Multi-type load side resource aggregation method, system, device and storage medium

Abstract

The present application belongs to the field of power system automation, and discloses a multi-type load side resource aggregation method, system, device and storage medium, comprising: obtaining the descending order ranking position of each load side resource based on the scene adaptability index under each type of balance scene; for each load side resource, respectively performing an aggregation step; the aggregation step comprises: obtaining the type of the balance scene corresponding to the maximum descending order ranking position of the current load side resource, and taking it as the target balance scene type, and judging according to the preset target balance scene type judgment rule, when the current load side resource meets the target balance scene type judgment rule, the current load side resource is aggregated to the target balance scene type set. By focusing on the adjustment characteristics of the load side resource, for the needs of different balance scenes, the load side resource more suitable for adjusting the balance scene is provided, the adjustment potential of the load side resource is maximized, and the control ability of the dispatching department to the load side resource is improved.

Description

Technical Field

[0001] This invention belongs to the field of power system automation and relates to a method, system, device and storage medium for aggregating resources on multiple types of load sides. Background Technology

[0002] With the rapid construction and development of new power systems, the traditional power source-side regulation capacity has reached its limit. Therefore, aggregating cross-regional and diverse load-side resources to fully leverage their flexibility and complementarity, and improving the regulation capacity of load-side resources, is of great significance. The load-side resources involved in aggregation include electric vehicles, temperature-controlled loads, and industrial loads. These loads can regulate their electricity demand in a short period without affecting or minimally affecting the performance of the equipment itself or the comfort of users. Through reasonable control measures, not only can intermittent energy load fluctuations be smoothed and the system peak-valley difference reduced, but compared with increasing installed capacity, the investment cost is low, resulting in good social and economic benefits.

[0003] However, load-side resources are characterized by their large number and significant performance differences. Analyzing their differentiated regulation capabilities and aggregating them into the regulation resources needed for grid dispatch is a critical and urgent challenge. Existing load-side resource aggregation methods generally focus on the load resources themselves, primarily aggregating them based on their physical characteristics. Furthermore, existing aggregation methods lack comprehensive analysis of the loads themselves, typically drawing parallels with generation-side energy and performing simple complementarity analyses of output. Based on the above analysis, existing load-side resource aggregation methods, when aggregating multiple types of load-side resources, do not fully consider the impact of the differences between these types of load-side resources on grid balance, resulting in the overall regulation capability of load-side resources not being fully utilized. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device and storage medium for aggregating multiple types of load-side resources.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] In a first aspect, the present invention provides a method for aggregating multiple types of load-side resources, comprising:

[0007] Obtain the descending ranking of each load-side resource based on scenario adaptability indicators under various types of balancing scenarios;

[0008] For each load-side resource, an aggregation step is performed. The aggregation step includes: obtaining the type of the balancing scenario corresponding to the maximum descending ranking of the current load-side resource and using it as the target balancing scenario type; and judging according to the preset target balancing scenario type judgment rule. When the current load-side resource meets the target balancing scenario type judgment rule, the current load-side resource is aggregated into the target balancing scenario type set.

[0009] Optionally, the types of balancing scenarios include hill climbing scenarios, peak shaving scenarios, and valley filling scenarios;

[0010] The ranking of each load-side resource in descending order based on scenario adaptability indicators under various types of balanced scenarios includes:

[0011] The scenario adaptability index Y of each load-side resource in the ramping scenario is obtained by the following formula. C,i :

[0012]

[0013] Where, ν i v represents the response speed of resource i on the load side. max p represents the maximum response speed of all load-side resources. r Adjustable capacity is required for hill climbing scenarios; p i The adjustable capacity of load-side resource i; p max This represents the maximum adjustable capacity of all load-side resources.

[0014] The following formula is used to obtain the scenario adaptability index Y of each load-side resource under the peak shaving scenario. P,i :

[0015]

[0016] Where pdown,r represents the adjustable capacity for peak shaving scenarios; pdown,i represents the adjustable capacity for load-side resource i; and pdown,max represents the maximum adjustable capacity for all load-side resources. r1 Adjust the duration for peak shaving scenarios; t i The duration of adjustment for load-side resource i; t max This represents the maximum adjustment duration for all load-side resources.

[0017] The scenario adaptability index Y of each load-side resource in the valley filling scenario is obtained by the following formula. V,i :

[0018]

[0019] Where, p up,r The capacity can be adjusted to meet the needs of valley filling scenarios; pup,i p is the adjustable capacity of load-side resource i. up,max The maximum adjustable capacity of all load-side resources; t r2 Adjust the duration to meet the needs of the valley filling scenario;

[0020] Based on the scenario adaptability index of each load-side resource in each type of balancing scenario, the load-side resources are sorted in descending order in each type of balancing scenario to obtain the descending ranking of each load-side resource based on the scenario adaptability index in each type of balancing scenario.

[0021] Optionally, the adjustable capacity for the climbing scenario, the adjustable capacity for the peak shaving scenario, the adjustable duration for the peak shaving scenario, the adjustable capacity for the valley filling scenario, and the adjustable duration for the valley filling scenario are obtained in the following ways:

[0022] Obtain adjustable capacity curves for load demand;

[0023] The rotating door algorithm is used to divide the adjustable capacity curve of load demand into several discrete segments, and adjacent discrete segments with the same trend of change are merged.

[0024] Based on the preset balanced scenario identification model, each discrete segment is identified to obtain the load demand time period for each type of balanced scenario.

[0025] Based on the load demand periods and adjustable capacity curves for various load balancing scenarios, we obtain the adjustable capacity for ramping scenarios, the adjustable capacity for peak shaving scenarios, the adjustable duration for peak shaving scenarios, and the adjustable capacity for valley filling scenarios.

[0026] Optionally, the types of balancing scenarios include hill climbing scenarios, peak shaving scenarios, and valley filling scenarios;

[0027] The rules for determining the target balance scenario type include:

[0028] When the target balance scenario type is a ramp scenario, during the scheduling period, if the difference in load power between any two time points of the current load side resources is greater than the maximum power generation output of the first preset ratio, the current load side resources meet the target balance scenario type judgment rule.

[0029] When the target balancing scenario type is peak shaving scenario, during the scheduling period, if the load power of the current load side resource is greater than the maximum generation side output of the second preset ratio between any two time points, the current load side resource meets the target balancing scenario type judgment rule.

[0030] When the target balance scenario type is valley filling scenario, during the scheduling period, if the load power of the current load side resource is less than the maximum power generation output of the third preset ratio between any two time points, the current load side resource meets the target balance scenario type judgment rule.

[0031] Optionally, the types of balancing scenarios include hill climbing scenarios, peak shaving scenarios, and valley filling scenarios;

[0032] The aggregation step further includes: when the current load-side resources have the same descending sorting position in each type of balancing scenario, when the descending sorting position is in the first 1 / 3, the current load-side resources are aggregated to the ramp-up scenario set; when the descending sorting position is in the middle 1 / 3, the current load-side resources are aggregated to the peak-shaving scenario set; when the descending sorting position is in the last 1 / 3, the current load-side resources are aggregated to the peak-shaving scenario set.

[0033] Optional, also includes:

[0034] Unaggregated load-side resources are grouped into several undetermined load-side resource groups by a preset number, and the complementarity index of each undetermined load-side resource group is obtained. The undetermined load-side resource groups with complementarity index greater than the preset complementarity index threshold are taken as the final load-side resource groups.

[0035] Obtain the descending ranking of each load-side resource group based on scenario adaptability indicators under various types of balanced scenarios; and for each load-side resource group, perform the aggregation step as the current load-side resource in the aggregation step.

[0036] Optionally, obtaining the complementarity index of each pending load-side resource group includes:

[0037] The complementarity index C for each undetermined load-side resource group is obtained using the following formula:

[0038]

[0039] Wherein, CCR represents the capacity complementarity adjustment ratio. Where, α k t1 represents the proportion of the k-th type of load-side resource in the current undetermined load-side resource group; t1 is the start time of the scheduling period; t2 is the end time of the scheduling period. Let be the adjustment capacity of the k-th load-side resource at time t;

[0040] RSCR is the response speed complementarity. Where, ν c The ramp-up rate of the current undetermined load-side resource group. Let be the ramp rate of the k-th load-side resource at time t;

[0041] SDCR is the complementarity rate of scheduling duration as a percentage of total duration. in, This represents the percentage of the scheduling duration for the currently pending load-side resource group. This represents the percentage of scheduling duration for the k-th type of load-side resource. The scheduling duration for the currently pending load-side resource group; T represents the scheduling duration of the k-th load-side resource; x Adjust the duration for the demand in the xth equilibrium scenario;

[0042] λ1 is the adjustment capacity complementarity weight, λ2 is the response speed complementarity weight, and λ3 is the scheduling duration percentage complementarity weight.

[0043] A second aspect of the present invention provides a multi-type load-side resource aggregation system, comprising:

[0044] The information acquisition module is used to obtain the descending ranking of each load-side resource based on scenario adaptability indicators under various types of balancing scenarios;

[0045] The first aggregation module is used to perform aggregation steps for each load-side resource. The aggregation steps include: obtaining the type of the balancing scenario corresponding to the maximum descending order of the current load-side resource and using it as the target balancing scenario type; and judging according to the preset target balancing scenario type judgment rules. When the current load-side resource meets the target balancing scenario type judgment rules, the current load-side resource is aggregated into the target balancing scenario type set.

[0046] Optionally, the types of balancing scenarios include hill climbing scenarios, peak shaving scenarios, and valley filling scenarios;

[0047] The information acquisition module is specifically used for:

[0048] The scenario adaptability index Y of each load-side resource in the ramping scenario is obtained by the following formula. C,i :

[0049]

[0050] Where, ν i v represents the response speed of resource i on the load side. max p represents the maximum response speed of all load-side resources. r Adjustable capacity is required for hill climbing scenarios; p i The adjustable capacity of load-side resource i; p max This represents the maximum adjustable capacity of all load-side resources.

[0051] The following formula is used to obtain the scenario adaptability index Y of each load-side resource under the peak shaving scenario.P,i :

[0052]

[0053] Where, p down,r Adjustable capacity to meet peak shaving requirements; p down,i p is the adjustable capacity of load-side resource i. down,max The maximum adjustable capacity of all load-side resources; t r1 Adjust the duration for peak shaving scenarios; t i The duration of adjustment for load-side resource i; t max This represents the maximum adjustment duration for all load-side resources.

[0054] The scenario adaptability index Y of each load-side resource in the valley filling scenario is obtained by the following formula. V,i :

[0055]

[0056] Where, p up,r The capacity can be adjusted to meet the needs of valley filling scenarios; p up,i p is the adjustable capacity of load-side resource i. up,max The maximum adjustable capacity of all load-side resources; t r2 Adjust the duration to meet the needs of the valley filling scenario;

[0057] Based on the scenario adaptability index of each load-side resource in each type of balancing scenario, the load-side resources are sorted in descending order in each type of balancing scenario to obtain the descending ranking of each load-side resource based on the scenario adaptability index in each type of balancing scenario.

[0058] Optional, also includes:

[0059] The combination module is used to group unaggregated load-side resources into a preset number to obtain several undetermined load-side resource groups, and to obtain the complementarity index of each undetermined load-side resource group. The undetermined load-side resource groups with complementarity index greater than the preset complementarity index threshold are taken as the final load-side resource groups.

[0060] The second aggregation module is used to obtain the descending ranking of each load-side resource group based on the scenario adaptability index under each type of balanced scenario; and to perform the aggregation step on each load-side resource group as the current load-side resource in the aggregation step.

[0061] In a third aspect, the present invention provides a computer device including 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 steps of the above-described multi-type load-side resource aggregation method.

[0062] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described multi-type load-side resource aggregation method.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] This invention presents a multi-type load-side resource aggregation method. It obtains the descending ranking of each load-side resource based on scenario adaptability indicators under various types of balanced scenarios, and then aggregates each load-side resource into different types of balanced scenario sets based on this descending ranking. This facilitates the classification and subsequent demand scheduling of load-side resources in each type of balanced scenario. Compared to current aggregation methods that only consider the physical characteristics of load-side resources' output, this invention also focuses on the adjustment characteristics of load-side resources. For the needs of different balanced scenarios, it provides load-side resource aggregation sets that are more suitable for adjusting the balanced scenario, maximizing the adjustment potential of load-side resources and improving the scheduling department's control over load-side resources. Attached Figure Description

[0065] Figure 1 This is a flowchart of a multi-type load-side resource aggregation method according to an embodiment of the present invention.

[0066] Figure 2 This is a block diagram of the multi-type load-side resource aggregation system according to an embodiment of the present invention. Detailed Implementation

[0067] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0068] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0069] The present invention will now be described in further detail with reference to the accompanying drawings:

[0070] See Figure 1 In one embodiment of the present invention, a method for aggregating multiple types of load-side resources is provided. This method not only considers the complementary characteristics of load-side resource output, but also analyzes the adjustment characteristics of load-side resources, thus establishing a more complete method for aggregating load-side resources.

[0071] Specifically, this multi-type load-side resource aggregation method includes the following steps:

[0072] S1: Obtain the descending ranking of each load-side resource based on scenario adaptability indicators in various balanced scenarios.

[0073] S2: For each load-side resource, perform an aggregation step. The aggregation step includes: obtaining the type of the balancing scenario corresponding to the maximum descending order of the current load-side resource and using it as the target balancing scenario type; and judging according to the preset target balancing scenario type judgment rule. When the current load-side resource meets the target balancing scenario type judgment rule, the current load-side resource is aggregated into the target balancing scenario type set.

[0074] The comparison of descending sort positions is based on whether the sort position is earlier. For example, among the three descending sort positions 1, 2 and 3, 1 is considered to be the largest descending sort position.

[0075] Specifically, the multi-type load-side resource aggregation method of the present invention obtains the descending order ranking of each load-side resource in each type of balanced scenario based on the scenario adaptability index, and then aggregates each load-side resource into different types of balanced scenario sets based on the descending order ranking, so as to facilitate the classification of the demand scheduling of load-side resources in each type of balanced scenario.

[0076] Compared to current aggregation methods that only consider the physical characteristics of load-side resources' output, this invention's multi-type load-side resource aggregation method also focuses on the regulation characteristics of load-side resources. For different balancing scenarios, it provides a more suitable load-side resource aggregation set for regulating the balancing scenario, maximizing the regulation potential of load-side resources, improving the dispatching department's control over load-side resources, and providing technical support for the participation of large-scale multi-type load-side regulation resources in the comprehensive balancing of large power grids across multiple time and space dimensions.

[0077] In one possible implementation, the types of balancing scenarios include climbing scenarios, peak shaving scenarios, and valley filling scenarios.

[0078] Specifically, a ramp-up event refers to a situation where the power system loses a significant amount of active power in a short period, making it difficult to maintain power balance and seriously jeopardizing the safe, stable, and economical operation of the power system. For example, due to the volatility and intermittency of wind power, the power output of wind power connected to the grid may fluctuate significantly in a short time; therefore, ramp-up events often occur in wind power generation. Analogous to wind power output scenarios, on the load side, due to an emergency requiring increased load, power consumption may suddenly surge in a short period. This phenomenon is called an up-ramp event. When a load fault or other sudden failure occurs, causing a sharp decrease in power consumption in a short period, this phenomenon is called a down-ramp event.

[0079] Peak shaving occurs during periods of high electricity demand from load-side users, characterized by a rapid initial increase followed by a gradual decrease in demand. Traditionally, peak demand in peak shaving scenarios involves increasing the output of conventional generators to maintain grid balance. However, in modern power systems, with the increasing penetration of renewable energy sources, the generation-side regulation capacity is becoming increasingly limited. Therefore, vigorously developing load-side adjustable resources to participate in power system regulation is a necessary approach for addressing energy endowments and constructing new power systems.

[0080] In contrast to peak shaving, valley filling refers to a scenario where, during periods of lower electricity consumption on the load side, the electricity load initially decreases gradually and then gradually increases over time. By utilizing adjustable resources on the load side, the electricity load from other periods can be shifted to the valley filling period, or the electricity consumption during the valley filling period can be increased, ultimately filling the low electricity demand.

[0081] In addition, considering that typical power grid balancing scenarios often occur simultaneously, when aggregating multiple types of load-side resources for a certain period of time, it is often necessary to establish corresponding scenario adaptability indicators based on the specific needs of different types of balancing scenarios, so as to provide a basis for the division and aggregation of load-side resources.

[0082] Therefore, in one possible implementation, the following methods for calculating scenario adaptability indices were constructed for different types of balance scenarios.

[0083] Specifically, for ramping scenarios, the faster the response speed and the shorter the response time of load-side resources, the better they fit the ramping scenario. Meanwhile, ramping scenarios generally do not have high requirements for the adjustable duration of load-side resources. Therefore, a scenario adaptability index Y for each load-side resource in a ramping scenario is defined. C,i :

[0084]

[0085] Where, ν i v represents the response speed of resource i on the load side. max p represents the maximum response speed of all load-side resources. r Adjustable capacity is required for hill climbing scenarios; p i The adjustable capacity of load-side resource i; p max This represents the maximum adjustable capacity of all load-side resources.

[0086] For peak shaving and valley filling scenarios, the requirements for response speed are generally moderate, while the duration requirement is relatively longer than frequency regulation, typically around 15 minutes. However, the peak shaving and valley filling call frequency is generally low, and load-side resources can usually meet the requirements. Based on this, the scenario adaptability index Y of each load-side resource in the peak shaving scenario is defined. P,i :

[0087]

[0088] Where, p down,r Adjustable capacity to meet peak shaving requirements; p down,i p is the adjustable capacity of load-side resource i. down,max The maximum adjustable capacity of all load-side resources; t r1 Adjust the duration for peak shaving scenarios; t i The duration of adjustment for load-side resource i; t max This represents the maximum adjustment duration for all load-side resources.

[0089] Meanwhile, define the scenario adaptability index Y for each load-side resource in the valley filling scenario. V,i :

[0090]

[0091] Where, p up,r The capacity can be adjusted to meet the needs of valley filling scenarios; p up,i p is the adjustable capacity of load-side resource i. up,max The maximum adjustable capacity of all load-side resources; t r2 Adjust the duration to meet the needs of the valley filling scenario.

[0092] Optionally, to make the scenario adaptability indicators more targeted, the above scenario adaptability indicator definition process has eliminated features that are not relevant to typical power grid balance scenarios, and after normalization, per-unit values ​​are taken. The indicator values ​​are between 0 and 1, and the larger the value, the higher the degree of fit to the balance scenario.

[0093] Based on the definition of the scenario adaptability index of each load-side resource under various types of balancing scenarios, in application, the scenario adaptability index of each load-side resource under various types of balancing scenarios is first calculated, and then the load-side resources are sorted in descending order under various types of balancing scenarios to obtain the descending ranking of each load-side resource based on the scenario adaptability index under various types of balancing scenarios.

[0094] In one possible implementation, in order to calculate the scenario adaptability index of each load-side resource under various types of balancing scenarios, it is necessary to first determine the adjustable capacity of demand in the ramping scenario, the adjustable capacity of demand in the peak shaving scenario, the adjustment duration of demand in the peak shaving scenario, the adjustable capacity of demand in the valley filling scenario, and the adjustment duration of demand in the valley filling scenario. Optionally, in this implementation, these parameters are obtained in the following way:

[0095] Obtain the adjustable capacity curve of load demand; use the rotating door algorithm to divide the adjustable capacity curve of load demand into several discrete segments, and merge adjacent discrete segments with the same trend of change; identify each discrete segment according to the preset balance scenario identification model to obtain the load demand time period of each type of balance scenario; based on the load demand time period of each type of balance scenario and the adjustable capacity curve of load demand, obtain the adjustable capacity of ramp scenario, the adjustable capacity of peak shaving scenario, the adjustment duration of peak shaving scenario, and the adjustable capacity of valley filling scenario.

[0096] Specifically, the adjustable capacity curve of load demand is first divided into several discrete segments using the rotating door algorithm. Then, adjacent discrete segments with the same trend in the direction of change are merged, including adjacent discrete segments with the same ramp direction and a higher ramp amplitude. All discrete segments are then examined; those with ramp amplitudes greater than a defined threshold are assigned a score using the evaluation function H, while the remaining discrete segments receive a score of 0. Finally, based on the balanced scenario identification model and the judgment rules for each type of balanced scenario, the load demand periods for each type of balanced scenario are determined.

[0097] For the balanced scene identification model, the objective function F of the model for the load at any two time points t1 and t2 is expressed as:

[0098]

[0099]

[0100] Where k' represents a certain time point between t1 and t2, and r(t1,t2) represents whether the judgment rule of the balanced scenario is satisfied. When the judgment rule of the balanced scenario is satisfied, r(t1,t2) is 1, otherwise, r(t1,t2) is 0.

[0101] Let's take the hill-climbing scenario analysis as an example. Assume there are M hill-climbing periods for wind power, and the m-th hill-climbing interval is denoted as Z. m =(a m ,b m The set of climbing intervals is represented as θ = {Z1, Z2, ..., Zn}. m ···Z M The m-th non-climbing interval is represented as: The set of non-climbing intervals is represented as For the m-th non-climbing section For any two time points t1 and t2 within its interval, have:

[0102]

[0103] Since the evaluation function H is a monotonically increasing function, for the m-th climbing interval Z... m =(a m ,b m In terms of the model objective function F, the optimal solution is... For a given wind power forecast period, the sequence of ramping and non-ramping intervals can be represented as follows: Solution of the objective function F of the model This can be obtained by continuously merging and scoring discrete segments of wind power output:

[0104]

[0105] This refers to the load demand period during the ramp-up scenario within the wind power forecast time period.

[0106] In one possible implementation, different judgment rules are set for different types of balancing scenarios. For example, for a ramp-up scenario, during the scheduling period, if the difference in load power between any two points in time corresponding to the current load-side resource is greater than the maximum generation output of the first preset proportion, the current load-side resource meets the judgment rules for the ramp-up scenario. For a peak-shaving scenario, during the scheduling period, if the load power between any two points in time is greater than the maximum generation output of the second preset proportion, the current load-side resource meets the judgment rules for the peak-shaving scenario. For a valley-filling scenario, during the scheduling period, if the load power between any two points in time is less than the maximum generation output of the third preset proportion, the current load-side resource meets the judgment rules for the valley-filling scenario.

[0107] It's important to note that the rule for determining the target balancing scenario type is the same as the rule for determining different types of balancing scenarios. Furthermore, the type of balancing scenario represented by the target balancing scenario type is not necessarily the same in each aggregation step. The target balancing scenario type is determined based on the type of balancing scenario corresponding to the maximum descending ranking of the current load-side resources. Similarly, the target balancing scenario type set also represents the ramp-up scenario set, peak-shaving scenario set, and valley-filling scenario set. Aggregating the current load-side resources into the target balancing scenario type set means aggregating the current load-side resources into the corresponding type of balancing scenario set.

[0108] Therefore, when judging according to the preset target balance scenario type judgment rules, when the target balance scenario type is a ramping scenario, during the scheduling period, if the difference in load power between any two time points of the current load-side resource is greater than the maximum generation output of the first preset ratio, the current load-side resource meets the target balance scenario type judgment rules; when the target balance scenario type is a peak shaving scenario, during the scheduling period, if the load power between any two time points of the current load-side resource is greater than the maximum generation output of the second preset ratio, the current load-side resource meets the target balance scenario type judgment rules; when the target balance scenario type is a valley filling scenario, during the scheduling period, if the load power between any two time points of the current load-side resource is less than the maximum generation output of the third preset ratio, the current load-side resource meets the target balance scenario type judgment rules.

[0109] Furthermore, during the aggregation step, the current load-side resources may have the same descending sorting position in various balancing scenarios. Therefore, in one possible implementation, when the current load-side resources have the same descending sorting position in various balancing scenarios, if the descending sorting position is in the first 1 / 3, the current load-side resources are aggregated into the ramp-up scenario set; if the descending sorting position is in the middle 1 / 3, the current load-side resources are aggregated into the peak-shaving scenario set; and if the descending sorting position is in the last 1 / 3, the current load-side resources are aggregated into the peak-shaving scenario set.

[0110] Furthermore, in power grid balancing and dispatching, due to the diverse types of load-side resources and their significant differences in spatiotemporal response characteristics, relying solely on a single type of load-side resource is insufficient to meet the balancing needs of a particular scenario. Effective combinations of diverse and flexible load-side resource outputs can enhance the overall regulation capacity of load-side resources, ultimately achieving the goal of power grid balance. Therefore, load-side resources that cannot be directly aggregated can be further aggregated by forming load-side resource groups based on their complementary characteristics. The complementary characteristics reflect the interrelationships between different types of load-side resources; that is, the comprehensive optimization of two or more load-side resources yields better power grid balancing results than utilizing individual load-side resources separately.

[0111] Meanwhile, the measurement of load-side resource complementarity is mainly presented by correlation and the regulation characteristics after aggregation. Therefore, the theoretical indicators of load-side resource complementarity are sorted out in two parts: correlation theory and regulation characteristic theory. The load-side resources that should be invested at each moment are continuously optimized, and a relatively stable scheduling potential can be obtained by regulating different load-side resources in different time periods.

[0112] Based on this, in one possible implementation, the multi-type load-side resource aggregation method further includes: grouping the unaggregated load-side resources into a preset number to obtain several undetermined load-side resource groups, obtaining the complementarity index of each undetermined load-side resource group, and taking the undetermined load-side resource groups with complementarity indices greater than a preset complementarity index threshold as the final load-side resource groups; obtaining the descending ranking of each load-side resource group based on scenario adaptability index in each type of balanced scenario; and for each load-side resource group, performing the aggregation step as the current load-side resource in the aggregation step.

[0113] Optionally, in this embodiment, the combined load-side resources are measured based on a complementarity index to determine whether such a combination is suitable as a new load-side resource to participate in load-side resource aggregation.

[0114] In this embodiment, the complementarity index is presented by adjusting the capacity complementarity rate, response speed complementarity rate, and scheduling duration ratio complementarity rate. The complementarity index is obtained by weighted superposition of the capacity complementarity rate, response speed complementarity rate, and scheduling duration ratio complementarity rate.

[0115] Specifically, the capacity complementarity rate (CCR) is quantified based on the capacity complementarity of joint scheduling of load-side resources, constructing a complementarity evaluation index applicable to joint scheduling of multiple load-side resources. The specific calculation method for the capacity complementarity rate (CCR) is as follows:

[0116]

[0117] Where, α k t1 represents the proportion of the k-th type of load-side resource in the current undetermined load-side resource group; t1 is the start time of the scheduling period; t2 is the end time of the scheduling period. Let be the adjustment capacity of the k-th load-side resource at time t. The adjustment capacity complementarity rate ranges from [0,1], and the larger the value, the stronger the complementarity between the load-side resources in the load-side resource group.

[0118] Response speed refers to the slope characteristic of a continuously rising or falling curve of the combined load power curve over a continuous time window. The specific calculation method for the Response Speed ​​Complementarity (RSCR) is as follows:

[0119]

[0120] Where, ν c The ramp-up rate of the current undetermined load-side resource group. Let be the ramp rate of the k-th load-side resource at time t. The range of the response speed complementarity rate is the same as that of the regulation capacity complementarity rate, which is also within the range of [0,1]. The larger the value, the better the complementarity between the load-side resources in the load-side resource group.

[0121] Dispatch duration refers to the maximum duration for which dispatchable loads can sustain under the dispatch capacity demand proposed by the dispatch center to maintain power grid balance. Similar to other complementary indicators, the Dispatch Duration Percentage Complementarity Ratio (SDCR) can be calculated using the proportion of joint dispatch duration and the proportion of independent dispatch duration. Specifically, the calculation method for the Dispatch Duration Percentage Complementarity Ratio (SDCR) is as follows:

[0122]

[0123]

[0124] in, This represents the percentage of the scheduling duration for the currently pending load-side resource group. This represents the percentage of scheduling duration for the k-th type of load-side resource. The scheduling duration for the currently pending load-side resource group; T represents the scheduling duration of the k-th load-side resource; x Adjust the duration for the demand in the xth equilibrium scenario.

[0125] After obtaining the complementarity rate of adjustment capacity, the complementarity rate of response speed, and the complementarity rate of scheduling duration as a percentage, the complementarity index C of each undetermined load-side resource group is obtained using the following formula:

[0126]

[0127] Wherein, λ1 is the adjustment capacity complementarity weight, λ2 is the response speed complementarity weight, and λ3 is the scheduling duration percentage complementarity weight, which can be taken as 0.5, 0.2, and 0.3 respectively.

[0128] Therefore, the complementarity index of each pending load-side resource group can be determined through the above method. Then, the pending load-side resource groups with complementarity indices greater than a preset complementarity threshold are designated as the final load-side resource groups. These load-side resource groups are then used as the new form of load-side resources. The ranking of each load-side resource group based on scenario adaptability indices under various types of balanced scenarios is obtained, and an aggregation step is performed. Finally, each load-side resource group is successfully assigned to the set of balanced scenarios of each type. Furthermore, load-side resources that have not yet been aggregated are considered not to meet scheduling requirements and will no longer be considered or used.

[0129] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0130] See Figure 2 In another embodiment of the present invention, a multi-type load-side resource aggregation system is provided, which can be used to implement the above-mentioned multi-type load-side resource aggregation method. Specifically, the multi-type load-side resource aggregation system includes an information acquisition module and a first aggregation module.

[0131] The information acquisition module is used to acquire the descending sorting position of each load-side resource based on the scenario adaptability index under each type of balance scenario; the first aggregation module is used to perform aggregation steps for each load-side resource; the aggregation steps include: acquiring the type of balance scenario corresponding to the maximum descending sorting position of the current load-side resource, and using it as the target balance scenario type, and judging according to the preset target balance scenario type judgment rules. When the current load-side resource meets the target balance scenario type judgment rules, the current load-side resource is aggregated into the target balance scenario type set.

[0132] In one possible implementation, the types of balancing scenarios include ramp-up scenarios, peak-shaving scenarios, and valley-filling scenarios; the information acquisition module is specifically used to: obtain the scenario adaptability index Y of each load-side resource under the ramp-up scenario using the following formula. C,i :

[0133]

[0134] The following formula is used to obtain the scenario adaptability index Y of each load-side resource under the peak shaving scenario. P,i :

[0135]

[0136] The scenario adaptability index Y of each load-side resource in the valley filling scenario is obtained by the following formula. V,i :

[0137]

[0138] Based on the scenario adaptability index of each load-side resource in each type of balancing scenario, the load-side resources are sorted in descending order in each type of balancing scenario to obtain the descending ranking of each load-side resource based on the scenario adaptability index in each type of balancing scenario.

[0139] In one possible implementation, the adjustable capacity of the climbing scenario, the adjustable capacity of the peak shaving scenario, the adjustment duration of the peak shaving scenario, the adjustable capacity of the valley filling scenario, and the adjustment duration of the valley filling scenario are obtained as follows: The adjustable capacity curve of the load demand is obtained; the adjustable capacity curve of the load demand is divided into several discrete segments using a rotating door algorithm, and adjacent discrete segments with the same trend of change are merged; each discrete segment is identified according to a preset balanced scenario identification model to obtain the load demand time period for each type of balanced scenario; based on the load demand time period of each type of balanced scenario and the adjustable capacity curve of the load demand, the adjustable capacity of the climbing scenario, the adjustable capacity of the peak shaving scenario, the adjustment duration of the peak shaving scenario, and the adjustment capacity of the valley filling scenario are obtained.

[0140] In one possible implementation, the types of balancing scenarios include ramp-up scenarios, peak-shaving scenarios, and valley-filling scenarios; the target balancing scenario type determination rules include: when the target balancing scenario type is a ramp-up scenario, during the scheduling period, if the difference in load power between any two time points of the current load-side resource is greater than the maximum power output of the first preset proportion, the current load-side resource meets the target balancing scenario type determination rules; when the target balancing scenario type is a peak-shaving scenario, during the scheduling period, if the load power between any two time points of the current load-side resource is greater than the maximum power output of the second preset proportion, the current load-side resource meets the target balancing scenario type determination rules; when the target balancing scenario type is a valley-filling scenario, during the scheduling period, if the load power between any two time points of the current load-side resource is less than the maximum power output of the third preset proportion, the current load-side resource meets the target balancing scenario type determination rules.

[0141] In one possible implementation, the types of balancing scenarios include ramping scenarios, peak shaving scenarios, and valley filling scenarios; the aggregation step further includes: when the current load-side resources have the same descending sorting position in each type of balancing scenario, when the descending sorting position is in the first 1 / 3, the current load-side resources are aggregated into the ramping scenario set; when the descending sorting position is in the middle 1 / 3, the current load-side resources are aggregated into the peak shaving scenario set; when the descending sorting position is in the last 1 / 3, the current load-side resources are aggregated into the peak shaving scenario set.

[0142] In one possible implementation, the multi-type load-side resource aggregation system further includes a combination module and a second aggregation module. The combination module is used to group unaggregated load-side resources into several undetermined load-side resource groups by a preset number, and obtain the complementarity index of each undetermined load-side resource group. Undetermined load-side resource groups with complementarity indices greater than a preset complementarity index threshold are selected as the final load-side resource groups. The second aggregation module is used to obtain the descending ranking of each load-side resource group based on a scenario adaptability index under various types of balanced scenarios; and for each load-side resource group, it is used as the current load-side resource in the aggregation step for aggregation.

[0143] In one possible implementation, obtaining the complementarity index of each undetermined load-side resource group includes: obtaining the complementarity index C of each undetermined load-side resource group using the following formula:

[0144]

[0145] Wherein, CCR represents the capacity complementarity adjustment ratio. RSCR is the response speed complementarity. SDCR is the complementarity rate of scheduling duration as a percentage of total duration. in, λ1 is the adjustment capacity complementarity weight, λ2 is the response speed complementarity weight, and λ3 is the scheduling duration percentage complementarity weight.

[0146] All relevant content of each step involved in the aforementioned embodiments of the multi-type load-side resource aggregation method can be referenced from the functional description of the corresponding functional module of the multi-type load-side resource aggregation system in the embodiments of the present invention, and will not be repeated here. The module division in the embodiments of the present invention is illustrative and is only a logical functional division. In actual implementation, there may be other division methods. In addition, each functional module in the various embodiments of the present invention can be integrated into a processor, or it can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of software functional modules.

[0147] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve corresponding method flows or corresponding functions. The processor described in this embodiment of the present invention can be used for the operation of multi-type load-side resource aggregation methods.

[0148] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the multi-type load-side resource aggregation method in the above embodiments.

[0149] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for aggregating multiple types of load-side resources, characterized in that, include: Obtain the descending ranking of each load-side resource based on scenario adaptability indicators under various types of balancing scenarios; For each load-side resource, an aggregation step is performed separately; The aggregation step includes: obtaining the type of the balancing scenario corresponding to the maximum descending sorting position of the current load-side resources and using it as the target balancing scenario type; and judging according to the preset target balancing scenario type judgment rules. When the current load-side resources meet the target balancing scenario type judgment rules, the current load-side resources are aggregated into the target balancing scenario type set. The types of balancing scenarios include hill climbing scenarios, peak shaving scenarios, and valley filling scenarios; The ranking of each load-side resource in descending order based on scenario adaptability indicators under various types of balanced scenarios includes: The following formula is used to obtain the scenario adaptability index of each load-side resource in the ramping scenario. : in, For load-side resources i Response speed This represents the maximum response speed for all load-side resources. Adjustable capacity to meet the needs of hill climbing scenarios; For load-side resources i Adjustable capacity; This represents the maximum adjustable capacity of all load-side resources. The following formula is used to obtain the scenario adaptability index of each load-side resource under peak shaving scenarios. : in, Capacity can be adjusted to meet peak shaving requirements; For load-side resources i Adjustable capacity This represents the maximum adjustable capacity of all load-side resources. Adjust the duration to meet peak demand scenarios; For load-side resources i The duration of adjustment; This represents the maximum adjustment duration for all load-side resources. The following formula is used to obtain the scenario adaptability index of each load-side resource in the valley filling scenario. : in, The capacity can be adjusted to meet the needs of valley filling scenarios; For load-side resources i Adjustable capacity This represents the maximum adjustable capacity of all load-side resources. Adjust the duration to meet the needs of the valley filling scenario; Based on the scenario adaptability index of each load-side resource in each type of balancing scenario, the load-side resources are sorted in descending order in each type of balancing scenario to obtain the descending ranking of each load-side resource based on the scenario adaptability index in each type of balancing scenario. The rules for determining the target balance scenario type include: When the target balance scenario type is a ramp scenario, during the scheduling period, if the difference in load power between any two time points of the current load side resources is greater than the maximum power generation output of the first preset ratio, the current load side resources meet the target balance scenario type judgment rule. When the target balancing scenario type is peak shaving scenario, during the scheduling period, if the load power of the current load side resource is greater than the maximum generation side output of the second preset ratio between any two time points, the current load side resource meets the target balancing scenario type judgment rule. When the target balance scenario type is valley filling scenario, during the scheduling period, if the load power of the current load side resource is less than the maximum power generation output of the third preset ratio between any two time points, the current load side resource meets the target balance scenario type judgment rule.

2. The method for aggregating multiple types of load-side resources according to claim 1, characterized in that, The adjustable capacity required for the hill climbing scenario, the adjustable capacity required for the peak shaving scenario, the adjustable duration required for the peak shaving scenario, the adjustable capacity required for the valley filling scenario, and the adjustable duration required for the valley filling scenario are obtained in the following ways: Obtain adjustable capacity curves for load demand; The rotating door algorithm is used to divide the adjustable capacity curve of load demand into several discrete segments, and adjacent discrete segments with the same trend of change are merged. Based on the preset balanced scenario identification model, each discrete segment is identified to obtain the load demand time period for each type of balanced scenario. Based on the load demand periods and adjustable capacity curves for various load balancing scenarios, we obtain the adjustable capacity for ramping scenarios, the adjustable capacity for peak shaving scenarios, the adjustable duration for peak shaving scenarios, and the adjustable capacity for valley filling scenarios.

3. The method for aggregating multiple types of load-side resources according to claim 1, characterized in that, The types of balancing scenarios include hill climbing scenarios, peak shaving scenarios, and valley filling scenarios; The aggregation step further includes: when the current load-side resources have the same descending sorting position in each type of balancing scenario, when the descending sorting position is in the first 1 / 3, the current load-side resources are aggregated to the ramp-up scenario set; when the descending sorting position is in the middle 1 / 3, the current load-side resources are aggregated to the peak-shaving scenario set; when the descending sorting position is in the last 1 / 3, the current load-side resources are aggregated to the peak-shaving scenario set.

4. The method for aggregating multiple types of load-side resources according to claim 1, characterized in that, Also includes: Unaggregated load-side resources are grouped into several undetermined load-side resource groups by a preset number, and the complementarity index of each undetermined load-side resource group is obtained. The undetermined load-side resource groups with complementarity index greater than the preset complementarity index threshold are taken as the final load-side resource groups. Obtain the descending ranking of each load-side resource group in various types of balanced scenarios based on scenario adaptability indicators; And for each load-side resource group, it is used as the current load-side resource in the aggregation step for aggregation.

5. The method for aggregating multiple types of load-side resources according to claim 4, characterized in that, The acquisition of complementarity indicators for each undetermined load-side resource group includes: The complementarity index of each undetermined load-side resource group is obtained using the following formula. C : in, To adjust the capacity complementarity rate, ,in, For the first k The proportion of each type of load-side resource in the current undetermined load-side resource group; This is the start time of the scheduling period; This is the end time of the scheduling period; For a moment t Inner k The adjustment capacity of load-side resources; For complementary response speeds, ,in, The ramp-up rate of the current undetermined load-side resource group. For the first k Load-side resources at time t The rate of climb; The complementary ratio of scheduling duration proportion, ,in, , This represents the percentage of the scheduling duration for the currently pending load-side resource group. For the first k The percentage of scheduling duration for each type of load-side resource; The scheduling duration for the currently pending load-side resource group; For the first k The duration of scheduling of load-side resources; For the first x The duration of demand adjustment in various balanced scenarios; To adjust the capacity complementarity ratio weight, Response speed complementarity weight, The scheduling duration is weighted relative to the complementary ratio.

6. A multi-type load-side resource aggregation system, characterized in that, include: The information acquisition module is used to obtain the descending ranking of each load-side resource based on scenario adaptability indicators under various types of balancing scenarios; The first aggregation module is used to perform aggregation steps for each load-side resource. The aggregation steps include: obtaining the type of the balancing scenario corresponding to the maximum descending order of the current load-side resource and using it as the target balancing scenario type; and judging according to the preset target balancing scenario type judgment rules. When the current load-side resource meets the target balancing scenario type judgment rules, the current load-side resource is aggregated into the target balancing scenario type set. The types of balancing scenarios include hill climbing scenarios, peak shaving scenarios, and valley filling scenarios; The information acquisition module is specifically used for: The following formula is used to obtain the scenario adaptability index of each load-side resource in the ramping scenario. : in, For load-side resources i Response speed This represents the maximum response speed for all load-side resources. Adjustable capacity to meet the needs of hill climbing scenarios; For load-side resources i Adjustable capacity; This represents the maximum adjustable capacity of all load-side resources. The following formula is used to obtain the scenario adaptability index of each load-side resource under peak shaving scenarios. : in, Capacity can be adjusted to meet peak shaving requirements; For load-side resources i Adjustable capacity This represents the maximum adjustable capacity of all load-side resources. Adjust the duration to meet peak demand scenarios; For load-side resources i The duration of adjustment; This represents the maximum adjustment duration for all load-side resources. The following formula is used to obtain the scenario adaptability index of each load-side resource in the valley filling scenario. : in, The capacity can be adjusted to meet the needs of valley filling scenarios; For load-side resources i Adjustable capacity This represents the maximum adjustable capacity of all load-side resources. Adjust the duration to meet the needs of the valley filling scenario; Based on the scenario adaptability index of each load-side resource in each type of balancing scenario, the load-side resources are sorted in descending order in each type of balancing scenario to obtain the descending ranking of each load-side resource based on the scenario adaptability index in each type of balancing scenario. The rules for determining the target balance scenario type include: When the target balance scenario type is a ramp scenario, during the scheduling period, if the difference in load power between any two time points of the current load side resources is greater than the maximum power generation output of the first preset ratio, the current load side resources meet the target balance scenario type judgment rule. When the target balancing scenario type is peak shaving scenario, during the scheduling period, if the load power of the current load side resource is greater than the maximum generation side output of the second preset ratio between any two time points, the current load side resource meets the target balancing scenario type judgment rule. When the target balance scenario type is valley filling scenario, during the scheduling period, if the load power of the current load side resource is less than the maximum power generation output of the third preset ratio between any two time points, the current load side resource meets the target balance scenario type judgment rule.

7. The multi-type load-side resource aggregation system according to claim 6, characterized in that, Also includes: The combination module is used to group unaggregated load-side resources into a preset number to obtain several undetermined load-side resource groups, and to obtain the complementarity index of each undetermined load-side resource group. The undetermined load-side resource groups with complementarity index greater than the preset complementarity index threshold are taken as the final load-side resource groups. The second aggregation module is used to obtain the descending ranking of each load-side resource group in various types of balanced scenarios based on scenario adaptability indicators. And for each load-side resource group, it is used as the current load-side resource in the aggregation step for aggregation.

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, When the processor executes the computer program, it implements the steps of the multi-type load-side resource aggregation method as described in any one of claims 1 to 5.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-type load-side resource aggregation method as described in any one of claims 1 to 5.

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