sampling period is determined, thereby calculating the feasible capacity range for different time periods within the sampling period.

[0120] The step involves calculating multiple resource information coefficients corresponding to multiple feasible capacities based on the calculated feasible capacity range for different time periods within the sampling period, including:

[0121]

[0122] In summary, this invention provides a capacity determination device for energy storage equipment. It determines the total number of sampling points by pre-determining the sampling time, clusters the total number of sampling points to determine the types of electricity loads at different times, combines the sampling period with the electricity load type to determine the charging and discharging capacity at different times, and then calculates the feasible capacity within the charging and discharging cycle interval. Each feasible capacity has corresponding resource information. Appropriate resource information is selected, and the corresponding feasible capacity is determined as the set capacity of the energy storage equipment. Compared to related technologies where the construction of energy storage equipment relies solely on human experience to determine equipment capacity, this method, by obtaining sampling points and basic operating parameters, calculates the charging and discharging capacity at different times and the feasible capacity within the charging and discharging cycle interval. By matching the feasible capacity with its corresponding resource information, the reasonable capacity of the energy storage equipment to be built can be intuitively viewed. This not only greatly saves time and avoids the need for later capacity expansion due to inaccurate estimation of energy storage equipment capacity, but also avoids increased costs and resource waste caused by constructing excessively large energy storage equipment.

[0123] Example 3

[0124] See Figure 3 The diagram shows the structure of an electronic device for a capacity-limiting processing method for energy storage devices. The electronic device includes a bus 501, a processor 502, a transceiver 503, a bus interface 504, a memory 505, and a user interface 506.

[0125] In this embodiment of the invention, the electronic device further includes a computer program stored in a memory 505 and executable on a processor 502. When the computer program is executed by the processor 502, it implements the various processes of the above-described device calibration processing method embodiment 1.

[0126] Transceiver 503 is used to receive and send data under the control of processor 502.

[0127] In this embodiment of the invention, a bus architecture (represented by bus 501) is used. Bus 501 may include any number of interconnected buses and bridges. Bus 501 connects various circuits, including one or more processors represented by processor 502 and memory represented by memory 505.

[0128] Bus 501 represents one or more of several types of bus architectures, including memory buses and memory controllers, peripheral buses, Accelerated Graphics Port (AGP), processors, or local buses using any bus architecture from various bus architectures. As an example and not a limitation, such architectures include: Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MCA) buses, Enhanced ISA (EISA) buses, Video Electronics Standards Association (VESA) buses, and Peripheral Component Interconnect (PCI) buses.

[0129] Processor 502 can be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The processors mentioned above include: general-purpose processors, central processing units (CPUs), network processors (NPs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), programmable logic arrays (PLAs), microcontroller units (MCUs) or other programmable logic devices, discrete gates, transistor logic devices, and discrete hardware components. They can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. For example, the processor can be a single-core processor or a multi-core processor, and the processor can be integrated on a single chip or located on multiple different chips.

[0130] Processor 502 can be a microprocessor or any conventional processor. The method steps disclosed in the embodiments of the present invention can be directly executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in readable storage media known in the art, such as Random Access Memory (RAM), Flash Memory, Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), registers, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0131] Bus 501 can also connect various other circuits, such as peripheral devices, voltage regulators, or power management circuits. Bus interface 504 provides an interface between bus 501 and transceiver 503, all of which are well known in the art. Therefore, the embodiments of the present invention will not be described further.

[0132] Transceiver 503 can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. For example, transceiver 503 receives external data from other devices and transmits data processed by processor 502 to other devices. Depending on the nature of the computer system, a user interface 506 may also be provided, such as a touchscreen, physical keyboard, monitor, mouse, speaker, microphone, trackball, joystick, or stylus.

[0133] It should be understood that, in embodiments of the present invention, memory 505 may further include memory remotely configured relative to processor 502, and such remotely configured memory can be connected to a server via a network. One or more portions of the aforementioned network may be an ad hoc network, intranet, extranet, virtual private network (VPN), local area network (LAN), wireless local area network (WLAN), wide area network (WAN), wireless wide area network (WWAN), metropolitan area network (MAN), Internet, public switched telephone network (PSTN), ordinary old-style telephone service (POTS), cellular telephone network, wireless network, Wi-Fi network, and combinations of two or more of the aforementioned networks. For example, cellular telephone networks and wireless networks can be Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), WiMAX, General Packet Radio Service (GPRS), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), Advanced Long Term Evolution (LTE-A), Universal Mobile Telecommunications System (UMTS), Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC), Ultra Reliable Low Latency Communications (uRLLC), etc.

[0134] It should be understood that the memory 505 in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory. Non-volatile memory includes: read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0135] Volatile memory includes random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 505 of the electronic device described in this embodiment includes, but is not limited to, the above and any other suitable types of memory.

[0136] In this embodiment of the invention, the memory 505 stores the following elements of the operating system 5051 and the application 5052: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0137] Specifically, the operating system 5051 includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 5052 includes various applications, such as a media player and a browser, used to implement various application functions. Programs implementing the methods of this embodiment can be included in the application program 5052. The application program 5052 includes applets, objects, components, logic, data structures, and other computer system executable instructions that perform specific tasks or implement specific abstract data types.

[0138] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the various processes of the above-described distributed cooperation method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0139] Computer-readable storage media include: permanent and non-permanent, removable and non-removable media, which are tangible devices capable of retaining and storing instructions for use by an instruction execution device. Computer-readable storage media include: electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, and any suitable combination thereof. Computer-readable storage media include: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape storage, magnetic disk storage or other magnetic storage devices, memory sticks, mechanical encoding devices (e.g., punched cards or raised structures in grooves on which instructions are recorded), or any other non-transfer medium that can be used to store information accessible by a computing device. As defined in the embodiments of the present invention, computer-readable storage media do not include temporary signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0140] In the embodiments provided by this invention, it should be understood that the disclosed apparatus, electronic devices, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or may be electrical, mechanical, or other forms of connection.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to solve the problems addressed by the embodiments of the present invention, depending on actual needs.

[0142] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (including: a personal computer, a server, a data center, or other network device) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media listed above that can store program code.

[0144] In the description of the embodiments of the present invention, those skilled in the art should understand that the embodiments of the present invention can be implemented as methods, apparatuses, electronic devices, and computer-readable storage media. Therefore, the embodiments of the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some embodiments, the embodiments of the present invention can also be implemented as a computer program product in one or more computer-readable storage media, the computer-readable storage media containing computer program code.

[0145] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any combination thereof. In embodiments of the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0146] The computer program code contained in the aforementioned computer-readable storage medium may be transmitted using any suitable medium, including wireless, wire, optical fiber, radio frequency (RF), or any suitable combination thereof.

[0147] Computer program code for performing the operations of the embodiments of the present invention can be written in assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or in one or more programming languages ​​or combinations thereof. The programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The computer program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer or an external computer via any type of network, including a local area network (LAN) or a wide area network (WAN).

[0148] The embodiments of the present invention describe the provided methods, apparatus, and electronic devices through flowcharts and / or block diagrams.

[0149] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine that, when executed by a computer or other programmable data processing apparatus, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0150] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to function in a particular manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction apparatus product that includes the functions / operations specified in the blocks of a flowchart and / or block diagram.

[0151] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable data processing apparatus provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0152] Example 4

[0153] A computer-readable storage medium is characterized in that it stores a computer program thereon, which, when executed by a processor, implements the steps of a capacity-limiting processing method for an energy storage device as described in Embodiment 1 above. For a detailed implementation, please refer to Embodiment 1, which will not be repeated here.

[0154] In summary, in this embodiment of the invention, the total number of sampling points is determined by pre-determining the sampling time. The types of electricity loads at different times are determined by clustering the total number of sampling points. The charging and discharging capacity at different times is determined by combining the sampling period with the electricity load type. Then, the feasible capacity within the charging and discharging cycle interval is calculated. Each feasible capacity has corresponding resource information. Appropriate resource information is selected, and the corresponding feasible capacity is determined as the set capacity of the energy storage device. Compared with related technologies where the construction of energy storage devices relies solely on human experience to determine the device capacity, this method, by obtaining sampling points and basic operating parameters, calculates the charging and discharging capacity at different times and the feasible capacity within the charging and discharging cycle interval. Then, by matching the feasible capacity with its corresponding resource information, the reasonable capacity of the energy storage device to be built can be intuitively viewed. This not only greatly saves time and avoids the need to extend the construction period and expand capacity later due to inaccurate estimation of the energy storage device capacity, but also avoids the situation where the constructed energy storage device capacity is too large, leading to increased costs and wasted resources.

[0155] In summary, the above are merely specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included within the protection scope of the present invention. Therefore, the protection scope of the embodiments of the present invention should be determined by the protection scope of the claims.

Claims

1. A method for calibrating an energy storage device, characterized in that, The method includes: The operating parameters and operating cost parameters of the energy storage device are obtained, and the sampling points within the sampling period are determined based on the operating parameters of the energy storage device; wherein, the sampling points include: the correspondence between sampling time and load data; Clustering is performed on the sampling points within the sampling period to obtain clustering results; wherein, the clustering results are used to indicate the power load type at different times within the sampling period, and to determine the power load type at different times within the sampling period; the power load type includes: load peak, load peak, and load valley; the power load type at different times within the sampling period includes: the sampling time sets corresponding to the load peak, load peak, and load valley within the sampling period, respectively; Based on the clustering results, the electricity load types for different time periods within the sampling period are determined. This yields matching electricity resource information for these different time periods. Furthermore, based on the electricity resource information and load data from sampling points within the sampling period, capacity analysis is performed on different time periods within the sampling period to determine the charging and discharging capacity for each period. This includes: Based on the clustering results, the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period are determined, thereby determining the electricity load type for different time periods within the sampling period based on the clustering results. Based on the electricity resource information corresponding to the load peak, load spike, and load trough, the electricity resource information matched by the sampling time sets corresponding to the load peak, load spike, and load trough is obtained. The discharge threshold of the energy storage device is obtained, and the capacity of the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period is analyzed using the following formula to determine the charge and discharge capacity of the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period: in, Indicates charging capacity; Indicates discharge capacity; Represents the sampling time in the set of sampling times. Corresponding load data; ∈{ , }; This represents the set of sampling times corresponding to the load trough. This represents the set of sampling times corresponding to peak load values; This represents the set of sampling times corresponding to the load peak. Indicates the discharge threshold of the energy storage device; Based on the power consumption information at different times within the sampling period, determine the number of charge-discharge cycles within the sampling period and the charge-discharge cycle interval that matches the number of charge-discharge cycles; Based on the charge and discharge capacity at different times within the sampling period and the number of charge and discharge cycles within the sampling period, the feasible capacity range at different times within the sampling period is calculated. Based on the calculated range of feasible capacity for different time periods within the sampling period, multiple resource information corresponding to multiple feasible capacities are calculated, and the feasible capacity corresponding to the largest resource information is selected from the multiple resource information as the set capacity of the energy storage device.

2. The energy storage device constant-capacity processing method according to claim 1, characterized in that, The electrical load type also includes: load parity; The types of electricity loads at different times within the sampling period also include: the set of sampling times corresponding to the average load value within the sampling period; The feasible capacity range for different time periods within the sampling period is calculated based on the charge / discharge capacity at different times within the sampling period and the number of charge / discharge cycles within the sampling period, including: Obtain the charge / discharge capacity from the sampling time set corresponding to the load peak, load spike, load trough and / or load average in each charge / discharge cycle interval; The minimum value of the charging and discharging capacity in the sampling time set corresponding to the load peak, load spike, load trough and / or load flat value in each charging and discharging cycle interval is determined as the feasible capacity in each charging and discharging cycle interval, and the power load type corresponding to the feasible capacity in each charging and discharging cycle interval is obtained. Based on the power load type corresponding to the feasible capacity within each charge-discharge cycle interval, the feasible capacity range corresponding to each power load type within the sampling period is determined, thereby calculating the feasible capacity range for different time periods within the sampling period.

3. The energy storage device constant-capacity processing method according to claim 2, characterized in that, The step involves calculating multiple resource information corresponding to multiple feasible capacities based on the calculated feasible capacity ranges for different time periods within the sampling period, including: Where n represents the planned operating cycle after the energy storage device is built. This represents the net resource information value in year t. This indicates the initial investment cost.

4. A constant-capacity processing device for energy storage equipment, characterized in that, The device includes: The acquisition module acquires the operating parameters and operating cost parameters of the energy storage device, and determines the sampling points within the sampling period based on the operating parameters of the energy storage device; wherein, the sampling points include: the correspondence between sampling time and load data; The clustering module clusters the sampling points within the sampling period to obtain clustering results; wherein, the clustering results are used to indicate the power load type at different times within the sampling period, and to determine the power load type at different times within the sampling period. The feature analysis module determines the power load type for different time periods within the sampling period based on the clustering results, obtains power resource information matching the power load type for different time periods within the sampling period, and performs capacity analysis on different time periods within the sampling period based on the power resource information for different time periods within the sampling period and the load data of the sampling points within the sampling period, to determine the charging and discharging capacity for different time periods within the sampling period. The cycle module determines the number of charge-discharge cycles within the sampling period and the charge-discharge cycle interval that matches the number of charge-discharge cycles based on the power consumption information at different times within the sampling period. The capacity calculation module calculates the feasible capacity range for different time periods within the sampling period based on the charge and discharge capacity at different times within the sampling period and the number of charge and discharge cycles within the sampling period. The determination module calculates multiple resource information corresponding to multiple feasible capacities based on the calculated feasible capacity range for different time periods within the sampling period, and selects the feasible capacity corresponding to the largest resource information from the multiple resource information as the set capacity of the energy storage device. The types of electrical loads include: peak load, peak load, and valley load. The types of electricity loads at different times within the sampling period include: the sets of sampling times corresponding to the load peak, load spike, and load trough within the sampling period, respectively; According to the feature analysis module, it includes: Based on the clustering results, the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period are determined, thereby determining the electricity load type for different time periods within the sampling period based on the clustering results. Based on the electricity resource information corresponding to the load peak, load spike, and load trough, the electricity resource information matched by the sampling time sets corresponding to the load peak, load spike, and load trough is obtained. The discharge threshold of the energy storage device is obtained, and the capacity of the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period is analyzed using the following formula to determine the charge and discharge capacity of the sampling time sets corresponding to the load peak, load spike, and load trough within the sampling period: in, Indicates charging capacity; Indicates discharge capacity; Represents the sampling time in the set of sampling times. Corresponding load data; ∈{ , }; This represents the set of sampling times corresponding to the load trough. This represents the set of sampling times corresponding to peak load values; This represents the set of sampling times corresponding to the load peak. This indicates the discharge threshold of the energy storage device.

5. The energy storage equipment constant-capacity processing device according to claim 4, characterized in that, The electrical load type also includes: load parity; The types of electricity loads at different times within the sampling period also include: the set of sampling times corresponding to the average load value within the sampling period; The capacity calculation module includes: Obtain the charge / discharge capacity from the sampling time set corresponding to the load peak, load spike, load trough and / or load average in each charge / discharge cycle interval; The minimum value of the charging and discharging capacity in the sampling time set corresponding to the load peak, load spike, load trough and / or load flat value in each charging and discharging cycle interval is determined as the feasible capacity in each charging and discharging cycle interval, and the power load type corresponding to the feasible capacity in each charging and discharging cycle interval is obtained. Based on the power load type corresponding to the feasible capacity within each charge-discharge cycle interval, the feasible capacity range corresponding to each power load type within the sampling period is determined, thereby calculating the feasible capacity range for different time periods within the sampling period.

6. The energy storage equipment constant-capacity processing device according to claim 5, characterized in that, The step involves calculating multiple resource information corresponding to multiple feasible capacities based on the calculated feasible capacity ranges for different time periods within the sampling period, including: Where n represents the planned operating cycle after the energy storage device is built. This represents the net resource information value in year t. This indicates the initial investment cost.

7. An electronic device, characterized in that, The device includes a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected via the bus. When the computer program is executed by the processor, it implements the steps in the energy storage device calibration processing method as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the steps in the energy storage device calibration processing method as described in any one of claims 1 to 3.

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