Distribution transformer district adjustable and controllable resource capacity evaluation method and system based on multilayer clustering strategy

Abstract

The invention discloses a method and a system for evaluating the adjustable and controllable resource capacity of a distribution transformer district based on a multilayer clustering strategy, and relates to the technical field of power demand side management. The method comprises the following steps: carrying out modeling on adjustable resources of a distribution transformer area by adopting an equivalent thermal parameter model, implementing multi-layer clustering, carrying out multiple control on an adjustable resource cluster formed after the multi-layer clustering, judging whether three-phase loads of the distribution transformer area are balanced or not, and if so, executing a three-phase load balance control scheme, if not, executing a three-phase load imbalance control scheme; and according to the result of the control scheme, constructing an adjustable resource capacity evaluation model in combination with the user comfort, the user intention and the user controllability, and evaluating the adjustable capacity of the adjustable resource. Through the method and the system provided by the invention, the regulation capability and the stability of the power grid are improved, the balance between the user demand and the power grid efficiency is met, the energy use efficiency is improved to the maximum extent, and the reduction of the energy consumption is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of power demand side management, and particularly to a method and system for evaluating the capacity of adjustable resources in a distribution transformer area based on a multi-layer clustering strategy. Background Art

[0002] Power demand side management (DSM) is an important means to improve energy efficiency, reduce energy consumption and environmental pollution. Power demand side management optimizes the grid load by regulating the power consumption pattern, reduces the system operation cost, and effectively improves the utilization rate of power resources. In the current power grid system dominated by renewable energy, the power load has volatility and uncertainty. How to efficiently manage adjustable resources has become a key issue in grid operation.

[0003] Existing methods for managing adjustable resources usually ignore factors such as user comfort, willingness and controllability, resulting in difficulty in balancing user needs and grid stability in practical applications, affecting the effect of demand response, making it impossible for the grid to accurately control resource scheduling, and thus reducing the energy utilization efficiency. In addition, existing adjustable resource models have limitations in dealing with three-phase load imbalance and three-phase load imbalance management, and fail to effectively aggregate and manage these adjustable resources, resulting in insufficient grid regulation ability and difficulty in achieving optimal resource allocation. Especially in large distribution transformer areas, the change of power demand is more complex, and the management and scheduling methods of existing technologies often cannot cope with the increasingly severe challenges of demand side management. Summary of the Invention

[0004] In view of the above problems, the present invention proposes a method and system for evaluating the capacity of adjustable resources in a distribution transformer area based on a multi-layer clustering strategy, which is used to optimize the management of adjustable resources in the distribution transformer area to improve the energy efficiency and reliability of the power grid.

[0005] The present invention realizes the above object through the following technical solutions:

[0006] On the one hand, the present invention proposes a method for evaluating the capacity of adjustable resources in a distribution transformer area based on a multi-layer clustering strategy, and the method includes:

[0007] Model the adjustable resources in the distribution transformer area by using an equivalent thermal parameter model, simplify the actual adjustable resources into a standard mathematical model, and the order of the equivalent circuit state space equation in the modeling method is determined by the environmental parameters in the equivalent process; the adjustable resources include resistance-type adjustable resources and asynchronous motor-type adjustable resources;

[0008] Implement multi-layer clustering on the model-processed adjustable resources to form multiple adjustable resource clusters;

[0009] The load status of the adjustable resource cluster formed after multi-layer clustering is judged and multiple controls are performed to judge whether the three-phase load of the distribution transformer area is balanced. The control strategy is switched according to the load status to ensure the stable operation of the system. If it is balanced, the three-phase load balance control scheme is executed; if it is unbalanced, the three-phase load unbalanced control scheme is executed;

[0010] According to the results of the control scheme, an adjustable resource capacity evaluation model is constructed in combination with user comfort, user willingness and user controllability to evaluate the adjustable capacity of the adjustable resources.

[0011] As a preferred solution of the present invention, the resistor-type adjustable resource refers to an adjustable resource whose load can be equivalent to a pure resistor. The general model is expressed as:

[0012]

[0013] In the formula, s t represents the switch operation state of the resistor-type adjustable resource at time t; Δt is the time interval; T i is the current ambient temperature; T set is the set temperature; δ is the temperature dead zone;

[0014] If the rated power of the resistor-type adjustable resource is P N , then the electric power P of the resistor-type controllable resource at time t is t , cooling or heating power Q t They are: t =P N ·s t , Q t =η·P t , where η is the energy efficiency ratio.

[0015] As a preferred solution of the present invention, the asynchronous motor type controllable resource refers to a controllable resource that can adjust the power output by changing the operating angular frequency. The general load model is expressed as:

[0016]

[0017] In the formula, ω I is the operating angular frequency of the adjustable resource of asynchronous motor, ω max and ω min They represent the maximum and minimum values ​​of the operating angular frequency respectively, ΔT is the difference between the current ambient temperature and the outdoor temperature, u and v represent the minimum and maximum temperature differences corresponding to the change of the operating angular frequency respectively;

[0018] Electric power P of adjustable resources such as asynchronous motors I , cooling or heating power Q I Simplified to the frequency f IThe relevant linear function is expressed as: Q I = η·P I , where a and b represent the constant coefficients of the function.

[0019] As a preferred embodiment of the present invention, the type clustering is specifically as follows: according to the power supply mode of the adjustable resources, the adjustable resources in the distribution transformer substation area are divided into two categories: three-phase power supply adjustable resources and single-phase power supply adjustable resources, and further according to different operation modes, the single-phase power supply adjustable resources are subdivided into resistance-type adjustable resources and asynchronous motor-type adjustable resources;

[0020] The phase type clustering is specifically as follows: the adjustable resources in the distribution transformer substation area are divided into two categories: three-phase type and single-phase type according to the phase type;

[0021] The parameter clustering is specifically as follows: analyze the thermal impedance parameters of different adjustable resources in the distribution transformer substation area, and cluster the adjustable resources with similar temperature change characteristics;

[0022] Among them, when the adjustable resources meet the following two conditions, they are regarded as having similar temperature change characteristics:

[0023] The difference in the time constant value τ does not exceed 5%, where τ = R×C, and R and C are the thermal resistance and heat capacity of the adjustable resources respectively;

[0024] Under the same external thermal disturbance, the difference in the steady-state temperature fluctuation amplitude is less than or equal to 2°C.

[0025] As a preferred embodiment of the present invention, the three-phase load balance control scheme specifically includes:

[0026] In the case of three-phase load balance, give priority to controlling the three-phase adjustable resources to avoid imbalance during power curtailment. If the power reduction amount ΔP that the three-phase adjustable resources can achieve C meets the total power reduction amount ΔP that the distribution transformer substation area needs to reduce n , then stop; otherwise, evenly distribute the remaining power reduction amount to the ABC three-phase adjustable resource clusters, and the formula is:

[0027]

[0028] In the formula, is the additional power reduction amount that each single phase needs to bear.

[0029] As a preferred embodiment of the present invention, the three-phase load imbalance control scheme specifically includes:

[0030] Denote the smallest phase among the ABC three phases as m, and the other two phases as α and β respectively. Calculate the power reduction amount of each phase relative to the smallest phase m, and the formula is:

[0031] Δζ M,α = P M,α - P M,m ;

[0032] Δζ M,β = P M,β - P M,m ;

[0033] Wherein, Δζ M,α , Δζ M,β are respectively the power reduction amounts of phase α and phase β relative to the minimum phase m; P M,α , P M,β are respectively the powers of phase α and phase β; P M,m is the power of the minimum phase m;

[0034] If the sum of the power reduction amounts of phase α and phase β, Δζ M,α + Δζ M,β is greater than or equal to the total power reduction amount ΔP n required to be reduced, then the power reduction amounts of phase α and phase β are allocated according to the following formula

[0035]

[0036] Conversely, if the sum of the power reduction amounts of phase α and phase β, Δζ M,α + Δζ M,β is less than the total power reduction amount ΔP n required to be reduced, then the remaining power reduction amount is borne by the three-phase adjustable resources, and the power reduction amount allocated to each phase is calculated according to the following formula:

[0037]

[0038] As a preferred embodiment of the present invention, the user comfort level is quantified by the predicted mean vote index, and the predicted mean vote index consists of four independent environmental parameters: ambient temperature, mean radiant temperature, air velocity, and air humidity. When the mean radiant temperature, air velocity, and air humidity are at a comfortable level, the relationship between the predicted mean vote index I PMV and the ambient temperature is:

[0039]

[0040] Wherein, I PMV is the predicted mean vote index value; T i is the current ambient temperature.

[0041] As a preferred embodiment of the present invention, the user preference is quantified by defining the user influence factor α i,t and the formula is:

[0042]

[0043] In the formula, p real is the current electricity price, p base is the basic electricity price, p max is the maximum electricity price, μ is the subjective response coefficient;

[0044] Temperature controllability refers to the set temperature range that the adjustable resources can dynamically adjust, which is obtained based on the user influence factor. The formula is:

[0045]

[0046] In the formula, and are the adjustable upper and lower limits of the initial temperature respectively; ΔT i,t is the temperature adjustment amount for the ith adjustable resource at time t; and They are the adjustable upper limit and lower limit of the set temperature after considering the user's wishes.

[0047] As a preferred solution of the present invention, the method of constructing an adjustable resource capacity evaluation model and evaluating the adjustable capacity of the adjustable resource includes:

[0048] When the adjustable resources are turned on and the terminal control device is installed, the lower limit of the adjustable range of the user-set temperature in each time period is calculated according to the electricity fee in each time period;

[0049] The reset temperature method is used to calculate the adjustable power of adjustable resources, taking the power consumed at the initial set temperature as the benchmark power P base , change the set temperature to the adjustable lower limit of the set temperature The formula for calculating the adjustable power of adjustable resources is obtained:

[0050]

[0051] In the formula, ΔP i is the adjustable power of the i-th adjustable resource; is the operating power of the i-th adjustable resource when the set temperature is at the adjustable lower limit of the set temperature;

[0052] The adjustable power of the adjustable resources running at different times of the day in the distribution transformer area is summed up and multiplied by the user controllability ξ to obtain the adjustable capacity evaluation model of the adjustable resources.

[0053] On the other hand, the present invention provides a system for evaluating the adjustable resource capacity of a distribution transformer area based on a multi-layer clustering strategy, which is applied to a method for evaluating the adjustable resource capacity of a distribution transformer area based on a multi-layer clustering strategy as described above, and includes:

[0054] A modeling module for modeling the adjustable resources in the distribution transformer area using an equivalent thermal parameter model, and simplifying the actual adjustable resources into a standard mathematical model;

[0055] A clustering module for performing multi-layer clustering on the modeled adjustable resources to form multiple adjustable resource clusters;

[0056] A control module for judging the load status and performing multiple controls on the adjustable resource clusters formed by the clustering module, judging whether the three-phase load of the distribution transformer area is balanced, and if it is balanced, executing a three-phase load balancing control scheme, and if it is unbalanced, executing a three-phase load unbalance control scheme;

[0057] A capacity evaluation module for constructing an adjustable resource capacity evaluation model based on the results of the control scheme, combined with user comfort, user willingness, and user controllability, and evaluating the adjustable capacity of the adjustable resources.

[0058] The beneficial effects of the present invention are as follows: Through refined modeling and resource management, the grid regulation ability and stability can be effectively improved; The equivalent thermal parameter model is used to model the resistance-type and asynchronous motor-type resources, and the resource scheduling is optimized through type, phase type, and parameter clustering to reduce the three-phase load imbalance problem; By comprehensively considering the thermal comfort, willingness, and controllability of users, the optimized management of adjustable resources is realized, the energy efficiency is improved, and the energy consumption is reduced, so as to achieve the goal of energy conservation and emission reduction. Not only the accuracy of grid load regulation is improved, but also the efficient operation of power demand side management is promoted. Description of the Drawings

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0060] Among them:

[0061] Figure 1 is the method flow chart in the embodiment of the present invention;

[0062] Figure 2 is the step block diagram of performing multi-layer clustering on adjustable resources in the embodiment of the present invention;

[0063] Figure 3It is a flowchart of a method for determining whether the three-phase load of a distribution transformer substation area is balanced in an embodiment of the present invention;

[0064] Figure 4 It is a schematic diagram of the system modular structure in an embodiment of the present invention. Specific embodiments

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the scope of protection of the present invention.

[0066] As Figure 1 shown, it is an embodiment of the present invention, and this embodiment provides a method for evaluating the adjustable resource capacity of a distribution substation area based on a multi-layer clustering strategy, including the following steps:

[0067] S1: Model the adjustable resources (including resistive adjustable resources and asynchronous motor adjustable resources) in the distribution substation area using an equivalent thermal parameter (ETP) model, simplify the actual adjustable resources into a standard mathematical model, and the order of the state space equation of the equivalent circuit in the modeling method is determined by the environmental parameters in the equivalent process;

[0068] Adjustable resources do not have a direct and measurable "form", but have certain electrical characteristics and dynamic behaviors, and these behaviors are affected by multiple factors such as the environment, load, and operating state. The purpose of modeling is to transform these abstract resource behaviors into a unified model form through a mathematical model (such as an equivalent thermal parameter model) to accurately describe the characteristics and operating laws of these resources, so that subsequent clustering, control scheme formulation, capacity evaluation, etc. can be optimized based on reliable data, enabling the system to manage and regulate them more precisely. In the large-scale adjustable resource aggregation model of the same distribution transformer substation area, although there are certain differences in building materials, house types, apartment types, etc., these differences have limited impact on the overall adjustable resource model. Therefore, in this embodiment, it is preferably to model the adjustable resources using a first-order ETP model, which can effectively reduce the model complexity and make the calculation more direct and fast.

[0069] Specifically, resistive adjustable resources refer to adjustable resources whose load can be equivalent to a pure resistor, and the working principle is: based on the thermal effect of the resistor, that is, using the heat generated when current passes through the resistor to achieve energy conversion and regulation, and its general model is expressed as:

[0070]

[0071] Where s t represents the switching operation state of the resistive adjustable resource at time t; Δt is the time interval, and s t-△t represents the switching operation state of the resistive adjustable resource at time t - Δt; T i is the current ambient temperature; T set is the set temperature; δ is the temperature dead zone; the temperature dead zone is an allowable temperature fluctuation range within which the adjustable resource will not change its operation state to avoid energy waste and equipment wear caused by frequent switching;

[0072] When the indoor temperature is lower than the set temperature minus half of the temperature dead zone, the resistive adjustable resource is turned off (s t = 0); when the indoor temperature is higher than the set temperature plus half of the temperature dead zone, the resistive adjustable resource is turned on (s t = 1); when the indoor temperature is within half of the temperature dead zone of the set temperature, the resistive adjustable resource maintains the state of the previous time interval Δt.

[0073] If the rated power of the resistive adjustable resource during operation is P N , then the electric power P t and the refrigeration or heating power Q t of the resistive adjustable resource at time t are respectively: P t = P N ·s t , Q t = η·P t ;

[0074] where η is the energy efficiency ratio, which represents the efficiency of the adjustable resource in converting electrical energy into refrigerating energy or heating energy, is a key indicator for measuring the performance of the adjustable resource, directly affects the energy consumption of the adjustable resource, and a high energy efficiency ratio means that the adjustable resource consumes less electrical energy while providing the same refrigerating capacity or heating capacity.

[0075] The adjustable resource of the asynchronous motor type refers to the adjustable resource that can adjust the power output by changing the operating angular frequency. Different from the resistive adjustable resource, the adjustable resource of the asynchronous motor type can dynamically adjust its operating angular frequency according to the temperature difference (ΔT), thereby more precisely controlling the ambient temperature and improving the energy efficiency ratio. The general load model is expressed as:

[0076]

[0077] Where ω I is the operating angular frequency of the adjustable resource of the asynchronous motor type, ω max and ω minrespectively represent the maximum and minimum values of the operating angular frequency, ΔT is the difference between the current ambient temperature and the outdoor temperature, and u and v respectively represent the minimum temperature difference and the maximum temperature difference corresponding to the change in the operating angular frequency;

[0078] The operating angular frequency ω of the adjustable resources of the asynchronous motor type I is adjusted according to the temperature difference ΔT. If the temperature difference is less than the minimum temperature difference u, the asynchronous motor operates at the minimum angular frequency ω min ; if the temperature difference is greater than the maximum temperature difference v, the asynchronous motor operates at the maximum angular frequency ω max ; between u and v, the operating angular frequency is calculated by linear interpolation;

[0079] The electric power P of the adjustable resources of the asynchronous motor type I and the refrigeration or heating power Q I are simplified to a linear function related to the frequency f I and expressed as: Q I = η·P I , where a and b represent the constant coefficients of the function. In practical applications, the constant coefficients a and b need to be determined through experiments or simulations to ensure the accuracy and reliability of the model.

[0080] S2: Implement multi-layer clustering on the adjustable resources after model processing to form multiple clusters of adjustable resources;

[0081] The multi-layer clustering includes type clustering, phase type clustering, and parameter clustering;

[0082] During the process of adjustable resources participating in demand response, it is unrealistic to give different instructions to adjustable resources, whether economically or feasibly. Therefore, it is necessary to consider implementing multi-layer clustering on adjustable resources.

[0083] In one specific embodiment, the type clustering is specifically as follows: According to the power supply mode of the adjustable resources, the adjustable resources in the distribution transformer substation area are divided into two categories: three-phase power supply and single-phase power supply. Further, according to different operating modes, the single-phase power supply is subdivided into resistive type adjustable resources and asynchronous motor type adjustable resources. This classification helps to identify the characteristics and requirements of adjustable resources under different power supply modes;

[0084] The phase type clustering is specifically as follows: The adjustable resources in the distribution transformer substation area are divided into two categories: three-phase type and single-phase type according to the phase type. This classification helps to consider phase type balance in power grid design and load management;

[0085] The parameter clustering is specifically as follows: Analyze the thermal impedance parameters of different adjustable resources in the distribution transformer substation area, and cluster the adjustable resources with similar temperature change characteristics. This classification helps to more accurately predict and manage different types of adjustable resources.

[0086] Further, when the controllable resources meet the following two conditions, they are regarded as having similar temperature change characteristics:

[0087] The difference in the time constant value τ does not exceed 5%, where τ = R × C, and R and C are the thermal resistance and heat capacity of the controllable resources respectively;

[0088] Under the same external thermal disturbance, the difference in the steady-state temperature fluctuation amplitude is less than or equal to 2°C.

[0089] Through the multi-layer clustering strategy, the controllable resources can be managed and controlled more meticulously, improving the stability and energy utilization efficiency of the power grid. It not only considers the power supply type and phase type of the controllable resources but also delves into parameter clustering, making the load management more refined and personalized.

[0090] S3: Judge the load status and perform multiple controls on the controllable resource clusters formed after multi-layer clustering. Judge whether the three-phase load of the distribution transformer substation area is balanced, and switch the control strategy according to the load status to ensure the stable operation of the system. If it is balanced, execute the three-phase load balance control scheme; if it is unbalanced, execute the three-phase load unbalance control scheme; it can effectively balance the three-phase load to the greatest extent, thereby reducing the imbalance of the power grid and improving the stability and reliability of the power grid.

[0091] In one preferred embodiment, the three-phase load balance control scheme specifically includes:

[0092] In the case of three-phase load balance, give priority to controlling the three-phase controllable resources to avoid causing imbalance during power curtailment. If the power reduction amount ΔP that the three-phase controllable resources can achieve C meets the total power reduction amount ΔP that the distribution transformer substation area needs to reduce n , then stop; otherwise, evenly distribute the remaining power reduction amount to the ABC three-phase controllable resource clusters, and the formula is:

[0093]

[0094] In the formula, is the additional power reduction amount that each single phase needs to bear.

[0095] The three-phase load unbalance control scheme specifically includes:

[0096] Denote the smallest phase among the ABC three phases as m, and the other two phases as α and β respectively. Calculate the power reduction amount of each phase relative to the smallest phase m, and the formula is:

[0097] Δζ M,α = P M,α - P M,m ;

[0098] ΔζM,β = P M,β -P M,m ;

[0099] Wherein, Δζ M,α , Δζ M,β are respectively the power reduction amounts of phase α and phase β relative to the minimum phase m; P M,α , P M,β are respectively the powers of phase α and phase β; P M,m is the power of the minimum phase m;

[0100] If the sum of the power reduction amounts of phase α and phase β, Δζ M,α + Δζ M,β is greater than or equal to the total power reduction amount ΔP n required to be reduced, then the power reduction amounts of phase α and phase β are allocated according to the following formula

[0101]

[0102] Conversely, if the sum of the power reduction amounts of phase α and phase β, Δζ M,α + Δζ M,β is less than the total power reduction amount ΔP n required to be reduced, then the remaining power reduction amount is borne by the three-phase adjustable resources, and the power reduction amount allocated to each phase is calculated according to the following formula:

[0103]

[0104] Taking phase A as the maximum load and phase C as the minimum load as an example, first calculate the power reduction amounts of phase A and phase B relative to phase C: Δζ M,A = P M,A - P M,C , Δζ M,B = P M,B - P M,C ;

[0105] If the sum of the reduction amounts of phase A and phase B, Δζ M,A + Δζ M,B is greater than or equal to the total power reduction amount ΔP n required to be reduced, then the power reduction amounts of phase A and phase B are allocated according to the following formula:

[0106]

[0107] Conversely, if the sum of the reduction amounts of phase A and phase B, Δζ M,A + Δζ M,B is less than the total power reduction amount ΔP n, the remaining power reduction is borne by the three-phase adjustable resources, and the reduction amount allocated to each phase is calculated according to the following formula:

[0108]

[0109] Through this multi-control strategy, it can be ensured that the adjustable resource cluster can effectively respond to the grid demand under both balanced and unbalanced three-phase loads, which is particularly suitable for grid demand-side management, such as load control during demand response and power curtailment.

[0110] S4: According to the results of the control scheme, combined with user comfort, user willingness, and user controllability, construct an adjustable resource capacity evaluation model to evaluate the adjustable capacity of the adjustable resources;

[0111] The evaluation of adjustable capacity refers to the evaluation of the ability of an object to participate in grid dispatching under a certain control method within a certain period. Using the results of the control scheme in step S3 and combining user factors, the actual adjustable capacity of the resources is output to facilitate guiding further power demand management decisions.

[0112] Specifically, user comfort is quantified by the Predicted Mean Vote (PMV) index. A certain degree of temperature change will not make users feel significantly uncomfortable. According to ISO7730 (Thermal comfort, a subjective satisfaction evaluation of the surrounding thermal environment by people), when the PMV value is between -0.5 and 0.5, the thermal comfort of users is within the acceptable range;

[0113] The Predicted Mean Vote index consists of four independent environmental parameters: ambient temperature, mean radiant temperature, air velocity, and air humidity. When the mean radiant temperature, air velocity, and air humidity are at a comfortable level, the Predicted Mean Vote index I PMV has the following relationship with the ambient temperature:

[0114]

[0115] where I PMV is the Predicted Mean Vote index value; T i is the current ambient temperature.

[0116] User willingness is quantified by defining the user impact factor α i,t . When the user willingness is high, their requirements for thermal comfort will decrease accordingly, thus affecting the adjustable range of the set temperature; on the contrary, when the user willingness is low, the user's requirements for thermal comfort will increase. The willingness of users to participate in demand response is mainly affected by electricity prices, and the sensitivity of users to electricity prices is affected by the user's household income, education level, and age. Therefore, the user impact factor α i,t is defined as:

[0117]

[0118] In the formula, p real is the current electricity price, p base is the basic electricity price, p max is the maximum electricity price, μ is the subjective response coefficient;

[0119] The set temperature range that can be dynamically adjusted by the adjustable resource is obtained based on the user influence factor. The formula is:

[0120]

[0121] In the formula, and are the adjustable upper and lower limits of the initial temperature respectively; ΔT i,t is the temperature adjustment amount for the ith adjustable resource at time t; and They are the adjustable upper limit and lower limit of the set temperature after considering the user's wishes.

[0122] Construct an adjustable resource capacity evaluation model to evaluate the adjustable capacity of adjustable resources. The method includes:

[0123] S41: When the adjustable resource is turned on and the terminal control device is installed, the lower limit of the adjustable range of the user-set temperature in each time period is calculated according to the electricity fee in each time period;

[0124] S42: Calculate the adjustable power of adjustable resources using the reset temperature method, taking the power consumed at the initial set temperature as the reference power P base , change the set temperature to the adjustable lower limit of the set temperature The formula for calculating the adjustable power of adjustable resources is obtained:

[0125]

[0126] In the formula, ΔP i is the adjustable power of the i-th adjustable resource; is the operating power of the i-th adjustable resource when the set temperature is at the adjustable lower limit of the set temperature;

[0127] S43: summing the adjustable powers of the adjustable resources running in the distribution transformer area at different times of the day and multiplying the sum by the user controllability ξ to obtain an adjustable capacity evaluation model for the adjustable resources;

[0128] Taking phase A as an example, the formula is:

[0129] In the formula, ΔP A is the total adjustable capacity of the adjustable resources of phase A; n Ais the total number of adjustable resources in phase A.

[0130] like Figure 4 As shown, another embodiment of the present invention provides a system for evaluating the capacity of adjustable resources in a distribution network area based on a multi-layer clustering strategy, which is applied to the method for evaluating the capacity of adjustable resources in a distribution network area based on a multi-layer clustering strategy as described above, including:

[0131] A modeling module is used to model the adjustable resources of the distribution area using an equivalent thermal parameter model, and to simplify the actual adjustable resources into a standard mathematical model;

[0132] A clustering module is used to perform multi-layer clustering on the adjustable resources after modeling to form multiple adjustable resource clusters;

[0133] The control module is used to judge the load status and perform multiple controls on the adjustable resource cluster formed by the clustering module, and judge whether the three-phase load of the distribution transformer area is balanced. If it is balanced, the three-phase load balance control scheme is executed; if it is unbalanced, the three-phase load unbalance control scheme is executed;

[0134] The capacity assessment module is used to build an adjustable resource capacity assessment model based on the results of the control scheme, combined with user comfort, user willingness and user controllability, and evaluate the adjustable capacity of adjustable resources. The assessment results can provide feedback to the modeling module to adjust the modeling parameters or optimize the model to further improve the system's control capability and accuracy.

[0135] In summary, the present invention uses an equivalent thermal parameter model to model the adjustable resources in the distribution area. Through the accurate description of the adjustable resources of resistors and asynchronous motors, the thermal effect of the resources and their adjustment behavior can be effectively simulated. Through the state space equations in the model, the order is automatically adjusted according to the environmental parameters, so that the modeling is more in line with the actual operating environment, thereby improving the accuracy and adaptability of the model.

[0136] The present invention manages resources in the distribution substation area in a refined manner by multi-layer clustering of types, phase types and parameters of adjustable resources. The combination of type clustering, phase clustering and parameter clustering enables various types of resources to be reasonably scheduled according to their characteristics and needs, optimizing load management and resource allocation strategies. This strategy ensures the load regulation capability of the power grid while avoiding management confusion caused by differences in resource types in traditional methods.

[0137] During the load management process, the present invention can automatically select to execute a three-phase load balance control scheme or an unbalance control scheme according to whether the three-phase load in the distribution substation area is balanced, thereby effectively reducing the three-phase unbalance phenomenon in the power grid. The three-phase load balance control scheme can preferentially adjust the three-phase controllable resources to ensure the power grid load balance and enhance the stability and reliability of the power grid. The three-phase load unbalance control scheme can, when the load is unbalanced, avoid the overload and unbalanced load problems of the power grid by reasonably distributing the power reduction amount. By finely managing the controllable resources, the present invention can effectively reduce the three-phase load unbalance when the controllable resources participate in the demand-side response, which helps the power grid operator more accurately predict and adjust the power demand, thereby improving the regulation ability and stability of the power grid.

[0138] Based on the comprehensive consideration of user comfort, user willingness, and user controllability, the present invention can accurately evaluate the adjustable capacity of the controllable resources. By quantifying user comfort and willingness and combining the controllability of user temperature adjustment, a more practical-demand-compliant controllable resource capacity evaluation model is constructed. This model can achieve flexible scheduling, meeting both user comfort requirements and the regulation requirements of the power grid, and enhancing the accuracy of power grid management and energy utilization efficiency.

[0139] By comprehensively considering resource types, user demands, and power grid load balance, the present invention effectively improves the regulation ability of the power grid. The combination of multi-layer clustering and fine control strategies ensures the efficient operation of the power grid in demand-side response, while maximizing the energy utilization efficiency. By flexibly allocating the controllable resources, it helps to achieve the balanced optimization of the load, thereby avoiding overload in the case of tight power supply, improving the energy utilization rate, and reducing energy waste. In addition, by comprehensively considering the thermal comfort, participation willingness, and controllability of users, the optimized management of the controllable resources is realized. This optimization not only improves the energy use efficiency but also helps to reduce energy consumption and achieve the goal of energy conservation and emission reduction.

[0140] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, it can be implemented in whole or in part in the form of a computer program product, which includes one or more computer instructions. When loading and executing the computer program instructions on a computer, the processes or functions according to the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another.

[0141] In addition, each functional unit in various embodiments of the present invention may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk or an optical disc, etc.

[0142] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various changes or substitutions, and these should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for evaluating the adjustable resource capacity of a distribution substation area based on a multi-layer clustering strategy, characterized in that, The method comprises: using an equivalent thermal parameter model to model the adjustable resources in the distribution station area, simplifying the actual adjustable resources into a standard mathematical model, wherein the order of the equivalent circuit state space equation in the modeling method is determined by the environmental parameters in the equivalent process; the adjustable resources include resistor-type adjustable resources and asynchronous motor-type adjustable resources; Implement multi-layer clustering on the adjustable resources after modeling to form multiple adjustable resource clusters; The load status of the adjustable resource cluster formed after multi-layer clustering is judged and multiple controls are performed to judge whether the three-phase load of the distribution transformer area is balanced. The control strategy is switched according to the load status to ensure the stable operation of the system. If it is balanced, the three-phase load balance control scheme is executed; if it is unbalanced, the three-phase load unbalanced control scheme is executed; According to the results of the control scheme, an adjustable resource capacity evaluation model is constructed in combination with user comfort, user willingness and user controllability to evaluate the adjustable capacity of the adjustable resources.

2. The capacity evaluation method of adjustable resources in a distribution substation area based on a multi-layer clustering strategy according to claim 1, wherein, The resistor-type adjustable resource refers to an adjustable resource whose load can be equivalent to a pure resistor. The general model is expressed as: where s t represents the switching operation state of the resistive adjustable resources at time t; Δt is the time interval; T i is the current ambient temperature; T set is the set temperature; δ is the temperature dead zone; If the rated power of the resistive adjustable resource during operation is P N , then the electric power P t and the refrigeration or heating power Q t of the resistive adjustable resource at time t are respectively: P t = P N ·s t , Q t = η·P t , where η is the energy efficiency ratio.

3. A method for evaluating the adjustable resource capacity of a distribution substation area based on a multi-layer clustering strategy according to claim 1, characterized in that The asynchronous motor type controllable resource refers to a controllable resource that can adjust the power output by changing the operating angular frequency. The general load model is expressed as: where ω I is the operating angular frequency of the adjustable resources of the asynchronous motor type, ω max and ω min represent the maximum and minimum values of the operating angular frequency respectively, ΔT is the difference between the current ambient temperature and the outdoor temperature, and u and v represent the minimum and maximum temperature differences corresponding to the change in the operating angular frequency; The electric power P of adjustable resources of asynchronous motor type I and the refrigeration or heating power Q I are simplified to linear functions related to the frequency f I and are expressed as: Q I = η·P I , where a and b represent the constant coefficients of the function.

4. A method for evaluating the adjustable resource capacity of a distribution transformer area based on a multi-layer clustering strategy according to claim 1, characterized in that The type clustering is specifically as follows: according to the power supply mode of the adjustable resources, the adjustable resources in the distribution transformer area are divided into two categories: three-phase power supply adjustable resources and single-phase power supply adjustable resources, and further according to different operation modes, the single-phase power supply adjustable resources are subdivided into resistor type adjustable resources and asynchronous motor type adjustable resources; The phase type clustering is specifically: dividing the controllable resources in the distribution transformer area into two types according to the phase type: three-phase type and single-phase type; The parameter clustering is specifically as follows: analyzing the thermal impedance parameters of different adjustable resources in the distribution transformer area, and clustering the adjustable resources with similar temperature change characteristics; When the adjustable resources meet the following two conditions, they are considered to have similar temperature change characteristics: The time constant value τ differs by no more than 5%, where τ = R × C, R and C are the thermal resistance and thermal capacity of the adjustable resource, respectively; Under the same external thermal disturbance, the steady-state temperature fluctuation amplitude difference is less than or equal to 2°C.

5. A method for evaluating the adjustable resource capacity of a distribution substation area based on a multi-layer clustering strategy according to claim 1, characterized in that, The three-phase load balancing control scheme specifically includes: Under the condition of balanced three-phase load, the three-phase controllable resources are preferentially controlled to avoid imbalance during power curtailment. If the power reduction amount ΔP C achieved by the three-phase controllable resources meets the total power ΔP n that needs to be reduced in the distribution transformer area, then stop; otherwise, evenly distribute the remaining power reduction amount to the ABC three-phase controllable resource clusters. The formula is: Wherein, is the reduction in power that each single phase needs to bear additionally.

6. The method for evaluating the adjustable resource capacity of a distribution transformer area based on a multi-layer clustering strategy according to claim 1, wherein The three-phase load unbalance control scheme specifically includes: Let m be the smallest phase among the three phases ABC, and the other two phases be α and β respectively. The power reduction of each phase relative to the smallest phase m is calculated using the formula: Δζ M,α = P M,α - P M,m ; Δζ M,β = P M,β - P M,m ; where, Δζ M,α and Δζ M,β are the power reduction amounts of phase α and phase β relative to the minimum phase m, respectively; P M,α and P M,β are the powers of phase α and phase β, respectively; P M,m is the power of the minimum phase m; If the sum of the power reduction amounts of phase α and phase β, Δζ M,α +Δζ M,β is greater than or equal to the total power reduction amount ΔP that needs to be reduced n , then the power reduction amounts of phase α and phase β are allocated according to the following formula Conversely, if the sum of the power reduction amounts of phase α and phase β, Δζ M,α +Δζ M,β is less than the total power reduction amount ΔP that needs to be reduced n , then the remaining power reduction amount is borne by the three-phase adjustable resources, and the power reduction amount allocated to each phase is calculated according to the following formula:

7. A method for evaluating the adjustable resource capacity of a distribution substation based on a multi-layer clustering strategy according to claim 1, characterized in that The user comfort level is quantified by the Predicted Mean Vote (PMV) index, which consists of four independent environmental parameters: ambient temperature, mean radiant temperature, air velocity, and air humidity. When the mean radiant temperature, air velocity, and air humidity are at comfortable levels, the relationship between the Predicted Mean Vote index I PMV and the ambient temperature is as follows: Where, I PMV is the predicted average voting index value; T i is the current ambient temperature.

8. A method for evaluating the adjustable resource capacity of a distribution substation based on a multi-layer clustering strategy according to claim 7, characterized in that The user's will is quantified by defining a user influence factor α i,t and the formula is as follows: where p real is the current electricity price, p base is the basic electricity price, p max is the highest electricity price, and μ is the subjective response coefficient; the temperature controllability refers to the set temperature range that the adjustable resources can dynamically adjust, which is obtained according to the user impact factor, and the formula is: In the formula, and are the adjustable upper and lower limits of the initial temperature respectively; ΔT i,t is the temperature adjustment amount for the ith adjustable resource at time t; and They are the adjustable upper limit and lower limit of the set temperature after considering the user's wishes.

9. The method for evaluating the adjustable resource capacity of a distribution substation area based on a multi-layer clustering strategy according to claim 8, wherein The adjustable resource capacity evaluation model is constructed to evaluate the adjustable capacity of the adjustable resource, and the method includes: when the adjustable resource is turned on and the terminal control device is installed, the lower limit of the adjustable range of the user-set temperature in each time period is calculated according to the electricity fee in each time period; The adjustable power of the adjustable resource is calculated using the reset temperature method, and the power consumed at the initial set temperature is used as the reference power P base , and the set temperature is changed to the adjustable lower limit of the set temperature The formula for calculating the adjustable power of the adjustable resource is obtained: where, ΔP i is the adjustable power of the i-th adjustable resource; is the operating power of the i-th adjustable resource when the set temperature is at the lower limit of the adjustable set temperature; The adjustable power of the adjustable resources running at different times of the day in the distribution transformer area is summed up and multiplied by the user controllability ξ to obtain the adjustable capacity evaluation model of the adjustable resources.

10. A capacity evaluation system for adjustable resources in a distribution substation area based on a multi-layer clustering strategy, which is applied to a capacity evaluation method for adjustable resources in a distribution substation area based on a multi-layer clustering strategy as described in any one of claims 1-9, and is characterized in that, include: A modeling module is used to model the adjustable resources of the distribution area using an equivalent thermal parameter model, and to simplify the actual adjustable resources into a standard mathematical model; A clustering module is used to perform multi-layer clustering on the adjustable resources after modeling to form multiple adjustable resource clusters; The control module is used to judge the load status and perform multiple controls on the adjustable resource cluster formed by the clustering module, and judge whether the three-phase load of the distribution transformer area is balanced. If it is balanced, the three-phase load balance control scheme is executed; if it is unbalanced, the three-phase load unbalance control scheme is executed; The capacity evaluation module is used to construct an adjustable resource capacity evaluation model based on the results of the control scheme, combined with user comfort, user willingness and user controllability, and evaluate the adjustable capacity of the adjustable resources.

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