Localized potential assessment method and system based on air conditioner load and charge and discharge resource regulation and control, and medium

Abstract

The invention discloses a localized potential evaluation method and system based on air conditioner load and charging and discharging resource regulation and control and a medium, and belongs to the technical field of intelligent power distribution network optimization regulation and control. The method comprises the steps that thermodynamic modeling is conducted on air conditioner load, air conditioner thermodynamic model parameters are identified, and air conditioner regulation potential is calculated; the method comprises the following steps: establishing a charge-discharge power constraint, an electric quantity constraint and a charge-discharge state mutual exclusion constraint of a single electric vehicle, introducing a state variable to represent a grid-connected state of the electric vehicle in a scheduling period to carry out definition domain continuation, and constructing an aggregation operation feasible region of an electric vehicle cluster to obtain an electric vehicle cluster scheduling potential; and outputting and superposing the air conditioner cluster scheduling potential and the electric vehicle cluster scheduling potential in the future time period by using the time sequence prediction model. According to the method, the problem that demand response or power grid optimization scheduling cannot be supported due to the fact that the aggregation adjustable potential of the air conditioner load and the electric vehicle charging and discharging resources in the local power grid cannot be accurately evaluated and predicted in the prior art is solved.

Description

Technical Field

[0001] This invention relates to a localized potential assessment method, system, and medium based on air conditioning load and charging / discharging resource regulation, belonging to the field of smart distribution network optimization and regulation technology. Background Technology

[0002] In recent years, the new power system has been undergoing a transformation from "source following load" to "source and load complementing each other." As the end of the power grid, distribution substations / feeders experience a spatiotemporal coupling relationship between air conditioning loads and electric vehicle charging in summer, leading to frequent risks of localized load exceeding limits and line overloads in the distribution network. Current control technologies mostly adopt single resource optimization strategies, such as the air conditioning ETP thermodynamic model and the EV travel chain model, which are costly to implement and difficult to adapt to dynamic scenarios at the distribution substation level.

[0003] Existing technologies lack strategies for the coordinated support of air conditioning load and electric vehicle charging / discharging resources, resulting in the underutilization of both resources. Traditional assessment methods require precise acquisition of physical parameters and user behavior data, leading to high implementation costs, poor adaptability, and an inability to accurately quantify the aggregated controllable potential of both in the local power grid, making it difficult to support demand response or grid optimization scheduling decisions. Although deep learning has improved the accuracy of potential assessment by integrating the advantages of physical models and data mining through a "hybrid model-data-driven" architecture, it still suffers from insufficient overall assessment and prediction capabilities, failing to meet the precise quantification requirements under the "source-load mutual support" transformation. Summary of the Invention

[0004] The purpose of this invention is to provide a localized potential assessment method, system, and medium based on air conditioning load and charging / discharging resource regulation. This method quantifies the air conditioning regulation potential by performing thermodynamic modeling on the air conditioning load and aggregate modeling on the electric vehicle cluster to quantify the electric vehicle cluster scheduling potential. Furthermore, it utilizes a time series prediction model to collaboratively predict the air conditioning regulation potential and the electric vehicle cluster scheduling potential. This addresses the problem that existing technologies cannot accurately assess and predict the aggregated controllable potential of air conditioning load and electric vehicle charging / discharging resources in the local power grid, leading to an inability to support demand response or grid optimization scheduling.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution.

[0006] In a first aspect, the present invention provides a method for assessing the localization potential based on air conditioning load and charging / discharging resource regulation, comprising:

[0007] Thermodynamic modeling of air conditioning load is performed based on historical air conditioning operation data and indoor and outdoor temperature data. The parameters of the air conditioning thermodynamic model are identified using historical daily air conditioning operation data. The air conditioning regulation potential for each time period is calculated based on the human thermal comfort temperature range.

[0008] Based on historical electric vehicle charging and discharging data, charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states are established for individual electric vehicles. State variables are introduced to characterize the grid connection state of electric vehicles within the scheduling cycle and the domain is extended. Within a unified scheduling cycle, the aggregated operational feasible domain of electric vehicle clusters is constructed to obtain the scheduling potential of electric vehicle clusters in each time period.

[0009] Based on the air conditioning scheduling potential and electric vehicle cluster scheduling potential for each time period, a pre-trained time series prediction model is used to predict and output the air conditioning cluster scheduling potential and electric vehicle cluster scheduling potential for future time periods, which are then superimposed to evaluate the localization potential.

[0010] Furthermore, a thermodynamic model of the air conditioning load is constructed based on historical air conditioning operation data and indoor and outdoor temperature data. The parameters of the air conditioning thermodynamic model are identified using historical daily air conditioning operation data. The air conditioning regulation potential for each time period is calculated based on the human thermal comfort temperature range, including:

[0011] Based on historical air conditioning operation data and indoor and outdoor temperature data, an air conditioning thermodynamic model is constructed based on a first-order equivalent thermal parameter model.

[0012] Based on the linear relationship between historical daily air conditioning operation data and outdoor temperature data, the parameters of the air conditioning thermodynamic model are identified using the linear regression method, including the air conditioning equivalent thermal resistance, air conditioning equivalent heat capacity, and air conditioning energy efficiency ratio.

[0013] Based on the temperature range of human thermal comfort, an optimization model is established according to the average value of indoor temperature setpoint and the average value of indoor heat source power. The optimization objective is to minimize the error between the actual value and the simulated value of the average operating power of the air conditioner in each time period. The optimal estimated values ​​of indoor temperature setpoint and indoor heat source power are obtained by iterative solution.

[0014] Based on the parameters of the air conditioning thermodynamic model, the indoor temperature setpoint and the optimal estimated value of the indoor heat source power, the average operating power of the air conditioner in each time period is calculated based on the energy balance equation.

[0015] The Franker thermal comfort equation is used to construct a predicted average vote index to approximate the range of human thermal comfort temperature and determine the adjustable range of indoor temperature.

[0016] Based on the average operating power of the air conditioner in each time period and the adjustable range of indoor temperature, the air conditioner's adjustment potential in each time period is calculated by perturbing the temperature setpoint, including the air conditioner's upward adjustment potential and downward adjustment potential.

[0017] Furthermore, the air conditioning thermodynamic model is expressed as follows:

[0018] ;

[0019] In the formula, Indicates the air conditioner at the current moment Cooling or heating power, Indicates the equivalent thermal resistance of the room. This represents the energy efficiency coefficient. Indicates the current time outdoor temperature, Indicates the indoor heat source at the current moment power, For indoor equivalent heat capacity, Indicates the current time The differential, Indicates the current time Indoor temperature The differential.

[0020] Furthermore, the predicted average vote count metric is expressed as:

[0021] ;

[0022] In the formula, This indicates the predicted average number of votes. , Indicates indoor temperature.

[0023] Furthermore, the optimization model is expressed as:

[0024] ;

[0025] In the formula, This indicates taking the minimum value. Indicates the first The operating power of the air conditioner during the time period, Indicates the first Average outdoor temperature during the period This represents the average value of the indoor temperature setpoint. This represents the average power of the indoor heat source. It means "to make... true". This indicates the minimum value of the indoor temperature setting. This indicates the maximum value of the indoor temperature setting. This represents the minimum value of the average power of the indoor heat source. For the first The average indoor heat source power during the time period. This represents the maximum value of the average power of the indoor heat source.

[0026] Furthermore, the average operating power of the air conditioner in each time period is expressed as follows:

[0027] ;

[0028] In the formula, For the first The average operating power of the air conditioner during the period, For the first Average indoor temperature during the time period.

[0029] Furthermore, the upward adjustment potential and downward adjustment potential of the air conditioner are respectively expressed as:

[0030] ;

[0031] In the formula, Indicates the first historical day The first air conditioning cluster The air conditioning adjustment potential during the time period Indicates the first historical day The first air conditioning cluster Air conditioning down-regulation potential during the time period , and They represent the historical days. Current time of each air conditioning cluster The air conditioner's operating power, maximum operating power, and minimum operating power;

[0032] Among them, the historical day Current time of each air conditioning cluster The operating power of the air conditioner, the maximum operating power and the minimum operating power are expressed as follows:

[0033] ;

[0034] In the formula, Indicates the first historical day Current time of each air conditioning cluster Average outdoor temperature, Indicates the first historical day Current time of each air conditioning cluster Average indoor temperature Indicates the first historical day Current time of each air conditioning cluster The average value of the indoor heat source power.

[0035] Furthermore, based on historical electric vehicle charging and discharging data, charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states are established for individual electric vehicles. State variables are introduced to characterize the grid connection state of electric vehicles within the scheduling cycle, extending the domain of definition. Within a unified scheduling cycle, an aggregated operational feasible domain for the electric vehicle cluster is constructed, yielding the scheduling potential of the electric vehicle cluster for each time period, including:

[0036] Establish charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states for individual electric vehicles based on historical electric vehicle charging and discharging data.

[0037] State variables are introduced to characterize the grid connection status of electric vehicles within the scheduling cycle, and the domain of the charging and discharging power constraints, energy constraints, and charging and discharging state mutual exclusion constraints of a single electric vehicle are extended to a unified scheduling cycle based on the state variables.

[0038] Based on the Minkowski summation method, the feasible domain spaces of all individual electric vehicles after the domain extension are aggregated to form an aggregated operational feasible domain that characterizes the operational characteristics of charging station-level generalized energy storage devices.

[0039] Based on the difference between the maximum chargeable and dischargeable power determined by the aggregated operational feasible domain and the actual chargeable and dischargeable power, the scheduling potential of the electric vehicle cluster in each time period is calculated, including the upward adjustment potential of the electric vehicle cluster and the downward adjustment potential of the electric vehicle cluster.

[0040] Furthermore, the charging and discharging power constraint is expressed as:

[0041] ;

[0042] In the formula, They represent electric vehicles. exist Charging and discharging power scheduling during different time periods They represent electric vehicles. exist The upper limit of charging scheduling power and the upper limit of discharging scheduling power for each time period. Indicates electric vehicles The set of grid connection times This indicates that any logical symbol can be chosen;

[0043] The energy constraint is expressed as:

[0044] ;

[0045] In the formula, They represent electric vehicles. exist Time period and Battery level during the period These represent charging efficiency and discharging efficiency, respectively. Indicates the scheduling time window. This represents the discharge compensation coefficient determined by discharge loss. They represent electric vehicles. The battery power safety boundary;

[0046] The mutual exclusion constraint of the charging and discharging states is expressed as follows:

[0047] ;

[0048] In the formula, and Charging stations exist Total charging power and total discharging power during the time period;

[0049] The state variable is represented as:

[0050] ;

[0051] In the formula, Indicates electric vehicles exist The state of the time period Indicates electric vehicles exist The period is in grid-connected state. Indicates electric vehicles exist The period was spent offline. Indicates electric vehicles The time of arrival at the charging station, Indicates electric vehicles During the period of leaving the charging station, the charging and discharging power constraints, whose domain is extended to the unified scheduling cycle, are expressed as follows:

[0052] ;

[0053] The upward adjustment potential and downward adjustment potential of the electric vehicle cluster are respectively expressed as follows:

[0054] ;

[0055] In the formula, Indicates the first The first electric vehicle cluster The potential for adjustment in electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The potential for adjustment of electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a certain period. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a certain time period. Indicates the first The first electric vehicle cluster The basic adjustment potential of electric vehicle clusters within a given time period;

[0056] Among them, the The first electric vehicle cluster The upper limit of the adjustment potential of the electric vehicle cluster during the time period and the representation of the first The first electric vehicle cluster The upper limit of the adjustment potential of electric vehicle clusters within the time period is expressed as follows:

[0057] .

[0058] Secondly, the present invention provides a localization potential assessment system based on air conditioning load and charging / discharging resource regulation, comprising:

[0059] The air conditioning potential assessment module is used to perform thermodynamic modeling of air conditioning load based on historical air conditioning operation data and indoor and outdoor temperature data, and to identify air conditioning thermodynamic model parameters using historical daily air conditioning operation data, and calculate the air conditioning regulation potential for each time period based on the human thermal comfort temperature range.

[0060] The electric vehicle potential assessment module is used to extend the domain of individual electric vehicles' charging and discharging power constraints, energy constraints, and grid connection status based on historical electric vehicle charging and discharging data. It constructs the aggregated operational feasible domain of electric vehicle clusters within a unified scheduling cycle and obtains the scheduling potential of electric vehicle clusters in each time period.

[0061] The time series prediction module is used to predict the air conditioning scheduling potential and electric vehicle cluster scheduling potential of each time period based on a pre-trained time series prediction model. It outputs the air conditioning cluster scheduling potential and electric vehicle cluster scheduling potential of future time periods and superimposes them to evaluate the localization potential of mutual regulation between air conditioning load and electric vehicle charging and discharging resources.

[0062] Furthermore, a thermodynamic model of the air conditioning load is constructed based on historical air conditioning operation data and indoor and outdoor temperature data. The parameters of the air conditioning thermodynamic model are identified using historical daily air conditioning operation data. The air conditioning regulation potential for each time period is calculated based on the human thermal comfort temperature range, including:

[0063] Based on historical air conditioning operation data and indoor and outdoor temperature data, an air conditioning thermodynamic model is constructed based on a first-order equivalent thermal parameter model.

[0064] Based on the linear relationship between historical daily air conditioning operation data and outdoor temperature data, the parameters of the air conditioning thermodynamic model are identified using the linear regression method, including the air conditioning equivalent thermal resistance, air conditioning equivalent heat capacity, and air conditioning energy efficiency ratio.

[0065] Based on the temperature range of human thermal comfort, an optimization model is established according to the average value of indoor temperature setpoint and the average value of indoor heat source power. The optimization objective is to minimize the error between the actual value and the simulated value of the average operating power of the air conditioner in each time period. The optimal estimated values ​​of indoor temperature setpoint and indoor heat source power are obtained by iterative solution.

[0066] Based on the parameters of the air conditioning thermodynamic model, the indoor temperature setpoint and the optimal estimated value of the indoor heat source power, the average operating power of the air conditioner in each time period is calculated based on the energy balance equation.

[0067] The Franker thermal comfort equation is used to construct a predicted average vote index to approximate the range of human thermal comfort temperature and determine the adjustable range of indoor temperature.

[0068] Based on the average operating power of the air conditioner in each time period and the adjustable range of indoor temperature, the air conditioner's adjustment potential in each time period is calculated by perturbing the temperature setpoint, including the air conditioner's upward adjustment potential and downward adjustment potential.

[0069] Furthermore, the air conditioning thermodynamic model is expressed as follows:

[0070] ;

[0071] In the formula, Indicates the air conditioner at the current moment Cooling or heating power, Indicates the equivalent thermal resistance of the room. This represents the energy efficiency coefficient. Indicates the current time outdoor temperature, Indicates the indoor heat source at the current moment power, For indoor equivalent heat capacity, Indicates the current time The differential, Indicates the current time Indoor temperature The differential.

[0072] Furthermore, the predicted average vote count metric is expressed as:

[0073] ;

[0074] In the formula, This indicates the predicted average number of votes. , Indicates indoor temperature.

[0075] Furthermore, the optimization model is expressed as:

[0076] ;

[0077] In the formula, This indicates taking the minimum value. Indicates the first The operating power of the air conditioner during the time period, Indicates the first Average outdoor temperature during the period This represents the average value of the indoor temperature setpoint. This represents the average power of the indoor heat source. It means "to make... true". This indicates the minimum value of the indoor temperature setting. This indicates the maximum value of the indoor temperature setting. This represents the minimum value of the average power of the indoor heat source. For the first The average indoor heat source power during the time period. This represents the maximum value of the average power of the indoor heat source.

[0078] Furthermore, the average operating power of the air conditioner in each time period is expressed as follows:

[0079] ;

[0080] In the formula, For the first The average operating power of the air conditioner during the period, For the first Average indoor temperature during the time period.

[0081] Furthermore, the upward adjustment potential and downward adjustment potential of the air conditioner are respectively expressed as:

[0082] ;

[0083] In the formula, Indicates the first historical day The first air conditioning cluster The air conditioning adjustment potential during the time period Indicates the first historical day The first air conditioning cluster Air conditioning down-regulation potential during the time period , and They represent the historical days. Current time of each air conditioning cluster The air conditioner's operating power, maximum operating power, and minimum operating power;

[0084] Among them, the historical day Current time of each air conditioning cluster The operating power of the air conditioner, the maximum operating power and the minimum operating power are expressed as follows:

[0085] ;

[0086] In the formula, Indicates the first historical day Current time of each air conditioning cluster Average outdoor temperature, Indicates the first historical day Current time of each air conditioning cluster Average indoor temperature Indicates the first historical day Current time of each air conditioning cluster The average value of the indoor heat source power.

[0087] Furthermore, based on historical electric vehicle charging and discharging data, charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states are established for individual electric vehicles. State variables are introduced to characterize the grid connection state of electric vehicles within the scheduling cycle, extending the domain of definition. Within a unified scheduling cycle, an aggregated operational feasible domain for the electric vehicle cluster is constructed, yielding the scheduling potential of the electric vehicle cluster for each time period, including:

[0088] Establish charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states for individual electric vehicles based on historical electric vehicle charging and discharging data.

[0089] State variables are introduced to characterize the grid connection status of electric vehicles within the scheduling cycle, and the domain of the charging and discharging power constraints, energy constraints, and charging and discharging state mutual exclusion constraints of a single electric vehicle are extended to a unified scheduling cycle based on the state variables.

[0090] Based on the Minkowski summation method, the feasible domain spaces of all individual electric vehicles after the domain extension are aggregated to form an aggregated operational feasible domain that characterizes the operational characteristics of charging station-level generalized energy storage devices.

[0091] Based on the difference between the maximum chargeable and dischargeable power determined by the aggregated operational feasible domain and the actual chargeable and dischargeable power, the scheduling potential of the electric vehicle cluster in each time period is calculated, including the upward adjustment potential of the electric vehicle cluster and the downward adjustment potential of the electric vehicle cluster.

[0092] Furthermore, the charging and discharging power constraint is expressed as:

[0093] ;

[0094] In the formula, They represent electric vehicles. exist Charging and discharging power scheduling during different time periods They represent electric vehicles. exist The upper limit of charging scheduling power and the upper limit of discharging scheduling power for each time period. Indicates electric vehicles The set of grid connection times This indicates that any logical symbol can be chosen;

[0095] The energy constraint is expressed as:

[0096] ;

[0097] In the formula, They represent electric vehicles. exist Time period and Battery level during the period These represent charging efficiency and discharging efficiency, respectively. Indicates the scheduling time window. This represents the discharge compensation coefficient determined by discharge loss. They represent electric vehicles. The battery power safety boundary;

[0098] The mutual exclusion constraint of the charging and discharging states is expressed as follows:

[0099] ;

[0100] In the formula, and Charging stations exist Total charging power and total discharging power during the time period;

[0101] The state variable is represented as:

[0102] ;

[0103] In the formula, Indicates electric vehicles exist The state of the time period Indicates electric vehicles exist The period is in grid-connected state. Indicates electric vehicles exist The period was spent offline. Indicates electric vehicles The time of arrival at the charging station, Indicates electric vehicles During the period of leaving the charging station, the charging and discharging power constraints, whose domain is extended to the unified scheduling cycle, are expressed as follows:

[0104] ;

[0105] The upward adjustment potential and downward adjustment potential of the electric vehicle cluster are respectively expressed as follows:

[0106] ;

[0107] In the formula, Indicates the first The first electric vehicle cluster The potential for adjustment in electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The potential for adjustment of electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a certain period. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a certain time period. Indicates the first The first electric vehicle cluster The basic adjustment potential of electric vehicle clusters within a given time period;

[0108] Among them, the The first electric vehicle cluster The upper limit of the adjustment potential of the electric vehicle cluster during the time period and the representation of the first The first electric vehicle cluster The upper limit of the adjustment potential of electric vehicle clusters within the time period is expressed as follows:

[0109] .

[0110] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the localization potential assessment method based on air conditioning load and charging / discharging resource regulation as described in the first aspect.

[0111] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0112] 1. This invention utilizes thermodynamic modeling and parameter identification of air conditioning load, electric vehicle charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states. It also introduces state variables to characterize the grid-connected state of electric vehicles within the scheduling cycle, extending the domain of definition and aggregating the feasible operating domain. By using a time-series prediction model to predict and superimpose the scheduling potential of air conditioning clusters and electric vehicle clusters in future periods, it achieves a precise quantitative assessment of the aggregated and controllable potential of air conditioning and electric vehicle resources in the local power grid. This solves the problem that existing technologies cannot accurately assess and predict the aggregated and controllable potential of air conditioning load and electric vehicle charging and discharging resources in the local power grid, resulting in the inability to support demand response or grid optimization scheduling.

[0113] 2. This invention reduces parameter dependence by coupling a first-order ETP model with linear regression, transforms the temperature regulation range using the Franker thermal comfort equation, and aggregates cluster energy storage characteristics based on state variable extension and Minkowski summation. Finally, it assesses localization potential based on time series predictions superimposed with the scheduling potential of air conditioning clusters and electric vehicle clusters in future periods, providing a quantitative decision-making basis that can be directly used for demand response and grid optimization scheduling in the substation-level source-load mutual assistance scenario.

[0114] 3. This invention unifies the charging and discharging constraints of individual electric vehicles to the scheduling cycle dimension by extending the domain of state variables. It combines Minkowski summation to achieve aggregate modeling of the feasible domain of the cluster. It directly quantifies the scheduling potential of electric vehicle clusters in each time period based on the difference between the maximum chargeable and dischargeable power and the actual power in the aggregated feasible domain. This solves the problem that traditional methods are inaccurate in assessing the collaborative potential of the dual attributes of "load-energy storage" of electric vehicle clusters and cannot support dynamic scheduling decisions at the distribution area level. It provides quantifiable cluster energy storage characteristics support for power grid optimization in the scenario of mutual support between source and load. Attached Figure Description

[0115] Figure 1 This is a flowchart illustrating a localization potential assessment method based on air conditioning load and charging / discharging resource regulation provided in an embodiment of the present invention.

[0116] Figure 2 This is a schematic diagram of the process for obtaining the air conditioning adjustment potential for each time period provided in an embodiment of the present invention;

[0117] Figure 3 This is a schematic diagram of the process for obtaining the scheduling potential of electric vehicle clusters in different time periods provided in the embodiments of the present invention. Detailed Implementation

[0118] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0119] Example 1

[0120] like Figure 1 As shown in the figure, this embodiment introduces a localization potential assessment method based on air conditioning load and charging / discharging resource regulation, including:

[0121] Step 1: Perform thermodynamic modeling of air conditioning load based on historical air conditioning operation data and indoor and outdoor temperature data, identify air conditioning thermodynamic model parameters using historical daily air conditioning operation data, and calculate the air conditioning regulation potential for each time period based on the human thermal comfort temperature range.

[0122] This embodiment uses thermodynamic modeling and model parameter identification based on historical air conditioning operation data and indoor and outdoor temperature data. It combines the temperature range of human thermal comfort to quantify the air conditioning adjustment potential at different times. This can accurately identify the adjustable space of air conditioning load during temperature regulation, providing a scientific basis for optimizing air conditioning operation strategies, improving energy efficiency and user comfort, while reducing ineffective energy consumption and achieving refined management of load-side resources and energy-saving goals.

[0123] Step 2: Based on historical electric vehicle charging and discharging data, establish charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states for individual electric vehicles. Introduce state variables to characterize the grid connection state of electric vehicles within the scheduling cycle and extend the domain. Construct the aggregated operational feasible domain of the electric vehicle cluster within a unified scheduling cycle to obtain the scheduling potential of the electric vehicle cluster in each time period.

[0124] This embodiment constructs an aggregated operational feasible domain based on historical charging and discharging data of electric vehicles, which includes power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states. It also achieves a unified representation of the cluster scheduling potential through the domain extension technology of state variables, effectively solving the contradiction between the randomness of individual electric vehicles and the coordinated scheduling of the cluster. This provides the power grid with quantifiable indicators of the electric vehicle cluster regulation capability, supporting load balancing and power grid stability improvement under large-scale electric vehicle grid connection.

[0125] Step 3: Based on the air conditioning scheduling potential and electric vehicle cluster scheduling potential for each time period, make predictions using a pre-trained time series prediction model, output the air conditioning cluster scheduling potential and electric vehicle cluster scheduling potential for future time periods, and superimpose them to evaluate the localization potential.

[0126] This embodiment uses a pre-trained time series prediction model to dynamically predict and overlay the scheduling potential of air conditioning and electric vehicle clusters, realizing the time-series collaborative potential assessment of multi-source load-side resources. It can not only accurately depict the localized potential characteristics of future periods, but also provide data support for real-time scheduling decisions in scenarios such as demand response and virtual power plants, enhance the power system's ability to flexibly call upon adjustable resources, and improve the level of new energy consumption and the economic efficiency of system operation.

[0127] Example 2

[0128] Based on the same inventive concept as Embodiment 1, this embodiment introduces the implementation steps of a localization potential assessment method based on air conditioning load and charging / discharging resource regulation, including:

[0129] Step 1: Perform thermodynamic modeling of the air conditioning load based on historical air conditioning operation data and indoor / outdoor temperature data. Identify the parameters of the air conditioning thermodynamic model using historical daily air conditioning operation data. Calculate the air conditioning regulation potential for each time period based on the human thermal comfort temperature range, such as... Figure 2 As shown.

[0130] This embodiment acquires historical air conditioning operation data and indoor and outdoor temperature data within the transformer area, and preprocesses missing points and outliers.

[0131] Step 1.1: Based on historical air conditioning operation data and indoor and outdoor temperature data, construct an air conditioning thermodynamic model based on a first-order equivalent thermal parameter model.

[0132] In this embodiment, the air conditioning thermodynamic model is expressed as follows:

[0133] ;

[0134] In the formula, Indicates the air conditioner at the current moment Cooling or heating power, Indicates the equivalent thermal resistance of the room. This represents the energy efficiency coefficient. Indicates the current time outdoor temperature, Indicates the indoor heat source at the current moment power, For indoor equivalent heat capacity, Indicates the current time The differential, Indicates the current time Indoor temperature The differential.

[0135] When the air conditioner is working normally, the real-time indoor temperature fluctuates around the set temperature value, therefore the first Average indoor temperature during the period A temperature setpoint can be used instead; the temperature setpoint is a constant value. Furthermore, the changes in outdoor temperature and indoor heat source power are relatively slow, occurring in the first... Within the time period, it can be considered that the first Average outdoor temperature during the period and the Average indoor heat source power during the time period It also remains constant, among which This can be obtained based on existing short-term temperature prediction models. Therefore, in the... During the period, the electrical energy consumed by the air conditioner, the heat energy lost indoors, and the heat energy generated by indoor heat sources are in a state of equilibrium.

[0136] Step 1.2: Based on the linear relationship between historical daily air conditioner operation data and outdoor temperature data, use the linear regression method to identify the parameters of the air conditioner thermodynamic model, including the air conditioner equivalent thermal resistance, air conditioner equivalent heat capacity, and air conditioner energy efficiency ratio.

[0137] Step 1.3: Based on the human body thermal comfort temperature range, establish an optimization model according to the average value of the indoor temperature setpoint and the average value of the indoor heat source power. The optimization objective is to minimize the error between the actual value and the simulation value of the average operating power of the air conditioner in each time period. The optimal estimated values ​​of the indoor temperature setpoint and the indoor heat source power are obtained by iterative solution.

[0138] In this embodiment, the optimization model is represented as:

[0139] ;

[0140] In the formula, This indicates taking the minimum value. Indicates the first The operating power of the air conditioner during the time period, Indicates the first Average outdoor temperature during the period This represents the average value of the indoor temperature setpoint. This represents the average power of the indoor heat source. It means "to make... true". This indicates the minimum value of the indoor temperature setting. This indicates the maximum value of the indoor temperature setting. This represents the minimum value of the average power of the indoor heat source. For the first The average indoor heat source power during the time period. This represents the maximum value of the average power of the indoor heat source.

[0141] Step 1.4: Based on the parameters of the air conditioning thermodynamic model, the indoor temperature setpoint, and the optimal estimated value of the indoor heat source power, calculate the average operating power of the air conditioner in each time period based on the energy balance equation.

[0142] In this embodiment, the average operating power of the air conditioner during each time period is expressed as:

[0143] ;

[0144] In the formula, For the first The average operating power of the air conditioner during the period, For the first Average indoor temperature during the time period.

[0145] Step 1.5: Using the Franker thermal comfort equation, construct a predicted average vote index to approximate the range of human thermal comfort temperature and determine the adjustable range of indoor temperature.

[0146] Average operating power of air conditioner at different times With the Average outdoor temperature during the period A linear relationship exists. Follow The slope of the linear relationship between the two changes with the change in the form of the variable. The intercept is For the same air conditioner, the slope and intercept are constant and unique; only the slope needs to be considered. The process involves identifying and calculating the average operating power of the air conditioner during its normal operating period. Finally, using linear regression fitting, the slope can be identified. To quantify the impact of temperature on human comfort, this implementation uses the Franker thermal comfort equation to construct a pre-predicted average vote index to approximate the temperature range of human thermal comfort and determine the adjustable range of indoor temperature. The smaller the pre-predicted average vote index, the higher the user comfort level.

[0147] In this embodiment, the predicted average vote count indicator is expressed as:

[0148] ;

[0149] In the formula, This indicates the predicted average number of votes. Indicates indoor temperature.

[0150] In this embodiment, The corresponding indoor temperature range is 24.8℃~27.3℃.

[0151] Step 1.6: Based on the average operating power of the air conditioner in each time period and the adjustable range of indoor temperature, calculate the air conditioner adjustment potential in each time period by perturbing the temperature setpoint, including the air conditioner's upward adjustment potential and downward adjustment potential.

[0152] The upward and downward adjustment potentials of the air conditioner are respectively expressed as:

[0153] ;

[0154] In the formula, Indicates the first historical day The first air conditioning cluster The air conditioning adjustment potential during the time period Indicates the first historical day The first air conditioning cluster Air conditioning down-regulation potential during the time period , and They represent the historical days. Current time of each air conditioning cluster The air conditioner's operating power, maximum operating power, and minimum operating power;

[0155] Among them, the historical day Current time of each air conditioning cluster The operating power of the air conditioner, the maximum operating power and the minimum operating power are expressed as follows:

[0156] ;

[0157] In the formula, Indicates the first historical day Current time of each air conditioning cluster Average outdoor temperature, Indicates the first historical day Current time of each air conditioning cluster Average indoor temperature Indicates the first historical day Current time of each air conditioning cluster The average value of the indoor heat source power.

[0158] Step 2: Based on historical electric vehicle charging and discharging data, establish charging and discharging power constraints, energy constraints, and mutual exclusion constraints for individual electric vehicles. Introduce state variables to represent the grid connection status of electric vehicles within the scheduling cycle and extend the domain. Construct the aggregated operational feasible domain of the electric vehicle cluster within a unified scheduling cycle to obtain the scheduling potential of the electric vehicle cluster in each time period, such as... Figure 3 As shown.

[0159] Step 2.1: Establish charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states for individual electric vehicles based on historical electric vehicle charging and discharging data.

[0160] In this embodiment, the charging and discharging power constraint is expressed as:

[0161] ;

[0162] In the formula, They represent electric vehicles. exist Charging and discharging power scheduling during different time periods They represent electric vehicles. exist The upper limit of charging scheduling power and the upper limit of discharging scheduling power for each time period. Indicates electric vehicles The set of grid connection times It indicates that any logical symbol can be chosen.

[0163] In this embodiment, the power constraint is expressed as:

[0164] ;

[0165] In the formula, They represent electric vehicles. exist Time period and Battery level during the period These represent charging efficiency and discharging efficiency, respectively. Indicates the scheduling time window. This represents the discharge compensation coefficient determined by discharge loss. They represent electric vehicles. The battery power safety boundary.

[0166] In this embodiment, the mutual exclusion constraint of the charge and discharge states is expressed as:

[0167] ;

[0168] In the formula, and Charging stations exist Total charging power and total discharging power during the time period;

[0169] The state variable is represented as:

[0170] ;

[0171] In the formula, Indicates electric vehicles exist The state of the time period Indicates electric vehicles exist The period is in grid-connected state. Indicates electric vehicles exist The period was spent offline. Indicates electric vehicles The time of arrival at the charging station, Indicates electric vehicles During the period of leaving the charging station, the charging and discharging power constraints, whose domain is extended to the unified scheduling cycle, are expressed as follows:

[0172] ;

[0173] The upward adjustment potential and downward adjustment potential of the electric vehicle cluster are respectively expressed as follows:

[0174] ;

[0175] In the formula, Indicates the first The first electric vehicle cluster The potential for adjustment in electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The potential for adjustment of electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a certain period. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a certain time period. Indicates the first The first electric vehicle cluster The basic adjustment potential of electric vehicle clusters within a given time period;

[0176] Among them, the The first electric vehicle cluster The upper limit of the adjustment potential of the electric vehicle cluster during the time period and the representation of the first The first electric vehicle cluster The upper limit of the adjustment potential of electric vehicle clusters within the time period is expressed as follows:

[0177] .

[0178] Step 2.2: Introduce state variables to characterize the grid connection status of electric vehicles within the scheduling cycle, and extend the domain of the charging and discharging power constraints, energy constraints, and charging and discharging state mutual exclusion constraints of individual electric vehicles to a unified scheduling cycle based on the state variables.

[0179] Step 2.3: Based on the Minkowski summation method, aggregate the feasible domain spaces of all individual electric vehicles after the domain extension to form an aggregated operational feasible domain that characterizes the operating characteristics of the charging station-level generalized energy storage device.

[0180] Step 2.4: Based on the difference between the maximum chargeable and dischargeable power determined by the aggregated operational feasible region and the actual chargeable and dischargeable power, calculate the scheduling potential of the electric vehicle cluster for each time period, including the upward adjustment potential and the downward adjustment potential of the electric vehicle cluster.

[0181] Step 3: Based on the air conditioning scheduling potential and electric vehicle cluster scheduling potential for each time period, make predictions using a pre-trained time series prediction model, output the air conditioning cluster scheduling potential and electric vehicle cluster scheduling potential for future time periods, and superimpose them to evaluate the localization potential.

[0182] In this embodiment, the time series prediction model is a long short-term memory network. On a time scale, the scheduling potential of air conditioners and electric vehicles will not change much in a short period of time. In addition, considering the impact of weekdays and non-weekdays, this embodiment makes predictions based on a pre-trained time series prediction model, outputting the scheduling potential of air conditioners and electric vehicles 1 day, 2 days, and 7 days before the prediction date as input variables. Based on the trained time series prediction model, the model is trained to output the scheduling potential of air conditioners and electric vehicles in future periods and superimpose them to evaluate the localization potential.

[0183] Example 3

[0184] Based on the same inventive concept as other embodiments, this embodiment introduces a localization potential assessment system based on air conditioning load and charging / discharging resource regulation, including:

[0185] The air conditioning potential assessment module is used to perform thermodynamic modeling of air conditioning load based on historical air conditioning operation data and indoor and outdoor temperature data, and to identify air conditioning thermodynamic model parameters using historical daily air conditioning operation data, and calculate the air conditioning regulation potential for each time period based on the human thermal comfort temperature range.

[0186] The electric vehicle potential assessment module is used to extend the domain of individual electric vehicles' charging and discharging power constraints, energy constraints, and grid connection status based on historical electric vehicle charging and discharging data. It constructs the aggregated operational feasible domain of electric vehicle clusters within a unified scheduling cycle and obtains the scheduling potential of electric vehicle clusters in each time period.

[0187] The time series prediction module is used to predict the air conditioning scheduling potential and electric vehicle cluster scheduling potential of each time period based on a pre-trained time series prediction model. It outputs the air conditioning cluster scheduling potential and electric vehicle cluster scheduling potential of future time periods and superimposes them to evaluate the localization potential of mutual regulation between air conditioning load and electric vehicle charging and discharging resources.

[0188] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0189] Example 4

[0190] Based on the same inventive concept as other embodiments, this embodiment describes a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the methods of Embodiment 1 or 2 described above.

[0191] In summary, this invention utilizes thermodynamic modeling and parameter identification of air conditioning load, electric vehicle charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states. It also introduces state variables to characterize the grid-connected state of electric vehicles within the scheduling cycle, extending the domain and aggregating the feasible operational domain. By using a time-series prediction model to predict and superimpose the scheduling potential of air conditioning clusters and electric vehicle clusters in future periods, it achieves a precise quantitative assessment of the aggregated and controllable potential of air conditioning and electric vehicle resources in the local power grid. This solves the problem that existing technologies cannot accurately assess and predict the aggregated and controllable potential of air conditioning load and electric vehicle charging and discharging resources in the local power grid, leading to an inability to support demand response or grid optimization scheduling.

[0192] This invention reduces parameter dependence by coupling a first-order ETP model with linear regression, transforms the temperature regulation range using the Franker thermal comfort equation, and aggregates cluster energy storage characteristics based on state variable extension and Minkowski summation. Finally, it assesses localization potential based on time series predictions superimposed with the scheduling potential of air conditioning clusters and electric vehicle clusters in future periods, providing a quantitative decision-making basis that can be directly used for demand response and grid optimization scheduling in the substation-level source-load mutual assistance scenario.

[0193] This invention unifies the charging and discharging constraints of individual electric vehicles to the scheduling cycle dimension by extending the domain of state variables. It combines Minkowski summation to achieve aggregate modeling of the cluster feasible domain and directly quantifies the scheduling potential of electric vehicle clusters in each time period based on the difference between the maximum chargeable and dischargeable power and the actual power in the aggregate feasible domain. This solves the problem that traditional methods are inaccurate in assessing the collaborative potential of electric vehicle clusters with the dual attributes of "load-energy storage" and cannot support dynamic scheduling decisions at the distribution area level. It provides quantifiable cluster energy storage characteristics to support power grid optimization in the scenario of source-load mutual assistance.

[0194] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0195] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0196] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0197] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0198] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for assessing the localization potential of air conditioning load and charging / discharging resource regulation, characterized in that, include: Thermodynamic modeling of air conditioning load is performed based on historical air conditioning operation data and indoor and outdoor temperature data. The parameters of the air conditioning thermodynamic model are identified using historical daily air conditioning operation data. The air conditioning regulation potential for each time period is calculated based on the human thermal comfort temperature range. Based on historical electric vehicle charging and discharging data, charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states are established for individual electric vehicles. State variables are introduced to characterize the grid connection state of electric vehicles within the scheduling cycle and the domain is extended. Within a unified scheduling cycle, the aggregated operational feasible domain of electric vehicle clusters is constructed to obtain the scheduling potential of electric vehicle clusters in each time period. Based on the air conditioning scheduling potential and electric vehicle cluster scheduling potential for each time period, a pre-trained time series prediction model is used to predict and output the air conditioning cluster scheduling potential and electric vehicle cluster scheduling potential for future time periods, which are then superimposed to evaluate the localization potential.

2. The method for assessing the localization potential based on air conditioning load and charging / discharging resource regulation according to claim 1, characterized in that, Thermodynamic modeling of air conditioning load is performed based on historical air conditioning operation data and indoor and outdoor temperature data. The parameters of the air conditioning thermodynamic model are identified using historical daily air conditioning operation data. The air conditioning regulation potential for each time period is calculated based on the human thermal comfort temperature range, including: Based on historical air conditioning operation data and indoor and outdoor temperature data, an air conditioning thermodynamic model is constructed based on a first-order equivalent thermal parameter model. Based on the linear relationship between historical daily air conditioning operation data and outdoor temperature data, the parameters of the air conditioning thermodynamic model are identified using the linear regression method, including the air conditioning equivalent thermal resistance, air conditioning equivalent heat capacity, and air conditioning energy efficiency ratio. Based on the temperature range of human thermal comfort, an optimization model is established according to the average value of indoor temperature setpoint and the average value of indoor heat source power. The optimization objective is to minimize the error between the actual value and the simulated value of the average operating power of the air conditioner in each time period. The optimal estimated values ​​of indoor temperature setpoint and indoor heat source power are obtained by iterative solution. Based on the parameters of the air conditioning thermodynamic model, the indoor temperature setpoint and the optimal estimated value of the indoor heat source power, the average operating power of the air conditioner in each time period is calculated based on the energy balance equation. The Franker thermal comfort equation is used to construct a predicted average vote index to approximate the range of human thermal comfort temperature and determine the adjustable range of indoor temperature. Based on the average operating power of the air conditioner in each time period and the adjustable range of indoor temperature, the air conditioner's adjustment potential in each time period is calculated by perturbing the temperature setpoint, including the air conditioner's upward adjustment potential and downward adjustment potential.

3. The method for assessing the localization potential based on air conditioning load and charging / discharging resource regulation according to claim 2, characterized in that, The air conditioning thermodynamic model is expressed as follows: ； In the formula, Indicates the air conditioner at the current moment Cooling or heating power, Indicates the equivalent thermal resistance of the room. This represents the energy efficiency coefficient. Indicates the current time outdoor temperature, Indicates the indoor heat source at the current moment power, For indoor equivalent heat capacity, Indicates the current time The differential, Indicates the current time Indoor temperature The differential.

4. The method for assessing the localization potential based on air conditioning load and charging / discharging resource regulation according to claim 3, characterized in that, The predicted average vote count indicator is expressed as follows: ； In the formula, This indicates the predicted average number of votes. , Indicates indoor temperature.

5. The method for assessing the localization potential based on air conditioning load and charging / discharging resource regulation according to claim 4, characterized in that, The optimization model is expressed as follows: ； In the formula, This indicates taking the minimum value. Indicates the first The operating power of the air conditioner during the period, Indicates the first Average outdoor temperature during the period This represents the average value of the indoor temperature setpoint. This represents the average power of the indoor heat source. It means "to make... true". This indicates the minimum value of the indoor temperature setting. This indicates the maximum value of the indoor temperature setting. This represents the minimum value of the average power of the indoor heat source. For the first The average indoor heat source power during the time period. This represents the maximum value of the average power of the indoor heat source.

6. The method for assessing the localization potential based on air conditioning load and charging / discharging resource regulation according to claim 5, characterized in that, The average operating power of the air conditioner in each time period is expressed as: ； In the formula, For the first The average operating power of the air conditioner during the period, For the first Average indoor temperature during the time period.

7. The method for assessing the localization potential based on air conditioning load and charging / discharging resource regulation according to claim 6, characterized in that, The upward and downward adjustment potentials of the air conditioner are respectively expressed as: ； In the formula, Indicates the first historical day The first air conditioning cluster The air conditioning adjustment potential during the time period Indicates the first historical day The first air conditioning cluster Air conditioning adjustment potential during the time period , and They represent the historical days. Current time of each air conditioning cluster The air conditioner's operating power, maximum operating power, and minimum operating power; Among them, the historical day Current time of each air conditioning cluster The operating power of the air conditioner, the maximum operating power and the minimum operating power are expressed as follows: ； In the formula, Indicates the first historical day Current time of each air conditioning cluster Average outdoor temperature, Indicates the first historical day Current time of each air conditioning cluster Average indoor temperature Indicates the first historical day Current time of each air conditioning cluster The average value of the indoor heat source power.

8. The method for assessing the localization potential based on air conditioning load and charging / discharging resource regulation according to claim 1, characterized in that, Based on historical electric vehicle charging and discharging data, charging and discharging power constraints, energy constraints, and mutual exclusion constraints for charging and discharging states are established for individual electric vehicles. State variables are introduced to characterize the grid connection state of electric vehicles within the scheduling cycle, extending the domain of definition. An aggregated operational feasible domain for the electric vehicle cluster is constructed within a unified scheduling cycle, yielding the scheduling potential of the electric vehicle cluster for each time period, including: Establish charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states for individual electric vehicles based on historical electric vehicle charging and discharging data. State variables are introduced to characterize the grid connection status of electric vehicles within the scheduling cycle, and the domain of the charging and discharging power constraints, energy constraints, and charging and discharging state mutual exclusion constraints of a single electric vehicle are extended to a unified scheduling cycle based on the state variables. Based on the Minkowski summation method, the feasible domain spaces of all individual electric vehicles after the domain extension are aggregated to form an aggregated operational feasible domain that characterizes the operational characteristics of charging station-level generalized energy storage devices. Based on the difference between the maximum chargeable and dischargeable power determined by the aggregated operational feasible domain and the actual chargeable and dischargeable power, the scheduling potential of the electric vehicle cluster in each time period is calculated, including the upward adjustment potential of the electric vehicle cluster and the downward adjustment potential of the electric vehicle cluster.

9. The method for assessing the localization potential based on air conditioning load and charging / discharging resource regulation according to claim 8, characterized in that, The charging and discharging power constraint is expressed as follows: ； In the formula, They represent electric vehicles. exist Charging and discharging power scheduling during different time periods They represent electric vehicles. exist The upper limit of charging scheduling power and the upper limit of discharging scheduling power for each time period. Indicates electric vehicles The set of grid connection times This indicates that any logical symbol can be chosen; The energy constraint is expressed as: ； In the formula, They represent electric vehicles. exist Time period and Battery level during the period These represent charging efficiency and discharging efficiency, respectively. Indicates the scheduling time window. This represents the discharge compensation coefficient determined by discharge loss. They represent electric vehicles. The battery power safety boundary; The mutual exclusion constraint of the charging and discharging states is expressed as follows: ； In the formula, and Charging stations exist Total charging power and total discharging power during the time period; The state variable is represented as: ； In the formula, Indicates electric vehicles exist The state of the time period Indicates electric vehicles exist The period is in grid-connected state. Indicates electric vehicles exist The period was spent offline. Indicates electric vehicles The time of arrival at the charging station, Indicates electric vehicles During the period of leaving the charging station, the charging and discharging power constraints, whose domain is extended to the unified scheduling cycle, are expressed as follows: ； The upward adjustment potential and downward adjustment potential of the electric vehicle cluster are respectively expressed as follows: ； In the formula, Indicates the first The first electric vehicle cluster The potential for adjustment in electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The potential for adjustment of electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a certain period of time. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a specific time period. Indicates the first The first electric vehicle cluster The basic adjustment potential of electric vehicle clusters within a given time period; Among them, the The first electric vehicle cluster The upper limit of the adjustment potential of the electric vehicle cluster during the time period and the representation of the first The first electric vehicle cluster The upper limit of the adjustment potential of electric vehicle clusters within the time period is expressed as follows: 。 10. A localization potential assessment system based on air conditioning load and charging / discharging resource regulation, characterized in that, include: The air conditioning potential assessment module is used to perform thermodynamic modeling of air conditioning load based on historical air conditioning operation data and indoor and outdoor temperature data, and to identify air conditioning thermodynamic model parameters using historical daily air conditioning operation data, and calculate the air conditioning regulation potential for each time period based on the human thermal comfort temperature range. The electric vehicle potential assessment module is used to extend the domain of individual electric vehicles' charging and discharging power constraints, energy constraints, and grid connection status based on historical electric vehicle charging and discharging data. It constructs the aggregated operational feasible domain of electric vehicle clusters within a unified scheduling cycle and obtains the scheduling potential of electric vehicle clusters in each time period. The time series prediction module is used to predict the air conditioning scheduling potential and electric vehicle cluster scheduling potential of each time period based on a pre-trained time series prediction model. It outputs the air conditioning cluster scheduling potential and electric vehicle cluster scheduling potential of future time periods and superimposes them to evaluate the localization potential of mutual regulation between air conditioning load and electric vehicle charging and discharging resources.

11. The localization potential assessment system based on air conditioning load and charging / discharging resource regulation according to claim 10, characterized in that, Thermodynamic modeling of air conditioning load is performed based on historical air conditioning operation data and indoor and outdoor temperature data. The parameters of the air conditioning thermodynamic model are identified using historical daily air conditioning operation data. The air conditioning regulation potential for each time period is calculated based on the human thermal comfort temperature range, including: Based on historical air conditioning operation data and indoor and outdoor temperature data, an air conditioning thermodynamic model is constructed based on a first-order equivalent thermal parameter model. Based on the linear relationship between historical daily air conditioning operation data and outdoor temperature data, the parameters of the air conditioning thermodynamic model are identified using the linear regression method, including the air conditioning equivalent thermal resistance, air conditioning equivalent heat capacity, and air conditioning energy efficiency ratio. Based on the temperature range of human thermal comfort, an optimization model is established according to the average value of indoor temperature setpoint and the average value of indoor heat source power. The optimization objective is to minimize the error between the actual value and the simulated value of the average operating power of the air conditioner in each time period. The optimal estimated values ​​of indoor temperature setpoint and indoor heat source power are obtained by iterative solution. Based on the parameters of the air conditioning thermodynamic model, the indoor temperature setpoint and the optimal estimated value of the indoor heat source power, the average operating power of the air conditioner in each time period is calculated based on the energy balance equation. The Franker thermal comfort equation is used to construct a predicted average vote index to approximate the range of human thermal comfort temperature and determine the adjustable range of indoor temperature. Based on the average operating power of the air conditioner in each time period and the adjustable range of indoor temperature, the air conditioner's adjustment potential in each time period is calculated by perturbing the temperature setpoint, including the air conditioner's upward adjustment potential and downward adjustment potential.

12. The localization potential assessment system based on air conditioning load and charging / discharging resource regulation according to claim 11, characterized in that, The air conditioning thermodynamic model is expressed as follows: ； In the formula, Indicates the air conditioner at the current moment Cooling or heating power, Indicates the equivalent thermal resistance of the room. This represents the energy efficiency coefficient. Indicates the current time outdoor temperature, Indicates the indoor heat source at the current moment power, For indoor equivalent heat capacity, Indicates the current time The differential, Indicates the current time Indoor temperature The differential.

13. The localization potential assessment system based on air conditioning load and charging / discharging resource regulation according to claim 12, characterized in that, The predicted average vote count indicator is expressed as follows: ； In the formula, This indicates the predicted average number of votes. , Indicates indoor temperature.

14. The localization potential assessment system based on air conditioning load and charging / discharging resource regulation according to claim 13, characterized in that, The optimization model is expressed as follows: ； In the formula, This indicates taking the minimum value. Indicates the first The operating power of the air conditioner during the period, Indicates the first Average outdoor temperature during the period This represents the average value of the indoor temperature setpoint. This represents the average power of the indoor heat source. It means "to make... true". This indicates the minimum value of the indoor temperature setting. This indicates the maximum value of the indoor temperature setting. This represents the minimum value of the average power of the indoor heat source. For the first The average indoor heat source power during the time period. This represents the maximum value of the average power of the indoor heat source.

15. The localization potential assessment system based on air conditioning load and charging / discharging resource regulation according to claim 14, characterized in that, The average operating power of the air conditioner in each time period is expressed as: ； In the formula, For the first The average operating power of the air conditioner during the period, For the first Average indoor temperature during the time period.

16. The localization potential assessment system based on air conditioning load and charging / discharging resource regulation according to claim 15, characterized in that, The upward and downward adjustment potentials of the air conditioner are respectively expressed as: ； In the formula, Indicates the first historical day The first air conditioning cluster The air conditioning adjustment potential during the time period Indicates the first historical day The first air conditioning cluster Air conditioning adjustment potential during the time period , and They represent the historical days. Current time of each air conditioning cluster The air conditioner's operating power, maximum operating power, and minimum operating power; Among them, the historical day Current time of each air conditioning cluster The operating power of the air conditioner, the maximum operating power and the minimum operating power are expressed as follows: ； In the formula, Indicates the first historical day Current time of each air conditioning cluster Average outdoor temperature, Indicates the first historical day Current time of each air conditioning cluster Average indoor temperature Indicates the first historical day Current time of each air conditioning cluster The average value of the indoor heat source power.

17. The localization potential assessment system based on air conditioning load and charging / discharging resource regulation according to claim 16, characterized in that, Based on historical electric vehicle charging and discharging data, charging and discharging power constraints, energy constraints, and mutual exclusion constraints for charging and discharging states are established for individual electric vehicles. State variables are introduced to characterize the grid connection state of electric vehicles within the scheduling cycle, extending the domain of definition. An aggregated operational feasible domain for the electric vehicle cluster is constructed within a unified scheduling cycle, yielding the scheduling potential of the electric vehicle cluster for each time period, including: Establish charging and discharging power constraints, energy constraints, and mutual exclusion constraints of charging and discharging states for individual electric vehicles based on historical electric vehicle charging and discharging data. State variables are introduced to characterize the grid connection status of electric vehicles within the scheduling cycle, and the domain of the charging and discharging power constraints, energy constraints, and charging and discharging state mutual exclusion constraints of a single electric vehicle are extended to a unified scheduling cycle based on the state variables. Based on the Minkowski summation method, the feasible domain spaces of all individual electric vehicles after the domain extension are aggregated to form an aggregated operational feasible domain that characterizes the operational characteristics of charging station-level generalized energy storage devices. Based on the difference between the maximum chargeable and dischargeable power determined by the aggregated operational feasible domain and the actual chargeable and dischargeable power, the scheduling potential of the electric vehicle cluster in each time period is calculated, including the upward adjustment potential of the electric vehicle cluster and the downward adjustment potential of the electric vehicle cluster.

18. The localization potential assessment system based on air conditioning load and charging / discharging resource regulation according to claim 17, characterized in that, The charging and discharging power constraint is expressed as follows: ； In the formula, They represent electric vehicles. exist Charging and discharging power scheduling during different time periods They represent electric vehicles. exist The upper limit of charging scheduling power and the upper limit of discharging scheduling power for each time period. Indicates electric vehicles The set of grid connection times This indicates that any logical symbol can be chosen; The energy constraint is expressed as: ； In the formula, They represent electric vehicles. exist Time period and Battery level during the period These represent charging efficiency and discharging efficiency, respectively. Indicates the scheduling time window. This represents the discharge compensation coefficient determined by discharge loss. They represent electric vehicles. The battery power safety boundary; The mutual exclusion constraint of the charging and discharging states is expressed as follows: ； In the formula, and Charging stations exist Total charging power and total discharging power during the time period; The state variable is represented as: ； In the formula, Indicates electric vehicles exist The state of the time period Indicates electric vehicles exist The period is in grid-connected state. Indicates electric vehicles exist The period was spent offline. Indicates electric vehicles The time of arrival at the charging station, Indicates electric vehicles During the period of leaving the charging station, the charging and discharging power constraints, whose domain is extended to the unified scheduling cycle, are expressed as follows: ； The upward adjustment potential and downward adjustment potential of the electric vehicle cluster are respectively expressed as follows: ； In the formula, Indicates the first The first electric vehicle cluster The potential for adjustment in electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The potential for adjustment of electric vehicle clusters within a given time period. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a certain period of time. Indicates the first The first electric vehicle cluster The upper limit of the adjustment potential for electric vehicle clusters within a specific time period. Indicates the first The first electric vehicle cluster The basic adjustment potential of electric vehicle clusters within a given time period; Among them, the The first electric vehicle cluster The upper limit of the adjustment potential of the electric vehicle cluster during the time period and the representation of the first The first electric vehicle cluster The upper limit of the adjustment potential of electric vehicle clusters within the time period is expressed as follows: 。 19. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the localization potential assessment method based on air conditioning load and charging / discharging resource regulation as described in any one of claims 1-9.

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