Energy storage capacity support verification method, device, equipment, medium and product for capacity compensation

Abstract

The invention discloses an energy storage capacity support verification method, device and equipment for capacity compensation, a medium and a product, and relates to the field of capacity compensation. The method comprises the following steps: acquiring information data; the information data comprises declaration parameters of the energy storage power station in a peak load period in dispatching operation; according to the information data, based on an energy storage capacity support verification model, capacity support verification processing is carried out to determine capacity compensation; the capacity compensation is used for maintaining the capacity adequacy of the power system; the energy storage capacity support verification model is determined based on an entropy weight method according to declaration parameters of the historical peak load period of the energy storage power station. According to the invention, the method can achieve the verification of the energy storage capacity supporting capability, so as to determine the capacity compensation, and enables the capacity compensation mode to be fair and accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of capacity compensation, in particular to a method and device for capacity compensation of energy storage capacity support, equipment, medium and product. BACKGROUND

[0002] The mechanism of capacity compensation guides the capacity construction of power generation resources through additional compensation, and guarantees the capacity adequacy of the power system. The accuracy and fairness of the capacity support ability of power generation resources will directly affect the effectiveness of the capacity compensation mechanism. As a capacity-limited resource, the accurate determination of the capacity support ability of new energy storage has always been a difficult problem, and a scientific method is urgently needed to evaluate its capacity support ability and guide it to play a capacity support role. The main factors affecting the capacity support ability of new energy storage include discharge power, discharge duration and dispatching strategy. Since the charging and discharging power, charging and discharging duration and SOC state of new energy storage will be declared in dispatching operation, the declared data of new energy storage in dispatching operation can be used to take the load-carrying capacity of new energy storage in peak load period as the evaluation index to determine the capacity support ability of new energy storage, and to provide a basis for the capacity compensation of new energy storage.

[0003] The current method for determining the capacity support capability of new energy storage mainly includes: (1) historical performance method: the capacity support capability of new energy storage is approximated by its historical performance during peak load period, which has the advantages of combining historical data analysis and simple calculation, but the future load and other conditions may change greatly, which may be quite different from the actual capacity support capability of new energy storage. (2) equivalent conversion method: the capacity of new energy storage is converted according to its actual discharge duration, for example, the capacity coefficient of new energy storage with energy capacity of 4MW·h and power capacity of 1MW is 100% when it can discharge continuously for 4h, and the capacity coefficient of new energy storage with energy capacity of 2MW·h and power capacity of 1MW is 50% when it can discharge continuously for 2h. Or according to the longest duration, the capacity conversion coefficient is determined, when the discharge power is lower than 1000MW, the capacity coefficients of new energy storage with the longest duration of 2, 4, 6 and 8 are 45%, 90%, 100% and 100% respectively; when the discharge power is higher than 1000MW, the capacity coefficients of new energy storage with the longest duration of 2, 4, 6 and 8 are 37.5%, 75%, 90% and 100% respectively. The advantages are simple calculation and consideration of the influence of discharge duration on the capacity support capability of new energy storage, and the disadvantages are that the interaction between different resources increases gradually, the influence of charging and discharging strategy on the capacity support capability of new energy storage cannot be described, and the actual capacity support capability of new energy storage may be quite different from the capacity support capability determined by considering the discharge duration alone. (3) credible capacity simulation method: the capacity support capability of new energy storage is determined by the effective load carrying capacity, if the expected system load loss is A under the basic scenario, the system load loss expectation becomes B when the installed capacity of the power resource is increased to X based on the scenario, and the system load loss expectation returns to A when the load is increased by Y. The effective load carrying capacity of the power resource is Y / X. This method determines the capacity support capability of new energy storage by simulation, which has the advantages of considering multiple factors affecting the capacity support capability and measuring the capacity support capability of different power resources by a unified standard. The disadvantages are that the simulation requires high accuracy of load curve shape and distribution, power supply structure and new energy storage configuration data, and there is a gap between the actual capacity support capability of new energy storage and the capacity support capability determined by simulation, and the method is not suitable for capacity compensation mechanism through prediction. SUMMARY

[0004] The purpose of the present application is to provide a method, device, equipment, medium and product for capacity compensation of energy storage capacity support, which can determine the capacity support capability of energy storage to determine capacity compensation, so that the capacity compensation is fair and accurate.

[0005] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0006] In a first aspect, the application provides a method for capacity compensation of energy storage capacity support, comprising:

[0007] obtaining information data; the information data comprises a declared parameter of the energy storage power station in a peak load period in dispatch operation;

[0008] based on the information data, performing capacity support determination processing based on an energy storage capacity support determination model to determine capacity compensation; the capacity compensation is used to maintain the capacity adequacy of the power system; the energy storage capacity support determination model is determined based on the entropy weight method according to the declared parameter of the historical peak load period of the energy storage power station.

[0009] Optionally, the method for determining the energy storage capacity support determination model comprises:

[0010] obtaining historical information data; the historical information data comprises a declared parameter of a historical peak load period of the energy storage power station;

[0011] based on the historical information data, determining the discharge capacity in the peak load period based on an optimization model; the optimization model is a physical simulation model constructed based on a preset security constrained unit commitment and a preset security constrained economic dispatch;

[0012] adopting a set deduction proportion coefficient to perform deduction processing on the historical information data to obtain deducted information data;

[0013] based on the optimization model and the deducted information data, determining the deducted discharge capacity in the peak load period;

[0014] adopting the entropy weight method to determine a weight according to the discharge capacity in the peak load period and the deducted discharge capacity in the peak load period;

[0015] determining the energy storage capacity support determination model according to the weight.

[0016] Optionally, the declared parameter comprises a storage charge-discharge price difference, a maximum charge power, a maximum discharge power, a maximum allowed power, a minimum allowed power, and a remaining power; the storage charge-discharge price difference is determined according to a storage discharge price coefficient and a storage charge price coefficient.

[0017] Optionally, the expression corresponding to the energy storage capacity support determination model is:

[0018]

[0019] wherein, is the expression corresponding to the energy storage capacity support determination model; w1 is a first weight; is the storage discharge price coefficient; is the storage charge price coefficient; w2 is a second weight; w1 is a first weight, w2 is a second weight, w3 is a third weight, w4 is a fourth weight, w5 is a fifth weight, w6 is a sixth weight, and w7 is a seventh weight. w1 is a first weight, w2 is a second weight, w3 is a third weight, w4 is a fourth weight, w5 is a fifth weight, w6 is a sixth weight, and w7 is a seventh weight. w1 is a first weight, w2 is a second weight, w3 is a third weight, w4 is a fourth weight, w5 is a fifth weight, w6 is a sixth weight, and w7 is a seventh weight. w1 is a first weight, w2 is a second weight, w3 is a third weight, w4 is a fourth weight, w5 is a fifth weight, w6 is a sixth weight, and w7 is a seventh weight. w1 is a first weight, w2 is a second weight, w3 is a third weight, w4 is a fourth weight, w5 is a fifth weight, w6 is a sixth weight, and w7 is a seventh weight.

[0020] Optionally, the entropy weight method is adopted to determine the weight according to the peak load period discharge quantity and the deducted peak load period discharge quantity, and specifically includes the following steps.

[0021] According to the peak load period discharge quantity and the deducted peak load period discharge quantity, the peak load period load change percentage is determined, and the index value is determined.

[0022] According to the index value, an index matrix is constructed.

[0023] The index matrix is normalized to obtain a normalized index matrix.

[0024] Based on the normalized index matrix, the probability of the occurrence of the index corresponding to different declaration parameters in the historical information data is determined, and the information entropy is determined according to the probability.

[0025] The weight is determined according to the information entropy.

[0026] Optionally, the calculation formula of the weight is as follows:

[0027]

[0028] wherein, w i is the weight corresponding to the i th declaration parameter; ξ i is the information entropy corresponding to the i th declaration parameter; and m is the number of declaration parameters.

[0029] In a second aspect, the present application provides a device for capacity compensation of energy storage capacity support determination, comprising:

[0030] An information data acquisition module is configured to acquire information data, wherein the information data includes declaration parameters of an energy storage power station in a peak load period in dispatching operation.

[0031] A determination module is configured to perform capacity support determination processing based on an energy storage capacity support determination model to determine capacity compensation according to the information data, wherein the capacity compensation is used to maintain the capacity adequacy of a power system, and the energy storage capacity support determination model is determined based on the historical peak load period declaration parameters of the energy storage power station according to the entropy weight method.

[0032] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for capacity compensation of energy storage capacity support rating described above.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the method for capacity compensation of energy storage capacity support rating described above.

[0034] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method for capacity compensation of energy storage capacity support rating described above.

[0035] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0036] The present application provides a method, device, equipment, medium and product for capacity compensation of energy storage capacity support rating. According to the obtained information data, capacity support rating processing is performed based on an energy storage capacity support rating model to determine capacity compensation. The capacity compensation is used to maintain the capacity adequacy of the power system. The energy storage capacity support rating model is determined based on the entropy weight method according to the declaration parameters of the historical peak load period of the energy storage power station. The present application aims at the new energy storage capacity support capability rating problem under the capacity compensation mechanism. According to the peak load period load carrying capacity of the energy storage power station in the dispatching operation, the weight of the contribution of the declaration parameter to the capacity support capability is determined based on the entropy weight method, and then the relationship model between the new energy storage declaration parameter and the capacity support capability, i.e. the energy storage capacity support rating model, is determined, so as to realize scientific, accurate and fair rating of the capacity support capability of the new energy storage. The capacity support capability of the energy storage can be rated to determine the capacity compensation, so that the capacity compensation is fair and accurate. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments or the related art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0038] Figure 1 The flowchart of the method for capacity compensation of energy storage capacity support rating;

[0039] Figure 2 The technical concept flowchart for energy storage capacity support rating in practical application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0041] The present application aims at the capacity support ability verification problem faced by the current new energy storage capacity compensation, and proposes a new energy storage capacity support ability verification method suitable for the capacity compensation mechanism, aiming at reflecting the actual capacity support ability of the new energy storage, and forming different capacity support ability verification index weights based on the entropy weight method according to the historical performance of the new energy storage in the peak load period.

[0042] The present application is a new energy storage capacity support ability verification and evaluation suitable for capacity compensation, taking the effective load carrying capacity of the new energy storage in the peak load period in dispatching operation as the evaluation index, which can reflect the capacity support ability of the energy storage. At the same time, the historical performance of the new energy storage power station in the peak load period is used as the weight coefficient, which can take into account the influence of the installed capacity, discharge power, discharge duration and dispatching mode of the energy storage power station on the capacity support ability of the energy storage. Through actual dispatching operation, the capacity support ability is evaluated by the load carrying capacity of the energy storage, which can more simply and accurately verify the capacity support ability of the new energy storage compared with the simulation method.

[0043] Based on the characteristics of the capacity compensation mechanism, a relationship model of the energy storage declaration parameters and the capacity support ability in the dispatching operation is established based on the entropy weight method, which can evaluate the capacity support ability by using the energy storage declaration parameters. Based on the present application, the new energy storage operator and the dispatching operation agency can fairly and transparently carry out capacity compensation on the basis of considering the capacity support ability of the energy storage. Since the energy storage declaration parameters are directly related to the operation, the operator can calculate and estimate the capacity compensation obtained by the self-parameter declaration, avoid excessive or insufficient capacity compensation, and also can promote the transition of the capacity compensation mechanism to a more fair and transparent capacity mechanism, and ensure the capacity adequacy of the power system.

[0044] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0045] In one exemplary embodiment, as shown in Figure 1 a new energy storage capacity support verification method for capacity compensation is provided, which comprises the following steps.

[0046] Step 100: Obtain information data. The information data includes the declaration parameters of the energy storage power station in the peak load period in the dispatching operation.

[0047] Step 200: According to the information data, capacity support rating processing is performed based on the energy storage capacity support rating model to determine the capacity compensation. The capacity compensation is used to maintain the capacity adequacy of the power system; the energy storage capacity support rating model is determined based on the entropy weight method according to the reported parameters of the historical peak load period of the energy storage power station.

[0048] In one embodiment, the method for determining the energy storage capacity support rating model comprises:

[0049] Obtain historical information data; the historical information data includes the reported parameters of the historical peak load period of the energy storage power station.

[0050] Determine the discharge capacity of the peak load period based on the optimization model according to the historical information data; the optimization model is a physical simulation model constructed based on the preset security constrained unit commitment and the preset security constrained economic dispatch.

[0051] The historical information data is processed by using a set of deduction proportion coefficients to obtain the deducted information data.

[0052] Determine the deducted discharge capacity of the peak load period based on the optimization model and the deducted information data.

[0053] Determine the weight according to the discharge capacity of the peak load period and the deducted discharge capacity of the peak load period by using the entropy weight method; determine the energy storage capacity support rating model according to the weight.

[0054] The reported parameters include: the energy storage charge-discharge price difference, the maximum charge power, the maximum discharge power, the maximum allowed power, the minimum allowed power, and the remaining power; the energy storage charge-discharge price difference is determined according to the energy storage discharge price coefficient and the energy storage charge price coefficient.

[0055] The expression corresponding to the energy storage capacity support rating model is:

[0056]

[0057] wherein, is the expression corresponding to the energy storage capacity support rating model; w1 is the first weight; is the energy storage discharge price coefficient; is the energy storage charge price coefficient; w2 is the second weight; is the maximum charge power; w3 is the third weight; is the maximum discharge power; w4 is the fourth weight; is the maximum allowed power; w5 is the fifth weight; is the minimum allowed power; w6 is the sixth weight; is the remaining power.

[0058] As an optional implementation, the entropy weight method is adopted, and the weight is determined according to the peak period discharge amount and the deducted peak period discharge amount, and specifically includes:

[0059] According to the peak period discharge amount and the deducted peak period discharge amount, the peak period load carrying amount change percentage is determined, and the index value is determined; the index matrix is constructed according to the index value; the index matrix is normalized to obtain the normalized index matrix; based on the normalized index matrix, the probability of the occurrence of the index corresponding to different declaration parameters in the historical information data is determined, and the information entropy is determined according to the probability. The weight is determined according to the information entropy.

[0060] The calculation formula of the weight is:

[0061]

[0062] Wherein, w i is the weight corresponding to the i th declaration parameter; ξ i is the information entropy corresponding to the i th declaration parameter; and m is the number of declaration parameters.

[0063] The process corresponding to the general idea of the application proposal is shown in Figure 2 .

[0064] (1) Obtain the declaration parameters of the new energy storage power station in the dispatching operation: the price difference of energy storage charging and discharging Maximum charging power Maximum discharging power Maximum allowable power Minimum allowable power And the remaining power after the optimization period ends

[0065] (2) Run the optimization model corresponding to SCUC and SCED to obtain and calculate the initial new energy storage power station in all N peak period discharge amounts (load carrying amount).

[0066] Wherein, SCUC refers to security constrained unit commitment, and SCED refers to security constrained economic dispatch.

[0067] Security-Constrained Unit Commitment (SCUC) and Security-Constrained Economic Dispatch (SCED) are two key technologies in power system dispatch. SCUC focuses on determining the unit commitment plan that meets the power grid security constraints and reserve requirements, and the calculation period is relatively long, while the requirement for dispatch plan is relatively low, as long as the power grid security and system supply-demand balance are met. SCED is to compile a generation plan that can be used for actual execution of dispatch based on the determined unit commitment. Compared with SCUC, SCED pays more attention to the refinement of dispatch plan.

[0068]

[0069] wherein, is the discharge power of the new energy storage power station in the peak load period, is the load carrying capacity of the energy storage power station in the peak load period, is the discharge power of the new energy storage power station, T1 and T2 are the start and end times of the peak load period respectively. n is the serial number of the peak load period.

[0070] (3) Set a certain deduction proportion coefficient a (0 < a < 1) of the declared parameters, and correspondingly deduct the declared parameters of the energy storage power station to obtain the deducted declared parameters and re-run the SCUC and SCED optimization models according to the new declared parameters.

[0071] (4) The discharge capacity of the new energy storage in the peak load period after the declared parameters are deducted by a certain proportion is calculated (load carrying capacity):

[0072]

[0073] is the load carrying capacity of the energy storage power station in the peak load period after deduction.

[0074] (5) The weight of the influence of different declared parameters of the energy storage on the load carrying capacity is obtained based on the entropy weight method. First, the declared parameters of the new energy storage power station are scored according to the deducted load carrying capacity of the peak load period, and an evaluation index matrix V is constructed, wherein the value of the index v ij in the ith row and jth column of the index matrix should be 1, 2,..., 100, corresponding to the load carrying capacity change percentage 1% ~ 100% (rounded) of the new energy storage in the peak load period after the declared parameters are deducted by a certain proportion.

[0075] (6) The initial evaluation matrix is obtained as:

[0076]

[0077] wherein, v 1j ~v 6j Corresponding to the energy storage power station energy storage charge and discharge price difference, the maximum charging power, the maximum discharge power, the maximum allowed power, the minimum allowed power, the remaining power, the contribution score of the capacity support ability.

[0078] The index value u ij Is divided into benefit type index whose declaration parameter is the larger the better and evaluation type index whose declaration parameter is the smaller the better, and is normalized respectively:

[0079]

[0080] The evaluation matrix U after normalization is:

[0081]

[0082] (7) Calculate the appearance probability μ of the evaluation index of different declaration parameters ij And information entropy ξ i :

[0083]

[0084] Wherein, k is the normalization factor, if μ ij =0, define

[0085] Get the evaluation index parameter The contribution weight w1, w2,..., w6 of the new energy storage capacity support ability.

[0086]

[0087] (8) Get the relationship model of new energy storage declaration parameters and the capacity support ability contributed by its peak load period:

[0088]

[0089] Each time the new energy storage participates in dispatching operation, the new energy storage operator and the dispatching operation agency evaluate the capacity support ability according to the relationship model with a unified standard, which is convenient for the operation agency to calculate the capacity compensation that may be obtained in the future, and at the same time reflects the capacity support ability of the new energy storage, so that the capacity compensation mode is more fair and accurate.

[0090] The application proposes a new energy storage capacity support capacity evaluation method suitable for capacity compensation, taking the effective load carrying capacity of the new energy storage in the peak load period of dispatching operation as the evaluation index, which can reflect the capacity support capacity of the energy storage. At the same time, the performance of the historical peak load period of the new energy storage power station is used as the weight coefficient, which can take into account the influence of the installed capacity, discharge power, discharge duration and dispatching mode of the energy storage power station on the capacity support capacity of the energy storage. Through actual dispatching operation, the capacity support capacity is evaluated by the load carrying capacity of the energy storage, which can more simply and accurately determine the capacity support capacity of the new energy storage compared with the simulation method.

[0091] Combined with the characteristics of the capacity compensation mechanism, a relationship model of the energy storage in the dispatching operation reporting parameters and the capacity support capacity is established based on the entropy weight method, which can evaluate the capacity support capacity by using the energy storage reporting parameters. Based on the method mentioned in the application, the new energy storage operator and the dispatching operation agency can fairly and transparently compensate the capacity based on the capacity support capacity of the energy storage. Since the energy storage reporting parameters are directly related to the operation, the operator can calculate and estimate the capacity compensation obtained by the reporting parameters, avoid excessive or insufficient capacity compensation, and also promote the transition of the capacity compensation mechanism to a more fair and transparent capacity mechanism, and ensure the capacity adequacy of the power system.

[0092] Based on the same inventive concept, the embodiments of the application also provide a device for capacity compensation of energy storage capacity support for implementing the method for capacity compensation of energy storage capacity support. The implementation scheme of the device for solving the problem is similar to the implementation scheme described in the above method, so the specific limitations in one or more device embodiments for capacity compensation of energy storage capacity support provided below can be referred to the limitations of the method for capacity compensation of energy storage capacity support in the above, which will not be repeated here.

[0093] In an exemplary embodiment, a device for capacity compensation of energy storage capacity support is provided, comprising:

[0094] An information data acquisition module is configured to acquire information data, wherein the information data includes reporting parameters of the energy storage power station in the peak load period of dispatching operation.

[0095] A determination module is configured to determine capacity compensation by performing capacity support determination processing based on an energy storage capacity support determination model according to the information data, wherein the capacity compensation is used to maintain the capacity adequacy of the power system, and the energy storage capacity support determination model is determined based on the entropy weight method according to the reporting parameters of the historical peak load period of the energy storage power station.

[0096] In an example embodiment, a computer device, which can be a server or a terminal, is provided, and the computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is configured to be executed by the processor to implement a method for capacity compensation of energy storage capacity support.

[0097] Those skilled in the art can understand that the structure of the computer device is only part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those described above, or combine certain components, or have a different arrangement of components. In an example embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor is configured to execute the computer program to implement the steps in the above method embodiments.

[0098] In an example embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is configured to be executed by a processor to implement the steps in the above method embodiments.

[0099] In an example embodiment, a computer program product is provided, which includes a computer program. The computer program is configured to be executed by a processor to implement the steps in the above method embodiments.

[0100] In the present application, all actions of obtaining signals, information, or data are performed in compliance with the corresponding data protection regulations and policies of the country where the device is located, and with the authorization of the corresponding device owner. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use, and processing of the relevant data need to comply with relevant regulations.

[0101] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0102] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0103] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0104] The principles and implementation modes of the present application are described by applying specific examples in the present application. The above-mentioned embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for capacity compensation of an energy storage capacity support rating, characterized by, The energy storage capacity support rating method for capacity compensation comprises: obtaining information data; the information data comprises declared parameters of the energy storage power station in a peak load period in dispatch operation; based on the information data, performing capacity support rating processing based on an energy storage capacity support rating model to determine capacity compensation; the capacity compensation is used to maintain the capacity adequacy of the power system; the energy storage capacity support rating model is determined based on the entropy weight method according to the declared parameters of the historical peak load period of the energy storage power station.

2. The method for rating an energy storage capacity support according to claim 1, wherein, The method for determining the energy storage capacity support rating model comprises: obtaining historical information data; the historical information data comprises declared parameters of the historical peak load period of the energy storage power station; based on the historical information data, determining the discharge capacity in the peak load period based on an optimization model; the optimization model is a physical simulation model constructed based on a preset security constrained unit commitment and a preset security constrained economic dispatch; performing deduction processing on the historical information data by using a set deduction proportion coefficient to obtain deduced information data; based on the optimization model and the deduced information data, determining the deduced discharge capacity in the peak load period; determining the weight based on the entropy weight method according to the discharge capacity in the peak load period and the deduced discharge capacity in the peak load period; determining the energy storage capacity support rating model according to the weight.

3. The method for rating an energy storage capacity support core for capacity compensation according to claim 2, characterized in that, The declared parameters comprise a storage charge-discharge price difference, a maximum charge power, a maximum discharge power, a maximum allowed electric quantity, a minimum allowed electric quantity and a residual electric quantity; the storage charge-discharge price difference is determined according to a storage discharge price coefficient and a storage charge price coefficient.

4. The method for rating an energy storage capacity support according to claim 3, wherein, The expression corresponding to the energy storage capacity support rating model is: wherein, is an expression corresponding to a model for supporting energy storage capacity; w1 is a first weight; is a coefficient of energy storage discharge price; is a coefficient of energy storage charging price; w2 is a second weight; is a maximum charging power; w3 is a third weight; is a maximum discharging power; w4 is a fourth weight; is a maximum allowed electric quantity; w5 is a fifth weight; is a minimum allowed electric quantity; w6 is a sixth weight; is a residual electric quantity.

5. The method for rating an energy storage capacity support according to claim 2, wherein, determining the weight based on the entropy weight method according to the discharge capacity in the peak load period and the deduced discharge capacity in the peak load period, specifically comprising: determining the load change percentage in the peak load period according to the discharge capacity in the peak load period and the deduced discharge capacity in the peak load period, and determining an index value; constructing an index matrix according to the index value; performing normalization processing on the index matrix to obtain a normalized index matrix; based on the normalized index matrix, determining the probability of the index corresponding to different declared parameters in the historical information data, and determining the information entropy according to the probability; determining the weight according to the information entropy.

6. The method for rating an energy storage capacity support according to claim 5, wherein, The calculation formula of the weight is: wherein w i is the weight corresponding to the i-th declaration parameter; ξ i is the information entropy corresponding to the i-th declaration parameter; and m is the number of declaration parameters.

7. An energy storage capacity support rating device for capacity compensation, characterized by, The energy storage capacity support rating device for capacity compensation comprises: an information data obtaining module, configured to obtain information data; the information data comprises declared parameters of the energy storage power station in a peak load period in dispatch operation; a determining module, configured to determine capacity compensation based on the information data and based on an energy storage capacity support rating model; the capacity compensation is used to maintain the capacity adequacy of the power system; the energy storage capacity support rating model is determined based on the entropy weight method according to the declared parameters of the historical peak load period of the energy storage power station.

8. A computer device comprising: A memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the energy storage capacity support rating method for capacity compensation according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, which when executed by a processor, implements the method for capacity compensation of a storage capacity support rating according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program, which when executed by a processor, implements the method for capacity compensation of a storage capacity support rating according to any one of claims 1 to 6.

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