Method and apparatus for multi-type flexible resource aggregation adjustment, terminal device and medium

Abstract

The application discloses a kind of multi-type flexibility resource aggregation regulation method, device, terminal equipment and medium, belong to electric power system resource regulation field, specifically: according to response time sequence parameter calculation associated index, based on associated index to technical attribute parameter and response time sequence parameter are weighted, obtain time sequence complementarity and power complementarity;Based on technical attribute parameter and response time sequence parameter determine resource regulation interval, based on network security constraint is boundary correction, obtain resource regulation capacity;According to technical attribute parameter, response time sequence parameter and associated index, calculate synergistic gain capacity, according to technical attribute parameter, time sequence complementarity and power complementarity, calculate conflict loss capacity;According to synergistic gain capacity and conflict loss capacity are corrected to resource regulation capacity, based on correction result, real-time operation data and power grid regulation demand generation regulation instruction.Therefore, by implementing the application, the accuracy of multi-type flexibility resource aggregation regulation can be improved.

Description

Technical Field

[0001] This invention relates to the field of power system resource regulation, and in particular to a method, apparatus, terminal equipment, and medium for aggregated regulation of multiple types of flexible resources. Background Technology

[0002] Existing methods for flexible resource regulation mainly focus on single resources or similar resources. Common methods include individual regulation control based on static characteristic modeling, virtual power plant aggregated regulation, and demand response optimization regulation. Existing methods typically model the capacity, power, response delay, and operating boundaries of resources such as electric vehicles, energy storage, air conditioning loads, and industrial interruptible loads separately. Based on this, they determine the aggregated regulation capacity using linear superposition or empirical estimation methods, and then generate regulation control commands.

[0003] However, existing technologies lack a systematic approach to identifying complementary and mutually exclusive relationships between different resource types, making it difficult to reveal the collaborative or conflicting mechanisms of different resources in response timing, power direction, and operational constraints. This results in poor coordination or even mutual cancellation among resources during regulation. Furthermore, current calculations of aggregated regulation capacity often employ simple linear superposition, failing to adequately account for collaborative gains and conflict losses between resources, leading to a mismatch between regulation commands and actual adjustable capacity. In addition, existing technologies do not adequately consider network security constraints such as the capacity of access points and transformers, potentially causing issued regulation commands to exceed the grid's carrying capacity, posing a security risk. Summary of the Invention

[0004] This invention provides a method, apparatus, terminal equipment, and medium for the aggregation and adjustment of multiple types of flexible resources, which can improve the accuracy of the aggregation and adjustment of multiple types of flexible resources.

[0005] This invention provides a method for aggregating and adjusting multiple types of flexible resources, including: Acquire the power grid regulation demand and the technical attribute parameters, response timing parameters, and real-time operation data of various flexible resources in the resource aggregate; Based on the response time series parameters, association rules are mined to obtain association indicators. Based on the association indicators, the technical attribute parameters and the response time series parameters are weighted and summed to obtain the time series complementarity and power complementarity. Based on the technical attribute parameters and the response timing parameters, the resource adjustment range is determined and network security constraints are corrected to obtain the resource adjustment capacity. The cooperative gain capacity is determined based on the correlation index, the conflict loss capacity is determined based on the timing complementarity and the power complementarity, and the resource adjustment capacity is corrected based on the cooperative gain capacity and the conflict loss capacity. Based on the correction results, the real-time operating data, and the power grid regulation requirements, regulation commands are generated and sent to the execution terminals of each of the flexibility resources to control the power regulation of each flexibility resource.

[0006] This invention provides a data foundation for subsequent complementary and mutually exclusive analysis and aggregation calculations by acquiring the technical attribute parameters and response timing parameters of various flexible resources. By calculating correlation indicators using response timing parameters and weighted summing to obtain timing complementarity and power complementarity, it quantifies the matching correlation between timing responses and power regulation among different flexible resources, providing complementary characteristic support for subsequent aggregation calculations. By determining the resource regulation range of the resource aggregate using technical attribute parameters and response timing parameters and correcting for network security constraints, it incorporates grid safety operation limitations into the aggregation results, ensuring that the regulation capacity does not exceed the actual carrying capacity of the grid. By calculating the cooperative gain capacity using correlation indicators and the conflict loss capacity using complementarity, it quantifies the additional regulation capacity brought by mutual cooperation among flexible resources and the reduction in regulation capacity caused by mutual conflict, overcoming the evaluation bias of simple linear superposition. By generating and issuing regulation commands based on the correction results, real-time operating data, and grid regulation needs, it transforms the aggregation regulation results into actual power control of various flexible resources, achieving closed-loop control from evaluation to execution. Compared to existing technologies that ignore the mutual exclusion and complementarity relationships between cross-type flexible resources, this application can improve the accuracy of aggregation regulation of multiple types of flexible resources.

[0007] Furthermore, before acquiring the technical attribute parameters, response timing parameters, and real-time operating data of various flexibility resources in the power grid regulation demand and resource aggregation system, the following steps are also included: Retrieve the technical attribute parameters of all preset types of flexibility resources; Based on the aforementioned technical attribute parameters, the K-means clustering algorithm is used to group the various flexibility resources to obtain the resource aggregates.

[0008] This invention obtains resource aggregates by acquiring the technical attribute parameters of various flexible resources and using the K-means clustering algorithm to group them. It can group flexible resources with similar static characteristics into the same aggregate, so that subsequent complementary and mutually exclusive analysis and aggregation calculation can be performed within the same characteristic range, thereby improving analysis efficiency.

[0009] Furthermore, the step of performing association rule mining based on the response time-series parameters to obtain association indicators includes: Based on the preset scheduling duration, the response timing parameters of each flexible resource are discretized to obtain the response status of each flexible resource in different scheduling periods; wherein, the response status includes no response, positive response, and negative response; Based on each of the aforementioned response states, the correlation index is calculated; wherein, the correlation index includes support and confidence; the support represents the frequency of a combination of response states occurring in all scheduling periods; the confidence represents the conditional probability that when one type of flexible resource presents a preset response state, another type of flexible resource corresponds to a preset response state.

[0010] This invention, by discretizing the response time-series parameters into three response states—positive response, negative response, and no response—and calculating support and confidence, can extract quantitative indicators that reflect the correlation between response behaviors of flexible resources, providing a basis for subsequent complementarity calculations.

[0011] Further, the step of weighted summing of the technical attribute parameters and the response time series parameters based on the correlation index to obtain the time series complementarity and power complementarity includes: Extract the inherent response delay, ramp rate limit, and maximum response duration from the technical attribute parameters; extract the response timing synchronization parameter and response timing lag parameter from the response timing parameters. Based on the aforementioned correlation indicators, the weights corresponding to the inherent response delay, the upper limit of the ramp rate, and the maximum response duration are determined using the entropy weight method, and the weighted sum is performed according to each weight to obtain the temporal complementarity. Extract the adjustment direction type, rated adjustable power and power adjustment range from the technical attribute parameters, and extract the power direction coordination parameter and power adjustment matching degree from the response timing parameter; Based on the aforementioned correlation indicators, the weights corresponding to the adjustment direction type, the rated adjustable power, and the power adjustment range are determined using the entropy weight method, and the power complementarity is obtained by weighted summation according to each weight.

[0012] This invention extracts multiple core parameters from technical attribute parameters and response timing parameters, and then performs weighted summation based on the entropy weight method determined by the correlation index. This objectively reflects the contribution of each parameter to the timing complementarity and power complementarity, making the quantification of complementary and mutually exclusive relationships more accurate.

[0013] Further, the step of determining the resource adjustment range and performing network security constraint correction based on the technical attribute parameters and the response timing parameters to obtain the resource adjustment capacity includes: Based on the maximum adjustable power in the technical attribute parameters and the adjustment direction characteristics in the response timing parameters, the individual adjustment range of each of the flexibility resources is determined; The resource adjustment interval is obtained by linearly superimposing the individual unit adjustment intervals; Based on the maximum transmission power, minimum transmission power, and baseline power flow of the resource aggregate, the upper and lower limits of the resource adjustment range are corrected to obtain the resource adjustment capacity.

[0014] The embodiments of the present invention determine the individual unit adjustment range by using the maximum adjustable power and adjustment direction characteristics and then linearly superimposing them. The boundary is then corrected based on the line transmission power at the access point and the reference power flow. This allows the theoretical adjustment capability of the aggregate to be obtained while ensuring that the adjustment capacity does not violate network security constraints.

[0015] Further, determining the cooperative gain capacity based on the correlation index and determining the conflict loss capacity based on the timing complementarity and the power complementarity includes: Based on the response timing parameters, the Pearson correlation coefficient between each of the flexibility resources is calculated, and the Pearson correlation coefficient is weighted and corrected based on the correlation index. The collaborative gain coefficient is determined based on the correction result. The cooperative gain capacity is calculated based on the cooperative gain coefficient and the maximum adjustable power in the technical attribute parameters. The overall mutual exclusion degree is determined based on the timing complementarity and the power complementarity, and the conflict loss coefficient is determined based on the overall mutual exclusion degree. The conflict loss capacity is calculated based on the conflict loss coefficient and the maximum adjustable power in the technical attribute parameters.

[0016] This invention calculates the Pearson correlation coefficient using response timing parameters and obtains the synergistic gain coefficient by weighting and correcting it with correlation indices. At the same time, it determines the comprehensive mutual exclusion and conflict loss coefficients through timing complementarity and power complementarity. This allows for the quantification of the positive improvement effect and negative reduction effect of flexible resource combination on aggregated capacity, providing specific values ​​for subsequent capacity correction.

[0017] Furthermore, the modification of the resource adjustment capacity based on the cooperative gain capacity and the conflict loss capacity includes: The corrected result is obtained by adding the resource adjustment capacity and the cooperative gain capacity, and then subtracting the conflict loss capacity.

[0018] By adding the resource adjustment capacity and the collaborative gain capacity and subtracting the conflict loss capacity, this embodiment of the invention can further consider the positive enhancement effect and negative reduction effect between flexibility resources on the basis of network security constraint correction, so that the final correction result is closer to the true adjustable capability of the resource aggregate.

[0019] Another embodiment of the present invention provides a multi-type flexible resource aggregation and adjustment device, including: a data acquisition module, a complementarity calculation module, an adjustment capacity calculation module, a correction amount calculation module, and an adjustment instruction generation module; The module includes a data acquisition module, a complementarity calculation module, a regulation capacity calculation module, a correction amount calculation module, and a regulation instruction generation module. The data acquisition module is used to acquire the power grid regulation demand and the technical attribute parameters, response timing parameters and real-time operation data of various flexible resources in the resource aggregate; The complementarity calculation module is used to perform association rule mining based on the response time series parameters to obtain association indicators, and to perform weighted summation of the technical attribute parameters and the response time series parameters based on the association indicators to obtain time series complementarity and power complementarity. The adjustment capacity calculation module is used to determine the resource adjustment range and perform network security constraint correction based on the technical attribute parameters and the response timing parameters to obtain the resource adjustment capacity. The correction calculation module is used to determine the cooperative gain capacity based on the correlation index, determine the conflict loss capacity based on the timing complementarity and the power complementarity, and correct the resource adjustment capacity based on the cooperative gain capacity and the conflict loss capacity. The regulation command generation module is used to generate regulation commands based on the correction results, the real-time operating data, and the power grid regulation requirements, and send them to the execution terminals of each of the flexibility resources to control each of the flexibility resources to perform power regulation.

[0020] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of a multi-type flexible resource aggregation and adjustment method as described in the present invention.

[0021] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform steps of a multi-type flexible resource aggregation and adjustment method as described in the present invention. Attached Figure Description

[0022] Figure 1 A flowchart illustrating one embodiment of the multi-type flexible resource aggregation and adjustment method provided by the present invention; Figure 2 This is a flowchart illustrating an embodiment of the K-means resource clustering algorithm provided by the present invention. Figure 3A schematic diagram of one embodiment of the elbow rule for determining the number of clusters provided by the present invention; Figure 4 A schematic diagram of one embodiment of the resource clustering scatter plot provided by the present invention; Figure 5 This is a schematic diagram of an embodiment of the comparison between the linear capacity and effective capacity of the resource aggregate provided by the present invention. Figure 6 A schematic diagram of an embodiment of the time-series curve of the aggregation regulation capability during the evening peak period provided by the present invention; Figure 7 This is a schematic diagram of one embodiment of the multi-type flexible resource aggregation and adjustment device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0025] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0028] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0029] See Figure 1 To address the problem that existing technologies neglect the mutually exclusive and complementary relationships between cross-type flexibility resources, an embodiment of the present invention provides a method for aggregating and adjusting multi-type flexibility resources, including steps S101 to S105: Step S101: Obtain the power grid regulation demand and the technical attribute parameters, response timing parameters and real-time operation data of various flexible resources in the resource aggregate.

[0030] It should be noted that obtaining the technical attribute parameters, response timing parameters, and real-time operating data of various flexible resources in the power grid regulation demand and resource aggregation system refers to: when executing an aggregated regulation task, obtaining the target power deficit that needs to be regulated at the current moment, retrieving the technical attribute parameters and response timing parameters of various flexible resources from a pre-built resource technical feature library, and collecting the actual operating data at the current moment from the execution terminals of various flexible resources. Among these, the technical attribute parameters and response timing parameters are data obtained through pre-modeling, while the real-time operating data is data collected each time a task is executed.

[0031] Preferably, before acquiring the power grid regulation demand and the technical attribute parameters, response timing parameters, and real-time operating data of various flexible resources in the resource aggregate, the method further includes: Retrieve the technical attribute parameters of all preset types of flexibility resources; Based on the aforementioned technical attribute parameters, the K-means clustering algorithm is used to group the various flexibility resources to obtain the resource aggregates.

[0032] In one embodiment, static technical attribute modeling and dynamic response mode modeling can be carried out for various types of flexible resources to construct a resource technical feature library; through the resource technical feature library, the technical attribute parameters and response timing parameters required for aggregation and adjustment can be obtained.

[0033] Furthermore, static technical attributes are the inherent technical parameters and constraints of flexible resources that do not change with the time of operation, including but not limited to maximum adjustable power, maximum number of start-stop cycles, inherent response delay, and ramp rate limit. Taking five types of flexible resources—electric vehicles, energy storage systems, industrial users, commercial users, and residential users—as examples, the static technical attribute modeling for each type of flexible resource includes, but is not limited to, the following: Maximum adjustable power of electric vehicles The power is determined by the smaller of the rated power of the charging pile and the maximum allowable power of the on-board charger, specifically: ; in, Rated power of the charging pile (kW); The maximum permissible power (kW) of the on-board charger. Typical values ​​range from 3 to 150 kW.

[0034] Maximum number of start-stop cycles for electric vehicles The daily limit is determined by taking the smaller value between the battery cycle life and the contactor mechanical life, specifically: ; in, The number of permissible charge-discharge cycles for the battery is converted to the number of start-stop cycles per day; The number of daily operations allowed by the contactor's mechanical life.

[0035] The inherent response delay of electric vehicles It is the sum of communication delay and controller response time, specifically: ; in, Communication delay (s); The controller response time (s); The typical value is 0.5 to 5 seconds.

[0036] Upper limit of hill climbing rate for electric vehicles The value is determined by the smaller of the power regulation capabilities of the battery management system and the charging pile, specifically: ; in, The maximum power change rate (kW / s) allowed by the battery management system. The charging pile power adjustment rate (kW / s); Typical values ​​range from 5 to 20 kW / s.

[0037] Maximum adjustable power of energy storage system The rated power of the energy storage converter determines the specific power output: ; in, Rated power (kW) of the energy storage converter; Typical values ​​range from 10 to 10,000 kW.

[0038] Maximum number of start-stop cycles for energy storage systems Due to limitations imposed by the cycle life of the energy storage medium and the lifespan of the converter switching devices, the minimum daily limit calculated from both is taken, specifically: ; in, The number of permissible cycles for the energy storage medium is converted to the number of start-stop cycles per day; The number of permissible daily operations for converter switching devices; The typical value is 50~200 times / day.

[0039] The inherent response delay of energy storage systems This is the sum of the converter controller response time and the communication delay, specifically: ; in, The converter controller response time (s); Communication delay (s); electrochemical energy storage system The typical value is 0.01~0.1 seconds, flywheel energy storage system Typical values ​​can be as low as 0.001 seconds.

[0040] Upper limit of ramp rate for energy storage systems It is determined by the ratio of the converter's rated power to its power rise time, specifically: ; in, The rated power of the converter (kW); The power rise time (s); Typical values ​​range from 50 to 200 kW / s.

[0041] Industrial users' flexible resources can be divided into the polysilicon industry and the electrolytic aluminum industry; among them, the maximum adjustable power of the polysilicon industry... The product of the number of reduction furnaces and the rated power of a single unit is as follows: ; in, This refers to the number of reduction furnaces; For the first Rated power of the reduction furnace (kW); Typical values ​​range from 1000 to 10000 kW.

[0042] Maximum number of start-stop cycles in the polysilicon industry Determined by the minimum shutdown time, specifically: ; in, The minimum shutdown time (h) is determined by the cooling and reheating time required after shutdown. The typical value is 1 to 2 times per day.

[0043] The inherent response delay in the polysilicon industry The sum of the control system response, actuator action, and furnace thermal inertia delay is as follows: ; in, The control system response time (s); The time of action of the actuator (s); The delay time (s) caused by the thermal inertia of the furnace body; The typical value is 5 to 15 seconds.

[0044] Upper limit of ramp rate in the polysilicon industry Constrained by the thermal inertia of the furnace body, specifically: ; in, This represents the change in power. It is a quantity that changes over time; Typical values ​​range from 10 to 50 kW / min.

[0045] Maximum adjustable power in the electrolytic aluminum industry The product of the number of electrolytic cells and the rated power of a single unit is as follows: ; in, This refers to the number of electrolytic cells; For the first Rated power of the electrolytic cell (kW); Typical values ​​range from 2000 to 20000 kW.

[0046] The maximum number of start-stop cycles per day in the electrolytic aluminum industry is 0 to 1; the inherent response delay in the electrolytic aluminum industry is the unified response time of the cluster, typically ranging from 5 to 20 seconds.

[0047] Upper limit of ramp-up rate in the electrolytic aluminum industry The current regulation rate is determined by the rated voltage of the electrolytic cell, specifically: ; in, The upper limit of the current regulation rate (A / s); Rated current (A); Rated power (kW); Typical values ​​range from 50 to 200 kW / min.

[0048] Commercial user-grade flexible resources can be divided into air conditioning and lighting; among them, the maximum adjustable power of air conditioning... It is determined by the building's usable area, the heating and cooling index per unit area, and the adjustment coefficient, specifically as follows: ; in, Usable floor area of ​​buildings serving air conditioning ( ); The heating and cooling index per unit area of ​​building ( For summer cooling, use 0.1-0.2. For winter heating, use 0.08-0.15. ); This is the air conditioning system adjustment coefficient (taken as 0.7-0.9, reflecting the actual adjustable proportion of the air conditioning system). Typical values ​​are 50-1000kW, with a single compressor rated power of 5-50kW. Air conditioning clusters consist of multiple compressors combined to match the building's cooling and heating load.

[0049] Maximum number of start-stop cycles for air conditioner The daily value is calculated by converting the hourly limit for a single compressor as follows: ; in, This refers to the number of times a single compressor can be started and stopped per hour. The minimum cycle time (h) of the refrigeration system; The typical value is 46 times / hour, which translates to 96,144 times / day. All compressors in the air conditioning cluster follow this limit for the number of start-stop cycles.

[0050] The inherent response delay of air conditioners The effects are influenced by the controller, compressor, and building thermal inertia, specifically: ; in, For controller response time; Delay the compressor start-up time; The building's thermal inertia coefficient; The building's usable floor area for air conditioning services; The typical value is 26 seconds. The larger the building area, the closer the response delay is to the upper limit.

[0051] Air conditioner ramp rate limit Constrained by frequency converter regulation capability and building heat capacity, specifically: ; in, This refers to the maximum adjustable power of the air conditioner (kW). The base time for full-range compressor power adjustment; The building heat capacity coefficient; Usable floor area of ​​buildings serving air conditioning ( ); Typical values ​​range from 5 to 25 kW / min. The larger the building area, the closer the climbing rate is to the lower limit.

[0052] Maximum adjustable power of lighting The product of the number of lamps and the rated power of a single unit is as follows: ; in, The number of lighting fixtures; For the first Rated power of the table lamp (kW); The typical power consumption for a single unit is 0.01~0.5kW, and for a cluster it is 10~200kW.

[0053] Maximum number of times the lighting can be turned on and off Due to the lifespan limitations of electronic switches, the maximum number of uses per day is ≥480. ; in, This refers to the number of times the light switch is allowed to start and stop per hour. The typical value is ≥20 times / hour, which translates to ≥480 times / day. In actual scheduling, it can be considered as having no strict limit.

[0054] Inherent response delay of lighting The sum of the operating times of the controller and the switching devices is as follows: ; in, The response time of the lighting controller (s); The switching device operating time (s); The typical value is 0.1 to 1 second.

[0055] The lighting's ramp rate can be adjusted instantaneously across the entire range, ≥100kW / min, with no rate limit.

[0056] Residential user-level flexible resources include air conditioners, water heaters, residential electric vehicles (EVs), and smart home devices, which participate in regulation in a clustered manner. The maximum adjustable power of each type of equipment is its rated value, with air conditioners at 1~3kW, water heaters at 1.5~4kW, and residential EVs at 3~7kW. The maximum number of start-stop cycles is constrained by the equipment's lifespan, with air conditioners and water heaters at 96~144 times per day and residential EVs at 3~10 times per day. The inherent response delay varies depending on the equipment type, with air conditioners at 1~3 seconds, water heaters at 2~5 seconds, and residential EVs at 0.5~2 seconds. The upper limit of ramp rate is limited by the adjustment capacity, with air conditioners and water heaters at 0.2~0.8kW / min and residential EVs at 2~10kW / min. Overall, the system exhibits characteristics of strong dispersion and a considerable total quantity.

[0057] Furthermore, dynamic response models characterize the behavior of flexible resources under dispatch commands as their output changes over time, including but not limited to four dimensions: load curve shape, response timing characteristics, adjustment direction characteristics, and multi-period coupling characteristics. Taking five types of flexible resources—electric vehicles, energy storage systems, industrial users, commercial users, and residential users—as examples, the dynamic response modeling for each type of flexible resource includes, but is not limited to, the following: The dynamic response of electric vehicles is influenced by user behavior, battery status, and charging constraints, exhibiting significant temporal and random characteristics. The load curve shows a bimodal pattern (18:00-22:00 and 22:00-06:00), with near-zero off-grid load during the day. Response timing is strongly correlated with time of day, with high potential during peak hours and extremely low response capability during the day, exhibiting a response delay of 5-30 seconds. It possesses bidirectional adjustment capabilities, with charging and discharging priorities determined by the battery's SOC (State of Charge). Multi-time-period coupling follows charging and discharging energy balance constraints, and the discharge amount is limited by the preceding charge amount. ; ; in, This refers to the discharge power. This refers to the charging power. The amount of electricity (kWh) that has been charged before discharge; Reserve electricity (kWh) for travel.

[0058] Energy storage systems are the most flexible resource with optimal dynamic response characteristics. Their load curves exhibit no fixed peak-valley patterns, allowing for flexible adjustment of charging and discharging power according to dispatch commands. Response timing reaches the millisecond level, with an inherent delay of 0.01~0.1 seconds, a ramp rate of 50~200kW / s, and a command following error of <±3%. They possess bidirectional adjustment capabilities, flexibly switching between charging and discharging states, with the adjustment range constrained by the upper and lower limits of SOC. Multi-time-period coupling is manifested in the continuous change of SOC over time, i.e.: ; in, and These are charging efficiency and discharging efficiency, respectively. The rated capacity (kWh) is the limit. This constraint restricts the energy storage system from charging and discharging simultaneously, and the cumulative charge and discharge capacity is subject to capacity boundary constraints.

[0059] The dynamic response of the polysilicon reduction furnace is constrained by the production process and thermal inertia. Its load curve exhibits characteristics of high base, low fluctuation, and continuous operation, maintaining a daily load of 145-155MW with fluctuations ≤±6%. The response delay is 5-15 seconds, the ramp rate is 10-50kW / min, and the adjustment process is smooth with no overshoot. Adjustment is primarily focused on power reduction, with bidirectional adjustment constrained by the process steady-state. Regarding multi-time-period coupling characteristics, the furnace's operating state is continuous in the time dimension, and power adjustment is constrained by the continuity of the production process. ; in, This is the upper limit for the ramp rate. Meanwhile, the duration of power reduction is constrained by the product batch, and a single adjustment cannot exceed the deviation time allowed by the process.

[0060] The dynamic response of the electrolytic aluminum cell is constrained by the coupling of current and cell temperature. The load curve exhibits extremely high base and very stable characteristics, with a daily load of 90-100MW and no significant peak-valley variations. The response delay is 5-20 seconds, the ramp rate is 50-200kW / min, and the current adjustment is slow and stable. It possesses bidirectional continuous adjustment capability within 50%-100% of the rated power range. The multi-time-period coupling is manifested in the strong correlation between cell temperature and current, namely: ; in, Electrolytic cell temperature ( ); This refers to the temperature response coefficient. Current (A); The rated current is required; temperature fluctuations must be controlled within the allowable range of the process.

[0061] The dynamic response of commercial air conditioning is dominated by building thermal inertia, with a load curve exhibiting a daytime peak from 8:00 to 22:00, a peak value from 12:00 to 18:00, and a nighttime trough. The response delay is 2–6 seconds, the ramp rate is 5–25 kW / min, and thermal inertia causes a regulation lag of 5–20 minutes. Regulation is primarily downward, with a downward adjustment range of 30%–60%, while small upward adjustments are limited. Multi-period coupling follows the building's thermal inertia energy balance constraints, namely: ; in, For the building's equivalent heat capacity ( ); For the building's equivalent thermal resistance ( ); This refers to the air conditioner's cooling / heating power (kW, negative for cooling and positive for heating). Heat dissipation for personnel and equipment (kW); Heat gain from solar radiation (kW); Indoor temperature ( ); Outdoor temperature ( ).

[0062] Commercial lighting is a purely electrical, fast-response resource. Its load curve is highly matched with commercial operating hours, exhibiting a stepped, stable load and zero load during non-operational periods. The response delay is only 0.1 to 1 second, enabling instantaneous full-range adjustment without inertial lag. It has bidirectional full-range adjustment capability with no constraints on the 0 to 100% adjustment range. There is no energy accumulation or thermal inertia, and the adjustment behavior at different times is completely independent, only needing to meet the minimum illuminance comfort requirements.

[0063] The dynamic response of flexible resources for residential users is influenced by user behavior and comfort constraints, exhibiting significant dispersion and randomness. The load curve shows a multi-peak pattern, with peaks between 7:00-9:00 AM and 6:00-10:00 PM. Individual household loads are low, but the total cluster load is large. Response timing varies considerably, with intelligent control taking 1-5 minutes and manual response taking 10-30 minutes. The ramp-up rate is limited by equipment type. Adjustment primarily involves reducing the power of air conditioners and water heaters, while residential electric vehicles possess bidirectional adjustment capabilities. Multi-time-period coupling follows the temperature inertia constraint of the temperature control equipment, i.e.: ; in, Indoor temperature; This refers to the temperature response coefficient. The reference power used to maintain the set temperature; This refers to the actual power. Similarly, water heater temperature changes must be controlled within a range acceptable to the user's comfort level.

[0064] In one embodiment, after obtaining technical attribute parameters from the resource technical feature library, core features can be extracted from these parameters to construct a standardized feature matrix. The K-means clustering algorithm is then used to group flexible resources and identify sets of flexible resources with similar inherent adjustment capabilities. Since the dimensions and numerical ranges of each feature index differ, to avoid a single feature dominating the clustering results, min-max standardization is used to normalize the initial feature matrix, eliminating the influence of dimensions. The standardization formula is: ; in, These are the standardized eigenvalues; For the first The first flexible resource The original values ​​of each feature; and The first The maximum and minimum values ​​of each feature are determined. The resulting standardized feature matrix serves as the core input to the K-means clustering algorithm.

[0065] Furthermore, the K-means clustering algorithm aims to minimize the sum of squared errors within clusters. It iteratively optimizes cluster centers and resource grouping, with the specific steps as follows: Figure 2 As shown, it includes: Determining the optimal number of clusters: The elbow rule is used to determine the optimal number of clusters, taking into account the actual number of samples for each type of flexibility resource. A curve was plotted with the number of clusters on the x-axis and the sum of squared errors within clusters (SSE) on the y-axis. The value corresponding to the inflection point where the curve transitions from steep to gentle was selected as the optimal number of clusters. .

[0066] Initialize cluster centers: randomly select from the normalized feature matrix The feature vectors of each flexible resource are used as the initial cluster centers. .

[0067] Sample allocation: Calculate the sample allocation for each flexibility resource. The Euclidean distance between cluster centers is used to assign samples to the clusters with the nearest cluster centers, achieving initial grouping. The formula for calculating the Euclidean distance is: ; in, For the first The first flexible resource sample to the The Euclidean distance between the cluster centers; For the first The first flexible resource One standardized feature value; For the first The first cluster center Each feature value.

[0068] Update cluster centers: For each cluster, calculate the average value of each feature of all flexibility resource samples within the cluster, and use this value as the new cluster center. The update formula is as follows: ; in, For the first The updated cluster centers of each cluster; For the first The number of flexible resource samples per cluster; For the first A sample set of flexible resources for each cluster.

[0069] Iterative convergence: Samples are continuously redistributed and cluster centers are updated until the cluster centers no longer change, or the sum of squared errors within clusters (SSE) tends to converge. The formula for calculating SSE is: ; At this point, the final cluster centers and resource grouping results are obtained.

[0070] Step S102: Based on the response time series parameters, perform association rule mining to obtain association indicators, and based on the association indicators, perform weighted summation on the technical attribute parameters and the response time series parameters to obtain time series complementarity and power complementarity.

[0071] It should be noted that, based on the response time-series parameters, association rule mining is performed to obtain association indicators. Then, based on these indicators, a weighted sum is applied to the technical attribute parameters and the response time-series parameters to obtain the time-series complementarity and power complementarity. This means that, in a single aggregation adjustment task, firstly, based on the response time-series parameters of various flexibility resources, quantitative indicators reflecting the degree of correlation between the response behaviors of any two types of flexibility resources are statistically calculated. Then, using these quantitative indicators as weight references, the core parameters extracted from the technical attribute parameters and the response time-series parameters are weighted and summed respectively, ultimately yielding two values ​​used to measure the degree of cooperation between resources: one is the time-series complementarity, which quantifies the cooperation effect of the two types of resources in terms of response time speed and duration; the other is the power complementarity, which quantifies the cooperation effect of the two types of resources in terms of adjustment direction and power magnitude.

[0072] Preferably, the step of performing association rule mining based on the response time-series parameters to obtain association indicators includes: Based on the preset scheduling duration, the response timing parameters of each flexible resource are discretized to obtain the response status of each flexible resource in different scheduling periods; wherein, the response status includes no response, positive response, and negative response; Based on each of the aforementioned response states, the correlation index is calculated; wherein, the correlation index includes support and confidence; the support represents the frequency of a combination of response states occurring in all scheduling periods; the confidence represents the conditional probability that when one type of flexible resource presents a preset response state, another type of flexible resource corresponds to a preset response state.

[0073] In one embodiment, firstly, based on the power system dispatching requirements, a 15-minute dispatching step size is selected, dividing the 24 hours into 96 continuous dispatching periods. Then, the response time-series parameters of various flexibility resources are uniformly discretized into three response states: no response, positive response, and negative response, constructing a typical daily time-series behavioral itemset. Simultaneously, effective rules for screening related indicators are set, and the calculation formula for the indicators is: Support: Represents the frequency of a set of behaviors occurring in 96 scheduling periods, reflecting the actual frequency of flexible resource combination response behaviors. The calculation formula is: ; in, , These represent the response status of two types of flexibility resources; A set of behavioral combinations of two types of flexible resources; For inclusion The number of itemsets; This represents the total number of itemsets.

[0074] Confidence level: indicates the likelihood of a behavior occurring. Under the premise that behavior occurs The conditional probability reflects the actual correlation strength between the response behaviors of flexible resources, and is calculated using the following formula: ; in, For inclusion The number of itemsets. Considering the coordination requirements of power system resource scheduling, a minimum confidence level of 70% is set.

[0075] Lifting degree: Indicates the degree at which a behavior occurs Under the premise that behavior occurs The probability and The ratio of the probabilities of two rules appearing alone reflects the actual effectiveness of the association rule. The calculation formula is as follows: ; When the lift is greater than 1, and A positive correlation exists, so the association rule is valid; when lift = 1, and They are independent of each other; when the lift is less than 1, and There is a negative correlation.

[0076] Specifically, association rule mining can be conducted using the Apriori algorithm based on association indicators to identify collaborative responses, substitutions, and conflicts among flexible resources. First, the discrete response states of each type of flexible resource are treated as 1-itemsets. Frequent 1-itemsets with sufficient support are selected. Then, frequent 2-itemsets are iteratively generated and selected based on these. These frequent 2-itemsets are then split into premise and conclusion items to generate candidate association rules. After calculating confidence and lift, valid rules are retained. Finally, the core features of the valid rules are extracted and classified to form a resource dynamic response association rule library. Based on this library, three types of relationships can be identified: collaborative response relationships refer to two types of flexible resources adjusting in the same direction with a lift of at least 1.5, where the adjustment capabilities can form a collaborative gain; substitution response relationships refer to two types of flexible resources achieving the same scheduling goal with a confidence of at least 80%, where the adjustment capabilities can complement each other; and conflict response relationships refer to two types of flexible resources adjusting in opposite directions with a lift of less than 1, where the adjustment capabilities will mutually deplete each other and violate the scheduling goal.

[0077] Preferably, the step of weighted summing of the technical attribute parameters and the response time series parameters based on the correlation index to obtain the time series complementarity and power complementarity includes: Extract the inherent response delay, ramp rate limit, and maximum response duration from the technical attribute parameters; extract the response timing synchronization parameter and response timing lag parameter from the response timing parameters. Based on the aforementioned correlation indicators, the weights corresponding to the inherent response delay, the upper limit of the ramp rate, and the maximum response duration are determined using the entropy weight method, and the weighted sum is performed according to each weight to obtain the temporal complementarity. Extract the adjustment direction type, rated adjustable power and power adjustment range from the technical attribute parameters, and extract the power direction coordination parameter and power adjustment matching degree from the response timing parameter; Based on the aforementioned correlation indicators, the weights corresponding to the adjustment direction type, the rated adjustable power, and the power adjustment range are determined using the entropy weight method, and the power complementarity is obtained by weighted summation according to each weight.

[0078] In one embodiment, the following core parameters can be selected from technical attribute parameters and response timing parameters to form the parameter system for timing complementary analysis: Inherent response delay The time it takes for a flexible resource to respond from receiving an instruction to initiating a response reflects the timeliness of the response. Climbing speed limit : The ability of flexible resources to change power per unit time reflects the speed of response; Maximum response duration The longest time that the flexible resources can maintain the rated regulating power reflects the continuity of the response; Response timing synchronization parameters The time synchronization coefficient for the response behavior of the two types of flexible resources has a value range of [0,1], and the closer it is to 1, the stronger the synchronization. Response timing lag Flexibility Resources Relative flexibility resources The response lag time is expressed in seconds (s).

[0079] The temporal complementarity factor comprehensively considers the above five core parameters and uses the entropy weight method to determine the weight of each parameter. Support and confidence levels are taken into account during the weight calculation process. Resource combinations with high support and strong confidence are assigned higher weights to their corresponding temporal parameters. The final result is obtained by weighted summation, and the calculation formula is as follows: ; in, Temporal complementarity is a core indicator for quantitatively evaluating the temporal complementarity of response between two types of flexible resources. The closer the value is to 1, the stronger the temporal complementarity; the closer the value is to 0, the weaker the temporal complementarity or even the existence of conflict. For the first The entropy weights of the parameters satisfy the following conditions: ; For the first A normalization function for each parameter converts the parameter values ​​into normalized values ​​within the range [0,1].

[0080] Specifically, using temporal complementarity Using the dividing point as a reference, and combining parameter characteristics with actual scheduling effects, we divide the core intervals into complementary and mutually exclusive ones: complementary regions This indicates that the response timing parameters and dynamic timing correlations of the two types of flexible resources are mutually compatible, and can form a timing-level coordination effect during the scheduling process, which can improve the overall time dimension adaptability of the adjustment and there is no obvious conflict at the timing level. Mutual Exclusive Intervals This indicates that the response timing parameters of the two types of flexibility resources conflict with each other. When combined, they can lead to timing disorder in grid power regulation and even exacerbate system power fluctuations, making it impossible to form an effective timing coordination.

[0081] In one embodiment, the following core parameters can be selected from technical attribute parameters and response timing parameters to form the parameter system for power direction complementarity analysis: Adjustment direction type : The ability to adjust the direction of flexible resources, 0 for unidirectional adjustment, 1 for bidirectional adjustment; Rated adjustable power The maximum adjustable power of flexible resources reflects the intensity of power regulation; Power adjustment range The ratio of adjustable power to rated power reflects the breadth of power regulation. Power Directional Coordination Parameters The coordination coefficient for the power adjustment direction of the two types of flexible resources, with a value range of [-1, 1]; Power regulation matching The matching coefficient of the adjustable power of the two types of flexible resources is calculated by the ratio of the rated adjustable power, and the value range is [0,1].

[0082] The calculation of power complementarity and timing complementarity adopts a unified modeling approach. Taking into account the five core parameters mentioned above, the entropy weight method is used to determine the weights of each parameter, and the final result is obtained by weighted summation. The calculation formula is as follows: ; in, Power complementarity is a core indicator for quantitatively evaluating the power direction complementarity between two types of flexible resources. The closer the value is to 1, the stronger the power direction complementarity; the closer the value is to 0, the weaker the power direction complementarity or even the existence of conflict. For the first The entropy weights of the power direction parameters satisfy the following conditions: ; For the first A normalization function for each parameter.

[0083] Specifically, in terms of power complementarity Using this as a dividing point, and combining the characteristics of power regulation direction with the actual effect of power grid operation, we divide the core intervals into complementary and mutually exclusive ones: complementary regions This indicates that the power regulation directions of the two types of flexible resources are effectively coordinated, which can cover the regulation needs of different dimensions in the process of power regulation of the power grid, or form positive and negative synergistic effects in the same regulation dimension, without any conflict at the power direction level. Mutual Exclusive Intervals This indicates that the power regulation directions of the two types of flexible resources are mutually conflicting. When combined, they will lead to an imbalance in the direction of power regulation in the power grid, and may even exacerbate voltage and frequency deviations, making it impossible to form an effective power direction coordination.

[0084] Step S103: Based on the technical attribute parameters and the response timing parameters, determine the resource adjustment range and perform network security constraint correction to obtain the resource adjustment capacity.

[0085] It should be noted that, based on the aforementioned technical attribute parameters and response timing parameters, determining the resource adjustment range and performing network security constraint corrections to obtain the resource adjustment capacity refers to the following: In a single aggregated adjustment task, firstly, based on the inherent adjustment capability and dynamic behavior characteristics of each type of flexible resource, the maximum power range that each type of flexible resource can adjust upwards and downwards during each scheduling period is determined. Then, the adjustment ranges of all flexible resources within the resource aggregate are linearly superimposed to obtain the theoretical total adjustment range of the aggregate. On this basis, this adjustment range is further compared with the line transmission capacity limit and transformer capacity limit of the access point where the resource aggregate is located. The adjustment range exceeding the grid's safe carrying capacity is discarded, and only the adjustment range within the grid's safe constraint range is retained, ultimately obtaining the actual resource adjustment capacity that can be used for scheduling.

[0086] Preferably, the step of determining the resource adjustment range and performing network security constraint correction based on the technical attribute parameters and the response timing parameters to obtain the resource adjustment capacity includes: Based on the maximum adjustable power in the technical attribute parameters and the adjustment direction characteristics in the response timing parameters, the individual adjustment range of each of the flexibility resources is determined; The resource adjustment interval is obtained by linearly superimposing the individual unit adjustment intervals; Based on the maximum transmission power, minimum transmission power, and baseline power flow of the resource aggregate, the upper and lower limits of the resource adjustment range are corrected to obtain the resource adjustment capacity.

[0087] In one embodiment, an interval format is used to uniformly describe the individual flexibility resources at time. Regulatory ability: ; in, and The first Flexible resources at any time The adjustable power lower and upper limits are determined by the power boundaries of different resource types, which are determined by their respective technical characteristics.

[0088] Specifically, the individual adjustment constraints for energy storage system-type flexibility resources are: ; ; in, and These are charging efficiency and discharging efficiency, respectively. and These are charging power and discharging power, respectively. Rated capacity; and These represent the lower and upper limits of SOC, typically ranging from 0.1 to 0.9. For temperature-controlled loads such as air conditioners and water heaters, their individual regulating capacity is constrained by the allowable fluctuation range of indoor and water temperatures. Taking air conditioners as an example, the individual regulating constraint for temperature-controlled loads is: ; ; in, Indoor temperature; This refers to the temperature response coefficient. The reference power used to maintain the set temperature; This refers to the actual power. and These represent the lower and upper limits of the indoor temperature, respectively. Constraints such as the maximum number of start-stop cycles, minimum operating and downtime for various types of flexibility resources limit the frequency and depth of adjustment of these resources per unit time. Taking industrial flexibility resources as an example, their start-stop constraints can be expressed as: ; ; in, For flexibility resources The operating status (1 indicates running, 0 indicates stopped); Maximum number of start / stop operations per day; and These are the minimum running time and the minimum downtime, respectively. and These are the running time and the downtime, respectively.

[0089] In one embodiment, for including Types of flexible resources, each type of flexible resource includes The total resource adjustment range of an aggregate of individual units can be expressed as: ; ; in, and These are the lower and upper limits of the resource adjustment range, respectively. and These represent the lower and upper limits of the individual unit's regulation range, respectively. This linear superposition model reflects the theoretical regulation capacity range of the resource aggregation. However, in actual operation, due to the asynchronicity of resource response timing, differences in regulation direction, and the existence of power grid physical constraints, the actual regulation capacity of the resource aggregation is often lower than this theoretical value. Therefore, it is necessary to introduce network security constraint correction and dynamic relationship correction based on this. Let the maximum transmission power of the line at the resource aggregation access point be... The minimum transmission power of the line is The baseline power flow of the line is The maximum power that the resource aggregate can inject into the power grid is limited by: ; When a resource aggregation absorbs power from the power grid, the following must be satisfied: ; Therefore, the resource regulation capacity limit under network security constraints is: ; ; in, and These are the lower and upper limits of the resource adjustment capacity, respectively; the revised resource adjustment capacity is the actual adjustable range under network security constraints.

[0090] Step S104: Determine the cooperative gain capacity based on the correlation index, determine the conflict loss capacity based on the timing complementarity and the power complementarity, and correct the resource adjustment capacity based on the cooperative gain capacity and the conflict loss capacity.

[0091] It should be noted that determining the synergistic gain capacity based on the aforementioned correlation indicators, determining the conflict loss capacity based on the aforementioned temporal complementarity and power complementarity, and correcting the resource adjustment capacity based on the synergistic gain capacity and the conflict loss capacity means that in a single aggregation adjustment task, two paths are used to calculate the positive gain generated by resource cooperation and the negative loss generated by resource conflict within the resource aggregate. The first path calculates the consistency of the response curves between any two types of flexible resources based on the response power time series data of various flexible resources, corrects this consistency using correlation indicators to obtain the synergistic gain coefficient, and then combines it with the maximum adjustable power of the flexible resources themselves to obtain the synergistic gain capacity. The second path determines the comprehensive mutual exclusion degree between flexible resources based on the previously calculated temporal complementarity and power complementarity, converts it into a conflict loss coefficient, and then combines it with the maximum adjustable power to obtain the conflict loss capacity. Both methods quantify the additional enhancement effect of flexible resource combinations on aggregation adjustment capabilities and the reduction effect caused by internal mutual constraints.

[0092] Preferably, determining the cooperative gain capacity based on the correlation index and determining the conflict loss capacity based on the timing complementarity and the power complementarity includes: Based on the response timing parameters, the Pearson correlation coefficient between each of the flexibility resources is calculated, and the Pearson correlation coefficient is weighted and corrected based on the correlation index. The collaborative gain coefficient is determined based on the correction result. The cooperative gain capacity is calculated based on the cooperative gain coefficient and the maximum adjustable power in the technical attribute parameters. The overall mutual exclusion degree is determined based on the timing complementarity and the power complementarity, and the conflict loss coefficient is determined based on the overall mutual exclusion degree. The conflict loss capacity is calculated based on the conflict loss coefficient and the maximum adjustable power in the technical attribute parameters.

[0093] In one embodiment, for any two types of flexibility resources and The Pearson correlation coefficient of its response power sequence Defined as: ; in, and These are the response power sequences for two types of flexibility resources, respectively. and These are the mean values ​​of the response power sequences for the two types of flexible resources, respectively. Positive values ​​indicate a positive correlation, and negative values ​​indicate a negative correlation. Based on support... With confidence level The Pearson correlation coefficient is weighted and adjusted to better reflect actual scheduling scenarios: ; in, and These are the weighting coefficients for support and confidence. Based on the modified Pearson correlation coefficient. Define the cooperative gain coefficient : ; in, This is the gain coefficient. The synergistic gain coefficient reflects the positive enhancement effect of flexible resource combinations on resource regulation capacity. The larger the value, the more significant the additional regulation capacity generated by the two types of flexible resources in synergistic regulation. Based on this, the synergistic gain capacity of the resource aggregate... It can be quantified as: ; in, The number of resource types within the resource aggregate; To minimize the maximum adjustable power, the resource should be set to a smaller value to avoid overestimating the gain.

[0094] In one embodiment, a combined mutual exclusion degree is determined based on temporal complementarity and power complementarity to quantify the loss of aggregation capability due to conflict. A conflict loss coefficient is defined based on the combined mutual exclusion degree. : ; in, The conflict loss coefficient reflects the negative impact of flexible resource combinations on resource regulation capacity. A larger value indicates a more significant loss of regulation capacity during coordinated regulation between the two types of flexible resources. Based on this, the conflict loss capacity of the resource aggregate... It can be quantified as: .

[0095] Preferably, the step of correcting the resource adjustment capacity based on the cooperative gain capacity and the conflict loss capacity includes: The corrected result is obtained by adding the resource adjustment capacity and the cooperative gain capacity, and then subtracting the conflict loss capacity.

[0096] In one embodiment, the ultimate effective regulation capability of the resource aggregate for: ; in, To adjust capacity for resources; For cooperative gain capacity; This is for conflict-induced capacity loss.

[0097] Step S105: Based on the correction results, the real-time operating data, and the power grid regulation requirements, generate regulation commands and send them to the execution terminals of each of the flexibility resources to control each of the flexibility resources to perform power regulation.

[0098] It should be noted that, based on the correction results, the real-time operating data, and the power grid regulation requirements, generating regulation commands and issuing them to the execution terminals of each of the aforementioned flexible resources to control the power regulation of each of the aforementioned flexible resources means that, in a single aggregate regulation task, the resource regulation capacity after network security constraint correction is added to the cooperative gain capacity, and the conflict loss capacity is subtracted to obtain the final effective regulation capacity; then, based on the actual power deficit of the power grid at the current moment, combined with the real-time operating data of each flexible resource, its current available regulation margin is determined, the effective regulation capacity is converted into the power regulation command corresponding to each flexible resource, and issued to the execution terminal through the communication link, so that each execution terminal drives the corresponding device to complete the actual power regulation action.

[0099] In one embodiment, the dispatching master station collects real-time physical operation measurement data such as power flow, transformer load rate, and system frequency of the power grid through the SCADA system and phasor measurement unit (PMU), calculates the power deviation between the current net load of the power grid and the target load curve, and obtains the total physical power regulation required during the dispatching period, i.e., the power grid regulation demand. (MW). The ultimate effective regulation capacity of multiple resource aggregates. Using the core constraint input, resource priority ranking is completed based on the physical regulation characteristics of the power system. The ranking rule is as follows: Level 1: Prioritize invoking inherent response latency Climbing speed Rapid bidirectional resource aggregators, such as energy storage systems and public charging stations for EVs, are used to undertake the task of emergency frequency regulation in seconds. Level 2: Call response delay It is a resource aggregator with flexible temperature control and traffic flexibility, with a temperature control margin of 1~5 seconds, enabling it to undertake the physical regulation task of peak shaving and valley filling at the minute level; Level 3: Utilize industrial load-stable resource aggregates with large rated capacity and stable continuous operation to undertake long-term power stabilization and physical regulation tasks.

[0100] The sorting result is an ordered sequence The output serves as the technical basis for subsequent physical instruction allocation.

[0101] Furthermore, based on the physical power balance constraints and capacity ratio allocation principles of the power grid, the power grid regulation demand is... The target physical power adjustment amount of each resource aggregate is obtained by decomposing it step by step to each resource aggregate. And satisfy: ; The inherent response delay is taken into account during the decomposition process. Climbing speed limit and maximum number of start-stops Given the physical constraints of the equipment, each generated instruction must contain at least five types of physical control parameters, namely the target physical power adjustment amount. (MW); Command issuance time When physical adjustment takes effect Continuous physical adjustment duration (min); Permissible power deviation band (MW). Among them, the time when physical regulation takes effect. satisfy: ; The above five types of parameters together constitute the structured power regulation command message, which serves as the standard data carrier for communication.

[0102] Furthermore, the dispatch master station transmits the generated adjustment commands via the power dispatch data network and encrypted secure communication links to the virtual power plant management platforms and edge control devices corresponding to each resource aggregate. After receiving the overall adjustment command, the aggregate control platform, based on the real-time operating data of each individual flexible resource under its jurisdiction—such as current output power, battery SOC, equipment temperature, and operating constraint margins—uses a secondary decomposition algorithm based on the physical state of the resources to process the overall command of the resource aggregate. Decomposed into physical control target values ​​for individual flexible resources ,satisfy Furthermore, the target control value of each individual unit does not exceed its physical adjustable power boundary. The decomposed unit control commands are sent to the execution terminals corresponding to the flexibility resources of each unit via local area communication hardware links such as RS485, ZigBee, and industrial Ethernet.

[0103] Furthermore, after receiving control commands, the execution terminals of each individual flexibility resource immediately drive the corresponding equipment to complete the actual physical power adjustment action. Taking five types of flexibility resources—energy storage systems, electric vehicles, commercial users, industrial users, and residential users—as examples, the physical execution paths for different types of flexibility resources are as follows: Energy storage system: The power conversion system (PCS) controller parses instructions, adjusts the PWM (Pulse Width Modulation) drive signal, controls the battery pack charging and discharging circuit, and realizes rapid and accurate adjustment of physical charging and discharging power; Electric vehicles: The charging pile control unit of electric vehicles adjusts the output current setting value through CP (Control Pilot) signal modulation and OCPP (Open Charge Point Protocol) communication, and drives the on-board charger to complete the physical charging and discharging power regulation. Commercial users: Commercial building automation systems send physical control signals to air conditioning and lighting controllers via the Modbus / BACnet protocol to drive compressor speed regulation and lighting dimming circuits to complete physical power adjustment; Industrial users: Industrial distributed control systems send power command signals to frequency converters and rectifiers through the Profibus / IP industrial bus to drive polysilicon reduction furnaces and electrolytic aluminum electrolytic cells to achieve stable physical power regulation. Residential users: The home energy management system sends commands to the indoor device controllers via Wi-Fi or Bluetooth to drive the air conditioner, water heater or household EV to complete the physical power adjustment.

[0104] All adjustment actions are in Starts at all times, physically constrained by the climbing rate. Smoothly climb to the target power and maintain it continuously. Ends after the allotted time.

[0105] Specifically, during the power regulation process, each execution terminal periodically collects the local actual physical output power. The data is reported level by level through the communication link to the aggregation control platform. The aggregation control platform summarizes the measured physical power of individual units and calculates the actual physical execution power of the resource aggregation. And calculate the execution deviation with the instruction target value. : ; when When the execution deviation exceeds a preset threshold, a physical closed-loop correction process will be automatically triggered. The aggregated control platform reassesses the remaining adjustment margin of each unit's flexibility resources, including physical constraints such as the number of overall start-stop cycles, SOC, and temperature, and adjusts the deviation proportionally according to the margin. The system allocates resources to individual flexible units with adjustment capabilities and generates correction commands, which are then sent to the corresponding execution terminals to achieve power compensation and deviation convergence. The aggregated control platform uploads physical execution deviation and correction process data to the scheduling master station, which then updates the resource priority and command allocation strategy for the next cycle, forming a closed-loop physical control mechanism encompassing physical quantity acquisition, command issuance, action execution, and deviation correction.

[0106] This invention provides a data foundation for subsequent complementary and mutually exclusive analysis and aggregation calculations by acquiring the technical attribute parameters and response timing parameters of various flexible resources. By calculating correlation indicators using response timing parameters and weighted summing to obtain timing complementarity and power complementarity, it quantifies the matching correlation between timing responses and power regulation among different flexible resources, providing complementary characteristic support for subsequent aggregation calculations. By determining the resource regulation range of the resource aggregate using technical attribute parameters and response timing parameters and correcting for network security constraints, it incorporates grid safety operation limitations into the aggregation results, ensuring that the regulation capacity does not exceed the actual carrying capacity of the grid. By calculating the cooperative gain capacity using correlation indicators and the conflict loss capacity using complementarity, it quantifies the additional regulation capacity brought by mutual cooperation among flexible resources and the reduction in regulation capacity caused by mutual conflict, overcoming the evaluation bias of simple linear superposition. By generating and issuing regulation commands based on the correction results, real-time operating data, and grid regulation needs, it transforms the aggregation regulation results into actual power control of various flexible resources, achieving closed-loop control from evaluation to execution. Compared to existing technologies that ignore the mutual exclusion and complementarity relationships between cross-type flexible resources, this application can improve the accuracy of aggregation regulation of multiple types of flexible resources.

[0107] Optionally, in this embodiment of the invention, before obtaining the technical attribute parameters, response timing parameters, and real-time operating data of various flexibility resources in the power grid regulation demand and resource aggregate, the following steps are further included: Retrieve the technical attribute parameters of all preset types of flexibility resources; Based on the aforementioned technical attribute parameters, the K-means clustering algorithm is used to group the various flexibility resources to obtain the resource aggregates.

[0108] This invention obtains resource aggregates by acquiring the technical attribute parameters of various flexible resources and using the K-means clustering algorithm to group them. It can group flexible resources with similar static characteristics into the same aggregate, so that subsequent complementary and mutually exclusive analysis and aggregation calculation can be performed within the same characteristic range, thereby improving analysis efficiency.

[0109] Optionally, in this embodiment of the invention, the step of performing association rule mining based on the response time-series parameters to obtain association indicators includes: Based on the preset scheduling duration, the response timing parameters of each flexible resource are discretized to obtain the response status of each flexible resource in different scheduling periods; wherein, the response status includes no response, positive response, and negative response; Based on each of the aforementioned response states, the correlation index is calculated; wherein, the correlation index includes support and confidence; the support represents the frequency of a combination of response states occurring in all scheduling periods; the confidence represents the conditional probability that when one type of flexible resource presents a preset response state, another type of flexible resource corresponds to a preset response state.

[0110] This invention, by discretizing the response time-series parameters into three response states—positive response, negative response, and no response—and calculating support and confidence, can extract quantitative indicators that reflect the correlation between response behaviors of flexible resources, providing a basis for subsequent complementarity calculations.

[0111] Optionally, in this embodiment of the invention, the step of weighted summing of the technical attribute parameters and the response time series parameters based on the correlation index to obtain the time series complementarity and power complementarity includes: Extract the inherent response delay, ramp rate limit, and maximum response duration from the technical attribute parameters; extract the response timing synchronization parameter and response timing lag parameter from the response timing parameters. Based on the aforementioned correlation indicators, the weights corresponding to the inherent response delay, the upper limit of the ramp rate, and the maximum response duration are determined using the entropy weight method, and the weighted sum is performed according to each weight to obtain the temporal complementarity. Extract the adjustment direction type, rated adjustable power and power adjustment range from the technical attribute parameters, and extract the power direction coordination parameter and power adjustment matching degree from the response timing parameter; Based on the aforementioned correlation indicators, the weights corresponding to the adjustment direction type, the rated adjustable power, and the power adjustment range are determined using the entropy weight method, and the power complementarity is obtained by weighted summation according to each weight.

[0112] This invention extracts multiple core parameters from technical attribute parameters and response timing parameters, and then performs weighted summation based on the entropy weight method determined by the correlation index. This objectively reflects the contribution of each parameter to the timing complementarity and power complementarity, making the quantification of complementary and mutually exclusive relationships more accurate.

[0113] Optionally, in this embodiment of the invention, determining the resource adjustment range and performing network security constraint correction based on the technical attribute parameters and the response timing parameters to obtain the resource adjustment capacity includes: Based on the maximum adjustable power in the technical attribute parameters and the adjustment direction characteristics in the response timing parameters, the individual adjustment range of each of the flexibility resources is determined; The resource adjustment interval is obtained by linearly superimposing the individual unit adjustment intervals; Based on the maximum transmission power, minimum transmission power, and baseline power flow of the resource aggregate, the upper and lower limits of the resource adjustment range are corrected to obtain the resource adjustment capacity.

[0114] The embodiments of the present invention determine the individual unit adjustment range by using the maximum adjustable power and adjustment direction characteristics and then linearly superimposing them. The boundary is then corrected based on the line transmission power at the access point and the reference power flow. This allows the theoretical adjustment capability of the aggregate to be obtained while ensuring that the adjustment capacity does not violate network security constraints.

[0115] Optionally, in this embodiment of the invention, determining the cooperative gain capacity based on the correlation index and determining the conflict loss capacity based on the timing complementarity and the power complementarity includes: Based on the response timing parameters, the Pearson correlation coefficient between each of the flexibility resources is calculated, and the Pearson correlation coefficient is weighted and corrected based on the correlation index. The collaborative gain coefficient is determined based on the correction result. The cooperative gain capacity is calculated based on the cooperative gain coefficient and the maximum adjustable power in the technical attribute parameters. The overall mutual exclusion degree is determined based on the timing complementarity and the power complementarity, and the conflict loss coefficient is determined based on the overall mutual exclusion degree. The conflict loss capacity is calculated based on the conflict loss coefficient and the maximum adjustable power in the technical attribute parameters.

[0116] This invention calculates the Pearson correlation coefficient using response timing parameters and obtains the synergistic gain coefficient by weighting and correcting it with correlation indices. At the same time, it determines the comprehensive mutual exclusion and conflict loss coefficients through timing complementarity and power complementarity. This allows for the quantification of the positive improvement effect and negative reduction effect of flexible resource combination on aggregated capacity, providing specific values ​​for subsequent capacity correction.

[0117] Optionally, in this embodiment of the invention, the step of correcting the resource adjustment capacity based on the cooperative gain capacity and the conflict loss capacity includes: The corrected result is obtained by adding the resource adjustment capacity and the cooperative gain capacity, and then subtracting the conflict loss capacity.

[0118] By adding the resource adjustment capacity and the collaborative gain capacity and subtracting the conflict loss capacity, this embodiment of the invention can further consider the positive enhancement effect and negative reduction effect between flexibility resources on the basis of network security constraint correction, so that the final correction result is closer to the true adjustable capability of the resource aggregate.

[0119] Based on the above-described method embodiments, this invention provides a specific embodiment, which selects the typical scenario of peak shaving during the evening rush hour on summer weekdays for simulation analysis. The simulation objects cover various types of flexible resources, including industrial, commercial, residential, transportation, and energy storage resources, with a scheduling time scale of 15 minutes.

[0120] Specifically, the peak shaving scenario during the evening rush hour from 18:00 to 21:00 on summer weekdays was selected in a mixed area of ​​industrial parks, buildings, and residential areas, as shown in Table 1.

[0121] Table 1 - Typical Scene Settings Furthermore, by selecting core parameters such as maximum adjustable power, number of start-stop cycles, response delay, and ramp rate, the characteristics of various flexibility resources differ significantly. Specific parameters are shown in Table 2.

[0122] Table 2 - Basic parameters of various flexibility resources during evening peak hours Furthermore, using four types of static indicators as clustering features, min-max standardization is first performed to eliminate dimensions; then the elbow rule is used to determine the optimal number of clusters K=4 (e.g., ...). Figure 3 (As shown in the figure); four types of resource clusters were obtained through K-means iteration, and the resource clustering scatter plots are as follows. Figure 4As shown in Table 3, combining clustering results and resource characteristics, various flexible resources can be divided into four categories.

[0123] Table 3 - Resource Clustering Results and Cluster Center Characteristics Furthermore, in the evening peak shaving scenario, four types of resource aggregates were constructed based on the above clustering results, and the adjustment capacity for each time period was calculated. The specific results are shown in Table 4.

[0124] Table 4 - Resource Aggregator Regulation Capacity at Different Times During Evening Peak Table 4 shows that the aggregation of various flexible resources during the evening peak period can form a strong peak-shaving capacity, reaching its peak between 19:30 and 19:45. Overall, the various types of flexible resources show good complementarity during the main peak-shaving phase; however, the aggregation capacity declines somewhat in the latter part of the evening peak. This indicates that in the latter part of the evening peak, there will still be some conflict losses between resources due to the superposition of recovery demand and the tightening of operating boundaries. To further analyze the aggregation correction effect, the peak period of 19:30-19:45 was selected to decompose the linear superposition capacity, cooperative gain, and conflict loss of each resource aggregation, and the specific results are shown in Table 5.

[0125] Table 5 - Resource Aggregate Adjustment Capacity and Correction Results As shown in Table 5, after correction for synergistic gain and conflict loss, the effective adjustment capability of the resource aggregate is higher than that of the linear superposition result, indicating that the overall relationship between the various types of flexible resources shows that the gain is greater than the loss.

[0126] Furthermore, the linear capacity and effective capacity of each resource aggregate are compared, for example... Figure 5 As shown, the effective capacity of each resource aggregate varies after correction. The effective capacity of the industrial stable load type has slightly decreased, while the effective capacity of the temperature-controlled flexible type, the rapid two-way type, and the traffic flexible type has increased.

[0127] Furthermore, the time-series curve of the aggregation regulation capacity during the evening peak period is as follows: Figure 6 As shown, the corrected aggregated capacity is generally higher than the linear superposition capacity throughout the evening peak period, indicating that the introduction of synergistic gain and conflict loss correction can more realistically reflect the actual aggregated adjustment capacity of various types of flexible resources.

[0128] This invention systematically identifies complementary and mutually exclusive relationships from the perspective of cross-type resources. It can not only determine whether flexible resources have adjustment capabilities, but also further determine in what response period and adjustment direction they are suitable for coordinated operation. By introducing correction mechanisms such as network security boundaries, cooperative gains, and conflict losses, the accuracy of aggregated adjustment capability assessment can be improved, making the results more consistent with the actual operating constraints of the power grid and the true adjustable level of resources. At the same time, the correction mechanism can improve the response accuracy and execution reliability of multiple types of flexible resources participating in regulation.

[0129] like Figure 7 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a multi-type flexible resource aggregation and adjustment device, including: a data acquisition module 701, a complementarity calculation module 702, an adjustment capacity calculation module 703, a correction amount calculation module 704, and an adjustment command generation module 705; The data acquisition module 701 is used to acquire the power grid regulation demand and the technical attribute parameters, response timing parameters and real-time operation data of various flexible resources in the resource aggregate; The complementarity calculation module 702 is used to perform association rule mining based on the response time series parameters to obtain association indicators, and to perform weighted summation of the technical attribute parameters and the response time series parameters based on the association indicators to obtain time series complementarity and power complementarity. The adjustment capacity calculation module 703 is used to determine the resource adjustment range and perform network security constraint correction based on the technical attribute parameters and the response timing parameters to obtain the resource adjustment capacity. The correction calculation module 704 is used to determine the cooperative gain capacity based on the correlation index, determine the conflict loss capacity based on the timing complementarity and the power complementarity, and correct the resource adjustment capacity based on the cooperative gain capacity and the conflict loss capacity. The regulation command generation module 705 is used to generate regulation commands based on the correction results, the real-time operating data, and the power grid regulation requirements, and send them to the execution terminals of each of the flexibility resources to control each of the flexibility resources to perform power regulation.

[0130] Optionally, in this embodiment of the invention, a data acquisition submodule and a resource clustering submodule are further included before the data acquisition module 701; The data acquisition submodule is used to acquire the technical attribute parameters of all preset types of flexible resources; The resource clustering submodule is used to group the flexible resources based on the technical attribute parameters using the K-means clustering algorithm to obtain the resource aggregate.

[0131] This invention obtains resource aggregates by acquiring the technical attribute parameters of various flexible resources and grouping them using the K-means clustering algorithm. This allows flexible resources with similar static characteristics to be grouped into the same aggregate, enabling subsequent complementary and mutually exclusive analysis and aggregation calculations to be performed within the same characteristic range, thereby improving analysis efficiency.

[0132] Optionally, in this embodiment of the invention, the complementarity calculation module 702 includes: a response state determination submodule and an association index calculation submodule; The response status determination submodule is used to discretize the response timing parameters of each flexible resource according to a preset scheduling duration to obtain the response status of each flexible resource in different scheduling periods; wherein, the response status includes no response, positive response, and negative response. The correlation index calculation submodule is used to calculate the correlation index based on each of the response states; wherein, the correlation index includes support and confidence; the support represents the frequency of a response state combination occurring in all scheduling periods; the confidence represents the conditional probability that when one type of flexible resource presents a preset response state, another type of flexible resource corresponds to a preset response state.

[0133] This invention, by discretizing the response time-series parameters into three response states—positive response, negative response, and no response—and calculating support and confidence, can extract quantitative indicators that reflect the correlation between response behaviors of flexible resources, providing a basis for subsequent complementarity calculations.

[0134] Optionally, in this embodiment of the invention, the complementarity calculation module 702 further includes: a first parameter extraction submodule, a time-series complementarity calculation submodule, a second parameter extraction submodule, and a power complementarity calculation submodule; The first parameter extraction submodule is used to extract inherent response delay, ramp rate limit and maximum response duration from the technical attribute parameters, and to extract response timing synchronization parameter and response timing lag parameter from the response timing parameters. The temporal complementarity calculation submodule is used to determine the weights corresponding to the inherent response delay, the upper limit of the ramp rate, and the maximum response duration based on the correlation index using the entropy weight method, and to perform a weighted summation according to each weight to obtain the temporal complementarity. The second parameter extraction submodule is used to extract the adjustment direction type, rated adjustable power and power adjustment range from the technical attribute parameters, and to extract the power direction coordination parameter and power adjustment matching degree from the response timing parameter; The power complementarity calculation submodule is used to determine the weights corresponding to the adjustment direction type, the rated adjustable power, and the power adjustment range based on the correlation index using the entropy weight method, and to perform a weighted summation according to each weight to obtain the power complementarity.

[0135] This invention extracts multiple core parameters from technical attribute parameters and response timing parameters, and then performs weighted summation based on the entropy weight method determined by the correlation index. This objectively reflects the contribution of each parameter to the timing complementarity and power complementarity, making the quantification of complementary and mutually exclusive relationships more accurate.

[0136] Optionally, in this embodiment of the invention, the adjustment capacity calculation module 703 includes: a single-unit adjustment range determination submodule, a single-unit adjustment range superposition submodule, and a boundary correction submodule; The single-unit adjustment range determination submodule is used to determine the single-unit adjustment range of each of the flexibility resources based on the maximum adjustable power in the technical attribute parameters and the adjustment direction characteristics in the response timing parameters. The single-unit adjustment interval superposition submodule is used to linearly superimpose the single-unit adjustment intervals to obtain the resource adjustment interval. The boundary correction submodule is used to correct the upper and lower limits of the resource adjustment range based on the maximum transmission power, minimum transmission power, and reference power flow of the resource aggregate, so as to obtain the resource adjustment capacity.

[0137] The embodiments of the present invention determine the individual unit adjustment range by using the maximum adjustable power and adjustment direction characteristics and then linearly superimposing them. The boundary is then corrected based on the line transmission power at the access point and the reference power flow. This allows the theoretical adjustment capability of the aggregate to be obtained while ensuring that the adjustment capacity does not violate network security constraints.

[0138] Optionally, in this embodiment of the invention, the correction calculation module 704 includes: a cooperative gain coefficient determination submodule, a cooperative gain capacity calculation submodule, a conflict loss coefficient determination submodule, and a conflict loss capacity calculation submodule; The collaborative gain coefficient determination submodule is used to calculate the Pearson correlation coefficient between each of the flexibility resources based on the response timing parameters, and to perform a weighted correction on the Pearson correlation coefficient based on the correlation index, and to determine the collaborative gain coefficient based on the correction result. The cooperative gain capacity calculation submodule is used to calculate the cooperative gain capacity based on the cooperative gain coefficient and the maximum adjustable power in the technical attribute parameters. The conflict loss coefficient determination submodule is used to determine the comprehensive mutual exclusion degree based on the timing complementarity and the power complementarity, and to determine the conflict loss coefficient based on the comprehensive mutual exclusion degree. The conflict loss capacity calculation submodule is used to calculate the conflict loss capacity based on the conflict loss coefficient and the maximum adjustable power in the technical attribute parameters.

[0139] This invention calculates the Pearson correlation coefficient using response timing parameters and obtains the synergistic gain coefficient by weighting and correcting it with correlation indices. At the same time, it determines the comprehensive mutual exclusion and conflict loss coefficients through timing complementarity and power complementarity. This allows for the quantification of the positive improvement effect and negative reduction effect of flexible resource combination on aggregated capacity, providing specific values ​​for subsequent capacity correction.

[0140] Optionally, in this embodiment of the invention, the correction calculation module 704 further includes: a resource adjustment capacity correction submodule; The resource adjustment capacity correction submodule is used to add the resource adjustment capacity to the cooperative gain capacity and subtract the conflict loss capacity to obtain the correction result.

[0141] By adding the resource adjustment capacity and the collaborative gain capacity and subtracting the conflict loss capacity, this embodiment of the invention can further consider the positive enhancement effect and negative reduction effect between flexibility resources on the basis of network security constraint correction, so that the final correction result is closer to the true adjustable capability of the resource aggregate.

[0142] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the multi-type flexible resource aggregation and adjustment method provided by any of the above-described method embodiments of the present invention.

[0143] This invention employs a data acquisition module 701 to acquire technical attribute parameters and response timing parameters of various flexible resources, providing a data foundation for subsequent complementary and mutually exclusive analysis and aggregation calculations. A complementarity calculation module 702 calculates correlation indicators and performs weighted summation to obtain timing complementarity and power complementarity, quantifying the matching correlation between timing responses and power regulation among different flexible resources, providing complementary characteristic support for subsequent aggregation calculations. A regulation capacity calculation module 703 determines the resource regulation range of the resource aggregate and performs network security constraint corrections, incorporating grid safety operation limitations into the aggregation results to ensure that the regulation capacity does not exceed the actual carrying capacity of the grid. A correction amount calculation module 704 calculates the cooperative gain capacity and conflict loss capacity, quantifying the additional regulation capacity brought about by mutual cooperation among flexible resources and the reduction in regulation capacity caused by mutual conflict, overcoming the evaluation bias of simple linear superposition. A regulation command generation module 705 generates and issues regulation commands based on the correction results, real-time operating data, and grid regulation needs, transforming the aggregation regulation results into actual power control of various flexible resources, achieving closed-loop control from evaluation to execution. Compared to existing technologies that ignore the mutually exclusive and complementary relationships between cross-type flexibility resources, this application can improve the accuracy of aggregation and adjustment of multi-type flexibility resources.

[0144] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0145] Based on the above-described embodiment of a multi-type flexible resource aggregation and adjustment method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a multi-type flexible resource aggregation and adjustment method according to any embodiment of the present invention.

[0146] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0147] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0148] The processor can 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0149] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the multi-type flexible resource aggregation and adjustment method described in any of the above-described method embodiments of the present invention.

[0150] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0151] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for aggregating and adjusting multiple types of flexible resources, characterized in that, include: Acquire the power grid regulation demand and the technical attribute parameters, response timing parameters, and real-time operation data of various flexible resources in the resource aggregate; Based on the response time series parameters, association rules are mined to obtain association indicators. Based on the association indicators, the technical attribute parameters and the response time series parameters are weighted and summed to obtain the time series complementarity and power complementarity. Based on the technical attribute parameters and the response timing parameters, the resource adjustment range is determined and network security constraints are corrected to obtain the resource adjustment capacity. The cooperative gain capacity is determined based on the correlation index, the conflict loss capacity is determined based on the timing complementarity and the power complementarity, and the resource adjustment capacity is corrected based on the cooperative gain capacity and the conflict loss capacity. Based on the correction results, the real-time operating data, and the power grid regulation requirements, regulation commands are generated and sent to the execution terminals of each of the flexibility resources to control the power regulation of each flexibility resource.

2. The method for aggregating and adjusting multiple types of flexible resources as described in claim 1, characterized in that, Before acquiring the technical attribute parameters, response timing parameters, and real-time operating data of various flexibility resources in the power grid regulation demand and resource aggregation system, the following steps are also included: Retrieve the technical attribute parameters of all preset types of flexibility resources; Based on the aforementioned technical attribute parameters, the K-means clustering algorithm is used to group the various flexibility resources to obtain the resource aggregates.

3. The method for aggregating and adjusting multiple types of flexible resources as described in claim 1, characterized in that, The step of performing association rule mining based on the response time-series parameters to obtain association indicators includes: Based on the preset scheduling duration, the response timing parameters of each flexible resource are discretized to obtain the response status of each flexible resource in different scheduling periods; wherein, the response status includes no response, positive response, and negative response; Based on each of the aforementioned response states, the correlation index is calculated; wherein, the correlation index includes support and confidence; the support represents the frequency of a combination of response states occurring in all scheduling periods; the confidence represents the conditional probability that when one type of flexible resource presents a preset response state, another type of flexible resource corresponds to a preset response state.

4. The method for aggregating and adjusting multiple types of flexible resources as described in claim 1, characterized in that, The step of weighted summation of the technical attribute parameters and the response time series parameters based on the correlation index to obtain the time series complementarity and power complementarity includes: Extract the inherent response delay, ramp rate limit, and maximum response duration from the technical attribute parameters; extract the response timing synchronization parameter and response timing lag parameter from the response timing parameters. Based on the aforementioned correlation indicators, the weights corresponding to the inherent response delay, the upper limit of the ramp rate, and the maximum response duration are determined using the entropy weight method, and the weighted sum is performed according to each weight to obtain the temporal complementarity. Extract the adjustment direction type, rated adjustable power and power adjustment range from the technical attribute parameters, and extract the power direction coordination parameter and power adjustment matching degree from the response timing parameter; Based on the aforementioned correlation indicators, the weights corresponding to the adjustment direction type, the rated adjustable power, and the power adjustment range are determined using the entropy weight method, and the power complementarity is obtained by weighted summation according to each weight.

5. The method for aggregating and adjusting multiple types of flexible resources as described in claim 1, characterized in that, The process of determining the resource adjustment range and performing network security constraint correction based on the technical attribute parameters and the response timing parameters to obtain the resource adjustment capacity includes: Based on the maximum adjustable power in the technical attribute parameters and the adjustment direction characteristics in the response timing parameters, the individual adjustment range of each of the flexibility resources is determined; The resource adjustment interval is obtained by linearly superimposing the individual unit adjustment intervals; Based on the maximum transmission power, minimum transmission power, and baseline power flow of the resource aggregate, the upper and lower limits of the resource adjustment range are corrected to obtain the resource adjustment capacity.

6. The method for aggregating and adjusting multiple types of flexible resources as described in claim 1, characterized in that, The determination of cooperative gain capacity based on the correlation index and the determination of conflict loss capacity based on the timing complementarity and the power complementarity include: Based on the response timing parameters, the Pearson correlation coefficient between each of the flexibility resources is calculated, and the Pearson correlation coefficient is weighted and corrected based on the correlation index. The collaborative gain coefficient is determined based on the correction result. The cooperative gain capacity is calculated based on the cooperative gain coefficient and the maximum adjustable power in the technical attribute parameters. The overall mutual exclusion degree is determined based on the timing complementarity and the power complementarity, and the conflict loss coefficient is determined based on the overall mutual exclusion degree. The conflict loss capacity is calculated based on the conflict loss coefficient and the maximum adjustable power in the technical attribute parameters.

7. The method for aggregating and adjusting multiple types of flexible resources as described in claim 1, characterized in that, The modification of the resource adjustment capacity based on the cooperative gain capacity and the conflict loss capacity includes: The corrected result is obtained by adding the resource adjustment capacity and the cooperative gain capacity, and then subtracting the conflict loss capacity.

8. A multi-type flexible resource aggregation and adjustment device, characterized in that, include: The module includes a data acquisition module, a complementarity calculation module, a regulation capacity calculation module, a correction amount calculation module, and a regulation instruction generation module. The data acquisition module is used to acquire the power grid regulation demand and the technical attribute parameters, response timing parameters and real-time operation data of various flexible resources in the resource aggregate; The complementarity calculation module is used to perform association rule mining based on the response time series parameters to obtain association indicators, and to perform weighted summation of the technical attribute parameters and the response time series parameters based on the association indicators to obtain time series complementarity and power complementarity. The adjustment capacity calculation module is used to determine the resource adjustment range and perform network security constraint correction based on the technical attribute parameters and the response timing parameters to obtain the resource adjustment capacity. The correction calculation module is used to determine the cooperative gain capacity based on the correlation index, determine the conflict loss capacity based on the timing complementarity and the power complementarity, and correct the resource adjustment capacity based on the cooperative gain capacity and the conflict loss capacity. The regulation command generation module is used to generate regulation commands based on the correction results, the real-time operating data, and the power grid regulation requirements, and send them to the execution terminals of each of the flexibility resources to control each of the flexibility resources to perform power regulation.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a multi-type flexible resource aggregation and adjustment method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a multi-type flexible resource aggregation and adjustment method as described in any one of claims 1-7.

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