Flexible resource individual behavior analysis method and related device

Abstract

The invention belongs to the technical field of power demand side adjustment, and discloses a flexible resource individual behavior analysis method and a related device. The flexible resource individual behavior analysis method comprises the following steps: acquiring preset parameters of a to-be-analyzed flexible resource; on the basis of the obtained preset parameters, the charging amount, the discharging amount or the electricity consumption of the flexible resources is obtained through a mechanism characteristic modeling algorithm; on the basis of the obtained charging amount, discharging amount or power consumption of the flexible resource, calculating and obtaining the power consumption transfer amount of the flexible resource in peak-average, peak-valley and average-valley periods by adopting a flexible resource individual behavior modeling algorithm under the influence of a price signal; wherein the load electricity consumption analysis of each time period is carried out by adopting the peak-normal time period transfer coefficient, the peak-valley time period transfer coefficient and the electricity saving coefficient. According to the method, the adjustment characteristics of various types of flexible resources under the multi-dimensional influence factors can be obtained, and the adjustment characteristics of the flexible resources are accurately grasped.

Description

Technical Field

[0001] The present invention belongs to the technical field of power demand side regulation, and in particular relates to a method for analyzing individual behavior of flexibility resources and related devices. Background Art

[0002] In recent years, the flexibility resources connected to regional power grids have seen explosive growth. Compared with traditional regulating resources such as large hydropower units and thermal power units, the above-mentioned flexibility resources have the characteristics of small single-unit capacity, huge quantity and potential, green environmental protection, flexible economy, etc. It can be foreseen that the flexibility resources in regional power grids in the future will show a massive development trend.

[0003] The above-mentioned flexibility resources are small in scale, but they can provide huge regulatory capabilities through efficient aggregation. The existing technical solutions for individual behavior analysis of flexibility resources cannot clearly define the regulatory characteristics of various types of flexibility resources under multi-dimensional influencing factors. It is urgent to study the behavior analysis methods of individual flexibility resources to provide support for the efficient aggregation and coordinated optimization of massive flexibility resources. Summary of the invention

[0004] The purpose of the present invention is to provide a method and related device for analyzing individual behavior of flexibility resources to solve one or more of the above-mentioned technical problems. The technical solution provided by the present invention is specifically a flexibility resource individual behavior analysis solution that takes into account multi-dimensional influencing factors such as mechanism characteristics and price signals, which can obtain the adjustment characteristics of various types of flexibility resources under multi-dimensional influencing factors and realize the accurate grasp of the adjustment characteristics of flexibility resources.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for analyzing individual behavior of flexibility resources, comprising the following steps:

[0007] Obtaining preset parameters of the flexibility resource to be analyzed;

[0008] Based on the obtained preset parameters, a mechanism characteristic modeling algorithm is used to obtain the charging amount, discharging amount or power consumption of the flexibility resource;

[0009] Based on the charging, discharging or electricity consumption of the obtained flexibility resources, the individual behavior modeling algorithm of the flexibility resources under the influence of price signals is used to calculate the electricity consumption transfer of the flexibility resources in the peak-flat, peak-valley and flat-valley periods;

[0010] Among them, the individual behavior modeling algorithm of flexibility resources under the influence of price signals adopts peak-to-normal period transfer coefficient, peak-to-valley period transfer coefficient and electricity saving coefficient to analyze the load power consumption in each period.

[0011] A further improvement of the method of the present invention is that

[0012] The peak-to-average period transfer coefficient is expressed as:

[0013]

[0014] In the formula, λ fp It indicates the percentage of electricity consumption shifted from peak hours to normal hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fg It indicates the amount of electricity consumption shifted from peak to normal period after the implementation of time-of-use electricity price; Q fNON Indicates the electricity consumption of users who do not implement time-of-use electricity prices;

[0015] The peak-to-valley time period transfer coefficient is expressed as:

[0016]

[0017] In the formula, λ fg It indicates the percentage of electricity consumption shifted from peak hours to valley hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fp It indicates the peak-valley electricity consumption shift after the implementation of time-of-use electricity price;

[0018] The power saving coefficient is expressed as:

[0019]

[0020] In the formula, λ fSAVE It indicates the percentage of electricity saved by users after reducing electricity consumption during peak hours after implementing time-of-use electricity prices and the electricity consumption when time-of-use electricity prices are not implemented; ΔQ fSAVE It indicates the reduction in electricity consumption during peak hours after the implementation of time-of-use electricity prices.

[0021] A further improvement of the method of the present invention is that in the step of using the peak-to-normal period transfer coefficient, the peak-to-valley period transfer coefficient and the power saving coefficient to analyze the load power consumption in each period,

[0022] The load during the peak period before and after the implementation of the time-of-use electricity price meets the following requirements:

[0023] L(k) 1 =L(k 0 ) 1 -μ fg L f -μ fp L f ;

[0024] Where, L(k) 1 represents the load during the kth peak period after the implementation of time-of-use electricity prices; L(k0 ) 1 represents the load during the kth peak period before the implementation of time-of-use electricity prices; μ fg It represents the ratio of the unit load reduction during the peak period due to the peak-valley electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; μ fp It represents the ratio of the load reduction per unit period during the peak period caused by the peak-to-flat electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; L f Indicates the average load during the pre-peak period when time-of-use electricity prices are implemented;

[0025] The load satisfaction in normal period before and after the implementation of time-of-use electricity price is:

[0026] L(k) 2 =L(k 0 ) 2 -μ pg L f +μ fp L f γ fp ;

[0027] Where, L(k) 2 represents the load power consumption in the kth normal period when the time-of-use electricity price is implemented; L(k 0 ) 2 represents the load power consumption in the kth normal period before the implementation of time-of-use electricity price; μ pg It indicates the ratio of the unit load reduction during normal hours due to the price difference between flat and valley hours to the average load during normal hours when time-of-use electricity prices are not implemented;

[0028] After the implementation of the peak-valley electricity price, the user's load during the valley period becomes:

[0029] L(k) 3 =L(k 0 ) 3 +μ fg L f γ fg +μ pg L p γ pg ;

[0030] Where, L(k) 3 represents the load power consumption in the kth valley period after the implementation of time-of-use electricity price; L(k 0 ) 3 It represents the load power consumption in the kth valley period before the implementation of time-of-use electricity price.

[0031] A further improvement of the method of the present invention is that

[0032] When the flexibility resource is a flexible resource of energy storage type, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is:

[0033] 1) The relationship between the energy storage power at adjacent moments is expressed as:

[0034]

[0035] In the formula, S stor (t) represents the energy stored at time t; u represents the self-discharge rate of the energy stored; S stor (t-1) represents the energy stored at time t-1; η abs Indicates the charging efficiency of energy storage; P abs (t) represents the charging power of the energy storage at time t; η relea Indicates the discharge efficiency of energy storage; P relea (t) represents the discharge power of the energy storage at time t; Δt represents the time interval;

[0036] 2) The relationship between energy storage charging and discharging power and stored energy is expressed as:

[0037]

[0038] In the formula, S stor (0) represents the power at the beginning of the energy storage operation cycle, S stor (end) indicates the energy at the end of the energy storage operation cycle; Indicates the minimum storage capacity of energy storage, Indicates the maximum storage capacity of energy storage; U abs (t) represents the charging state of the energy storage at time t, U relea (t) represents the discharge state of the stored energy at time t; Indicates the minimum charging and discharging power of energy storage, Indicates the maximum charging and discharging power of energy storage;

[0039] 3) The interaction constraints between energy storage and external devices are expressed as:

[0040]

[0041] In the formula, N represents the number of energy storage, P ess,b (t) represents the power purchased by the energy storage from external devices at time t, P ess,s (t) represents the power sold by the energy storage to external devices at time t; P ess,abs (t) represents the charging power of the energy storage at time t, P ess,relea (t) represents the discharge power of the energy storage at time t; Indicates the maximum interaction power between energy storage and external devices.

[0042] A further improvement of the method of the present invention is that

[0043] When the flexibility resource is an air conditioning type flexibility resource, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is:

[0044] 1) The human comfort evaluation index is expressed as:

[0045]

[0046] In the formula, SSD 夏 Indicates the comfort level of human body in summer, T indicates the ambient temperature, RH indicates the relative humidity, α 1 Indicates the wind direction correction coefficient in summer, V represents wind speed, S represents sunshine hours; SSD 冬 Indicates the comfort level of human body in winter, α 2 Indicates the wind direction correction coefficient during winter period;

[0047] 2) The equivalent thermal parameter model is expressed as:

[0048] When C = 0,

[0049] When C = 1,

[0050] In the formula, C represents the start and stop state of the air conditioner. When C is 0, it means the air conditioner is in the off state, and when C is 1, it means the air conditioner is in the running state. t in represents the indoor temperature at time t; represents the indoor temperature at time t+1; T t out represents the outdoor temperature at time t; represents the outdoor temperature at time t+1; ε represents the heat dissipation coefficient; α represents the air conditioning energy efficiency duty cycle; S represents the thermal conductivity of the air conditioner; P x represents the rated energy consumption of the air conditioner at time x, αP x Indicates the rated cooling capacity of the air conditioner;

[0051] 3) The energy balance equation is expressed as:

[0052]

[0053] Where, T max Indicates the highest temperature of the air conditioner in the current environment; T min Indicates the lowest temperature of the air conditioner in the current environment; t on Indicates the opening time of the air conditioner; t off Indicates the shut-off time of the air conditioner; It represents the average rated power of the air conditioner during the x period;

[0054] 4) The adjustable capacity is expressed as:

[0055]

[0056] In the formula, C x represents the adjustable capacity of the air conditioner during the x period; t c represents the control period of the air conditioner, t c =t on +t off .

[0057] A further improvement of the method of the present invention is that

[0058] When the flexibility resource is an electric vehicle type flexibility resource, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is:

[0059] 1) Charging behavior characteristic index is expressed as:

[0060]

[0061] Where, T min Indicates the shortest charging duration of an electric vehicle; E exp E represents the amount of electricity that electric vehicle owners expect to obtain when leaving the charging station; ini Indicates the initial battery capacity of the electric vehicle; P ch.max Indicates the maximum charging power acceptable to electric vehicles; E bat Indicates the total battery capacity of the electric vehicle; S ini Indicates the initial charge state of the electric vehicle when it is connected to the power grid; S exp Indicates the state of charge that electric vehicle owners expect to achieve when charging is completed;

[0062] 2) Obtain the electric vehicle classification results through the K-means clustering method.

[0063] A second aspect of the present invention provides a flexibility resource individual behavior analysis system, comprising:

[0064] A parameter acquisition module, used to acquire preset parameters of the flexibility resource to be analyzed;

[0065] An electricity calculation module, used to obtain the charging amount, discharging amount or electricity consumption of the flexible resource based on the acquired preset parameters by using a mechanism characteristic modeling algorithm;

[0066] An analysis module is used to calculate the electricity consumption transfer of the flexibility resources in the peak-flat, peak-valley, and flat-valley periods based on the charging, discharging or electricity consumption of the obtained flexibility resources by using an individual behavior modeling algorithm of the flexibility resources under the influence of price signals;

[0067] Among them, the individual behavior modeling algorithm of flexibility resources under the influence of price signals adopts peak-to-normal period transfer coefficient, peak-to-valley period transfer coefficient and electricity saving coefficient to analyze the load power consumption in each period.

[0068] A further improvement of the system of the present invention is that:

[0069] The peak-to-average period transfer coefficient is expressed as:

[0070]

[0071] In the formula, λ fp It indicates the percentage of electricity consumption shifted from peak hours to normal hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fg It indicates the amount of electricity consumption shifted from peak to normal period after the implementation of time-of-use electricity price; Q fNON Indicates the electricity consumption of users who do not implement time-of-use electricity prices;

[0072] The peak-to-valley time period transfer coefficient is expressed as:

[0073]

[0074] In the formula, λ fg It indicates the percentage of electricity consumption shifted from peak hours to valley hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fp It indicates the peak-valley electricity consumption shift after the implementation of time-of-use electricity price;

[0075] The power saving coefficient is expressed as:

[0076]

[0077] In the formula, λ fSAVE It indicates the percentage of electricity saved by users after reducing electricity consumption during peak hours after implementing time-of-use electricity prices and the electricity consumption when time-of-use electricity prices are not implemented; ΔQ fSAVE It indicates the reduction in electricity consumption during peak hours after the implementation of time-of-use electricity prices.

[0078] A further improvement of the system of the present invention is that the step of using the peak-to-normal period transfer coefficient, the peak-to-valley period transfer coefficient and the power saving coefficient to analyze the load power consumption in each period includes:

[0079] The load during the peak period before and after the implementation of the time-of-use electricity price meets the following requirements:

[0080] L(k) 1 =L(k 0 ) 1 -μ fg L f -μ fp Lf ;

[0081] Where, L(k) 1 represents the load during the kth peak period after the implementation of time-of-use electricity prices; L(k 0 ) 1 represents the load during the kth peak period before the implementation of time-of-use electricity prices; μ fg It represents the ratio of the unit load reduction during the peak period due to the peak-valley electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; μ fp It represents the ratio of the load reduction per unit period during the peak period caused by the peak-to-flat electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; L f Indicates the average load during the pre-peak period when time-of-use electricity prices are implemented;

[0082] The load satisfaction in normal period before and after the implementation of time-of-use electricity price is:

[0083] L(k) 2 =L(k 0 ) 2 -μ pg L f +μ fp L f γ fp ;

[0084] Where, L(k) 2 represents the load power consumption in the kth normal period when the time-of-use electricity price is implemented; L(k 0 ) 2 represents the load power consumption in the kth normal period before the implementation of time-of-use electricity price; μ pg It indicates the ratio of the unit load reduction during normal hours due to the price difference between flat and valley hours to the average load during normal hours when time-of-use electricity prices are not implemented;

[0085] After the implementation of the peak-valley electricity price, the user's load during the valley period becomes:

[0086] L(k) 3 =L(k 0 ) 3 +μ fg L f γ fg +μ pg L p γ pg ;

[0087] Where, L(k) 3 represents the load power consumption in the kth valley period after the implementation of time-of-use electricity price; L(k 0 ) 3 It represents the load power consumption in the kth valley period before the implementation of time-of-use electricity price.

[0088] A further improvement of the system of the present invention is that:

[0089] When the flexibility resource is a flexible resource of energy storage type, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is:

[0090] 1) The relationship between the energy storage power at adjacent moments is expressed as:

[0091]

[0092] In the formula, S stor (t) represents the energy stored at time t; u represents the self-discharge rate of the energy stored; S stor( t-1) represents the energy stored at time t-1; η abs Indicates the charging efficiency of energy storage; P abs (t) represents the charging power of the energy storage at time t; η relea Indicates the discharge efficiency of energy storage; P relea (t) represents the discharge power of the energy storage at time t; Δt represents the time interval;

[0093] 2) The relationship between energy storage charging and discharging power and stored energy is expressed as:

[0094]

[0095] In the formula, S stor (0) represents the power at the beginning of the energy storage operation cycle, S stor (end) indicates the energy at the end of the energy storage operation cycle; Indicates the minimum storage capacity of energy storage, Indicates the maximum storage capacity of energy storage; U abs (t) represents the charging state of the energy storage at time t, U relea (t) represents the discharge state of the stored energy at time t; Indicates the minimum charging and discharging power of energy storage, Indicates the maximum charging and discharging power of energy storage;

[0096] 3) The interaction constraints between energy storage and external devices are expressed as:

[0097]

[0098] In the formula, N represents the number of energy storage, P ess,b (t) represents the power purchased by the energy storage from external devices at time t, P ess,s (t) represents the power sold by the energy storage to external devices at time t; P ess,abs (t) represents the charging power of the energy storage at time t, P ess,relea (t) represents the discharge power of the energy storage at time t; Indicates the maximum interaction power between energy storage and external devices.

[0099] A further improvement of the system of the present invention is that:

[0100] When the flexibility resource is an air conditioning type flexibility resource, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is:

[0101] 1) The human comfort evaluation index is expressed as:

[0102]

[0103] In the formula, SSD 夏 Indicates the comfort level of human body in summer, T indicates the ambient temperature, RH indicates the relative humidity, α 1 Indicates the wind direction correction coefficient in summer, V represents wind speed, S represents sunshine hours; SSD 冬 Indicates the comfort level of human body in winter, α 2 Indicates the wind direction correction coefficient during winter period;

[0104] 2) The equivalent thermal parameter model is expressed as:

[0105] When C = 0,

[0106] When C = 1,

[0107] In the formula, C represents the start and stop state of the air conditioner. When C is 0, it means the air conditioner is in the off state, and when C is 1, it means the air conditioner is in the running state. t in represents the indoor temperature at time t; represents the indoor temperature at time t+1; T t out represents the outdoor temperature at time t; represents the outdoor temperature at time t+1; ε represents the heat dissipation coefficient; α represents the air conditioning energy efficiency duty cycle; S represents the thermal conductivity of the air conditioner; P x represents the rated energy consumption of the air conditioner at time x, αP x Indicates the rated cooling capacity of the air conditioner;

[0108] 3) The energy balance equation is expressed as:

[0109]

[0110] Where, T max Indicates the highest temperature of the air conditioner in the current environment; T min Indicates the lowest temperature of the air conditioner in the current environment; t on Indicates the opening time of the air conditioner; t off Indicates the shut-off time of the air conditioner; It represents the average rated power of the air conditioner during the x period;

[0111] 4) The adjustable capacity is expressed as:

[0112]

[0113] In the formula, C x represents the adjustable capacity of the air conditioner during the x period; t c represents the control period of the air conditioner, t c =t on +t off .

[0114] A further improvement of the system of the present invention is that:

[0115] When the flexibility resource is an electric vehicle type flexibility resource, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is:

[0116] 1) Charging behavior characteristic index is expressed as:

[0117]

[0118] Where, T min Indicates the shortest charging duration of an electric vehicle; E exp E represents the amount of electricity that electric vehicle owners expect to obtain when leaving the charging station; ini Indicates the initial battery capacity of the electric vehicle; P ch.max Indicates the maximum charging power acceptable to electric vehicles; E bat Indicates the total battery capacity of the electric vehicle; S ini Indicates the initial charge state of the electric vehicle when it is connected to the power grid; S exp Indicates the state of charge that electric vehicle owners expect to achieve when charging is completed;

[0119] 2) Obtain the electric vehicle classification results through the K-means clustering method.

[0120] According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for analyzing individual behavior of flexibility resources as described in any one of the first aspect of the present invention is implemented.

[0121] According to a fourth aspect of the present invention, there is provided a non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for analyzing individual behaviors of flexibility resources as described in any one of the first aspect of the present invention is implemented.

[0122] Compared with the prior art, the present invention has the following beneficial effects:

[0123] The present invention provides a method for analyzing individual behavior of flexibility resources, which is an individual behavior analysis scheme for flexibility resources that takes into account multi-dimensional influencing factors such as mechanism characteristics and price signals. By analyzing the transfer power of flexibility resources in multiple time periods under the influence of prices, the regulation characteristics of various types of flexibility resources under multi-dimensional influencing factors can be obtained, and the precise grasp of the regulation characteristics of flexibility resources can be achieved, which can provide effective support for the efficient aggregation and coordinated optimization of flexibility resources. Specifically, in the existing technical scheme, the behavior analysis under the influence of prices focuses on the transfer power of a single specific time period, lacks the analysis of the transfer power of flexibility resources in multiple time periods under the influence of prices, and cannot accurately grasp the regulation characteristics of flexibility resources; in response to the above-mentioned existing technical problems, the technical scheme of the present invention adopts the peak-to-normal period transfer coefficient, the peak-to-valley period transfer coefficient and the power saving coefficient for analysis. This technical means can refine the user's electricity transfer amount in different time periods, clarify the user's transfer period under the time-of-use electricity price, and is beneficial to the improvement of user benefits and the formulation of demand response strategies.

[0124] In the existing technical solutions, the modeling of the mechanism characteristics of energy storage focuses on the transformation of its internal energy relations, lacks the analysis of the interactive coupling relationship between energy storage and the external environment, and cannot accurately grasp the adjustment characteristics of flexible resources. In response to the problems existing in the mechanism characteristics modeling algorithms of the above-mentioned existing energy storage types of flexible resources, the technical solution of the present invention adopts technical means to characterize the relationship between the power of energy storage at adjacent moments, the relationship between the energy storage charging and discharging power and the stored energy, and the interactive constraints between energy storage and external devices. This technical means can more comprehensively quantify and analyze the coupling relationship between the internal energy storage and its interaction with the external environment, and can improve the accuracy of the energy storage generation strategy.

[0125] In the existing technical solutions, the modeling of the mechanism characteristics of the air conditioner focuses on the relationship between it and the ambient temperature, lacks an analysis of the comfort impact caused by the regional differences in the air conditioner, and cannot accurately grasp the flexibility resource adjustment characteristics; in response to the problems existing in the mechanism characteristics modeling algorithm of the flexibility resources of the above-mentioned existing air-conditioning types, the technical solution of the present invention adopts a human comfort evaluation index that takes into account ambient temperature, relative humidity, wind direction correction coefficient, and sunshine hours, an equivalent thermal parameter model, an energy balance equation, and an adjustable capacity for characterization. This technical means can reflect the differences between different regions, increase the versatility of the modeling method and improve the accuracy of the calculation results.

[0126] In the existing technical solutions, the modeling of the mechanism characteristics of electric vehicles focuses on individual electric vehicles, lacks electric vehicle clustering analysis, and cannot accurately grasp the flexibility resource adjustment characteristics; in order to solve the problems existing in the mechanism characteristics modeling algorithms of the flexibility resources of the above-mentioned existing electric vehicle types, the technical solution of the present invention adopts the shortest charging duration, the expected amount of power when leaving the charging station, the initial battery power, the acceptable maximum charging power, the total battery capacity, the initial state of charge when connected to the power grid, the expected state of charge to be achieved at the end of charging, and K-means clustering for analysis. This technical means can effectively aggregate individual electric vehicle resources with similar characteristics, and can improve the analysis capabilities for large-scale electric vehicle clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0127] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below; obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0128] Figure 1 It is a flow chart of a method for analyzing individual behavior of flexibility resources in an embodiment of the present invention.

[0129] Figure 2 It is a schematic diagram of a flexibility resource individual behavior analysis system in an embodiment of the present invention. DETAILED DESCRIPTION

[0130] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments and technical solutions are only part of the embodiments of the present invention, not all of the embodiments.

[0131] All other embodiments obtained by those of ordinary skill in the art without creative work based on the technical solutions disclosed in the embodiments of the present invention belong to the scope of protection of the present invention. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0132] See also Figure 1 In an embodiment of the present invention, a method for analyzing individual behavior of flexibility resources is disclosed, which specifically includes the following steps:

[0133] Step 1, obtaining preset parameters of the flexibility resource to be analyzed;

[0134] In a specific exemplary technical solution, the preset parameters that need to be obtained for the flexible resources of the energy storage type include: self-discharge rate, charging efficiency, discharge efficiency, power at the beginning of the operation cycle, minimum storage power, maximum storage power, minimum charge and discharge power, maximum charge and discharge power, and maximum interaction power between energy storage and external devices;

[0135] In a specific exemplary technical solution, the preset parameters that need to be obtained for the flexibility resource of the air-conditioning type include: relative humidity, wind direction correction coefficient in summer / winter period, wind speed, sunshine hours, indoor temperature, outdoor temperature, heat dissipation coefficient, air-conditioning energy efficiency duty cycle, thermal conductivity, air-conditioning rated energy consumption, rated cooling capacity, the highest / lowest temperature of the current environment, start / stop time, average rated power and control cycle;

[0136] In a specific exemplary technical solution, the preset parameters that need to be obtained for the flexibility resources of electric vehicles include: the shortest charging duration, the amount of electricity expected to be obtained when the electric vehicle leaves the charging station, the initial battery power, the maximum charging power acceptable to the electric vehicle, the total battery capacity of the electric vehicle, the initial state of charge of the electric vehicle when it is connected to the power grid, the state of charge that the electric vehicle owner expects to reach at the end of charging, the electric vehicle user sample data set, the number of iterations, and the number of initial cluster centers;

[0137] Step 2: Based on the preset parameters obtained in step 1, a mechanism characteristic modeling algorithm is used to calculate the charging amount, discharging amount or power consumption of the flexibility resource;

[0138] In a specific exemplary technical solution, for the flexible resources of energy storage type, the relationship between the power of energy storage at adjacent moments, the relationship between the charging and discharging power of energy storage and the stored energy, and the interaction constraints between energy storage and external devices are used to characterize the mechanism characteristic modeling algorithm; the flexible resources of energy storage type calculate their charging and discharging amounts according to their mechanism characteristic modeling algorithm;

[0139] In the specific exemplary technical solution, for the flexibility resources of the air-conditioning type, the mechanism characteristic modeling algorithm adopts the human comfort evaluation index (further explanatory, specifically the human comfort evaluation index taking into account the ambient temperature, relative humidity, wind direction correction coefficient, and sunshine hours), the equivalent thermal parameter model, the energy balance equation, and the adjustable capacity for characterization; the flexibility resources of the air-conditioning type calculate their power consumption according to their mechanism characteristic modeling algorithm;

[0140] In a specific exemplary technical solution, for flexibility resources of electric vehicle type, charging behavior characteristic index and K-means clustering are used in the mechanism characteristic modeling algorithm for analysis; for flexibility resources of electric vehicle type, its power consumption is obtained according to its mechanism characteristic modeling algorithm;

[0141] Step 3, based on the charging, discharging or power consumption of the flexibility resources calculated in step 2, use the flexibility resource individual behavior modeling algorithm under the influence of price signals to calculate the power consumption transfer of the flexibility resources in the peak-flat, peak-valley and flat-valley periods; wherein, the flexibility resource individual behavior modeling algorithm under the influence of price signals uses the peak-flat period transfer coefficient, the peak-valley period transfer coefficient and the power saving coefficient to analyze the load power consumption in each period.

[0142] The embodiment of the present invention specifically proposes a method for analyzing the individual behavior of flexibility resources. The method not only takes into account the basic mechanism characteristics of flexibility resources, but also integrates multi-dimensional influencing factors such as price signals to form a comprehensive and in-depth analysis framework. The core of the technical solution of the embodiment of the present invention is to reveal the unique regulation characteristics of various types of flexibility resources under the influence of various complex factors through a detailed analysis of how the price mechanism drives the transfer of electricity from flexibility resources in different time periods. The technical solution of the embodiment of the present invention greatly enhances the accurate understanding of the regulation capabilities of flexibility resources and lays a solid foundation for subsequent efficient aggregation and coordinated optimization work. Specifically, current technical solutions are often limited to analyzing the impact of prices on electricity transfer within a single specific period of time, ignoring the dynamic behavior changes of flexible resources over a wider time span. This limitation leads to an inaccurate grasp of the regulation characteristics of flexible resources, making it difficult to effectively guide the optimal allocation and utilization of resources. To overcome the above technical difficulties, the technical solution of the embodiment of the present invention introduces key analysis indicators such as peak-to-normal period transfer coefficient, peak-to-valley period transfer coefficient, and electricity saving coefficient. The application of these coefficients enables us to quantify the electricity transfer of users between different electricity price periods (such as peak hours, normal hours, and valley hours) in detail, and clearly define the specific behavior patterns of users when responding to time-of-use electricity price policies. In this way, it not only promotes the maximization of user economic benefits, but also provides a scientific basis for formulating more accurate demand response strategies. In summary, the embodiment of the present invention successfully breaks through the limitations of traditional methods in analyzing the individual behavior of flexible resources by constructing a multi-dimensional and refined analysis system, and provides strong technical support for realizing efficient management of flexible resources, optimizing scheduling, and promoting the flexibility and stability of power systems.

[0143] In a specific embodiment of the present invention, the individual behavior modeling of flexibility resources under the influence of price signals includes:

[0144] The peak-to-average period transfer coefficient is expressed as:

[0145]

[0146] In the formula, λ fp It indicates the percentage of electricity consumption shifted from peak hours to normal hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fg It indicates the amount of electricity consumption shifted from peak to (→) normal periods after the implementation of time-of-use electricity prices; Q fNON Indicates the electricity consumption of users who do not implement time-of-use electricity prices;

[0147] The peak-to-valley time period transfer coefficient is expressed as:

[0148]

[0149] In the formula, λ fg It indicates the percentage of electricity consumption shifted from peak hours to valley hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fp It indicates the peak-to-valley electricity consumption shift after the implementation of time-of-use electricity price;

[0150] The power saving coefficient is expressed as:

[0151]

[0152] In the formula, λ fSAVE It indicates the percentage of electricity saved by users after reducing electricity consumption during peak hours after implementing time-of-use electricity prices and the electricity consumption when time-of-use electricity prices are not implemented; ΔQ fSAVE It indicates the reduction in electricity consumption during peak hours after the implementation of time-of-use electricity prices.

[0153] Furthermore, the analysis process of the peak period specifically includes:

[0154] Step (1), define ΔL fgk is the load increase caused by the peak-valley electricity price difference in the kth valley section, ΔL gfk is the load reduction in the k-th peak period caused by the peak-valley electricity price difference. The amount of electricity transferred from a certain period is evenly distributed according to time; the amount of electricity transferred to a certain period is also evenly distributed according to time, as shown in the following formula:

[0155]

[0156] ΔL fg *N g =ΔQ fg =ΔL gf *N f ;

[0157] Where N g Indicates the number of valley periods; N fIndicates the number of peak hours.

[0158] Step (2), define L f To implement the average load of the pre-peak period of time-of-use electricity price, as shown in the following formula:

[0159] L f *N f =Q fNON ;

[0160] Step (3), substitute the above formula and use μ fg Alternative Lambda fg , as shown below:

[0161]

[0162] Step (4) calculates the load change in each peak and valley period after the implementation of the electricity price, as shown in the following formula:

[0163] ΔL gf =μ fg *L f ;

[0164]

[0165] In the formula, γ fg =N f / N g represents the peak-to-valley transfer coefficient;

[0166] Step (5), define the peak period user response as:

[0167]

[0168] In the formula, μ fg It represents the ratio of the unit load reduction during the peak period due to the peak-valley electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; μ fp It represents the ratio of the load reduction per unit period during the peak period caused by the peak-to-flat electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; P f represents the electricity price during peak hours, P p represents the electricity price during normal hours, Δ fp Represents the peak-valley electricity price difference, which can be expressed by Δ fp =P f -P p Calculated; a fp Indicates the upper limit of the price difference between peak hours and normal hours in the dead zone; b fp K represents the upper limit of the price difference between peak and normal hours in the linear region; fp represents the slope of the linear region;

[0169] Step (6): The load during the peak period before and after the implementation of the time-of-use electricity price meets the following requirements:

[0170] L(k)=L(k 0 )-ΔL gf -ΔL pf =L(k 0 )-μ fg L f -μ fp L f k∈T f ;

[0171] Where L(k) represents the load during the kth peak period after the implementation of time-of-use electricity prices; L(k 0 ) represents the load during the kth peak period before the implementation of time-of-use electricity pricing;

[0172] It should be noted that μ fp and μ fg They all reflect the electricity consumption characteristics of a certain region or a certain type of load; in fact, the normal period electricity price P p When the range of change is not large, P f and P g The impact on the peak time response is P p Much bigger.

[0173] Step (7): Assume that η is the ratio of the pull-out ratio, the load transfer rate is η, and the peak, valley and normal period electricity price P f , P p , P g The function of

[0174] μ fi =f(P f ,P p ,P g ,η)(μ fi =μ fp ,μ fg ).

[0175] The analysis process during normal periods specifically includes:

[0176] Definition of user response during normal period: μ pg It represents the ratio of the unit load reduction during normal hours due to the price difference between flat and valley hours to the average load during normal hours when time-of-use electricity prices are not implemented. Then:

[0177]

[0178] According to the above definition, the load in normal period before and after the implementation of time-of-use electricity price meets:

[0179] L(k)=L(k 0 )-ΔL gp +ΔLfp =L(k 0 )-μ pg L f +μ fp L f γ fp k∈T p ;

[0180] Where, L(k) represents the load power consumption in the kth normal period when time-of-use electricity price is not implemented; L(k 0 ) represents the load power consumption in the kth normal period before the implementation of time-of-use electricity price;

[0181] With μ pg It is also related to the load characteristics and can be expressed as:

[0182] μ pi =f(P f ,P p ,P g ,η)(μ pi =μ pg ).

[0183] The specific analysis process of the valley period includes:

[0184] After the implementation of the peak-valley electricity price, the user's load during the valley period becomes:

[0185] L(k)=L(k 0 )+ΔL fg +ΔL pg =L(k 0 )+μ fg L f γ fg +μ pg L p γ pg k∈T g ;

[0186] Where, L(k) represents the load power consumption in the kth valley period after the implementation of time-of-use electricity price; L(k 0 ) represents the load power consumption in the kth valley period before the implementation of time-of-use electricity price.

[0187] In the technical solution provided by the embodiment of the present invention, the amount of electricity transfer is mainly characterized by constructing a functional relationship between flexibility resources (such as energy storage, air conditioning, and electric vehicles) and prices to evaluate the user's sensitivity to prices. The user's electricity consumption is directly obtained by integrating the power of the above three resources during the operation cycle, which is the original electricity consumption of the user when not participating in the regulation; for example, taking the peak period as an example, it is expressed as:

[0188] ΔQ fg =f 峰 (max{Pess,abs (t),P ess,relea (t)},C x ,E exp -E ini )

[0189]

[0190] In a specific embodiment of the present invention, when the flexibility resource is a flexible resource of energy storage type, the result of the mechanism characteristic modeling is:

[0191] 1) The relationship between the energy storage power at adjacent moments is expressed as:

[0192]

[0193] In the formula, S stor (t) represents the energy of the energy storage at time t; u represents the self-discharge rate of the energy storage (explanatory, this value is close to 0 and can be ignored when the time interval is small); S stor (t-1) represents the energy stored at time t-1; η abs Indicates the charging efficiency of energy storage; P abs (t) represents the charging power of the energy storage at time t; η relea Indicates the discharge efficiency of energy storage (explanatory, usually similar to the charging efficiency); P relea (t) represents the discharge power of the energy storage at time t; Δt represents the time interval;

[0194] 2) The relationship between energy storage charging and discharging power and stored energy is expressed as:

[0195]

[0196] In the formula, S stor (0) represents the power at the beginning of the energy storage operation cycle, S stor (end) represents the energy at the end of the energy storage operation cycle. Explanatoryally, since the energy storage itself does not generate or consume energy, the amount of electricity at the beginning and end should be balanced; Indicates the minimum storage capacity of energy storage, Indicates the maximum storage capacity of energy storage; U abs (t) represents the charging state of the energy storage at time t, U relea (t) represents the discharge state of the energy storage at time t. Explanation: the above two variables are 0-1 variables. When the value is 0, it means that the energy storage is not in this state, and when the value is 1, it means that the energy storage is in this state. Indicates the minimum charging and discharging power of energy storage, Indicates the maximum charging and discharging power of energy storage;

[0197] 3) The interaction constraints between energy storage and external devices are expressed as:

[0198]

[0199] In the formula, N represents the number of energy storage, P ess,b (t) represents the power purchased by the energy storage from external devices at time t, P ess,s (t) represents the power sold by the energy storage to external devices at time t; P ess,abs (t) represents the charging power of the energy storage at time t, P ess,relea (t) represents the discharge power of the energy storage at time t; Indicates the maximum interaction power between energy storage and external devices.

[0200] In a specific embodiment of the present invention, when the flexibility resource is an air conditioning type flexibility resource, the result of the mechanism characteristic modeling is:

[0201] 1) The human comfort evaluation index is expressed as:

[0202]

[0203] In the formula, SSD 夏 Indicates the comfort level of human body in summer, T indicates the ambient temperature, RH indicates the relative humidity, α 1 Indicates the wind direction correction coefficient in summer, V represents wind speed, S represents sunshine hours; SSD 冬 Indicates the comfort level of human body in winter, α 2 Indicates the wind direction correction coefficient during winter period;

[0204] 2) The equivalent thermal parameter model is expressed as:

[0205] When C = 0,

[0206] When C = 1,

[0207] In the formula, C represents the start / stop state of the air conditioner. Explanation: when C is 0, it means the air conditioner is in the off state, and when C is 1, it means the air conditioner is in the running state; T t in represents the indoor temperature at time t; represents the indoor temperature at time t+1; T t out represents the outdoor temperature at time t; represents the outdoor temperature at time t+1; ε represents the heat dissipation coefficient, which can be explained by It is calculated that the value can also be 0.96, where τ represents the simulation time interval, t krepresents the time constant; α represents the air conditioning energy efficiency duty cycle; S represents the thermal conductivity of the air conditioner, which is generally 0.18; P x represents the rated energy consumption of the air conditioner at time x, αP x Indicates the rated cooling capacity of the air conditioner;

[0208] 3) The energy balance equation is expressed as:

[0209]

[0210] Where, T max Indicates the highest temperature of the air conditioner in the current environment; T min Indicates the lowest temperature of the air conditioner in the current environment; t on Indicates the opening time of the air conditioner; t off Indicates the shut-off time of the air conditioner; It represents the average rated power of the air conditioner during the x period;

[0211] 4) The adjustable capacity is expressed as:

[0212]

[0213] In the formula, C x represents the adjustable capacity of the air conditioner during the x period; t c Represents the control cycle of the air conditioner, which can be expressed by t c =t on +t off Calculated.

[0214] In a specific embodiment of the present invention, when the flexibility resource is an electric vehicle type flexibility resource, the result of the mechanism characteristic modeling is:

[0215] 1) Charging behavior characteristic index is expressed as:

[0216]

[0217] Where, T min Indicates the shortest charging duration of an electric vehicle; E exp E represents the amount of electricity that electric vehicle owners expect to obtain when leaving the charging station; ini Indicates the initial battery capacity of the electric vehicle; P ch.max Indicates the maximum charging power acceptable to electric vehicles; E bat Indicates the total battery capacity of the electric vehicle; S ini Indicates the initial charge state of the electric vehicle when it is connected to the power grid; S exp Indicates the state of charge that electric vehicle owners expect to achieve when charging is completed;

[0218] 2) K-means clustering to obtain electric vehicle classification results;

[0219] Specific exemplary steps include:

[0220] Step (1): select a sample data set of n electric vehicle users {x 1 ,x 2 ,...,x n}, let the number of iterations be R, randomly select k (k≤n) electric vehicle users from the sample as the initial clustering center μ 1 ,μ 2 ,...,μ k , divide the original samples into k clusters S = {S 1 ,S 2 ,...,S k};

[0221] Step (2), calculate the relationship between the i-th user in the data set and each cluster center μ j distance, and divide the user into the cluster center μ that is closest to the user. m The corresponding cluster S m In the above formula, the following equation is satisfied:

[0222]

[0223] Step (3): Based on the above clustering results, calculate new k cluster centers for each cluster obtained, as shown in the following formula:

[0224]

[0225] Where n j For cluster S j The number of data contained in ;

[0226] Step (4), calculate the clustering criterion function D(r+1), and use it to determine whether the clustering result converges; wherein,

[0227]

[0228] Step (5): Repeat steps (2) to (4) until the clustering criterion function converges, that is, satisfies the following formula:

[0229] ||D(r+1)-D(r)||<ε;

[0230] In the formula, ε is a small positive number.

[0231] Explanatory, given that electric vehicles have small capacity, dispersed locations, and are greatly influenced by the users' own wishes, it is not enough to only implement single-unit modeling when analyzing individual resource behaviors. This is a major difference from air conditioning and energy storage. Therefore, it is necessary to analyze the charging and discharging behaviors of electric vehicle clusters based on individual resource modeling. Considering that the current scale of electric vehicles is growing very fast, direct analysis is difficult, so a feasible path is to put electric vehicles with similar characteristics into one category, which is also the purpose of clustering.

[0232] The embodiment of the present invention focuses on the individual behavior analysis method of three types of flexibility resources, which is mainly divided into two steps. The first step focuses on the mechanism characteristic modeling of the three types of resources, and the second step focuses on the impact of price signals on the electricity consumption behavior of the three types of resources (the impact is mainly reflected in the transfer of electricity consumption). To be realistic, there is no correlation between the modeling of the three types of resources, because from the perspective of individual resource behavior analysis, the mechanism characteristic modeling of the three types of resources is independent, that is, it will not affect the modeling of other types of resources; in addition, from the perspective of the impact of price signals, the impact of the three types of resources is similar, which is reflected in the transfer of electricity consumption during peak hours when electricity prices are high to normal and valley hours on the basis of meeting their basic needs. The improvements of the technical solution of the embodiment of the present invention are mainly reflected in two aspects: one is the mechanism characteristic modeling of individual resources, that is, the mechanism modeling method and process of the three types of resources; the second is the transfer of electricity consumption in different time periods under the influence of prices. In summary, the embodiment of the present invention proposes a method for analyzing individual behaviors of flexibility resources that takes into account multi-dimensional influencing factors such as mechanism characteristics, price signals, and user experience; wherein, mechanism characteristics are boundaries that can determine the operating logic of flexibility resources; price signals are conditions that can trigger the adjustment range and time period of flexibility resources; user experience is the bottom line that provides boundaries for the adjustment range of flexibility resources; the method disclosed in the embodiment of the present invention helps to provide an individual behavior analysis basis for the efficient aggregation and coordinated optimization of massive flexibility resources. The technical solution of the embodiment of the present invention is suitable for application in demand-side resource regulation and response platforms, including but not limited to virtual power plant operation and management platforms, new load management platforms, demand response management platforms, etc., and can effectively improve the efficient aggregation and coordinated optimization of massive flexibility resources.

[0233] The following are device embodiments of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0234] See also Figure 2 In an embodiment of the present invention, a flexibility resource individual behavior analysis system is provided, comprising:

[0235] A parameter acquisition module, used to acquire preset parameters of the flexibility resource to be analyzed;

[0236] An electricity calculation module, used to obtain the charging amount, discharging amount or electricity consumption of the flexible resource based on the acquired preset parameters by using a mechanism characteristic modeling algorithm;

[0237] An analysis module is used to calculate the electricity consumption transfer of the flexibility resources in the peak-flat, peak-valley, and flat-valley periods based on the charging, discharging or electricity consumption of the obtained flexibility resources by using an individual behavior modeling algorithm of the flexibility resources under the influence of price signals;

[0238] Among them, the individual behavior modeling algorithm of flexibility resources under the influence of price signals adopts peak-to-normal period transfer coefficient, peak-to-valley period transfer coefficient and electricity saving coefficient to analyze the load power consumption in each period.

[0239] In one embodiment of the present invention, a computer device is provided, the computer device comprising a processor and a memory, the memory being used to store a computer program, the computer program comprising program instructions, and the processor being used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used to perform the operation of the flexibility resource individual behavior analysis method.

[0240] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM (Random Access Memory) memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the individual behavior analysis method of flexibility resources in the above embodiment.

[0241] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) that contain computer-usable program code.

[0242] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0243] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0244] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0245] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing individual behavior of flexibility resources, characterized in that: The following steps are involved: Obtaining preset parameters of the flexibility resource to be analyzed; Based on the obtained preset parameters, a mechanism characteristic modeling algorithm is used to obtain the charging amount, discharging amount or power consumption of the flexibility resource; Based on the charging, discharging or electricity consumption of the obtained flexibility resources, the individual behavior modeling algorithm of the flexibility resources under the influence of price signals is used to calculate the electricity consumption transfer of the flexibility resources in the peak-flat, peak-valley and flat-valley periods; Among them, the individual behavior modeling algorithm of flexible resources under the influence of price signals adopts peak-to-normal period transfer coefficient, peak-to-valley period transfer coefficient and power saving coefficient to analyze the load power consumption in each period.

2. A method for analyzing individual behavior of flexibility resources according to claim 1, characterized in that: The peak-to-average period transfer coefficient is expressed as: In the formula, λ fp It indicates the percentage of electricity consumption shifted from peak hours to normal hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fg It indicates the amount of electricity consumption shifted from peak to normal period after the implementation of time-of-use electricity price; Q fNON Indicates the electricity consumption of users who do not implement time-of-use electricity prices; The peak-to-valley time period transfer coefficient is expressed as: In the formula, λ fg It indicates the percentage of electricity consumption shifted from peak hours to valley hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fp It indicates the peak-valley electricity consumption shift after the implementation of time-of-use electricity price; The power saving coefficient is expressed as: In the formula, λ fSAVE It indicates the percentage of electricity saved by users after reducing electricity consumption during peak hours after implementing time-of-use electricity prices and the electricity consumption when time-of-use electricity prices are not implemented; ΔQ fSAVE It indicates the reduction in electricity consumption during peak hours after the implementation of time-of-use electricity prices.

3. A method for analyzing individual behavior of flexibility resources according to claim 2, characterized in that: In the step of using the peak-to-normal period transfer coefficient, the peak-to-valley period transfer coefficient and the power saving coefficient to analyze the load power consumption in each period, The load during the peak period before and after the implementation of the time-of-use electricity price meets the following requirements: L(k)1=L(k0)1-μ fg L f -m fp L f ; Where, L(k)1 represents the load of the kth peak period after the implementation of time-of-use electricity price; L(k0)1 represents the load of the kth peak period before the implementation of time-of-use electricity price; μ fg It represents the ratio of the unit load reduction during the peak period due to the peak-valley electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; μ fp It represents the ratio of the load reduction per unit period during the peak period caused by the peak-to-flat electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; L f Indicates the average load during the pre-peak period when time-of-use electricity prices are implemented; The load satisfaction in normal period before and after the implementation of time-of-use electricity price is: L(k)2=L(k0)2-μ pg L f +μ fp Lγ fp ; Where, L(k)2 represents the load power consumption in the kth normal period when the time-of-use electricity price is implemented; L(k0)2 represents the load power consumption in the kth normal period before the time-of-use electricity price is implemented; μ pg It indicates the ratio of the unit load reduction during normal hours due to the price difference between flat and valley hours to the average load during normal hours when time-of-use electricity prices are not implemented; After the implementation of the peak-valley electricity price, the user's load during the valley period becomes: L(k)3=L(k0)3+μ fg L f c fg +m pg L p c pg ; Where, L(k)3 represents the load power consumption in the kth valley period after the implementation of time-of-use electricity prices; L(k0)3 represents the load power consumption in the kth valley period before the implementation of time-of-use electricity prices.

4. A method for analyzing individual behavior of flexibility resources according to claim 1, characterized in that: When the flexibility resource is a flexible resource of energy storage type, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is: 1) The relationship between the energy storage power at adjacent moments is expressed as: In the formula, S stor (t) represents the energy stored at time t; u represents the self-discharge rate of the energy stored; S stor (t-1) represents the energy stored at time t-1; η abs Indicates the charging efficiency of energy storage; P abs (t) represents the charging power of the energy storage at time t; η relea Indicates the discharge efficiency of energy storage; P relea (t) represents the discharge power of the energy storage at time t; Δt represents the time interval; 2) The relationship between energy storage charging and discharging power and stored energy is expressed as: In the formula, S stor (0) represents the power at the beginning of the energy storage operation cycle, S stor (end) indicates the energy at the end of the energy storage operation cycle; Indicates the minimum storage capacity of energy storage, Indicates the maximum storage capacity of energy storage; U abs (t) represents the charging state of the energy storage at time t, U relea (t) represents the discharge state of the stored energy at time t; Indicates the minimum charging and discharging power of energy storage, Indicates the maximum charging and discharging power of energy storage; 3) The interaction constraints between energy storage and external devices are expressed as: In the formula, N represents the number of energy storage, P ess,b (t) represents the power purchased by the energy storage from external devices at time t, P ess,s (t) represents the power sold by the energy storage to external devices at time t; P ess,abs (t) represents the charging power of the energy storage at time t, P ess,relea (t) represents the discharge power of the energy storage at time t; Indicates the maximum interaction power between energy storage and external devices.

5. The method for analyzing individual behavior of flexibility resources according to claim 1, characterized in that: When the flexibility resource is an air conditioning type flexibility resource, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is: 1) The human comfort evaluation index is expressed as: In the formula, SSD 夏 Indicates the comfort level of human body in summer, T indicates the ambient temperature, RH indicates the relative humidity, α1 indicates the wind direction correction coefficient in summer, V indicates the wind speed, and S indicates the number of sunshine hours; SSD 冬 It represents the comfort level of human body in winter, and α2 represents the wind direction correction coefficient in winter; 2) The equivalent thermal parameter model is expressed as: When C = 0, When C = 1, In the formula, C represents the start and stop state of the air conditioner. When C is 0, it means the air conditioner is in the off state, and when C is 1, it means the air conditioner is in the running state. t in represents the indoor temperature at time t; represents the indoor temperature at time t+1; T t out represents the outdoor temperature at time t; represents the outdoor temperature at time t+1; ε represents the heat dissipation coefficient; α represents the air conditioning energy efficiency duty cycle; S represents the thermal conductivity of the air conditioner; P x represents the rated energy consumption of the air conditioner at time x, αP x Indicates the rated cooling capacity of the air conditioner; 3) The energy balance equation is expressed as: Where, T max Indicates the highest temperature of the air conditioner in the current environment; T min Indicates the lowest temperature of the air conditioner in the current environment; t on Indicates the opening time of the air conditioner; t off Indicates the shut-off time of the air conditioner; It represents the average rated power of the air conditioner during the x period; 4) The adjustable capacity is expressed as: In the formula, C x represents the adjustable capacity of the air conditioner during the x period; t c represents the control period of the air conditioner, t c =t on +t off .

6. A method for analyzing individual behavior of flexibility resources according to claim 1, characterized in that: When the flexibility resource is an electric vehicle type flexibility resource, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is: 1) Charging behavior characteristic index is expressed as: Where, T min Indicates the shortest charging duration for electric vehicles; E exp E represents the amount of electricity that electric vehicle owners expect to obtain when leaving the charging station; ini Indicates the initial battery capacity of the electric vehicle; P ch.max Indicates the maximum charging power acceptable to electric vehicles; E bat Indicates the total battery capacity of the electric vehicle; S ini Indicates the initial charge state of the electric vehicle when it is connected to the power grid; S exp Indicates the state of charge that electric vehicle owners expect to achieve when charging is completed; 2) Obtain the electric vehicle classification results through the K-means clustering method.

7. A flexibility resource individual behavior analysis system, characterized in that: include: A parameter acquisition module, used to acquire preset parameters of the flexibility resource to be analyzed; An electricity calculation module, used to obtain the charging amount, discharging amount or electricity consumption of the flexible resource based on the acquired preset parameters by using a mechanism characteristic modeling algorithm; An analysis module is used to calculate the electricity consumption transfer of the flexibility resources in the peak-flat, peak-valley, and flat-valley periods based on the charging, discharging or electricity consumption of the obtained flexibility resources by using the flexibility resource individual behavior modeling algorithm under the influence of price signals; Among them, the individual behavior modeling algorithm of flexible resources under the influence of price signals adopts peak-to-normal period transfer coefficient, peak-to-valley period transfer coefficient and power saving coefficient to analyze the load power consumption in each period.

8. The flexibility resource individual behavior analysis system according to claim 7, characterized in that: The peak-to-average period transfer coefficient is expressed as: In the formula, λ fp It indicates the percentage of electricity consumption shifted from peak hours to normal hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fg It indicates the amount of electricity consumption shifted from peak to normal period after the implementation of time-of-use electricity price; Q fNON Indicates the electricity consumption of users who do not implement time-of-use electricity prices; The peak-to-valley time period transfer coefficient is expressed as: In the formula, λ fg It indicates the percentage of electricity consumption shifted from peak hours to valley hours after the implementation of time-of-use electricity price and the electricity consumption when time-of-use electricity price is not implemented; ΔQ fp It indicates the peak-valley electricity consumption shift after the implementation of time-of-use electricity price; The power saving coefficient is expressed as: In the formula, λ fSAVE It indicates the percentage of electricity saved by users after reducing electricity consumption during peak hours after implementing time-of-use electricity prices and the electricity consumption when time-of-use electricity prices are not implemented; ΔQ fSAVE It indicates the reduction in electricity consumption during peak hours after the implementation of time-of-use electricity prices.

9. A flexible resource individual behavior analysis system according to claim 8, characterized in that: In the step of using the peak-to-normal period transfer coefficient, the peak-to-valley period transfer coefficient and the power saving coefficient to analyze the load power consumption in each period, The load during the peak period before and after the implementation of the time-of-use electricity price meets the following requirements: L(k)1=L(k0)1-μ fg L f -m fp L f ; Where, L(k)1 represents the load of the kth peak period after the implementation of time-of-use electricity price; L(k0)1 represents the load of the kth peak period before the implementation of time-of-use electricity price; μ fg It represents the ratio of the unit load reduction during the peak period due to the peak-valley electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; μ fp It represents the ratio of the load reduction per unit period during the peak period caused by the peak-to-flat electricity price difference to the average load during the peak period when time-of-use electricity prices are not implemented; L f Indicates the average load during the pre-peak period when time-of-use electricity prices are implemented; The load satisfaction in normal period before and after the implementation of time-of-use electricity price is: L(k)2=L(k0)2-μ pg L f +m fp L f c fp ; Where, L(k)2 represents the load power consumption in the kth normal period when the time-of-use electricity price is implemented; L(k0)2 represents the load power consumption in the kth normal period before the time-of-use electricity price is implemented; μ pg It indicates the ratio of the unit load reduction during normal hours due to the price difference between flat and valley hours to the average load during normal hours when time-of-use electricity prices are not implemented; After the implementation of the peak-valley electricity price, the user's load during the valley period becomes: L(k)3=L(k0)3+μ fg L f c fg +m pg L p c pg ; Where, L(k)3 represents the load power consumption in the kth valley period after the implementation of time-of-use electricity prices; L(k0)3 represents the load power consumption in the kth valley period before the implementation of time-of-use electricity prices.

10. The flexibility resource individual behavior analysis system according to claim 7, characterized in that: When the flexibility resource is a flexible resource of energy storage type, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is: 1) The relationship between the energy storage power at adjacent moments is expressed as: In the formula, S stor (t) represents the energy stored at time t; u represents the self-discharge rate of the energy stored; S stor (t-1) represents the energy stored at time t-1; η abs Indicates the charging efficiency of energy storage; P abs (t) represents the charging power of the energy storage at time t; η relea Indicates the discharge efficiency of energy storage; P relea (t) represents the discharge power of the energy storage at time t; Δt represents the time interval; 2) The relationship between energy storage charging and discharging power and stored energy is expressed as: In the formula, S stor (0) represents the power at the beginning of the energy storage operation cycle, S stor (end) indicates the energy at the end of the energy storage operation cycle; Indicates the minimum storage capacity of energy storage, Indicates the maximum storage capacity of energy storage; U abs (t) represents the charging state of the energy storage at time t, U relea (t) represents the discharge state of the stored energy at time t; Indicates the minimum charging and discharging power of energy storage, Indicates the maximum charging and discharging power of energy storage; 3) The interaction constraints between energy storage and external devices are expressed as: In the formula, N represents the number of energy storage, P ess,b (t) represents the power purchased by the energy storage from external devices at time t, P ess,s (t) represents the power sold by the energy storage to external devices at time t; P ess,abs (t) represents the charging power of the energy storage at time t, P ess,relea (t) represents the discharge power of the energy storage at time t; Indicates the maximum interaction power between energy storage and external devices.

11. The flexible resource individual behavior analysis system according to claim 7, characterized in that: When the flexibility resource is an air conditioning type flexibility resource, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is: 1) The human comfort evaluation index is expressed as: In the formula, SSD 夏 Indicates the comfort level of human body in summer, T indicates the ambient temperature, RH indicates the relative humidity, α1 indicates the wind direction correction coefficient in summer, V indicates the wind speed, and S indicates the number of sunshine hours; SSD 冬 It represents the comfort level of human body in winter, and α2 represents the wind direction correction coefficient in winter; 2) The equivalent thermal parameter model is expressed as: When C = 0, When C = 1, In the formula, C represents the start and stop state of the air conditioner. When C is 0, it means the air conditioner is in the off state, and when C is 1, it means the air conditioner is in the running state. t in represents the indoor temperature at time t; represents the indoor temperature at time t+1; T t out represents the outdoor temperature at time t; represents the outdoor temperature at time t+1; ε represents the heat dissipation coefficient; α represents the air conditioning energy efficiency duty cycle; S represents the thermal conductivity of the air conditioner; P x represents the rated energy consumption of the air conditioner at time x, αP x Indicates the rated cooling capacity of the air conditioner; 3) The energy balance equation is expressed as: Where, T max Indicates the highest temperature of the air conditioner in the current environment; T min Indicates the lowest temperature of the air conditioner in the current environment; t on Indicates the opening time of the air conditioner; t off Indicates the shut-off time of the air conditioner; It represents the average rated power of the air conditioner during the x period; 4) The adjustable capacity is expressed as: In the formula, C x represents the adjustable capacity of the air conditioner during the x period; t c represents the control period of the air conditioner, t c =t on +t off .

12. The flexibility resource individual behavior analysis system according to claim 7, characterized in that: When the flexibility resource is an electric vehicle type flexibility resource, the mechanism characteristic modeling result of the mechanism characteristic modeling algorithm is: 1) Charging behavior characteristic index is expressed as: Where, T min Indicates the shortest charging duration for electric vehicles; E exp E represents the amount of electricity that electric vehicle owners expect to obtain when leaving the charging station; ini Indicates the initial battery capacity of the electric vehicle; P ch.max Indicates the maximum charging power acceptable to electric vehicles; E bat Indicates the total battery capacity of the electric vehicle; S ini Indicates the initial charge state of the electric vehicle when it is connected to the power grid; S exp Indicates the state of charge that electric vehicle owners expect to achieve when charging is completed; 2) Obtain the electric vehicle classification results through the K-means clustering method.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the flexibility resource individual behavior analysis method according to any one of claims 1 to 6 is implemented.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for analyzing individual behaviors of flexibility resources according to any one of claims 1 to 6 is implemented.

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