Distributed energy storage aggregation method and device for scheduling needs

Abstract

This application relates to the field of new power system optimization operation technology, and particularly to a distributed energy storage aggregation method and device oriented towards dispatch requirements. The method includes: analyzing the comprehensive dispatch cost of distributed energy storage based on an improved energy storage cost per kilowatt-hour index; determining the aggregation method of distributed energy storage according to target dispatch requirements, using balancing net load fluctuations as the dispatch target for distributed energy storage to divide the energy storage cluster capacity, and determining the energy storage capacity division boundaries; based on the energy storage capacity division boundaries and the comprehensive dispatch cost, grouping individual energy storage units with pre-set similar cost per kilowatt-hour conditions into the same cluster, and dividing the distributed energy storage into multiple energy storage clusters based on the boundary values ​​of the energy storage capacity division boundaries. This application can realize the aggregation of multiple energy storage units into large-scale energy storage clusters for dispatch, reducing the variable dimensionality and computational complexity of optimized dispatch, and providing good technical support for the collaborative optimization operation of multiple energy storage units in new power systems.

Description

Technical Field

[0001] This application relates to the field of new power system optimization operation technology, and in particular to a distributed energy storage aggregation method and device oriented towards dispatching needs. Background Technology

[0002] In related technologies, with the rapid development of new power systems, energy storage is moving towards large-scale, industrialized, and market-oriented development, and the coordinated and optimized operation of energy storage and new energy sources is becoming increasingly important. Energy storage resources in new power systems are diverse in type, widely distributed, and complex in characteristics. Due to limitations imposed by technology, economics, and geographical conditions, current new energy storage is mainly distributed, with energy storage devices geographically widespread, individual power and capacity limited, and operational characteristics varying. Simultaneously, the integration of large amounts of energy storage makes the dispatch and operation modes of new power systems increasingly complex. Numerous energy storage systems operate independently, lacking coordination and mutual support, potentially leading to problems such as disordered charging and discharging. Traditional control methods are insufficient for the unified coordination of large-scale energy storage.

[0003] However, in related technologies, traditional control methods are difficult to coordinate large amounts of energy storage in a unified manner. The regulation potential and operating efficiency of energy storage are low, making it difficult to play a greater role. The surge in the number of energy storage leads to an increase in variables to be optimized, resulting in excessively high computational dimensionality, making the solution complex and difficult. It is difficult to quickly respond to power fluctuations in the power system and cannot support the distributed energy storage control needs under complex and uncertain scenarios, which urgently needs to be addressed. Summary of the Invention

[0004] This application provides a distributed energy storage aggregation method and device for scheduling needs, in order to solve the problems in related technologies, such as the difficulty of unified coordination of a large amount of energy storage by traditional control methods, the low regulation potential and operating efficiency of energy storage, the inability to play a greater role, the increase in the number of energy storage leading to an increase in the number of variables to be optimized, resulting in excessively high computational dimensionality, complex and difficult solutions, difficulty in quickly responding to power fluctuations in the power system, and inability to support the distributed energy storage control needs under complex and uncertain scenarios.

[0005] The first aspect of this application provides a distributed energy storage aggregation method oriented towards scheduling needs, comprising the following steps: analyzing the comprehensive dispatch cost of distributed energy storage based on an improved energy storage cost per kilowatt-hour index; determining the aggregation method of the distributed energy storage according to the target scheduling needs, dividing the energy storage cluster capacity with balancing net load fluctuations as the scheduling target of the distributed energy storage, and determining the energy storage capacity division boundary of the distributed energy storage; based on the energy storage capacity division boundary and the comprehensive dispatch cost, grouping energy storage individuals with energy storage costs reaching a preset close condition into the same cluster, and dividing the distributed energy storage into multiple energy storage clusters based on the boundary values ​​of the energy storage capacity division boundary to obtain the final distributed energy storage aggregation result.

[0006] Optionally, in one embodiment of this application, determining the energy storage capacity boundary of the distributed energy storage includes: dividing the distributed energy storage based on the energy and power demand during the charging phase to obtain the energy storage clusters of the distributed energy storage; and sorting the energy storage clusters according to the capacity demand of the distributed energy storage in the target scenario to obtain the energy storage capacity boundary of the distributed energy storage.

[0007] Optionally, in one embodiment of this application, the energy storage clusters are sorted according to the capacity requirements of the distributed energy storage in the target scenario to obtain the energy storage capacity boundary of the distributed energy storage, including: obtaining the total number of the energy storage clusters; and merging the energy storage clusters in the same range when the total number of the energy storage clusters is greater than a preset threshold to obtain the energy storage capacity boundary of the distributed energy storage.

[0008] Optionally, in one embodiment of this application, the formula for calculating the comprehensive call cost is:

[0009]

[0010] Among them, C E C represents the unit capacity cost during energy storage construction. P Let represent the unit power cost, d represent the discharge duration (i.e., the ratio of energy storage capacity to rated power), n(t) represent the number of cycles per year, T represent the service life, M(t) represent the maintenance cost in year t, r represent the discount rate, DOD represent the equivalent rated cycle depth, and P represent the equivalent rated cycle depth. c P represents the unit charging cost. punish This represents the penalty cost corresponding to the loss of one unit of energy due to energy loss during the energy storage cycle, η. d η represents the discharge efficiency, and η represents the total cycle efficiency.

[0011] A second aspect of this application provides a distributed energy storage aggregation device oriented towards scheduling needs, comprising: an analysis module for analyzing the comprehensive dispatch cost of distributed energy storage based on an improved energy storage cost per kilowatt-hour index; a partitioning module for determining the aggregation method of the distributed energy storage according to the target scheduling needs, partitioning the energy storage cluster capacity with balancing net load fluctuations as the scheduling target of the distributed energy storage, and determining the energy storage capacity partitioning boundary of the distributed energy storage; and an aggregation module for grouping individual energy storage units with a pre-set proximity in cost per kilowatt-hour into the same cluster based on the energy storage capacity partitioning boundary and the comprehensive dispatch cost, and dividing the distributed energy storage into multiple energy storage clusters based on the boundary values ​​of the energy storage capacity partitioning boundary to obtain the final distributed energy storage aggregation result.

[0012] Optionally, in one embodiment of this application, the partitioning module includes: a partitioning unit, used to partition the distributed energy storage based on the energy and power requirements of the charging phase, to obtain the energy storage clusters of the distributed energy storage; and a processing unit, used to sort the energy storage clusters according to the capacity requirements of the distributed energy storage in the target scenario, so as to obtain the energy storage capacity partitioning boundary of the distributed energy storage.

[0013] Optionally, in one embodiment of this application, the processing unit includes: an acquisition subunit, used to acquire the total number of the energy storage clusters; and a merging subunit, used to merge the energy storage clusters within the same range when the total number of the energy storage clusters is greater than a preset threshold, so as to obtain the energy storage capacity division boundary of the distributed energy storage.

[0014] Optionally, in one embodiment of this application, the formula for calculating the comprehensive call cost is:

[0015]

[0016] Among them, C E C represents the unit capacity cost during energy storage construction. P Let represent the unit power cost, d represent the discharge duration (i.e., the ratio of energy storage capacity to rated power), n(t) represent the number of cycles per year, T represent the service life, M(t) represent the maintenance cost in year t, r represent the discount rate, DOD represent the equivalent rated cycle depth, and P represent the equivalent rated cycle depth. c P represents the unit charging cost. punish This represents the penalty cost corresponding to the loss of one unit of energy due to energy loss during the energy storage cycle, η. d η represents the discharge efficiency, and η represents the total cycle efficiency.

[0017] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the distributed energy storage aggregation method for scheduling needs as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described distributed energy storage aggregation method oriented towards scheduling requirements.

[0019] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the above-described distributed energy storage aggregation method oriented towards scheduling requirements.

[0020] This application's embodiments can analyze the comprehensive energy storage dispatch cost based on an improved energy storage levelized cost of electricity (LCOS) index, divide the energy storage cluster capacity according to the energy storage dispatch objectives, and finally divide distributed energy storage into several energy storage clusters based on the determined energy storage capacity division boundaries, thereby realizing the aggregation of distributed energy storage. This enables the aggregation of multiple energy storage sources into large-scale energy storage clusters for dispatch, optimizes the energy storage participation in dispatch, and thus supports the safe and economical operation of power grids with a high proportion of new energy sources, serving the efficient operation of new power systems. This solves the problems in related technologies, such as the difficulty of traditional control methods in coordinating large amounts of energy storage, the low regulation potential and operating efficiency of energy storage, the increase in the number of energy storage sources leading to an increase in variables to be optimized, resulting in excessively high computational dimensionality, complex and difficult solutions, difficulty in quickly responding to power system power fluctuations, and inability to support the distributed energy storage control needs under complex and uncertain scenarios.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0023] Figure 1 This is a flowchart of a distributed energy storage aggregation method for scheduling needs provided according to an embodiment of this application;

[0024] Figure 2 This is a schematic diagram of a distributed energy storage aggregation device oriented towards scheduling requirements according to an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0026] Figure label:

[0027] 10-Distributed energy storage aggregation device for scheduling needs: 100-Analysis module, 200-Division module and 300-Aggregation module; 301-Memory, 302-Processor and 303-Communication interface. Detailed Implementation

[0028] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0029] The following description, with reference to the accompanying drawings, describes a distributed energy storage aggregation method and apparatus oriented towards scheduling needs, based on embodiments of this application. Addressing the issues mentioned in the background art, traditional control methods struggle to coordinate large-scale energy storage, resulting in low regulation potential and operational efficiency, hindering the full potential of energy storage. The dramatic increase in the number of energy storage units leads to an increase in variables requiring optimization, resulting in excessively high computational dimensionality, complex and difficult solutions, and an inability to quickly respond to power system fluctuations, thus failing to support the distributed energy storage control needs under complex and uncertain scenarios. This application provides a distributed energy storage aggregation method oriented towards scheduling needs. In this method, the comprehensive energy storage dispatch cost can be analyzed based on an improved energy storage levelized cost of electricity (LCOS) index. The capacity of energy storage clusters is divided according to energy storage scheduling objectives. Finally, based on the determined energy storage capacity division boundaries, distributed energy storage is divided into several energy storage clusters, thereby achieving distributed energy storage aggregation. This enables the aggregation of multiple energy storage units into large-scale energy storage clusters for scheduling, optimizes the energy storage participation in scheduling, and supports the safe and economical operation of power grids with a high proportion of new energy sources, serving the efficient operation of new power systems. This solves the problems in related technologies, such as the difficulty of traditional control methods in coordinating large amounts of energy storage, the low regulation potential and operating efficiency of energy storage, the inability to play a greater role, the increase in the number of variables to be optimized due to the surge in the number of energy storage, the excessively high computational dimensionality, the complexity and difficulty in solving the problem, the inability to quickly respond to power fluctuations in the power system, and the inability to support the distributed energy storage control needs under complex and uncertain scenarios.

[0030] Specifically, Figure 1 This is a flowchart illustrating a distributed energy storage aggregation method for scheduling needs, provided as an embodiment of this application.

[0031] like Figure 1 As shown, this distributed energy storage aggregation method oriented towards scheduling needs includes the following steps:

[0032] In step S101, the comprehensive dispatch cost of distributed energy storage is analyzed based on the improved energy storage cost per kilowatt-hour index. The formula for calculating the comprehensive dispatch cost can be expressed as:

[0033]

[0034] Among them, C E C represents the unit capacity cost during energy storage construction. P Let represent the unit power cost, d represent the discharge duration (i.e., the ratio of energy storage capacity to rated power), n(t) represent the number of cycles per year, T represent the service life, M(t) represent the maintenance cost in year t, r represent the discount rate, DOD represent the equivalent rated cycle depth, and P represent the equivalent rated cycle depth. c P represents the unit charging cost. punishThis represents the penalty cost corresponding to the loss of one unit of energy due to energy loss during the energy storage cycle, η. d η represents the discharge efficiency, and η represents the total cycle efficiency.

[0035] In practical implementation, the operating cost of the power system is a key aspect of the dispatching process. Lower-cost energy storage should be prioritized for participation in charge-discharge cycles. By allowing low-cost energy storage to cycle more times per unit period, greater economic benefits can be achieved. Therefore, before aggregating distributed energy storage, the embodiments of this application can first analyze the charge-discharge costs of energy storage.

[0036] For example, for capacity-type energy storage, cost analysis can be conducted based on the levelized cost of storage (LCOS), which involves dividing the total cost of the energy storage over its entire lifecycle by the total energy it can process, thus allocating the total cost per unit of electricity. The total cost of energy storage primarily refers to, but is not limited to, construction investment costs, operating costs, and charging costs.

[0037] Specifically, the investment cost of energy storage mainly includes, but is not limited to, the cost of materials, civil engineering, and construction during the initial stage, which is a one-time investment. The operating cost mainly includes, but is not limited to, the operation and maintenance cost of energy storage, which is related to the installed capacity, power, and operating conditions of energy storage. The charging cost can be understood as the electricity required for energy storage charging multiplied by the corresponding price, which can be selected and adopted according to the electricity price at the actual location. Furthermore, considering the different situations when new energy is absorbed, if the power output of nearby new energy that cannot be directly supplied to the load is used for charging, the marginal cost can be considered as 0. If the power output of long-distance new energy is used for charging, its marginal cost can be the product of the electricity consumed during transmission and the local electricity price.

[0038] When improving upon the traditional LCOS in this application, the following formula can be used to comprehensively evaluate the levelized cost of energy storage under rated operating conditions:

[0039]

[0040] Among them, C E C represents the unit capacity cost during energy storage construction. P The value represents the unit power cost, d represents the discharge duration (the ratio of energy storage capacity to rated power), n(t) represents the number of cycles per year, T represents the service life, M(t) represents the maintenance cost in year t, r represents the discount rate, and DOD represents the equivalent rated depth of cycle. The DOD of battery energy storage is generally no higher than 90%, while the equivalent depth of cycle for compressed air and hydrogen energy storage can reach 100%. c P represents the unit charging cost. punishThis represents the penalty cost corresponding to the loss of one unit of energy due to energy loss during the energy storage cycle, η. d η represents the discharge efficiency, and η represents the total cycle efficiency.

[0041] The embodiments of this application can analyze the comprehensive dispatch cost of distributed energy storage based on the improved energy storage cost per kilowatt-hour index, ensuring that power dispatch is carried out at the lowest cost while meeting power demand, balancing power demand and supply, helping to optimize resource allocation, reduce power production costs, and improve the economy and stable operation of the entire power system.

[0042] Step S102: Determine the aggregation method of distributed energy storage according to the target scheduling requirements, divide the energy storage cluster capacity with the goal of balancing net load fluctuations as the scheduling target of distributed energy storage, and determine the energy storage capacity division boundary of distributed energy storage.

[0043] In some embodiments, given that energy storage resources in new power systems are diverse, widely distributed, and have complex characteristics, and that the distribution and scenarios of numerous energy storage systems are complex and highly uncertain, the aggregation method for distributed energy storage can be determined according to scheduling requirements when aggregating distributed energy storage in this application embodiment, and the goal of balancing net load fluctuations is taken as the scheduling objective of energy storage.

[0044] When using net load fluctuations as the scheduling objective for energy storage, the sum of the power of energy storage and net load should be kept as stable as possible. Ideally, the charging and discharging power of energy storage should be the opposite of all AC components of the net load, which can be expressed as follows:

[0045]

[0046] Among them, Pideal t,s Pnet t,s , Let Pideal represent the ideal energy storage power, net load power, and average net load power at time t in the s-th scenario, respectively. t,s When the value is negative, the energy storage is charged; otherwise, the energy storage is discharged.

[0047] Next, we can find the ideal energy storage charging and discharging state transition node in the s-th scenario and denote it as {t}. s,1 ,t s,2 ,t s,3 ,t s,4 ,t s,5 …}, and using this as a dividing point, the charging and discharging behavior of ideal energy storage within a day is divided into multiple stages, which can be denoted as {T}. s,1 ,T s,2 ,T s,3 ,T s,4 ,T s,5…}, then find the absolute value of the maximum charging and discharging power for each stage, and calculate the maximum power and energy storage capacity required for that stage, which can be expressed as follows:

[0048] Pneed s,i =max{|Pideal t,s |},t∈T i

[0049]

[0050] Among them, Pneed s,i Eney represents the maximum power required for the i-th stage in the s-th scenario. s,i T represents the energy storage capacity required for the i-th stage in the s-th scenario, Δt represents the unit scheduling time, and T represents the energy storage capacity required for the ith stage in the s-th scenario. i This indicates the different charging and discharging stages of ideal energy storage within a day.

[0051] Furthermore, the distributed energy storage aggregation method in this embodiment, when using balancing net load fluctuations as the scheduling objective of distributed energy storage, can also divide the energy storage cluster capacity based on this scheduling objective, thereby determining the energy storage capacity division boundary of distributed energy storage. This process will be further elaborated below.

[0052] Optionally, in one embodiment of this application, determining the energy storage capacity boundary of distributed energy storage includes: dividing distributed energy storage based on the energy and power demand during the charging phase to obtain distributed energy storage clusters; and sorting the energy storage clusters according to the capacity demand of distributed energy storage in the target scenario to obtain the energy storage capacity boundary of distributed energy storage.

[0053] As one possible approach, in the embodiments of this application, when performing distributed energy storage aggregation with scheduling needs as the objective, the division of cluster capacity may, but is not limited to, be a key focus. When dividing the cluster capacity, the energy and power requirements during the charging phase can be used as the basis for division to divide the distributed energy storage, thereby obtaining a distributed energy storage cluster.

[0054] For example, assuming each energy storage unit cycles 1-2 times per day, the energy storage units participating in the same charge-discharge cycle can be considered as a single entity, i.e., an energy storage cluster. When the net load power is positive, thermal power units can supplement the output to assist in regulation. However, when the net load power is negative or changes rapidly, the system mainly regulates through energy storage charging. Therefore, the regulation pressure on energy storage is mainly during the charging period. Thus, in this application embodiment, when grouping energy storage into several clusters, the energy and power demand of the charging phase can be used as the basis for division. That is, the total capacity of the energy storage cluster participating in the s-th scenario and the ci-th charging phase should be greater than Eneed. s,ciThe total power it can provide should be greater than Pneed s,ci .

[0055] To simplify the division of cluster capacity, the total cluster capacity required for the ci-th stage in the s-th scenario is Ccolony. s,ci It can be represented as follows:

[0056]

[0057] in, Let represent the average discharge time of all stored energy, then the k-th stored energy... The equivalent capacity provided by internal energy, Cind k The calculation method can be expressed as follows:

[0058]

[0059] Among them, Ce k and Pe k Let C and K be the rated capacity and rated power of the k-th energy storage unit, respectively. At this point, it is sufficient that the sum of the equivalent capacities of all energy storage units within the unit is greater than or equal to Ccolony. ci This allows for the simultaneous satisfaction of both energy and power requirements during this stage, as can be represented as follows:

[0060]

[0061] Among them, Ccolony s,ci This represents the total cluster capacity requirement corresponding to the ci-th stage in the s-th scenario.

[0062] Furthermore, in this embodiment of the application, the energy storage clusters can be sorted according to the capacity requirements of distributed energy storage in the target scenario, so as to obtain the energy storage capacity division boundary of distributed energy storage.

[0063] For example, in the s-th scenario, the energy storage capacity requirements of each stage can be arranged from smallest to largest, and can be denoted as {Ccolony} s,1 Ccolony s,2 Ccolony s,3 …Ccolony s,cn The capacity threshold for an energy storage cluster can then be expressed as {Cdiv}. s,1 , Cdiv s,2 , Cdiv s,3 …Cdiv s,cn-1 This allows for the division of various energy storage systems at capacity boundaries. The calculation formula can be expressed as follows:

[0064]

[0065] Wherein, cn represents the number of charging stages on that day.

[0066] Since the capacity threshold values ​​differ across different scenarios, to ensure that the needs of each scenario are met, we can first retain the capacity threshold values ​​for all scenarios and arrange them in ascending order. If a threshold value exceeds the total effective capacity of all energy storage in that area, then that threshold value is invalid and can be deleted. This way, we can obtain all the capacity threshold values, which can be denoted as {Cdiv1, Cdiv2, Cdiv3, Cdiv4…Cdiv…} n}

[0067] Optionally, in one embodiment of this application, the energy storage clusters are sorted according to the capacity requirements of distributed energy storage in the target scenario to obtain the energy storage capacity division boundary of distributed energy storage, including: obtaining the total number of energy storage clusters; and merging the energy storage clusters in the same range when the total number of energy storage clusters is greater than a preset threshold to obtain the energy storage capacity division boundary of distributed energy storage.

[0068] Based on the descriptions of other embodiments, it is understood that, considering the validity of energy storage capacity limit values, the embodiments of this application can arrange the capacity division limit values ​​of all scenarios in ascending order, and set the limit values ​​that exceed the total effective capacity of all energy storage in the region to invalid.

[0069] Additionally, this application embodiment also considers that if there are many limit values, the number of energy storage clusters that can be segmented will also be large, and the calculation process and cost will increase accordingly. Therefore, this application embodiment can consider merging similar capacity limit values ​​while meeting the capacity and power requirements of each stage. The merging method can be expressed as follows:

[0070] (1) Let k = 1, where k represents any limit value.

[0071] (2) Find the k-th boundary value Cdiv k and the (k+1)th boundary value Cdiv k+1 Given the scenarios f and g in which the two values ​​reside, calculate the difference ΔCdiv between the two boundary values. k .

[0072] (3) If f≠g, then find the maximum bound value retained in the f-th scene. Then proceed to step (4), otherwise let k = k + 1 and jump to step (2).

[0073] (4) If the following formula is satisfied, the k-th and (k+1)-th boundary values ​​can be merged. If the following formula is not satisfied, let k = k+1, and then jump to step (2):

[0074]

[0075] Where N represents the total number of energy storage facilities in the region, Cind j Indicates the j-th energy storage The equivalent capacity provided by internal energy. When this formula is satisfied, the k-th boundary value can be deleted, and the boundary values ​​originally ordered after the k-th boundary value in the f-th scenario can be corrected according to the following formula:

[0076] Cdiv f,j =Cdiv f,j +ΔCdiv k ,Cdiv f,j ≥Cdiv k

[0077] Among them, Cdiv f,j Let represent the energy storage capacity boundary value for the j-th scenario. The boundary values ​​for all scenarios after correction are rearranged in ascending order to obtain a new boundary value sequence {Cdiv1, Cdiv2, Cdiv3, Cdiv4…Cdiv}. n-1}, and keep k unchanged, then jump to step (2).

[0078] (5) Repeat steps (2) to (4) until all boundary values ​​are processed.

[0079] The embodiments of this application can determine the final energy storage capacity division boundary of distributed energy storage by comprehensively considering the needs of various energy storage scenarios and the total number of energy storage clusters. It is applicable to energy storage in multiple scenarios with wide distribution and complex characteristics, effectively expanding the scope of application and practicality of this application, reducing the calculation process and complexity while preserving the accuracy of the calculation results to the greatest extent.

[0080] Step S103: Based on the energy storage capacity division boundary and the comprehensive call cost, energy storage individuals whose cost per kilowatt-hour reaches a preset close condition are grouped into the same cluster, and distributed energy storage is divided into multiple energy storage clusters based on the boundary values ​​of the energy storage capacity division boundary, so as to obtain the final distributed energy storage aggregation result.

[0081] In actual implementation, after determining the boundary values ​​for dividing energy storage capacity, this embodiment of the application can divide all energy storage into multiple clusters at the boundary values, and the final boundary value sequence can be recorded as {Cdiv1, Cdiv2, Cdiv3, Cdiv4…Cdiv...} q Then, the ideal capacity calculation formula for each cluster can be expressed as follows:

[0082]

[0083] It should be noted that if the charging and discharging operation parameters of a certain type of energy storage in the region differ significantly from those of other types of energy storage, and the installed capacity of this type of energy storage is large, this relatively special type of energy storage can be treated as a separate cluster to avoid excessive fluctuations in operation parameters.

[0084] Furthermore, since day-ahead dispatch issues power commands to the entire energy storage cluster, the control center can only obtain overall parameters. To ensure economic efficiency, energy storage systems with similar levelized cost of electricity (LCOE) can be prioritized for aggregation, resulting in relatively stable LCOE within the cluster. Cost differences between different clusters allow day-ahead dispatch to prioritize lower-cost energy storage clusters. Under specific conditions, lower-cost energy storage systems can be cycled more frequently while higher-cost systems are cycled less, avoiding the uneconomical effects of indiscriminate charging and discharging of all energy storage systems. Simultaneously, since the LCOE of the same type of energy storage is relatively similar, aggregation based on LCOE also helps maintain the relative stability of cluster operating characteristics.

[0085] It should be noted that since only secondary power allocation is performed within the cluster and there is no internal power circulation, the geographical distance of energy storage within the cluster can be disregarded during aggregation in this embodiment of the application.

[0086] Specifically, based on the energy storage levelized cost of electricity (LCOE) calculation method, it can be assumed that each sub-energy storage system cycles twice a day. The comprehensive LCOE cost of each sub-energy storage system is calculated and arranged in ascending order of comprehensive LCOE cost. The corresponding effective capacity sequence of each energy storage system can be represented as follows: When forming the first energy storage cluster, the sub-energy storage units should be added sequentially in order of increasing cost per kilowatt-hour. That is, after adding the z1th energy storage unit, the total energy storage capacity in the cluster is closest to the ideal capacity. Then, the first to z1th energy storage units form the first cluster, until the following equation is satisfied:

[0087]

[0088] Following this pattern, after obtaining the (i-1)th cluster, the remaining energy storage is sequentially added to the ith cluster, i.e., the zth cluster. i-1 +1 to the zth i The energy storage forms the i-th cluster. After the allocation of the first q-1 clusters is completed, the remaining energy storage automatically forms the q-th cluster, until the following equation is satisfied:

[0089]

[0090] This application embodiment can combine energy storage capacity delineation boundaries and comprehensive dispatch costs to divide distributed energy storage into multiple energy storage clusters, obtaining the final distributed energy storage aggregation result. By grouping individual energy storage units with similar per-kilowatt-hour costs into the same cluster, the boundary values ​​of the energy storage capacity delineation boundaries are obtained, reducing the computational dimensionality and complexity of the aggregation process. Considering meeting power demand while minimizing dispatch costs, the aggregation of distributed energy storage improves the economy and security of power operation.

[0091] The distributed energy storage aggregation method proposed in this application, oriented towards scheduling needs, analyzes the comprehensive energy storage dispatch cost based on an improved energy storage levelized cost of electricity (LCOS) index, divides the energy storage cluster capacity according to energy storage scheduling objectives, and finally divides the distributed energy storage into several energy storage clusters based on the determined energy storage capacity division boundaries, thereby realizing the aggregation of distributed energy storage. This enables the aggregation of multiple energy storage systems into large-scale energy storage clusters for scheduling, optimizes the energy storage participation in scheduling, and supports the safe and economical operation of power grids with a high proportion of new energy sources, serving the efficient operation of new power systems. This solves the problems in related technologies, such as the difficulty of traditional control methods in coordinating large amounts of energy storage, the low regulation potential and operating efficiency of energy storage, the increase in the number of energy storage systems leading to an increase in variables to be optimized, resulting in excessively high computational dimensionality, complex and difficult solutions, difficulty in quickly responding to power system power fluctuations, and inability to support the distributed energy storage control needs under complex and uncertain scenarios.

[0092] Next, referring to the accompanying drawings, a distributed energy storage aggregation device oriented towards scheduling needs is described according to an embodiment of this application.

[0093] Figure 2 This is a schematic diagram of the structure of a distributed energy storage aggregation device oriented towards scheduling needs according to an embodiment of this application.

[0094] like Figure 2 As shown, the distributed energy storage aggregation device 10 for scheduling needs includes: an analysis module 100, a partitioning module 200, and an aggregation module 300.

[0095] The analysis module 100 is used to analyze the overall call cost of distributed energy storage based on the improved energy storage cost per kilowatt-hour index.

[0096] The partitioning module 200 is used to determine the aggregation method of distributed energy storage according to the target scheduling requirements, and to partition the capacity of the energy storage cluster by taking the balancing of net load fluctuations as the scheduling target of distributed energy storage, and to determine the energy storage capacity partitioning boundary of distributed energy storage.

[0097] The aggregation module 300 is used to group individual energy storage units whose cost per kilowatt-hour reaches a preset close condition into the same cluster based on the energy storage capacity division boundary and the comprehensive call cost, and to divide distributed energy storage into multiple energy storage clusters based on the boundary values ​​of the energy storage capacity division boundary, so as to obtain the final distributed energy storage aggregation result.

[0098] Optionally, in one embodiment of this application, the partitioning module 200 includes: a partitioning unit and a processing unit.

[0099] The partitioning unit is used to divide distributed energy storage based on the energy and power demand during the charging phase, thereby obtaining distributed energy storage clusters.

[0100] The processing unit is used to sort the energy storage clusters according to the capacity requirements of distributed energy storage in the target scenario, so as to obtain the energy storage capacity division boundary of distributed energy storage.

[0101] Optionally, in one embodiment of this application, the processing unit includes: an acquisition subunit and a merging subunit.

[0102] The acquisition sub-unit is used to obtain the total number of energy storage clusters.

[0103] The merging sub-unit is used to merge energy storage clusters within the same range when the total number of energy storage clusters exceeds a preset threshold, so as to obtain the energy storage capacity division boundary of distributed energy storage.

[0104] Optionally, in one embodiment of this application, the formula for calculating the overall call cost can be expressed as:

[0105]

[0106] Among them, C E C represents the unit capacity cost during energy storage construction. P The value represents the unit power cost, d represents the discharge duration (i.e., the ratio of energy storage capacity to rated power), n(t) represents the number of cycles per year, represents the service life, M(t) represents the maintenance cost in year t, r represents the discount rate, DOD represents the equivalent rated cycle depth, and P... c P represents the unit charging cost. punish This represents the penalty cost corresponding to the loss of one unit of energy due to energy loss during the energy storage cycle, η. d η represents the discharge efficiency, and η represents the total cycle efficiency.

[0107] It should be noted that the foregoing explanation of the distributed energy storage aggregation method embodiment for scheduling needs also applies to the distributed energy storage aggregation device for scheduling needs in this embodiment, and will not be repeated here.

[0108] According to the distributed energy storage aggregation device proposed in this application, which is geared towards scheduling needs, the comprehensive energy storage dispatch cost can be analyzed based on an improved energy storage levelized cost of electricity (LCOS) index. Combined with energy storage scheduling objectives, the capacity of energy storage clusters is divided, and finally, based on the determined energy storage capacity division boundaries, distributed energy storage is divided into several energy storage clusters, thereby realizing the aggregation of distributed energy storage. This enables the aggregation of multiple energy storage sources into large-scale energy storage clusters for scheduling, optimizes the energy storage participation in scheduling, and supports the safe and economical operation of power grids with a high proportion of new energy sources, serving the efficient operation of new power systems. This solves the problems in related technologies, such as the difficulty of traditional control methods in coordinating large amounts of energy storage, the low regulation potential and operating efficiency of energy storage, the increase in the number of energy storage sources leading to an increase in variables to be optimized, resulting in excessively high computational dimensionality, complex and difficult solutions, difficulty in quickly responding to power system fluctuations, and inability to support the distributed energy storage control needs under complex and uncertain scenarios.

[0109] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0110] The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0111] When the processor 302 executes the program, it implements the distributed energy storage aggregation method for scheduling requirements provided in the above embodiments.

[0112] Furthermore, electronic devices also include:

[0113] Communication interface 303 is used for communication between memory 301 and processor 302.

[0114] The memory 301 is used to store computer programs that can run on the processor 302.

[0115] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0116] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0117] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0118] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0119] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described distributed energy storage aggregation method oriented towards scheduling requirements.

[0120] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the distributed energy storage aggregation method for scheduling needs provided in this application.

[0121] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0122] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0123] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0125] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0126] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0127] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0128] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A distributed energy storage aggregation method oriented towards scheduling requirements, characterized in that, Includes the following steps: Based on the improved energy storage cost per kilowatt-hour index, the overall dispatch cost of distributed energy storage is analyzed; The aggregation method of the distributed energy storage is determined according to the target scheduling requirements. The energy storage cluster capacity is divided with balancing net load fluctuations as the scheduling target of the distributed energy storage, and the energy storage capacity division boundary of the distributed energy storage is determined. Based on the energy storage capacity division boundary and the comprehensive dispatch cost, energy storage individuals whose cost per kilowatt-hour reaches a preset close condition are grouped into the same cluster, and the distributed energy storage is divided into multiple energy storage clusters based on the boundary values ​​of the energy storage capacity division boundary to obtain the final distributed energy storage aggregation result. The formula for calculating the overall call cost is as follows: ， in, This represents the unit capacity cost during energy storage construction. Indicates cost per unit of power. This indicates the discharge duration, which is the ratio of energy storage capacity to rated power. Indicates the number of cycles in a year. T Indicates the service life. Indicates the first Annual maintenance costs This represents the discount rate. Indicates the equivalent rated cycle depth. Indicates the unit charging cost. This indicates the penalty cost corresponding to the loss of a unit of energy due to energy loss during the energy storage cycle. Indicates discharge efficiency. Indicates the overall cycle efficiency; The step of using the balancing of net load fluctuations as the scheduling target for distributed energy storage to divide the energy storage cluster capacity and determine the energy storage capacity division boundary for distributed energy storage includes: using the balancing of net load fluctuations as the scheduling target for distributed energy storage, calculating the maximum power required for each charging stage. and energy storage capacity : ， ， in, Indicates the first In each scenario The ideal energy storage power at any given time, that is, the energy storage charging and discharging power under ideal conditions. Indicates the first In the first scenario The maximum power required for each stage Indicates the first In the first scenario The energy storage capacity required for each stage Indicates the unit scheduling duration. This represents the different charging and discharging stages of ideal energy storage within a day; wherein, when the scheduling objective of the distributed energy storage is to balance net load fluctuations, the energy storage charging and discharging power under ideal conditions is the opposite of all AC components of the net load. Based on the energy and power requirements during the charging phase, the following is obtained: In the first scenario Total cluster capacity requirements for each stage : in, Representing the average discharge time of all stored energy, then the first... Energy storage Equivalent capacity provided by internal energy The expression is: in, and The first The rated capacity and rated power of each energy storage unit are determined by the requirement that the sum of the equivalent capacities of all energy storage units within the unit is greater than or equal to the rated capacity and rated power of the energy storage unit. This allows for the simultaneous satisfaction of both energy and power requirements during this phase, as expressed in the following expression: in, Indicates the first In the first scenario The total cluster capacity requirements for each stage; Based on the distributed energy storage capacity requirements of the target scenario, the energy storage clusters are sorted to obtain the energy storage capacity division boundaries for distributed energy storage; in the s-th scenario, the energy storage capacity requirements of each stage are arranged from smallest to largest, resulting in: ； The threshold value for energy storage cluster capacity is: ； The energy storage will be divided at the defined boundary value: ， in, Indicates the number of charging stages on that day; Retain the capacity delineation boundary values ​​for all scenarios and arrange them in ascending order. Remove invalid boundary values ​​that exceed the total effective capacity of all energy storage in the area, resulting in a complete set of delineation boundary values: ； The process involves grouping individual energy storage units with similar cost per kilowatt-hours (kWh) based on the energy storage capacity delineation boundaries and the overall dispatch cost into the same cluster, and further dividing the distributed energy storage into multiple energy storage clusters based on the boundary values ​​of the energy storage capacity delineation boundaries to obtain the final distributed energy storage aggregation result. This includes: calculating the ideal capacity of each cluster based on the delineation boundary values. : ； in, Indicates the first Energy storage The equivalent capacity provided by internal energy; Arrange the overall levelized cost of electricity (LCOE) of each sub-energy storage unit from smallest to largest, and determine the corresponding effective capacity sequence of each energy storage unit: When forming the first energy storage cluster, the sub-energy storage units are added sequentially according to their cost per kilowatt-hour, from lowest to highest, until the total energy storage capacity of the first cluster is closest to the ideal capacity. Each energy storage unit forms the first cluster, until the following equation is satisfied: And so on, to obtain the first After the first cluster, the remaining energy storage will be added to the second cluster in sequence. In the cluster, that is, the first cluster... One to the first One energy storage unit forms the first... Cluster, complete the previous After the allocation of the first cluster, the remaining energy storage automatically forms the second cluster. Clusters, until the following equation is satisfied: 。 2. The method according to claim 1, characterized in that, The process of sorting energy storage clusters according to the capacity requirements of distributed energy storage in the target scenario to obtain the capacity delineation boundaries for distributed energy storage includes: Obtain the total number of the energy storage clusters; If the total number of energy storage clusters exceeds a preset threshold, the energy storage clusters within the same range will be merged to obtain the energy storage capacity division boundary of the distributed energy storage.

3. A distributed energy storage aggregation device oriented towards scheduling needs, characterized in that, The distributed energy storage aggregation method oriented towards scheduling needs as described in any one of claims 1-2 includes: The analysis module is used to analyze the overall utilization cost of distributed energy storage based on the improved energy storage cost per kilowatt-hour index. The partitioning module is used to determine the aggregation method of the distributed energy storage according to the target scheduling requirements, and to partition the energy storage cluster capacity with the balancing of net load fluctuations as the scheduling target of the distributed energy storage, and to determine the energy storage capacity partitioning boundary of the distributed energy storage. The aggregation module is used to group individual energy storage units whose cost per kilowatt-hour reaches a preset close condition into the same cluster based on the energy storage capacity division boundary and the comprehensive call cost, and to divide the distributed energy storage into multiple energy storage clusters based on the boundary values ​​of the energy storage capacity division boundary, so as to obtain the final distributed energy storage aggregation result.

4. The apparatus according to claim 3, characterized in that, The partitioning module includes: A partitioning unit is used to partition the distributed energy storage based on the energy and power demand during the charging phase, thereby obtaining the energy storage cluster of the distributed energy storage. The processing unit is used to sort the energy storage clusters according to the capacity requirements of the distributed energy storage in the target scenario, so as to obtain the energy storage capacity division boundary of the distributed energy storage.

5. The apparatus according to claim 4, characterized in that, The processing unit includes: Obtain a sub-unit, used to obtain the total number of the energy storage cluster; The merging subunit is used to merge the energy storage clusters within the same range when the total number of energy storage clusters is greater than a preset threshold, so as to obtain the energy storage capacity division boundary of the distributed energy storage.

6. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the distributed energy storage aggregation method for scheduling needs as described in any one of claims 1-2.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the distributed energy storage aggregation method for scheduling requirements as described in any one of claims 1-2.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the distributed energy storage aggregation method for scheduling requirements as described in any one of claims 1-2.

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