A method and system for evaluating adjustable resource capacity of a distribution transformer area based on a multi-layer clustering strategy

Abstract

The application discloses a kind of based on multi-layer clustering strategy's adjustable resource capacity evaluation method and system of distribution transformer area, and relates to the technical field of power demand side management.The method comprises: using equivalent thermal parameter model to model the adjustable resource of distribution area, and implementing multi-layer clustering, multiple control is carried out to the adjustable resource cluster formed after multi-layer clustering, whether the three-phase load of distribution transformer area is balanced is judged, if balanced, three-phase load balance control scheme is executed, if unbalanced, three-phase load imbalance control scheme is executed;According to the result of control scheme, user comfort, user willingness and user controllability are combined to build adjustable resource capacity evaluation model, and the adjustable capacity of adjustable resource is evaluated.The method and system of the application not only improve the regulation ability and stability of power grid, but also meet the balance between user demand and power grid efficiency, maximize energy use efficiency, and help reduce energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power demand side management, in particular to a distribution area controllable resource capacity evaluation method and system based on a multi-layer clustering strategy. BACKGROUND

[0002] Power demand side management (DSM) is an important means to improve energy efficiency, reduce energy consumption and reduce environmental pollution. Power demand side management optimizes power grid load by regulating power consumption patterns, reduces system operation costs, and effectively improves the utilization rate of power resources. In the current power grid system dominated by renewable energy, power load is volatile and uncertain, and how to efficiently manage controllable resources has become a key problem in power grid operation.

[0003] Existing controllable resource management methods usually ignore user comfort, willingness and controllability, etc., making it difficult to balance user demand and grid stability in actual applications, affecting the effectiveness of demand response, making it difficult for the power grid to accurately control resource scheduling, and thus reducing energy efficiency. In addition, existing controllable resource models have limitations in dealing with three-phase load imbalance and three-phase load imbalance management, and are unable to effectively aggregate and manage these controllable resources, resulting in insufficient grid regulation capacity and difficulty in achieving optimal resource allocation. In particular, in large distribution areas, power demand changes are more complex, and existing management and scheduling methods often cannot cope with the increasingly severe demand side management challenges. SUMMARY

[0004] To solve the above problems, the present application provides a distribution area controllable resource capacity evaluation method and system based on a multi-layer clustering strategy, which is used for optimizing the management of controllable resources in distribution areas to improve the energy efficiency and reliability of the power grid.

[0005] The present application achieves the above-mentioned purposes through the following technical solutions:

[0006] On the one hand, the present application provides a distribution area controllable resource capacity evaluation method based on a multi-layer clustering strategy, which comprises:

[0007] The controllable resources of the distribution area are modeled using an equivalent thermal parameter model, which simplifies the actual controllable 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 controllable resources include resistance type controllable resources and asynchronous motor type controllable resources;

[0008] The controllable resources after modeling are subjected to multi-layer clustering to form a plurality of controllable resource clusters;

[0009] The load state of the adjustable resource cluster formed after multi-layer clustering is judged and multiple controlled, whether the three-phase load of the distribution transformer area is balanced is judged, and the control strategy is switched according to the load state to ensure the stable operation of the system, if balanced, the three-phase load balance control scheme is executed, if unbalanced, the three-phase load unbalance control scheme is executed;

[0010] According to the result of the control scheme, the user comfort, the user willingness and the user controllability are combined to build an adjustable resource capacity evaluation model, and the adjustable capacity of the adjustable resource is evaluated.

[0011] As a preferred scheme of the present application, the resistance type adjustable resource refers to the adjustable resource whose load can be equivalent to a pure resistance, and the general model is represented as:

[0012]

[0013] In the formula, s t represents the switching operation state of the resistance type adjustable resource at t time; Δt is a time interval; T i is the current environment temperature; T set is the set temperature; and δ is the temperature dead zone.

[0014] If the rated power of the resistance type adjustable resource when working is P N , then the electric power P t , the refrigeration or heating power Q t of the resistance type adjustable resource at t time are respectively: P t =P N ·s t , Q t =η·P t , wherein η is the energy efficiency ratio.

[0015] As a preferred scheme of the present application, the asynchronous motor type adjustable resource refers to the adjustable resource whose power output can be adjusted by changing the operating angular frequency, and the general load model is represented as:

[0016]

[0017] In the formula, ω I is the operating angular frequency of the asynchronous motor type adjustable resource, ω max and ω min respectively represent the maximum value and the minimum value of the operating angular frequency, ΔT is the difference between the current environment temperature and the outdoor temperature, and u and v respectively represent the minimum value of the temperature difference and the maximum value of the temperature difference corresponding to the change of the operating angular frequency.

[0018] The electric power P I , the refrigeration or heating power Q I of the asynchronous motor type adjustable resource are simplified as f IThe relevant linear function is expressed as: Q I = η·P I wherein a and b represent constant coefficients of the function.

[0019] As a preferred scheme of the present application, the phase type clustering is specifically: dividing the controllable resources of the distribution transformer area into three-phase and single-phase types according to the power supply mode of the controllable resources;

[0020] The phase type clustering is specifically: dividing the controllable resources of the distribution transformer area into three-phase and single-phase types according to the phase type;

[0021] The parameter clustering is specifically: analyzing the thermal impedance parameters of different controllable resources in the distribution transformer area, and clustering the controllable resources with similar temperature variation characteristics;

[0022] Wherein, the controllable resources are considered to have similar temperature variation characteristics when they meet the following two conditions:

[0023] The time constant value τ difference is not more than 5%, wherein τ = R × C, R and C are the thermal resistance and heat capacity of the controllable resource, respectively;

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

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

[0026] In the case of three-phase load balance, the three-phase controllable resources are preferentially controlled to avoid causing imbalance during power reduction, and if the power reduction amount ΔP C meets the total power ΔP n required to be reduced by the distribution transformer area, it is stopped; otherwise, the remaining power reduction amount is evenly distributed to the ABC three-phase controllable resource cluster, and the formula is:

[0027]

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

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

[0030] Let the minimum phase in ABC three-phase be m, and the other two phases be α and β, respectively, calculate the power reduction amount of each phase relative to the minimum 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] In the formula, Δζ M,α and Δζ M,β are the power reduction amounts of phase α and phase β relative to the minimum phase m; P M,α and P M,β are the powers of phase α and phase β; and P M,m is the power of the minimum phase m.

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

[0035]

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

[0037]

[0038] As a preferred scheme of the present application, the user comfort is quantified by a predicted average voting index composed of four independent environmental parameters of ambient temperature, average radiant temperature, air flow rate and air humidity. When the average radiant temperature, air flow rate and air humidity are at a comfortable level, the predicted average voting index I PMV is related to the ambient temperature as follows:

[0039]

[0040] In the formula, I PMV is the predicted average voting index value; and T i is the current ambient temperature.

[0041] As a preferred scheme of the present application, the user willingness is quantified by defining a user influence factor α i,t , which is calculated according to the following formula:​

[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, and μ is a subjective response coefficient;

[0044] The temperature controllability refers to a set temperature range that can be dynamically adjusted by the adjustable resource, and is obtained according to a user influence factor, and the formula is:

[0045]

[0046] In the formula, T and T are an adjustable upper limit and an adjustable lower limit of the initial temperature respectively, and ΔT i,t is a temperature adjustment amount of the i th adjustable resource at time t; and T are an adjustable upper limit and an adjustable lower limit of the set temperature after considering the user's willingness respectively.

[0047] As a preferred scheme of the present application, the adjustable resource capacity evaluation model is constructed to evaluate the adjustable capacity of the adjustable resource, and the method comprises the following steps:

[0048] In the case that the adjustable resource is started and the terminal control device is installed, the lower limit of the adjustable range of the user set temperature in each period is calculated according to the electricity fee of each period;

[0049] The adjustable power of the adjustable resource is calculated by using the reset temperature method, and the power consumed at the initial set temperature is taken 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:

[0050]

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

[0052] The adjustable powers of the adjustable resources operated in different periods in a day in the distribution transformer area are summed up, multiplied by the user controllability ξ, and the adjustable capacity evaluation model of the adjustable resource is obtained.

[0053] In another aspect, the application provides a power distribution area controllable resource capacity evaluation system based on a multi-layer clustering strategy, applied to the power distribution area controllable resource capacity evaluation method based on a multi-layer clustering strategy as described above, comprising:

[0054] A modeling module is configured to model the controllable resources of the power distribution area using an equivalent thermal parameter model, and simplify the actual controllable resources into a standard mathematical model.

[0055] A clustering module is configured to implement multi-layer clustering on the controllable resources after the modeling process, and form a plurality of controllable resource clusters.

[0056] A control module is configured to perform load state judgment and multiple control on the controllable resource clusters formed by the clustering module, judge whether the three-phase load of the distribution transformer area is balanced, and if balanced, execute a three-phase load balancing control scheme, and if not balanced, execute a three-phase load unbalanced control scheme.

[0057] A capacity evaluation module is configured to construct a controllable resource capacity evaluation model according to the results of the control scheme, in combination with user comfort, user willingness and user controllability, and evaluate the adjustable capacity of the controllable resources.

[0058] The application has the beneficial effects that: through fine modeling and resource management, the grid regulation capacity 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, so as to reduce the three-phase load unbalance problem; the user's thermal comfort, willingness and controllability are comprehensively considered to realize the optimization management of the controllable resources, improve the energy efficiency and reduce the energy consumption, so as to achieve the energy saving and emission reduction goal, not only improve the accuracy of the grid load regulation, but also promote the efficient operation of the power demand side management. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0060] Among them:

[0061] Figure 1 The method flowchart in the embodiment of the application;

[0062] Figure 2 The step block diagram of the multi-layer clustering on the controllable resources in the embodiment of the application;

[0063] Figure 3Flowchart of a method for determining whether the three-phase load of a distribution transformer area is balanced according to an embodiment of the present invention;

[0064] Figure 4 Schematic diagram of the modular structure of the system in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0066] like Figure 1 FIG. 1 is an embodiment of the present invention, which provides a method for evaluating the controllable resource capacity of a distribution network area based on a multi-layer clustering strategy, including the following steps:

[0067] S1: The controllable resources in the distribution area (including resistor-type controllable resources and asynchronous motor-type controllable resources) are modeled using the equivalent thermal parameter (ETP) model. The actual controllable resources are simplified into a standard mathematical model. The order of the equivalent circuit state space equation in the modeling method is determined by the environmental parameters in the equivalent process.

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

[0069] Specifically, resistor-type controllable resources refer to controllable resources whose loads can be equivalent to pure resistors. The working principle is based on the thermal effect of resistors, that is, the heat generated when current passes through the resistor is used to achieve energy conversion and control. Its general model is expressed as:

[0070]

[0071] where s t represents the switch operating state of the resistance type controllable resource at time t; Δt is the time interval, s t-△t represents the switch operating state of the resistance type controllable 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 allowed temperature fluctuation range, within which the controllable resource will not change its operating state to avoid energy waste and device wear caused by frequent switching;

[0072] When the indoor temperature is lower than the set temperature minus half of the temperature dead zone, the resistance type controllable 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 resistance type controllable resource is turned on (s t = 1); when the indoor temperature is within the half dead zone range of the set temperature, the resistance type controllable resource maintains the state of the previous time interval Δt.

[0073] If the rated power of the resistance type controllable resource when working is P N , then the electric power P t , the refrigeration or heating power Q t of the resistance type controllable resource at time t are respectively: P t = P N ·s t , Q t = η·P t ;

[0074] wherein η is the energy efficiency ratio, indicating the efficiency of the controllable resource in converting electric energy into refrigeration or heating energy, which is a key indicator of the performance of the controllable resource and directly affects the energy consumption of the controllable resource. High energy efficiency ratio means that the controllable resource consumes less electric energy while providing the same refrigeration or heating capacity.

[0075] The asynchronous motor type controllable resource refers to a controllable resource capable of adjusting the power output by changing the operating angular frequency. Unlike the resistance type controllable resource, the asynchronous motor type controllable resource can dynamically adjust its operating angular frequency according to the temperature difference (ΔT), thereby more accurately controlling the ambient temperature and improving the energy efficiency ratio. The general load model is represented as:

[0076]

[0077] where ω I is the operating angular frequency of the asynchronous motor type controllable resource, ω max and ω minThey represent the maximum and minimum values ​​of the operating angular frequency, 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 in the operating angular frequency, respectively.

[0078] The operating angular frequency ω of the adjustable resources of asynchronous motors I According to the temperature difference ΔT, if the temperature difference is less than the minimum temperature difference u, the asynchronous motor will operate at the minimum angular frequency ω. min If the temperature difference is greater than the maximum temperature difference v, the asynchronous motor runs at the maximum angular frequency ω max Run; between u and v, the running angular frequency is calculated by linear interpolation;

[0079] Electric power P of adjustable resources such as asynchronous motors I , cooling or heating power Q I Simplified to the frequency f I The related linear function is expressed as: Q I =η·P I , where a and b represent the constant coefficients of the function. In practical applications, constants a and b need to be determined through experiments or simulations to ensure the accuracy and reliability of the model.

[0080] S2: Perform multi-layer clustering on the modeled controllable resources to form multiple controllable resource clusters;

[0081] Multi-level clustering includes type clustering, phase type clustering and parameter clustering;

[0082] In the process of regulating resources participating in demand response, giving different instructions to the regulating resources is unrealistic both economically and feasibly. Therefore, it is necessary to consider implementing multi-layer clustering for regulating resources.

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

[0084] Phase type clustering specifically involves classifying the controllable resources in the distribution substation area into two types: three-phase and single-phase. This classification helps consider phase balance in power grid design and load management.

[0085] Parameter clustering specifically involves analyzing the thermal impedance parameters of different adjustable resources within the distribution transformer area and clustering adjustable resources with similar temperature change characteristics. This classification helps to more accurately predict and manage different types of adjustable resources.

[0086] Furthermore, when the adjustable resources meet the following two conditions, they are considered to have similar temperature change characteristics:

[0087] The time constant value τ differs by no more than 5%, where τ = R × C, where R and C are the thermal resistance and thermal capacity of the controllable resource, respectively;

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

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

[0090] S3: Perform load status judgment and multiple controls on the adjustable resource clusters formed after multi-layer clustering to determine whether the three-phase load of the distribution transformer area is balanced. Switch the control strategy according to the load status to ensure stable system operation. If balanced, the three-phase load balance control scheme is executed; if unbalanced, the three-phase load unbalance control scheme is executed. 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 balancing control scheme specifically includes:

[0092] In the case of three-phase load balancing, the three-phase controllable resources are controlled first to avoid imbalance during power reduction. If the power reduction ΔP that the three-phase controllable resources can achieve is C Meet the total power reduction ΔP required in the distribution transformer area n , then stop; otherwise, distribute the remaining power reduction evenly to the ABC three-phase adjustable resource clusters, the formula is:

[0093]

[0094] Where, The additional power reduction required for each single phase.

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

[0096] Let m be the smallest phase among the three phases ABC, and the other two phases be α and β. The power reduction of each phase relative to the smallest phase m is calculated using the following formula:

[0097] Δζ M,α =P M,α -P M,m ;

[0098] ΔζM,β =P M,β -P M,m ;

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

[0100] If the sum of the power reductions of phase α and phase β is Δζ M,α +Δζ M,β Greater than or equal to the total power that needs to be reduced ΔP n , the power reduction of phase α and phase β is distributed according to the following formula

[0101]

[0102] On the contrary, if the sum of the power reductions of phase α and phase β is Δζ M,α +Δζ M,β Less than the total power that needs to be reduced ΔP n , then the remaining power reduction is borne by the three-phase controllable resources, and the power reduction allocated to each phase is Calculated using 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 of phases A and 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 reductions of phase A and phase B Δζ M,A +Δζ M,B Greater than or equal to the total power that needs to be reduced ΔP n , the power reduction of phase A and phase B is distributed according to the following formula:

[0106]

[0107] On the contrary, if the sum of the reductions of phase A and phase B Δζ M,A +Δζ M,B Less than the total power that needs to be reduced ΔP nThe remaining power reduction is then borne by the three-phase controllable resources, and the reduction allocated to each phase is calculated according to the following formula:

[0108]

[0109] Through this multi-control strategy, the controllable resource cluster can effectively respond to the demand of the power grid in the case of three-phase load balance and imbalance, and is particularly suitable for power demand side management, such as load control during demand response and power reduction.

[0110] S4: According to the results of the control scheme, a controllable resource capacity evaluation model is constructed combining user comfort, user willingness and user controllability, and the adjustable capacity of the controllable resource is evaluated;

[0111] Controllable capacity evaluation refers to the evaluation of the ability of the object to participate in grid dispatching within a certain period under a certain control method. The actual adjustable capacity of the resource is output by combining user factors with the control scheme results of step S3, so as to guide 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 the user feel obvious discomfort. According to ISO7730 (thermal comfort, subjective satisfaction evaluation of people on the surrounding thermal environment), when the PMV value is between -0.5 and 0.5, the thermal comfort of the user is within an acceptable range;

[0113] The predicted mean vote index is composed of four independent environmental parameters: ambient temperature, mean radiant temperature, air flow rate and air humidity. When the mean radiant temperature, air flow rate and air humidity are at a comfortable level, the predicted mean vote index I PMV The relationship with the ambient temperature is:

[0114]

[0115] In the formula, I PMV is the predicted mean vote index value; T i is the current ambient temperature.

[0116] User willingness is quantified by defining a user influence factor a i,t When the user's willingness is high, the user's requirement for thermal comfort will decrease accordingly, thereby affecting the adjustable range of the set temperature; on the contrary, when the user's willingness is low, the user's requirement for thermal comfort will increase. The user's willingness to participate in demand response is mainly affected by the electricity price, and the user's sensitivity to the electricity price is affected by the user's family income, education level and age. Therefore, the user influence factor a 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, and μ is a subjective response coefficient;

[0119] According to the user influence factor, the set temperature range that can be dynamically adjusted by the controllable resource is obtained, and the formula is:

[0120]

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

[0122] A controllable resource capacity evaluation model is constructed to evaluate the adjustable capacity of the controllable resource, and the method comprises:

[0123] S41: In the case that the controllable resource is started and the terminal control device is installed, the lower limit of the adjustable range of the user set temperature in each period is calculated according to the electricity fee of each period;

[0124] S42: The adjustable power of the controllable resource is calculated using the reset temperature method, and the power consumed at the initial set temperature is taken as the reference power P base , and the set temperature is changed to the adjustable lower limit of the set temperature to obtain the formula for calculating the adjustable power of the controllable resource:

[0125]

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

[0127] S43: The adjustable powers of the controllable resources operated in different periods of a day in the distribution transformer area are summed up, multiplied by the user controllability ξ, and the adjustable capacity evaluation model of the controllable resource is obtained;

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

[0129] In the formula, ΔP A is the total adjustable capacity of the controllable resource of phase A; and n ATotal number of regulatable resources of phase A.

[0130] As Figure 4 As shown in another embodiment of the application, the embodiment provides a power distribution substation regulatable resource capacity evaluation system based on a multi-layer clustering strategy, applied to a power distribution substation regulatable resource capacity evaluation method based on a multi-layer clustering strategy, comprising:

[0131] A modeling module for modeling the regulatable resources of the power distribution substation using an equivalent thermal parameter model, simplifying the actual regulatable resources into a standard mathematical model;

[0132] A clustering module for implementing multi-layer clustering on the modelized regulatable resources to form a plurality of regulatable resource clusters;

[0133] A control module for performing load state judgment and multiple control on the regulatable resource clusters formed by the clustering module, judging whether the three-phase load of the distribution substation is balanced, and if balanced, executing a three-phase load balancing control scheme, and if unbalanced, executing a three-phase load unbalancing control scheme;

[0134] A capacity evaluation module for constructing a regulatable resource capacity evaluation model according to the results of the control scheme, combining user comfort, user willingness and user controllability, and evaluating the regulatable capacity of the regulatable resources. The evaluation results can be fed back to the modeling module for adjusting the modeling parameters or optimizing the model, further improving the regulation ability and accuracy of the system.

[0135] As described above, the application uses an equivalent thermal parameter model to model the regulatable resources in the power distribution substation, accurately describes the regulatable resources of the resistance type and asynchronous motor type, and effectively simulates the thermal effect and regulation behavior of the resources. Through the state space equation in the model, the order is automatically adjusted according to the environmental parameters, so that the modeling is more consistent with the actual operating environment, thereby improving the precision and adaptability of the model.

[0136] The application finely manages the resources in the power distribution substation by multi-layer clustering of the type, phase type and parameters of the regulatable resources. The combination of type clustering, phase type clustering and parameter clustering enables various resources to be reasonably scheduled according to their characteristics and needs, optimizing the load management and resource allocation strategy. This strategy ensures the load regulation capacity of the power grid, while avoiding the management confusion caused by the difference in resource types in traditional methods.

[0137] In the load management process, the application can automatically select the three-phase load balance control scheme or the unbalance control scheme according to whether the three-phase load of the power distribution 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 balance of the power grid load and enhance the stability and reliability of the power grid. The three-phase load unbalance control scheme can reduce the power reduction amount by reasonable allocation when the load is unbalanced, thereby avoiding the problems of overload and unbalanced load of the power grid. Through fine management of controllable resources, the application can effectively reduce the three-phase load unbalance of controllable resources participating in demand side response, which helps the power grid operator to more accurately predict and adjust power demand, thereby improving the regulation capacity and stability of the power grid.

[0138] Based on the comprehensive consideration of user comfort, user willingness and user controllability, the application can accurately evaluate the adjustable capacity of controllable resources. By quantifying user comfort and willingness, combined with the controllability of users to temperature adjustment, a controllable resource capacity evaluation model that better meets actual demand is constructed. This model can realize flexible scheduling, which not only meets the user comfort demand, but also takes into account the regulation demand of the power grid, thereby improving the accuracy of power grid management and energy utilization efficiency.

[0139] By comprehensively considering resource type, user demand and power grid load balance, the application effectively improves the regulation capacity of the power grid. The combination of multi-layer clustering and fine control strategy ensures efficient operation of the power grid in demand side response, while maximizing energy utilization efficiency. Through flexible allocation of controllable resources, it helps to realize the balance optimization of load, thereby avoiding overload in the case of tight power supply, improving energy utilization rate and reducing energy waste. In addition, the user's thermal comfort, willingness to participate and controllability are comprehensively considered to realize the optimal management of controllable resources. This optimization not only improves the efficiency of energy use, but also helps to reduce energy consumption and achieve the goal of energy saving and emission reduction.

[0140] In the above embodiments, all or part can be realized by software, hardware, firmware or any other combination. When realized by software, all or part can be realized in the form of a computer program product including one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the application are generated. 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 transferred from one computer readable storage medium to another.

[0141] In addition, each function unit in each embodiment of the present application can be integrated in one processing module, or each unit can exist physically independently, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware, or in the form of a software function module. When the integrated module is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium. The storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.

[0142] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for evaluating the controllable resource capacity of a distribution network area based on a multi-layer clustering strategy, characterized in that: The method includes: using an equivalent thermal parameter model to model the controllable resources in the distribution station area, simplifying the actual controllable 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 controllable resources include resistor-type controllable resources and asynchronous motor-type controllable resources; 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 asynchronous motor type controllable resource, ω max and ω min They represent the maximum and minimum values ​​of the operating angular frequency, 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 in the operating angular frequency, respectively. Electric power P of adjustable resources such as asynchronous motors I , cooling or heating power Q I Simplified to the frequency f I The related linear function is expressed as: Q I =η·P I , where a and b represent the constant coefficients of the function; Implement multi-layer clustering on the modeled controllable resources to form multiple controllable resource clusters; The load status of the controllable resource cluster formed after multi-layer clustering is judged and multiple controls are performed to determine whether the three-phase load of the distribution transformer area is balanced. The control strategy is switched according to the load status to ensure stable system operation. If balanced, the three-phase load balance control scheme is implemented; if unbalanced, the three-phase load unbalance control scheme is implemented; The three-phase load balancing control scheme specifically includes: In the case of three-phase load balancing, the three-phase controllable resources are controlled first to avoid imbalance during power reduction. If the power reduction ΔP that the three-phase controllable resources can achieve is C Meet the total power reduction ΔP required in the distribution transformer area n , then stop; otherwise, distribute the remaining power reduction evenly to the ABC three-phase adjustable resource clusters, the formula is: Where, The additional power reduction required for each single phase; According to the results of the control scheme, a controllable resource capacity evaluation model is constructed in combination with user comfort, user willingness and user controllability to evaluate the adjustable capacity of the controllable resources.

2. The method for evaluating the controllable resource capacity of a distribution network area based on a multi-layer clustering strategy according to claim 1 is characterized in that: The resistor-type controllable resource refers to a controllable resource whose load can be equivalent to a pure resistor. The general model is expressed as: Where s t represents the switch operation state of the resistor-type controllable 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; 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: P t =P N ·s t , Q t =η·P t , where η is the energy efficiency ratio.

3. The method for evaluating the controllable resource capacity of a distribution network area based on a multi-layer clustering strategy according to claim 1 is characterized in that: The multi-layer clustering includes type clustering, phase type clustering and parameter clustering; Specifically, the type clustering is as follows: based on 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 based on 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 as follows: the controllable resources of the distribution transformer area are divided into two types according to the phase type: three-phase type and single-phase type; The parameter clustering specifically includes: analyzing the thermal impedance parameters of different controllable resources in the distribution transformer area, and clustering the controllable 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, where R and C are the thermal resistance and thermal capacity of the controllable resource, respectively; Under the same external thermal disturbance, the difference in steady-state temperature fluctuation amplitude is less than or equal to 2°C.

4. The method for evaluating the controllable resource capacity of a distribution network area based on a multi-layer clustering strategy according to claim 1, characterized in that: The three-phase load imbalance control scheme specifically includes: Let m be the smallest phase among the three phases ABC, and the other two phases be α and β. The power reduction of each phase relative to the smallest phase m is calculated using the following formula: Δζ M,α =P M,α -P M,m ; Δζ M,β =P M,β -P M,m ; Where Δζ M,α , Δζ M,β are the power reduction of phase α and phase β relative to the minimum phase m; P M,α 、P M,β are the power of phase α and phase β respectively; P M,m is the power of the minimum phase m; If the sum of the power reductions of phase α and phase β is Δζ M,α +Δζ M,β Greater than or equal to the total power that needs to be reduced ΔP n , the power reduction of phase α and phase β is distributed according to the following formula On the contrary, if the sum of the power reductions of phase α and phase β is Δζ M,α +Δζ M,β Less than the total power that needs to be reduced ΔP n , then the remaining power reduction is borne by the three-phase controllable resources, and the power reduction allocated to each phase is Calculated using the following formula:

5. The method for evaluating the controllable resource capacity of a distribution network area based on a multi-layer clustering strategy according to claim 1 is characterized in that: The user comfort is quantified by predicting the average voting index, which is composed of four independent environmental parameters: ambient temperature, average radiant temperature, air velocity and air humidity. When the average radiant temperature, air velocity and air humidity are at a comfortable level, the predicted average voting index I PMV The relationship with ambient temperature is: Where, I PMV is the predicted average voting index value; T i is the current ambient temperature.

6. The method for evaluating the controllable resource capacity of a distribution network area based on a multi-layer clustering strategy according to claim 5, characterized in that: The user willingness is defined by the user influence factor α i,t To quantify, the formula is: Where 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; temperature controllability refers to the set temperature range within which the controllable resource can be dynamically adjusted, which is obtained based on the user influence factor and is expressed as follows: Where, and are the adjustable upper and lower limits of the initial temperature respectively; ΔT i,t is the temperature adjustment amount for the i-th controllable resource at time t; and They are the upper and lower adjustable limits of the set temperature after considering the user's wishes.

7. The method for evaluating the controllable resource capacity of a distribution network area based on a multi-layer clustering strategy according to claim 6, characterized in that: 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, calculating the lower limit of the adjustable range of the user set temperature in each time period based on the electricity fee in each time period; The reset temperature method is used to calculate the adjustable power of the adjustable resource, with 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: 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 set temperature; The adjustable power of the adjustable resources running at different times of the day in the distribution transformer area is summed and multiplied by the user controllability ξ to obtain the adjustable capacity evaluation model of the adjustable resources.

8. A system for evaluating the capacity of controllable resources in a power distribution area based on a multi-layer clustering strategy, applied to a method for evaluating the capacity of controllable resources in a power distribution area based on a multi-layer clustering strategy as claimed in any one of claims 1 to 7, characterized in that: include: The modeling module is used to model the controllable resources in the distribution area using an equivalent thermal parameter model, simplifying the actual controllable resources into a standard mathematical model; A clustering module is used to perform multi-layer clustering on the controllable resources after modeling to form multiple controllable 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 balanced, the three-phase load balance control scheme is executed; if 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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