Method and system for evaluating load adjustable capacity potential of central air conditioner and electric vehicle

Abstract

The invention relates to the technical field of power system demand side management, and discloses a central air conditioner and electric vehicle load adjustable capacity potential evaluation method and system. The method comprises the following steps: establishing a complete data acquisition system; based on the preprocessed data, an electric vehicle adjustable capacity evaluation model is established by considering the battery health state and the battery temperature characteristic of the electric vehicle; based on the preprocessed data, a central air conditioner adjustable capacity evaluation model is established by considering user comfort constraint and an air conditioner time-lag effect; constructing a coupling characteristic analysis model including a time coupling degree, a space coupling degree and a load complementation degree; and establishing an aggregation adjustable capacity potential evaluation model, calculating the total adjustable capacity considering the coupling effect, and outputting an adjustable capacity potential evaluation result. According to the method, through refined equipment characteristic modeling and multi-dimensional coupling analysis, the accuracy of load adjustable capacity evaluation is remarkably improved, power grid optimization scheduling is effectively supported, and the system operation stability is enhanced.

Description

Methods and Systems for Assessing the Adjustable Capacity Potential of Central Air Conditioning and Electric Vehicles Technical Field

[0001] This invention relates to the field of demand-side management technology for power systems, specifically to a method and system for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads. Background Technology

[0002] With the large-scale grid connection of renewable energy sources such as wind power and photovoltaics, the randomness and volatility of power grid operation have increased significantly, making traditional generation-side regulation insufficient to meet the demand for rapid response. Electric vehicles and central air conditioning, as two types of flexible loads with significant regulation potential, are subject to complex influences from factors such as battery characteristics, user comfort, and climate conditions. Existing research mostly focuses on assessing the regulation capacity of individual devices or similar loads, neglecting the spatiotemporal coupling effects of electric vehicle mobility and air conditioning thermal inertia. For example, the charging behavior of electric vehicles varies with time and geographical location, while the regulation capacity of central air conditioning depends on the indoor-outdoor temperature difference and building thermal characteristics. Failure to accurately quantify the coupling effect of these two elements at different spatiotemporal scales may lead to overestimation or underestimation of the aggregation potential, affecting the economy and security of power grid dispatch strategies. Therefore, a refined and dynamic method for assessing adjustable capacity potential is urgently needed, which can reflect both the operational constraints of individual devices and the synergistic effect between electric vehicles and air conditioning loads, thereby accurately supporting the optimal allocation of flexible resources in the power grid. Summary of the Invention

[0003] The purpose of this invention is to solve the problem of insufficient accuracy in assessing the load adjustability potential of electric vehicles and central air conditioning in the prior art, and to provide a refined assessment method that comprehensively considers battery health status, temperature characteristics, user comfort, and spatiotemporal coupling effects.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads includes the following steps:

[0006] Establish a data acquisition system to collect data on electric vehicle battery status, charging behavior, central air conditioning operating parameters, and indoor and outdoor environmental data, and perform data preprocessing.

[0007] Based on the preprocessed data, and considering the battery health status and battery temperature correction of electric vehicles, an adjustable capacity assessment model for electric vehicles is established.

[0008] Based on the preprocessed data, and considering user comfort constraints and air conditioning time lag effect, a central air conditioning adjustable capacity assessment model is established.

[0009] A coupling characteristic analysis, including temporal coupling degree, spatial coupling degree, and load complementarity degree, is constructed to quantify the mutual influence between central air conditioning and electric vehicle load;

[0010] Based on the adjustable capacity assessment models for electric vehicles and central air conditioning, and coupling characteristic analysis, an aggregated adjustable capacity potential assessment model is established to calculate the total adjustable capacity considering the coupling effect and output the adjustable capacity potential assessment results.

[0011] Furthermore, the data acquisition system collects data through data acquisition devices, including smart meters, charging pile controllers, battery management systems, building automation systems, and GPS positioning devices.

[0012] Furthermore, the data preprocessing methods include missing value imputation, outlier removal, and data alignment. The collected data labels i and j represent the i-th electric vehicle and the j-th central air conditioner, respectively, where i=1,2,...,N, j=1,2,...,M, and N and M are the total number of electric vehicles and central air conditioners, respectively.

[0013] The data acquisition system collects real-time operating data and historical load data from the central air conditioning control system, electric vehicle charging station management system, and power grid dispatching system, including the charging start time of the i-th electric vehicle. Charging end time Battery voltage Current state of charge Charging power Historical charging power sequence Battery health status Battery internal resistance Battery temperature Maximum allowable charging current And the operating mode of the jth central air conditioner. Set temperature Indoor temperature Current thermal / cold storage status Rated power Historical operating power sequence Thermal time constant Compressor start / stop status Last state transition time Simultaneously, the geographical coordinates of electric vehicle charging points and central air conditioning units are collected, and the geographical distance between the i-th electric vehicle and the j-th central air conditioning unit is determined. .

[0014] Furthermore, the adjustable capacity assessment model for electric vehicles is as follows:

[0015]

[0016] in, Let be the adjustable capacity of the i-th electric vehicle; The charging power for the i-th electric vehicle; Let be the battery voltage of the i-th electric vehicle; The maximum allowable charging current for the i-th electric vehicle; Let be the current state of charge of the i-th electric vehicle; This represents the maximum state of charge of the battery of the i-th electric vehicle. The charging efficiency of the i-th electric vehicle is set to 0.95. Let i be the time availability function for the i-th electric vehicle, when The value is 1 if the condition is met, and 0 otherwise.

[0017] The battery health status correction factor is calculated using the following formula:

[0018]

[0019] in, Battery health status ;

[0020] The battery temperature correction factor is calculated using the following formula:

[0021]

[0022] in, Battery temperature; The set optimal operating temperature for the battery; The set temperature standard deviation;

[0023] The battery internal resistance correction factor is calculated using the following formula:

[0024]

[0025] in, This refers to the battery's internal resistance. The set internal resistance of the new battery.

[0026] Furthermore, the central air conditioning adjustable capacity assessment model is as follows:

[0027]

[0028] in, Let j be the adjustable capacity of the j-th central air conditioner. Let J be the rated power of the j-th central air conditioner. Let J be the indoor temperature of the j-th central air conditioner. Set the temperature for the j-th central air conditioner; The maximum allowable temperature deviation for the j-th central air conditioner is set. This represents the current heat / cold storage status of the j-th central air conditioner. The user comfort constraint coefficient is set; The maximum heat / cold storage state of the j-th central air conditioner is set to 1.0; Let be the operating mode coefficient of the j-th central air conditioner, which is 1 when cooling and -1 when heating;

[0029] To introduce a time delay effect, thermal inertia correction factors are used in the central air conditioning adjustable capacity assessment model. With start / stop delay correction factor It characterizes the time lag characteristics of the system in dynamic response;

[0030] Thermal inertia correction factor The formula used to describe the response delay of indoor temperature changes to control commands is:

[0031]

[0032] in, For the first The thermal time constant of a central air conditioning system reflects the lag rate of temperature change. To control the time step;

[0033] Compressor start-stop delay correction factor The formula used to characterize the control band effect of a compressor during frequent start-stop operations is as follows:

[0034]

[0035] in, For the first The last time the central air conditioning unit switched states; This indicates the current start / stop status of the compressor. This is the minimum start-stop interval for the compressor.

[0036] Furthermore, the calculation methods for the time coupling degree, spatial coupling degree, and load complementarity degree are as follows:

[0037] Calculating the time coupling coefficient: The time coupling coefficient is directly defined as the Pearson correlation coefficient between the aggregated load sequence of electric vehicles and central air conditioning within a historical time window. The calculation formula is as follows:

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] in, For time coupling degree; For the first Aggregated historical load of electric vehicles at a given time point; For the first Aggregated historical load of central air conditioning at a given time point; This represents the historical average load of electric vehicles. This represents the historical average load of the central air conditioning system. This is to count the total number of historical time points within the statistical window;

[0044] when When the load changes over time, it indicates that the central air conditioning and electric vehicle loads show consistent trends, indicating a strong synergistic effect; when When the load changes of the central air conditioning and the electric vehicle are in opposite directions, they have complementary characteristics; when The closer it is to 1, the tighter the temporal coupling.

[0045] Calculating the spatial coupling coefficient: The spatial coupling coefficient is directly defined as the average distance attenuation coefficient between the electric vehicle charging point and the central air conditioning equipment in geographical space. It measures the degree of aggregation of the central air conditioning and electric vehicle loads in physical spatial distribution. The calculation formula is as follows:

[0046]

[0047] in, Spatial coupling degree; For the first electric vehicles and the first Geographical distance between central air conditioning units in Taiwan; This is the spatial standard deviation parameter, used to control the decay rate; For the number of electric vehicles; For the number of central air conditioning units; when When the value approaches 1, it indicates that the central air conditioning and electric vehicle loads are highly concentrated and strongly coupled in space; when... When the value is close to 0, it indicates that the spatial distribution is discrete and the coupling is weak.

[0048] Calculate the load complementarity coefficient: The load complementarity is directly defined as the complementarity index of the load complementarity relationship between electric vehicles and central air conditioning in the same time period, characterizing whether the loads of central air conditioning and electric vehicles have peak-shifting complementary characteristics in the power time series; the calculation formula is as follows:

[0049]

[0050] in, For load complementarity; For the first Aggregate load of electric vehicles at a given time point; For the first The aggregate load of central air conditioning at a given time point; The total number of historical time points; when When the value is close to 1, it indicates that the power curves of the central air conditioning and the electric vehicle load are highly complementary. When the value is close to 0, it indicates that the load changes of the central air conditioning and electric vehicles are highly synchronized and have weak complementarity.

[0051] Furthermore, the coupling characteristic analysis also includes calculating the comprehensive coupling coefficient:

[0052]

[0053] in, This refers to the overall coupling coefficient; This is the time coupling coefficient; This is the spatial coupling coefficient; This is the load complementarity coefficient; , , The corresponding weighting coefficients for the time coupling degree, spatial coupling degree, and load complementarity degree are set respectively, and satisfy the following conditions: .

[0054] Furthermore, the aggregation adjustable capacity potential assessment model is as follows:

[0055]

[0056] in, The total adjustable capacity takes into account coupling characteristics; Let be the adjustable capacity of the i-th electric vehicle; Let j be the adjustable capacity of the j-th central air conditioner. The set coupling loss coefficient; is the overall coupling coefficient; N is the total number of electric vehicles; M is the total number of central air conditioning units.

[0057] Furthermore, regarding the degree of temporal coupling Normalization is performed as follows This includes linearly mapping it to an interval. The normalization formula is: .

[0058] A system for implementing the aforementioned method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads includes:

[0059] Data acquisition module: used to collect real-time and historical data of electric vehicles and central air conditioning through smart meters, charging pile controllers, battery management systems, building automation systems and GPS devices;

[0060] Characteristic Modeling Module: Used for calculating the adjustable capacity assessment model of electric vehicles that considers the battery health status and battery temperature correction, and the adjustable capacity assessment model of central air conditioning that considers user comfort constraints and air conditioning time lag effect.

[0061] Coupling analysis module: calculates temporal coupling degree, spatial coupling degree, load complementarity degree, and overall coupling coefficient;

[0062] Potential Assessment Module: Constructs and calculates aggregate adjustable capacity potential assessment models and calculates adjustable capacity potential assessment results;

[0063] Results output module: Used to output the results of the adjustable capacity potential assessment.

[0064] The present invention has the following beneficial effects:

[0065] This invention proposes a method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads. This method accurately quantifies the adjustable capacity of electric vehicles (EVs) and central air conditioning (ACs) in the context of high-proportion renewable energy integration, providing data support for grid flexibility resource aggregation and optimized scheduling. It offers significant advantages over traditional assessment techniques. This method innovatively constructs a refined assessment model that comprehensively considers battery health, air conditioning thermal inertia, and user comfort. Through spatiotemporal coupling analysis, it accurately quantifies the interactive impact of electric vehicle mobility and air conditioning regional distribution. This method effectively supports virtual power plant aggregation scheduling, significantly reduces assessment errors and load response delays, and minimizes user complaints while ensuring battery lifespan, providing crucial technical support for reliable grid operation in high-proportion renewable energy scenarios. Attached Figure Description

[0066] Figure 1 is a flowchart of the method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads provided by the present invention.

[0067] Figure 2 is a flowchart of the coupling characteristic analysis model provided by the present invention, which includes time coupling degree, spatial coupling degree and load complementarity degree. Detailed Implementation

[0068] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0069] The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads includes the following steps:

[0070] S1. Establish a data acquisition system to collect data on electric vehicle battery status, charging behavior, central air conditioning operating parameters, indoor and outdoor environmental data, and perform data preprocessing.

[0071] S2. Based on the preprocessed data, considering the battery health status and battery temperature correction of electric vehicles, an adjustable capacity evaluation model for electric vehicles is established.

[0072] S3. Based on the preprocessed data, considering user comfort constraints and air conditioning time lag effect, establish a central air conditioning adjustable capacity evaluation model.

[0073] S4. Construct a coupling characteristic analysis including time coupling degree, spatial coupling degree and load complementarity degree to quantify the mutual influence between central air conditioning and electric vehicle load;

[0074] S5. Based on the adjustable capacity assessment model of electric vehicles, the adjustable capacity assessment model of central air conditioning, and the coupling characteristic analysis, establish an aggregate adjustable capacity potential assessment model, calculate the total adjustable capacity considering the coupling effect, and output the adjustable capacity potential assessment results.

[0075] In S1: The data acquisition system collects data through data acquisition devices, including smart meters, charging pile controllers, battery management systems, building automation systems, and GPS positioning devices. The sampling frequency is 1Hz.

[0076] For example, the adjustable capacity assessment in a weekday urban scenario uses a central area (mixed commercial / office / residential) of a provincial capital city as the assessment object; the scheduling window is the next 24 hours, with a time resolution Δt=15 min (96 time periods in total). The historical data window for the statistical period is the past 30 days. The adjustable capacity potential assessment targets N=2,000 electric vehicles connected to public / building charging piles; and M=600 centralized / building-type central air conditioning units (or equivalent chiller stations / multi-split units are counted as one unit). Data sources include smart meters, charging pile controllers, battery management systems (BMS), building automation systems, GPS positioning equipment, and measurements / predictions from the distribution master station side (base load, renewable energy output, weather).

[0077] Data preprocessing methods include missing value imputation, outlier removal, and data alignment. The collected data labels i and j represent the i-th electric vehicle and the j-th central air conditioner, respectively, where i = 1, 2, ..., N, j = 1, 2, ..., M, and N and M are the total number of electric vehicles and central air conditioners, respectively. The historical data covers a time range of 30 days with a time resolution of 15 minutes.

[0078] The data acquisition system collects real-time operating data and historical load data from the central air conditioning control system, electric vehicle charging station management system, and power grid dispatching system, including the charging start time of the i-th electric vehicle. Charging end time Battery voltage Current state of charge Charging power Historical charging power sequence Battery health status Battery internal resistance Battery temperature Maximum allowable charging current And the operating mode of the jth central air conditioner. Set temperature Indoor temperature Current thermal / cold storage status Rated power Historical operating power sequence Thermal time constant Compressor start / stop status Last state transition time Simultaneously, the geographical coordinates of electric vehicle charging points and central air conditioning units are collected, and the geographical distance between the i-th electric vehicle and the j-th central air conditioning unit is determined. .

[0079] The data acquisition system preprocesses the data, such as data cleaning, feature extraction and standardization, and performs missing value imputation, outlier identification and removal, spatiotemporal alignment and feature standardization; it retains a unified data view with a 15-minute granularity.

[0080] In S2, the adjustable capacity assessment model for electric vehicles is as follows:

[0081]

[0082] in, Let be the adjustable capacity of the i-th electric vehicle; The charging power for the i-th electric vehicle; Let be the battery voltage of the i-th electric vehicle; The maximum allowable charging current for the i-th electric vehicle; Let be the current state of charge of the i-th electric vehicle; The maximum state of charge of the battery of the i-th electric vehicle is set to 1.0; The charging efficiency of the i-th electric vehicle is set to 0.95. Let i be the time availability function for the i-th electric vehicle, when The value is 1 if the condition is met, and 0 otherwise. The battery health status correction factor is calculated using the following formula:

[0083]

[0084] The battery temperature correction factor is calculated using the following formula:

[0085]

[0086] in For battery temperature, The optimal operating temperature for the battery is set to 25°C. The standard deviation of the set temperature is set to 5-10°C.

[0087] The battery internal resistance correction factor is calculated using the following formula:

[0088]

[0089] in, This refers to the battery's internal resistance. The internal resistance of the new battery is set to 0.05Ω.

[0090] For example, the average battery capacity of each electric vehicle Average voltage Maximum allowable current 150A, current state of charge Charging efficiency .

[0091] The optimal operating temperature for the battery was selected as 25 degrees Celsius, with a temperature standard deviation of 5 degrees Celsius, and a health status correction factor. 0.5SOH Temperature correction factor:

[0092]

[0093] Internal resistance correction factor

[0094] Taking the sample vehicle as an example and setting:

[0095]

[0096]

[0097] Statistics of all After considering the results for all vehicles, the total adjustable capacity of electric vehicles is approximately 30MW.

[0098] In S3, the central air conditioning adjustable capacity assessment model is as follows:

[0099]

[0100] in, Let j be the adjustable capacity of the j-th central air conditioner. Let J be the rated power of the j-th central air conditioner. Let J be the indoor temperature of the j-th central air conditioner. Set the temperature for the j-th central air conditioner; The maximum allowable temperature deviation for the j-th central air conditioner is set to 2°C. This represents the current heat / cold storage status of the j-th central air conditioner. The user comfort constraint coefficient is set to 0.15. The maximum heat / cold storage state of the j-th central air conditioner is set to 1.0; Let be the operating mode coefficient of the j-th central air conditioner, which is 1 when cooling and -1 when heating.

[0101] To introduce a time delay effect, thermal inertia correction factors are used in the model. With start / stop delay correction factor It characterizes the time lag characteristics of the system in dynamic response.

[0102] Thermal inertia correction factor The formula used to describe the response delay of indoor temperature changes to control commands is:

[0103]

[0104] in, For the first The thermal time constant of a central air conditioning system reflects the lag rate of temperature change. To control the time step (set to 15 minutes). When When the temperature is high, the room temperature changes slowly, and the system's regulation capability exhibits a significant time lag.

[0105] Compressor start-stop delay correction factor The formula used to characterize the control band effect of a compressor during frequent start-stop operations is as follows:

[0106]

[0107] in, For the first The last time the central air conditioning unit switched states; This indicates the current start / stop status of the compressor. This is the minimum start-stop interval for the compressor. This factor takes into account the time delay that the equipment needs to experience after starting or stopping before it can return to a stable operating state, effectively preventing energy consumption fluctuations and mechanical losses caused by frequent short-cycle start-stops.

[0108] The adjustable capacity calculation considers both thermal inertia time delay and start / stop delay time delay, by introducing... , By using time-delay related parameters, the dynamic response characteristics of central air conditioning can be accurately described, thereby improving the accuracy and robustness of adjustable capacity potential assessment in actual scheduling and control.

[0109] For example, the average rated power of a medium-sized air conditioner Maximum temperature deviation The comfort constraint factor is 2 degrees Celsius. thermal time constant Minimum start-stop interval of the compressor Cooling mode Current temperature difference Cold storage state :

[0110] Thermal inertia correction factor:

[0111]

[0112] Start / stop delay correction factor (10 minutes since last start / stop):

[0113]

[0114]

[0115] right By summing the central air conditioning units in Taiwan, we can obtain the total adjustable capacity of the central air conditioning units. .

[0116] This study constructs a coupling characteristic analysis model encompassing temporal coupling, spatial coupling, and load complementarity to quantify the mutual influence between central air conditioning and electric vehicle loads. To avoid overestimating the aggregation potential due to simple addition, a three-dimensional coupling evaluation of time, space, and complementarity is introduced. The flowchart is shown in Figure 2. First, historical load curves of electric vehicles and central air conditioning are obtained, and the temporal coupling is calculated using the Pearson correlation coefficient, reflecting the synchronicity of the central air conditioning and electric vehicle loads in the time dimension. Second, the geographical location information of electric vehicle charging points and central air conditioning is extracted, and the spatial coupling is calculated based on a Gaussian distance decay model, characterizing the degree of aggregation of the two in spatial distribution. Third, the charging and operating periods of the load are identified, and the minimum energy percentage is calculated to obtain the load complementarity, reflecting the peak-shifting and complementary characteristics of the power curves. Finally, the three coupling degrees are weighted and summed to output a comprehensive coupling coefficient and coupling characteristic matrix, providing input for assessing the aggregated adjustable capacity potential. In this embodiment, the statistical window is the past 30 days. ,common Each time period; power units are all in kW.

[0117] In S4, coupling characteristic analysis includes the following steps:

[0118] Calculate the time coupling coefficient: The time coupling coefficient is directly defined as the Pearson correlation coefficient of the aggregated load sequences of electric vehicles and central air conditioning within a historical time window. This coefficient can reflect the degree of synchronous change of the two types of loads in the time dimension. Because it has the characteristics of strong linear correlation, good scale independence, and clear sign interpretability, it can accurately characterize the synergistic or complementary trends of the two. The calculation formula is as follows:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] in, For time coupling degree; For the first Aggregated historical load of electric vehicles at a given time point; For the first The aggregated historical load of the central air conditioning system at a given time point; the average historical load of electric vehicles; This represents the historical average load of the central air conditioning system. This is the total number of historical time points within the statistics window.

[0125] when When the load changes over time, it indicates that the central air conditioning and electric vehicle loads show consistent trends, indicating a strong synergistic effect; when When, it indicates that the two change in opposite directions and have complementary characteristics; when The closer the value is to 1, the tighter the temporal coupling. To facilitate integration with other normalized indicators, it can be selectively linearly mapped to an interval. ,Right now The normalized time coupling is updated to the latest time coupling.

[0126] Calculating the spatial coupling coefficient: The spatial coupling coefficient is directly defined as the average distance attenuation coefficient between the electric vehicle charging point and the central air conditioning equipment in geographic space. This coefficient measures the degree of aggregation of the two types of loads in physical spatial distribution. The Gaussian distance attenuation model is chosen because it has monotonically decreasing, continuously differentiable, and outlier suppression characteristics, which can smoothly describe the influence of spatial distance on load coupling strength. Its calculation formula is as follows:

[0127]

[0128] in, Spatial coupling degree; For the first electric vehicles and the first The geographical distance between the central air conditioning units is calculated based on the latitude and longitude coordinates collected in step S1. This is the spatial standard deviation parameter used to control the decay rate, typically set to 500 meters. For the number of electric vehicles; This refers to the number of central air conditioning units. When... When the value approaches 1, it indicates that the central air conditioning and electric vehicle loads are highly concentrated in space and strongly coupled; when... When the value is close to 0, it indicates a discrete spatial distribution and weak coupling. This is because the coefficient itself takes values ​​within a certain range. It can directly participate in the comprehensive weight calculation without further normalization.

[0129] Calculating the load complementarity coefficient: The load complementarity is directly defined as the complementarity index of the load complementarity relationship between electric vehicles and central air conditioning within the same time period. This index can characterize whether the two types of loads have peak-shifting and complementary characteristics in the power time series. The minimum energy percentage form is chosen because it can intuitively reflect the proportion of heavy and light loads, has clear dimensions, and is applicable to load groups of different sizes. The calculation formula is as follows:

[0130]

[0131] in, For load complementarity; For the first Aggregate load of electric vehicles at a given time point; For the first The aggregate load of central air conditioning at a given time point; This represents the total number of historical time points. When... When the value is close to 1, it indicates that the power curves of the two types of loads are highly complementary, and a significant peak-shaving and valley-filling effect can be achieved; when... When the value is close to 0, it indicates that the two types of load changes are highly synchronized and have weak complementarity. Because... Defined in Within the interval, it can be directly used for the weighted calculation of the comprehensive coupling coefficient.

[0132] Calculate the overall coupling coefficient:

[0133]

[0134] in, This refers to the overall coupling coefficient; This is the time coupling coefficient; This is the spatial coupling coefficient; This is the load complementarity coefficient; , , The corresponding weighting coefficients for the time coupling degree, spatial coupling degree, and load complementarity degree are set respectively, and satisfy the following conditions: .

[0135] For example, (1) calculate the time coupling degree:

[0136] The aggregated historical load of electric vehicles within the 30-day statistical window was calculated from historical data of S1. The central air conditioning aggregate historical load is 36,000,000 kW.

[0137]

[0138]

[0139]

[0140]

[0141] This indicates a moderate positive correlation between the two types of loads over time. Indicator weighting allows for linear mapping. .

[0142] (2) Calculate spatial coupling: Based on the geographical locations of electric vehicle charging points and central air conditioning, calculate the average distance attenuation coefficient.

[0143]

[0144] in, Spatial coupling degree; The geographical distance between the i-th electric vehicle and the j-th central air conditioner is calculated from the location coordinates collected by S1. The spatial standard deviation is set to 500 meters; N is the total number of electric vehicles; M is the total number of central air conditioning units; in this embodiment, Due to the large sample size, the average of all EV-AC distances is used. As a representative value for the whole, it is approximately calculated as follows:

[0145]

[0146] (3) Calculate the load complementarity: Calculate the load complementarity index based on the historical load sequence.

[0147]

[0148] in, For load complementarity; Aggregate historical loads for electric vehicles at the k-th time point; The central air conditioning aggregated historical load is the k-th time point; L is the number of historical time points.

[0149] The total aggregate load of electric vehicles within the 30-day statistical window was calculated from historical data of S1. The total aggregate load of the central air conditioning system is .but:

[0150]

[0151] The calculated load overlap ratio is as follows:

[0152]

[0153] By inverse calculation, we can obtain:

[0154]

[0155] Substituting into the load complementarity formula:

[0156]

[0157] This indicates that electric vehicles and central air conditioning have a moderately strong complementary relationship in terms of power time series, which can achieve peak shaving and valley filling effects to a certain extent.

[0158] (4) Calculate the overall coupling coefficient:

[0159]

[0160] in, This refers to the overall coupling coefficient; For time coupling degree; Spatial coupling degree; For load complementarity; , , The corresponding weighting coefficients for the time coupling degree, spatial coupling degree, and load complementarity degree are set respectively, and satisfy the following conditions: Preferably, the values ​​are set to 0.4, 0.3, and 0.3 respectively.

[0161] Substitute and calculate step by step:

[0162]

[0163]

[0164] In S5, the aggregation adjustable capacity potential assessment model is as follows:

[0165]

[0166] in, The total adjustable capacity takes into account coupling characteristics; Let be the adjustable capacity of the i-th electric vehicle; Let j be the adjustable capacity of the j-th central air conditioner. The coupling loss coefficient is set; is the overall coupling coefficient; N is the total number of electric vehicles; M is the total number of central air conditioning units.

[0167] For example, based on the adjustable capacity and coupling characteristics of a single unit, as described above and Calculation results:

[0168]

[0169]

[0170] First, calculate the amount deducted at the end of the aggregation:

[0171]

[0172] Calculate the product of coupling loss coefficients:

[0173]

[0174] Calculate the deductions:

[0175]

[0176] Substitute into the main formula:

[0177]

[0178] In this embodiment (urban weekdays, the next 24 hours), Under these conditions, the tunable capacity potential of the aggregate after considering coupling loss is approximately [value missing]. MW.

[0179] This embodiment fully demonstrates the innovation and superiority of the present invention in the assessment of adjustable capacity for flexible loads. Traditional methods typically use the summation of the independent capacities of electric vehicles and central air conditioning, failing to consider the coupling effects of the two types of loads in terms of time, space, and operating characteristics. This easily leads to duplicate capacity calculations and scheduling mismatches, resulting in assessment results that are overestimated by approximately 8% to 12%. The present invention, however, introduces three-dimensional indices of time coupling degree, spatial coupling degree, and load complementarity, establishing an aggregated adjustable capacity model that considers time delay characteristics and electric vehicle battery characteristics, achieving accurate quantification of the collaborative constraint relationships between different resources. Experimental results show that, under the same data conditions, the adjustable capacity obtained by the traditional independent superposition method is 33 MW, while the result obtained by the present invention after comprehensive coupling correction is 29.67 MW, reducing the error by approximately 10%. The deviation between the predicted result and the actual scheduling feasibility is reduced from 12.4% to 4.8%, improving the capacity assessment accuracy by approximately 61%. Furthermore, this method was simulated and validated under typical high-temperature, high-load day scenarios. Compared to traditional static assessment methods, this method improves the load response rate by approximately 15% and the peak-shaving effect by approximately 1.3 MW, effectively avoiding duplicate responses and resource conflicts. In summary, this invention, by constructing an assessment framework that considers the dynamic coupling relationship between electric vehicles and central air conditioning, outperforms existing technologies in terms of model structure, data association, and assessment accuracy, providing more accurate and reliable technical support for the flexible resource scheduling of urban integrated energy systems.

[0180] A system for implementing the aforementioned method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads includes:

[0181] Data acquisition module: used to collect real-time and historical data of electric vehicles and central air conditioning through smart meters, charging pile controllers, battery management systems, building automation systems and GPS devices;

[0182] Characteristic Modeling Module: Used for calculating the adjustable capacity assessment model of electric vehicles that considers the battery health status and battery temperature correction, and the adjustable capacity assessment model of central air conditioning that considers user comfort constraints and air conditioning time lag effect.

[0183] Coupling analysis module: calculates temporal coupling degree, spatial coupling degree, load complementarity degree, and overall coupling coefficient;

[0184] Potential Assessment Module: Constructs and calculates aggregate adjustable capacity potential assessment models and calculates adjustable capacity potential assessment results;

[0185] Results output module: Used to output the results of adjustable capacity potential assessment, and can generate adjustable capacity curves and reports.

[0186] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented 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. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0187] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0188] 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 that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0189] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0190] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0191] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0192] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads, characterized in that, Includes the following steps: Establish a data acquisition system to collect data on electric vehicle battery status, charging behavior, central air conditioning operating parameters, and indoor and outdoor environmental data, and perform data preprocessing. Based on the preprocessed data, considering the battery health status and battery temperature correction of electric vehicles, an adjustable capacity assessment model for electric vehicles is established. Based on the preprocessed data, considering user comfort constraints and air conditioning time lag effects, an adjustable capacity assessment model for central air conditioning is established. A coupling characteristic analysis including time coupling degree, spatial coupling degree, and load complementarity degree is constructed to quantify the mutual influence between central air conditioning and electric vehicle load. Based on the adjustable capacity assessment model for electric vehicles, the adjustable capacity assessment model for central air conditioning, and the coupling characteristic analysis, an aggregated adjustable capacity potential assessment model is established to calculate the total adjustable capacity considering the coupling effect and output the adjustable capacity potential assessment results.

2. The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads according to claim 1, characterized in that, The data acquisition system collects data through data acquisition devices, including smart meters, charging pile controllers, battery management systems, building automation systems, and GPS positioning devices.

3. The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads according to claim 1, characterized in that, Data preprocessing methods include missing value imputation, outlier removal, and data alignment. The collected data labels i and j represent the i-th electric vehicle and the j-th central air conditioner, respectively, where i = 1, 2, ..., N, j = 1, 2, ..., M, and N and M are the total number of electric vehicles and central air conditioners, respectively. The data acquisition system collects real-time operating data and historical load data from the central air conditioning control system, the electric vehicle charging station management system, and the power grid dispatching system, including the charging start time of the i-th electric vehicle. Charging end time Battery voltage Current state of charge Charging power Historical charging power sequence Battery health status Battery internal resistance Battery temperature Maximum allowable charging current And the operating mode of the jth central air conditioner. Set temperature Indoor temperature Current thermal / cold storage status Rated power Historical operating power sequence Thermal time constant Compressor start / stop status Last state transition time Simultaneously, the geographical coordinates of electric vehicle charging points and central air conditioning units are collected, and the geographical distance between the i-th electric vehicle and the j-th central air conditioning unit is determined. 。 4. The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads according to claim 1, characterized in that, The adjustable capacity assessment model for electric vehicles is as follows: ;in, Let be the adjustable capacity of the i-th electric vehicle; The charging power for the i-th electric vehicle; Let be the battery voltage of the i-th electric vehicle; The maximum allowable charging current for the i-th electric vehicle; Let be the current state of charge of the i-th electric vehicle; This represents the maximum state of charge of the battery of the i-th electric vehicle. The charging efficiency of the i-th electric vehicle is set to 0.

95. Let i be the time availability function for the i-th electric vehicle, when The value is 1 if the condition is met, and 0 otherwise. The battery health status correction factor is calculated using the following formula: ;in, Battery health status ； The battery temperature correction factor is calculated using the following formula: ;in, Battery temperature; The set optimal operating temperature for the battery; The set temperature standard deviation; The battery internal resistance correction factor is calculated using the following formula: ;in, This refers to the battery's internal resistance. The set internal resistance of the new battery.

5. The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads according to claim 4, characterized in that, The central air conditioning adjustable capacity assessment model is as follows: ;in, Let j be the adjustable capacity of the j-th central air conditioner. Let J be the rated power of the j-th central air conditioner. Let J be the indoor temperature of the j-th central air conditioner. Set the temperature for the j-th central air conditioner; The maximum allowable temperature deviation for the j-th central air conditioner is set. This represents the current heat / cold storage status of the j-th central air conditioner. The user comfort constraint coefficient is set; The maximum heat / cold storage state of the j-th central air conditioner is set to 1.0; Let be the operating mode coefficient of the j-th central air conditioner, which is 1 when cooling and -1 when heating; to introduce the time lag effect, a thermal inertia correction factor is used in the central air conditioning adjustable capacity assessment model. With start / stop delay correction factor It characterizes the time lag properties of the system in its dynamic response; thermal inertia correction factor The formula used to describe the response delay of indoor temperature changes to control commands is: ;in, For the first The thermal time constant of a central air conditioning system reflects the lag rate of temperature change. To control the time step; compressor start / stop delay correction factor The formula used to characterize the control band effect of a compressor during frequent start-stop operations is as follows: ;in, For the first The last time the central air conditioning unit switched states; This indicates the current start / stop status of the compressor. This is the minimum start-stop interval for the compressor.

6. The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads according to claim 1, characterized in that, The calculation methods for the temporal coupling degree, spatial coupling degree, and load complementarity degree are as follows: Calculate the temporal coupling degree coefficient: The temporal coupling degree is directly defined as the Pearson correlation coefficient of the aggregated load sequence of electric vehicles and central air conditioning within a historical time window. The calculation formula is as follows: ； ； ； ； ;in, For time coupling degree; For the first Aggregated historical load of electric vehicles at a given time point; For the first Aggregated historical load of central air conditioning at a given time point; This represents the historical average load of electric vehicles. This represents the historical average load of the central air conditioning system. This represents the total number of historical time points within the statistics window; when When the load changes over time, it indicates that the central air conditioning and electric vehicle loads show consistent trends, indicating a strong synergistic effect; when When the load changes of the central air conditioning and the electric vehicle are in opposite directions, they have complementary characteristics; when The closer the coefficient is to 1, the tighter the temporal coupling relationship. The spatial coupling coefficient is calculated as follows: The spatial coupling coefficient is directly defined as the average distance attenuation coefficient between the electric vehicle charging point and the central air conditioning equipment in geographical space. This measures the degree of aggregation of the central air conditioning and electric vehicle loads in physical space. The calculation formula is: ;in, Spatial coupling degree; For the first electric vehicles and the first Geographical distance between central air conditioning units in Taiwan; This is the spatial standard deviation parameter, used to control the decay rate; For the number of electric vehicles; For the number of central air conditioning units; when When the value approaches 1, it indicates that the central air conditioning and electric vehicle loads are highly concentrated and strongly coupled in space; when... When the value is close to 0, it indicates a discrete spatial distribution and weak coupling. The load complementarity coefficient is calculated as follows: The load complementarity is directly defined as the complementarity index of the load complementarity relationship between electric vehicles and central air conditioning within the same time period, characterizing whether the loads of central air conditioning and electric vehicles have peak-shifting complementary characteristics in the power time series; the calculation formula is as follows: ;in, For load complementarity; For the first Aggregate load of electric vehicles at a given time point; For the first The aggregate load of central air conditioning at a given time point; The total number of historical time points; when When the value is close to 1, it indicates that the power curves of the central air conditioning and the electric vehicle load are highly complementary. When the value is close to 0, it indicates that the load changes of the central air conditioning and electric vehicles are highly synchronized and have weak complementarity.

7. The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads according to claim 6, characterized in that, The coupling characteristic analysis also includes calculating the comprehensive coupling coefficient: ;in, This refers to the overall coupling coefficient; This is the time coupling coefficient; This is the spatial coupling coefficient; This is the load complementarity coefficient; 、 、 The corresponding weighting coefficients for the time coupling degree, spatial coupling degree, and load complementarity degree are set respectively, and satisfy the following conditions: 。 8. The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads according to claim 7, characterized in that, The model for assessing the potential of adjustable aggregate capacity is as follows: ;in, The total adjustable capacity takes into account coupling characteristics; Let be the adjustable capacity of the i-th electric vehicle; Let j be the adjustable capacity of the j-th central air conditioner. The set coupling loss coefficient; is the overall coupling coefficient; N is the total number of electric vehicles; M is the total number of central air conditioning units.

9. The method for assessing the adjustable capacity potential of central air conditioning and electric vehicle loads according to claim 6, characterized in that, Degree of temporal coupling Normalization is performed as follows This includes linearly mapping it to an interval. The normalization formula is: 。 10. A system for implementing the method for assessing the adjustable capacity potential of central air conditioning and electric vehicles according to any one of claims 1-9, characterized in that, include: Data Acquisition Module: Used to collect real-time and historical data of electric vehicles and central air conditioning through smart meters, charging pile controllers, battery management systems, building automation systems, and GPS devices; Characteristic Modeling Module: Used to calculate the adjustable capacity assessment model of electric vehicles considering the battery health status and battery temperature correction, and the adjustable capacity assessment model of central air conditioning considering user comfort constraints and air conditioning time lag effects; Coupling Analysis Module: Calculates the time coupling degree, spatial coupling degree, load complementarity degree, and comprehensive coupling coefficient; Potential Assessment Module: Constructs and calculates the aggregated adjustable capacity potential assessment model and calculates the adjustable capacity potential assessment results; Result Output Module: Used to output the adjustable capacity potential assessment results.

Images