Method and device for predicting electrical load response potential, and storage medium

Abstract

The application discloses a kind of with electric load response potential prediction method, device and storage medium.Therein, the method includes: obtaining the multiple calculation modes corresponding to the system temperature initial setting value of power system, multiple calculation modes are and multiple predetermined temperature adjustment values one-to-one corresponding to the way of calculating the electric load response potential according to ambient temperature, predetermined temperature adjustment value is the temperature value for adjusting system temperature initial setting value, determine the target system temperature initial setting value, target temperature adjustment value and target ambient temperature of power system, according to target system temperature initial setting value, target temperature adjustment value and multiple calculation modes corresponding to multiple system temperature initial setting values respectively determine target calculation mode, according to target ambient temperature and target calculation mode, obtain the electric load response potential of target object.The application solves the technical problem that the electric load response potential in the power consumption side is not predicted in the related art.

Description

Technical Field

[0001] This invention relates to the field of power control, and more specifically, to a method, apparatus, and storage medium for predicting the response potential of electrical loads. Background Technology

[0002] As the party responsible for instantaneous power balance in the power system, the load characteristics and behavior of the electricity consumer side significantly influence the security and stability of the power grid. The difficulty of power grid dispatching and operation increases with the continuous growth of peak power loads and the rapid development of intermittent energy sources. This poses new and significant challenges to the power system's regulation capabilities, necessitating the prediction of the electricity load response potential on the consumer side and conducting grid dispatching based on the prediction results. However, current technologies lack methods for predicting the electricity load response potential on the consumer side.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and storage medium for predicting the response potential of electricity load, thereby at least addressing the technical problem in the related art of lacking a method for predicting the response potential of electricity load on the electricity consumption side.

[0005] According to one aspect of the present invention, a method for predicting electricity load response potential is provided, comprising: determining a plurality of initial system temperature settings for a target object's electricity system; for each of the plurality of initial system temperature settings: acquiring a plurality of calculation methods corresponding to the initial system temperature settings, wherein the plurality of calculation methods are a plurality of methods for calculating electricity load response potential based on ambient temperature, the plurality of calculation methods correspond one-to-one with a plurality of predetermined temperature adjustment values, the predetermined temperature adjustment values ​​being temperature values ​​used to adjust the initial system temperature settings; determining a target initial system temperature setting, a target temperature adjustment value, and a target ambient temperature for the electricity system, wherein the target initial system temperature setting is one of the plurality of initial system temperature settings, and the target temperature adjustment value is one of the plurality of predetermined temperature adjustment values; determining a target calculation method based on the target initial system temperature setting and the target temperature adjustment value, and the plurality of calculation methods corresponding to the plurality of initial system temperature settings; and acquiring the electricity load response potential of the target object based on the target ambient temperature and the target calculation method.

[0006] Optionally, for each of the multiple initial system temperature settings of the power system, obtaining multiple calculation methods corresponding to the initial system temperature setting includes: determining multiple initial system temperature settings of the power system of the target object on a typical weather day; for each of the multiple initial system temperature settings: obtaining a first load curve corresponding to the initial system temperature setting, and multiple second load curves corresponding one-to-one with the multiple predetermined temperature adjustment values; wherein, the first load curve is obtained based on the power load of the power system at multiple predetermined times on the typical weather day when the set temperature of the power system is the initial system temperature setting; each of the multiple second load curves is obtained based on the power load of the power system at the multiple predetermined times on the typical weather day when the set temperature of the power system is the sum of the initial system temperature setting and the corresponding predetermined temperature adjustment value; and obtaining multiple calculation methods corresponding to the initial system temperature setting based on the first load curve corresponding to the initial system temperature setting, the multiple second load curves corresponding one-to-one with the multiple predetermined temperature adjustment values, and the temperature curve of the typical weather day.

[0007] Optionally, acquiring the first load curve corresponding to the initial system temperature setting value and the multiple second load curves corresponding one-to-one with the multiple predetermined temperature adjustment values ​​includes: acquiring multiple historical load curves and multiple historical temperature curves corresponding to the multiple typical weather days, respectively, when the set temperature of the target object's power system is the multiple initial system temperature setting values; the multiple typical weather days are obtained by dividing the multiple dates based on meteorological data and social factors within a predetermined historical time period, wherein the meteorological data includes ambient temperature and the social factors include the multiple... The dates are either working days or non-working days; based on multiple historical load curves and multiple historical temperature curves of the multiple typical meteorological days, the correspondence model between electricity load and the set temperature of the power system is optimized to obtain a thermodynamic simulation model; the historical load curves are obtained by fitting the electricity load of the power system at multiple times on the corresponding typical meteorological days, and the historical temperature curves are obtained by fitting the ambient temperature at multiple times on the corresponding typical meteorological days; based on the thermodynamic simulation model, a first load curve corresponding to the initial set value of the system temperature and multiple second load curves corresponding one-to-one with the multiple predetermined temperature adjustment values ​​are obtained.

[0008] Optionally, the step of acquiring multiple historical load curves and multiple historical temperature curves corresponding to the multiple typical weather days for the target object, where the set temperature of the target object's power system is a multiple initial system temperature setpoint, includes: for each typical weather day for the target object, where the set temperature of the power system is a multiple initial system temperature setpoint, collecting the power load of the power system at multiple predetermined times on the typical weather day, and fitting the historical load curve based on the multiple power loads and the corresponding times; for each typical weather day for the target object, collecting the ambient temperature at multiple predetermined times on the typical weather day, and fitting the historical temperature curve based on the multiple ambient temperatures and the corresponding times.

[0009] Optionally, the step of optimizing the correspondence model between electricity load and the set temperature of the power system based on multiple historical load curves and multiple historical temperature curves on multiple typical meteorological days to obtain a thermodynamic simulation model includes: for each typical meteorological day among the multiple typical meteorological days, obtaining the historical temperature curve corresponding to the typical meteorological day; based on the historical temperature curve and the correspondence model between electricity load and the set temperature of the power system, obtaining the predicted load curve of the target object on the typical meteorological day when the system set temperature is the initial set value of the system temperature; and optimizing the correspondence model between electricity load and the set temperature of the power system based on the predicted load curve and the corresponding historical load curve to obtain a thermodynamic simulation model.

[0010] Optionally, it further includes: constructing a correspondence model between electrical load and electrical system set temperature based on the heat capacity of the target building, the system temperature setpoint of the target electrical system, the cooling power of multiple loads in the target electrical system, the sum of the heat conduction of all external surfaces of the target building, the surface convective heat transfer coefficients of multiple external surfaces of the target building, the area of ​​multiple external surfaces of the target building, the temperature of the external surfaces of the target building, the internal ambient temperature and air heat exchange coefficient of the target building, and the heat capacity of the air in the target building.

[0011] Optionally, the correspondence model between the electrical load and the set temperature of the electrical system includes:

[0012]

[0013] Among them, C Z T is the heat capacity of the building of the target object. ZThe system temperature setpoint for the HVAC system of the target object, where t is time; Q i Let i be the cooling power of the i-th load. For all N in the building of the target object si The sum of the cooling power of each load; For all N in the building of the target object surface The sum of heat conducted through each outer surface, h j Let A be the surface convective heat transfer coefficient of the j-th outer surface of the target building. j T is the area of ​​the j-th outer surface of the building of the target object. sup T is the temperature of the outer surface of the building of the target object. si The internal ambient temperature of the target building is m. inf C p (T si -T Z ) represents the heat exchange rate per unit time between the load and the internal environment of the target building, in m. inf C is the air heat exchange coefficient. p The heat capacity of the air in the building containing the target object, m inf C p (T sup -T Z ) represents the heat exchange rate between the internal and external environments of the building containing the target object per unit time.

[0014] Optionally, determining the target temperature adjustment value for the target object includes: acquiring historical behavioral data of the target object's participation in demand response, and determining the target temperature adjustment value corresponding to the target object based on the historical behavioral data; or, acquiring survey data of the target object's participation in demand response, and determining the target temperature adjustment value corresponding to the target object based on the survey data.

[0015] According to another aspect of the embodiments, a device for predicting electricity load response potential is also provided, comprising: a first determining module, configured to determine multiple initial system temperature settings of an electricity system of a target object; a first acquiring module, configured to, for each of the multiple initial system temperature settings of the electricity system, acquire multiple calculation methods corresponding to the initial system temperature settings, wherein the multiple calculation methods are multiple methods for calculating electricity load response potential based on ambient temperature, the multiple calculation methods correspond one-to-one with multiple predetermined temperature adjustment values, and the predetermined temperature adjustment values ​​are temperature values ​​used to adjust the initial system temperature settings; a second determining module, configured to determine a target initial system temperature setting, a target temperature adjustment value, and a target ambient temperature of the electricity system, wherein the target initial system temperature setting is one of the multiple initial system temperature settings, and the target temperature adjustment value is one of the multiple predetermined temperature adjustment values; a third determining module, configured to determine a target calculation method based on the target initial system temperature setting and the target temperature adjustment value, and the multiple calculation methods corresponding to the multiple initial system temperature settings; and a second acquiring module, configured to acquire the electricity load response potential of the target object based on the target ambient temperature and the target calculation method.

[0016] According to another aspect of the present invention, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein, when the program is executed, the device on which the storage medium is located executes the power load response potential prediction method described in any one of the preceding embodiments.

[0017] In this embodiment of the invention, multiple initial system temperature settings for the target object's power system are determined. For each initial system temperature setting, multiple calculation methods corresponding to the initial system temperature setting are obtained. These multiple calculation methods are methods for calculating the power load response potential based on ambient temperature. Each calculation method corresponds one-to-one with multiple predetermined temperature adjustment values, where the predetermined temperature adjustment value is the temperature value used to adjust the initial system temperature setting. A target initial system temperature setting, a target temperature adjustment value, and a target ambient temperature are determined for the target object's power system. The target initial system temperature setting is one of the multiple initial system temperature settings, and the target temperature adjustment value is one of the multiple predetermined temperature adjustment values. A target calculation method is determined based on the target initial system temperature setting and the target temperature adjustment value, as well as the multiple calculation methods corresponding to the multiple initial system temperature settings. The power load response potential of the target object is obtained based on the target ambient temperature and the target calculation method. This solves the technical problem in related technologies of lacking a method for predicting the power load response potential on the power consumption side. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 This is a flowchart of an optional method for predicting the electrical load response potential according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of a historical load curve and a historical temperature curve according to an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of another historical load curve and historical temperature curve according to an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram illustrating the prediction results of a user's electricity load response potential according to an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram illustrating the predicted response potential of an electrical load at different time periods according to an embodiment of the present invention.

[0024] Figure 6 This is a schematic diagram illustrating the response potential prediction results of another electrical load at different time periods according to an embodiment of the present invention;

[0025] Figure 7 This is a schematic diagram illustrating the response potential prediction results of another type of electrical load at different time periods according to an embodiment of the present invention;

[0026] Figure 8 This is a schematic diagram illustrating the relationship between ambient temperature and response potential according to an embodiment of the present invention;

[0027] Figure 9 This is a schematic diagram illustrating another relationship between ambient temperature and response potential according to an embodiment of the present invention;

[0028] Figure 10 This is a schematic diagram illustrating another relationship between ambient temperature and response potential according to an embodiment of the present invention;

[0029] Figure 11 This is a schematic diagram illustrating another relationship between ambient temperature and response potential according to an embodiment of the present invention;

[0030] Figure 12 This is a framework diagram of an optional power load response potential prediction device according to an embodiment of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] Example 1

[0034] According to an embodiment of the present invention, a method embodiment for predicting the adjustable potential of electrical load is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0035] Figure 1 This is a flowchart of an optional method for predicting the electrical load response potential according to an embodiment of the present invention.

[0036] Reference Figure 1 As shown, the method may include the following steps:

[0037] Step S102: Determine the initial temperature settings of multiple systems in the power system of the target object.

[0038] In some alternative embodiments, the target object can be of various types, such as commercial buildings, non-commercial buildings, etc.

[0039] In some alternative embodiments, the electrical system can be of various types, including, for example, HVAC (Heating, Ventilation, Air-conditioning and Cooling) systems, lighting systems, and so on.

[0040] Step S104: For each of the multiple initial system temperature settings of the power system: obtain multiple calculation methods corresponding to the initial system temperature setting, wherein the multiple calculation methods are multiple ways to calculate the power load response potential based on the ambient temperature, and the multiple calculation methods correspond one-to-one with multiple predetermined temperature adjustment values, and the predetermined temperature adjustment value is the temperature value used to adjust the initial system temperature setting.

[0041] Step S106: Determine the target system temperature initial setting value, target temperature adjustment value, and target ambient temperature of the target object's power system. The target system temperature initial setting value is one of multiple system temperature initial setting values, and the target temperature adjustment value is one of multiple predetermined temperature adjustment values.

[0042] Step S108: Determine the target calculation method based on the target system temperature initial setting value and the target temperature adjustment value, as well as the multiple calculation methods corresponding to the multiple system temperature initial setting values.

[0043] Step S110: Based on the target ambient temperature and the target calculation method, obtain the electrical load response potential of the target object.

[0044] In this optional embodiment, multiple initial system temperature settings for the target object's power system are determined. For each initial system temperature setting, multiple calculation methods corresponding to the initial system temperature setting are obtained. These multiple calculation methods are methods for calculating the power load response potential based on ambient temperature. Each calculation method corresponds one-to-one with multiple predetermined temperature adjustment values, where the predetermined temperature adjustment value is the temperature value used to adjust the initial system temperature setting. A target initial system temperature setting, a target temperature adjustment value, and a target ambient temperature are determined for the target object's power system. The target initial system temperature setting is one of the multiple initial system temperature settings, and the target temperature adjustment value is one of the multiple predetermined temperature adjustment values. A target calculation method is determined based on the target initial system temperature setting and the target temperature adjustment value, as well as the multiple calculation methods corresponding to the multiple initial system temperature settings. The power load response potential of the target object is obtained based on the target ambient temperature and the target calculation method. This solves the technical problem in related technologies of lacking a method for predicting the power load response potential on the power consumption side.

[0045] As an optional embodiment, a method for obtaining multiple calculation methods corresponding to each of the multiple initial system temperature settings of an electrical system may include the following steps: determining multiple initial system temperature settings of the target electrical system on a typical weather day; for each of the multiple initial system temperature settings: obtaining a first load curve corresponding to the initial system temperature setting, and multiple second load curves corresponding one-to-one with multiple predetermined temperature adjustment values; wherein, the first load curve is obtained based on the electrical load of the electrical system at multiple predetermined times on a typical weather day when the set temperature of the electrical system is the initial system temperature setting; each of the multiple second load curves is obtained based on the electrical load of the electrical system at multiple predetermined times on a typical weather day when the set temperature of the electrical system is the sum of the initial system temperature setting and the corresponding predetermined temperature adjustment value; and obtaining multiple calculation methods corresponding to the initial system temperature setting based on the first load curve corresponding to the initial system temperature setting, the multiple second load curves corresponding one-to-one with the multiple predetermined temperature adjustment values, and the temperature curve of the typical weather day. By determining a first load curve corresponding to the initial system temperature setpoint, and adjusting multiple predetermined temperature adjustment values ​​based on the initial system temperature setpoint, a second load curve corresponding to each predetermined temperature adjustment value is obtained. Based on the difference between the first and second load curves, the power load response potential at multiple times is obtained. The ambient temperature at multiple times is obtained based on meteorological curves. Based on the power load response potential at multiple times and the ambient temperature values, a calculation method for calculating the power load response potential based on the ambient temperature is obtained. Thus, the correspondence between ambient temperature and power load response potential can be accurately obtained. In an optional embodiment, multiple initial system temperature setpoints for the target object's power system are determined on a typical meteorological day. For each of the multiple temperature adjustment values ​​corresponding to each initial system temperature setpoint: based on the corresponding first and second load curves, the power load response potential of the target object's power system at multiple times within a predetermined time period is obtained, and multiple ambient temperatures of the area where the target object is located within the predetermined time period are obtained. Based on the multiple power load response potentials and multiple ambient temperatures, a corresponding method for calculating the power load response potential based on the ambient temperature is obtained. Therefore, the correlation between ambient temperature and electrical load response potential can be accurately obtained.

[0046] In one optional embodiment, based on the first load curve and the second load curve, the difference in electricity load at each predetermined time point within a series of predetermined times on a typical weather day is obtained. The corresponding electricity load response potential is then calculated based on the electricity load difference. Based on the temperature curve of a typical weather day, the ambient temperature value at each predetermined time point within a series of predetermined times on the corresponding typical weather day is obtained. Based on the multiple electricity load response potentials and the corresponding ambient temperature values, a correlation between the ambient temperature and the electricity load response potential can be fitted. This correlation is the method for calculating the electricity load response potential based on the ambient temperature.

[0047] In one optional embodiment, multiple initial system temperature setpoints for the target object's power system are determined on multiple typical weather days. For each initial system temperature setpoint on each typical weather day, multiple predetermined temperature adjustment values ​​corresponding to each initial system temperature setpoint are determined, and the power load response potential corresponding to each of the multiple predetermined temperature adjustment values ​​is obtained. For each predetermined temperature adjustment value corresponding to each initial system temperature setpoint: based on the power load response potential corresponding to the multiple typical weather days and the ambient temperature value, a corresponding method for calculating the power load response potential based on the ambient temperature is obtained. The method for calculating the power load response potential based on ambient temperature, obtained from data of multiple typical weather days, has high accuracy.

[0048] As an optional embodiment, obtaining a first load curve corresponding to the initial system temperature setting value and multiple second load curves corresponding one-to-one with multiple predetermined temperature adjustment values ​​may include the following steps: obtaining multiple historical load curves and multiple historical temperature curves corresponding to multiple typical weather days for the target object, where the set temperature of the target object's power system is the initial system temperature setting value; wherein, the multiple typical weather days are obtained by dividing multiple dates based on meteorological data and social factors within a predetermined historical time period, the meteorological data including ambient temperature, and the social factors including whether the multiple dates are working days or non-working days; optimizing the correspondence model between power load and power system set temperature based on the multiple historical load curves and multiple historical temperature curves of the multiple typical weather days to obtain a thermodynamic simulation model; the historical load curves are obtained by fitting the power load of the power system at multiple times on the corresponding typical weather days, and the historical temperature curves are obtained by fitting the ambient temperature at multiple times on the corresponding typical weather days; obtaining the first load curve corresponding to the initial system temperature setting value and multiple second load curves corresponding one-to-one with multiple predetermined temperature adjustment values ​​based on the thermodynamic simulation model. The model of the correspondence between electricity load and the set temperature of the power system is optimized based on historical load curves and historical temperature curves to obtain a thermodynamic simulation model. The thermodynamic simulation model has high accuracy, and the first load curve and the second load curve can be accurately obtained based on this thermodynamic simulation model.

[0049] As an optional embodiment, acquiring multiple historical load curves and multiple historical temperature curves corresponding to multiple typical weather days for the target object, assuming the set temperature of the target object's power system is one of multiple initial system temperature setpoints, may include the following steps: For each typical weather day for the target object, with the set temperature of the power system being one of the multiple initial system temperature setpoints, collecting the power load of the power system at multiple predetermined times on the typical weather day, and fitting a historical load curve based on the multiple power loads and corresponding times; For each typical weather day for the target object, collecting the ambient temperature at multiple predetermined times on the typical weather day, and fitting a historical temperature curve based on the multiple ambient temperatures and corresponding times. Based on the collected power load and ambient temperature data, the historical load curve and historical temperature curve can be accurately obtained.

[0050] As an optional embodiment, based on multiple historical load curves and multiple historical temperature curves over multiple typical weather days, the correspondence model between electricity load and the set temperature of the power system is optimized to obtain a thermodynamic simulation model. This includes: for each typical weather day among the multiple typical weather days, obtaining the historical temperature curve corresponding to the typical weather day; based on the correspondence model between the historical temperature curve and the electricity load and the set temperature of the power system, obtaining the predicted load curve of the target object on the typical weather day, with the system set temperature at the initial set value of the system temperature; and optimizing the correspondence model between the electricity load and the set temperature of the power system based on the predicted load curve and the corresponding historical load curve to obtain a thermodynamic simulation model. In an optional embodiment, based on the comparison difference between the predicted load curve and the historical load curve, the parameters in the correspondence model between the electricity load and the set temperature of the power system are optimized to obtain an accurate thermodynamic simulation model.

[0051] As an optional embodiment, the method further includes: constructing a correspondence model between electrical load and electrical system setpoint temperature based on the heat capacity of the target building, the system temperature setpoint of the target building's electrical system, the cooling power of multiple loads in the target building's electrical system, the sum of heat conduction from all external surfaces of the target building, the surface convective heat transfer coefficients of multiple external surfaces of the target building, the area of ​​multiple external surfaces of the target building, the temperature of the external surfaces of the target building, the internal ambient temperature and air heat exchange coefficient of the target building, and the heat capacity of the air in the target building. Based on the aforementioned multiple parameters related to the building, a correspondence model between electrical load and electrical system setpoint temperature for different buildings can be accurately obtained. Based on this model, an electrical load curve corresponding to the electrical system setpoint temperature can be accurately obtained.

[0052] As an optional embodiment, the correspondence model between electrical load and the set temperature of the electrical system includes:

[0053]

[0054] Among them, C Z For the heat capacity of the target building, T Z Q represents the system temperature setpoint of the target HVAC system, where t is time; i Let i be the cooling power of the i-th load. For all N in the target object's buildings si The sum of the cooling power of each load; For all N in the target object's buildings surface The sum of heat conducted through each outer surface, h j Let A be the surface convective heat transfer coefficient of the j-th outer surface of the target building.j Let T be the area of ​​the j-th outer surface of the target building. sup T represents the temperature of the building's exterior surface. si The internal ambient temperature of the target building, m inf C p (T si -T Z (m) represents the heat exchange rate between the load and the internal environment of the target building per unit time. inf C is the air heat exchange coefficient. p m is the heat capacity of the air in the building of the target object. inf C p (T sup -T Z ) represents the heat exchange rate between the internal and external environments of the target building per unit time.

[0055] As an optional embodiment, determining the target temperature adjustment value for the target object includes at least one of the following: acquiring historical behavioral data of the target object's participation in demand response, and determining the target temperature adjustment value corresponding to the target object based on the historical behavioral data; acquiring survey data of the target object's participation in demand response, and determining the target temperature adjustment value corresponding to the target object based on the survey data. It should be understood that the target temperature adjustment value is the magnitude of the adjustment temperature that the target object intends to adjust to the set temperature of the power system when a demand response event occurs in the area where the target object is located, and when the current set temperature of the power system is the system temperature setpoint. Based on historical behavioral data or survey data, the target temperature adjustment value for the target object can be obtained quickly and accurately.

[0056] Based on the foregoing embodiments and optional embodiments, an optional implementation method for predicting electricity load response potential is provided. In this disclosure, the target object on the electricity consumption side is a commercial user, and the electricity system is an HVAC system as an example for illustration.

[0057] Openness and interaction are key characteristics of smart grids. As the component responsible for instantaneous power balancing within the power system, the load characteristics and behavior of the electricity consumer side significantly impact the grid's security and stability. The difficulty of grid dispatching and operation increases with the continuous growth of peak power loads and the rapid development of intermittent energy sources, posing new and significant challenges to the power system's regulation capabilities. Currently, there is a lack of methods for predicting the load response potential on the electricity consumer side.

[0058] Step 1: Obtain the historical load curve and historical temperature curve of a typical weather day for commercial users. After adjusting the system temperature setpoint, use a thermodynamic simulation model to solve for the power consumption and response potential of commercial users on a typical weather day.

[0059] It's important to understand that commercial users can be further categorized into administrative offices, commercial operations, finance, services, culture and entertainment, sports, education and research, healthcare, and so on. These industries primarily consume public utility loads, mainly air conditioning (HVAC) systems and lighting. HVAC and lighting power can be considered interruptible loads and participate in demand response management. The interruptible load capacity can be quantified by dividing it into HVAC interruptible capacity and lighting interruptible capacity.

[0060] Taking the research results of a certain laboratory as an example, we obtained the HVAC interruptibility and lighting interruptibility of different industries as shown in Table 1.

[0061] Table 1

[0062]

[0063] In this optional implementation, HVAC-related 2-hour and 20-minute interruptible load behaviors primarily include adjusting temperature setpoints, shutting down compressors in package units, using a reduction mode in facilities with multiple package units serving the same space, and completely shutting down some package units under normal control. A package unit can be a single electricity user; specifically, it can be a large commercial user or a group of multiple smaller users.

[0064] In this alternative implementation, the estimation of the interruptibility level for a given equipment type can depend on field testing during peak load periods and the HVAC's evaluation of the interruptibility potential for each target equipment type. It is assumed that a 2-hour interruptibility is similar to a 4-hour peak load in field testing. Testing has shown that, in most cases, a 2-hour reduction in availability is more than one-third higher than a 20-minute reduction. Furthermore, reduction levels for multiple time periods can be combined. For example, a rooftop unit can shut down its compressor (e.g., in small offices, warehouse office areas, schools, residences, office areas, and other facilities), achieving 60% HVAC-related 2-hour interruptibility plus 80% 20-minute interruptibility. Alternatively, 50% 2-hour interruptibility plus 20-minute interruptibility can be used for setpoint adjustments to reduce the power demand of commercial users such as large offices and university buildings; 30% 2-hour interruptibility plus 40% 20-minute interruptibility can be used for commercial users such as restaurants to shut down or adjust the compressor settings of rooftop units. It should be noted that for commercial users such as restaurants, the interruptibility limit may not include other facilities such as air conditioning within their premises. This is because these commercial users need to maintain a ventilation supply rate to balance the exhaust rate of the kitchen. Similarly, hospitals and medical facilities are not included in the measurement of interruptibility.

[0065] Interruption capability studies, based on field tests conducted on peak days in multiple commercial buildings, show that dimming or turning off lights during demand response periods (up to four hours) can reduce lighting electricity demand by 33%. The retail sector demonstrated a 25% interruptibility capability for lighting electricity. In contrast, restaurants and restrooms, where dimming or turning off lights is not possible, had a reduction capability of 0%.

[0066] Based on the above data, for most commercial users, the most significant interruptible load is concentrated in the building's air conditioning system, specifically the HVAC system used for cooling, heating, and ventilation. Therefore, in this optional embodiment, taking the HVAC system as an example and commercial users as the target group, the method for predicting the load response potential is explained. It should be understood that the electrical system in this optional embodiment is not limited to the HVAC system, but also includes other electrical systems such as lighting and water supply.

[0067] For each of several typical weather days, the following operations are performed: The temperature of the commercial user's HVAC system is set to a predetermined initial setpoint. The HVAC power load is collected every hour. This yields the power load of the commercial user's system at multiple predetermined times on a typical weather day, with the HVAC system temperature at the predetermined initial setpoint. Based on the collected power load data at these predetermined times, a historical load curve for the commercial user is generated. This provides the historical load curve for the commercial user on multiple typical weather days, corresponding to the set temperature of the HVAC system.

[0068] For each typical weather day among multiple typical weather days, perform the following operations: collect atmospheric temperature data of the area where the commercial user is located at multiple predetermined times, and generate the historical temperature curve of the commercial user based on the temperature data at multiple predetermined times.

[0069] It's important to understand that a typical weather day refers to multi-dimensional information generated by aggregating various meteorological parameters, such as atmospheric temperature, humidity, diurnal temperature range, sunshine duration, and season. Additionally, a typical weather day may include non-meteorological factors, such as social factors. Specifically, social factors can include weekdays, non-weekdays, and so on.

[0070] In this optional implementation, multiple typical weather days can be pre-generated, each of which may include multiple meteorological and social factor parameters of different dimensions. When predicting the user response potential for the current or future date, information on one or more of the closest typical weather days can be extracted from the dataset of multiple typical weather days. For example, the historical load curve and historical temperature curve corresponding to that typical weather day can be extracted. After extracting information on one or more of the closest typical weather days from the dataset of multiple typical weather days, the response potential corresponding to the current or future date can be predicted based on the historical load curve and historical temperature curve.

[0071] Figure 2 This is a schematic diagram of a historical load curve and a historical temperature curve according to an embodiment of the present invention; Figure 3 This is a schematic diagram of another historical load curve and historical temperature curve according to an embodiment of the present invention. (Refer to...) Figure 2 and Figure 3 As shown, the horizontal axis represents 24 time points within a typical meteorological day, and the vertical axis represents the total load power and temperature values, respectively.

[0072] Continue to refer to Figure 2 and Figure 3 As shown, Figure 2 and Figure 3 This corresponds to a typical office building. Figure 2 and Figure 3 The data are respectively compared to the data of two typical weather days during the summer months of July to September for this office building. According to Figure 2 The historical load and temperature curves shown indicate that the temperature reaches its lowest point of around 23 degrees Celsius at 5:00 AM and its highest point of around 34 degrees Celsius at 4:00 PM. For weekdays, the electricity load is relatively high from around 8:00 AM to around 10:00 PM, reaching its peak power of approximately 210 kW around 4:00 PM. Figure 3 On the typical weather day shown, the ambient temperature fluctuates between 24 and 29 degrees Celsius. This typical weather day is a non-working day, so the power consumption is relatively low. During the 24 hours of this typical weather day, the power consumption remains below 60 kW.

[0073] In this optional implementation, the thermodynamic model can be implemented using thermodynamic simulation tools such as EnergyPlus. EnergyPlus is a building energy consumption hourly simulation engine that includes integrated and synchronized load / system / equipment simulation methods. When using this software to calculate system load, the time step can be selected by the user, for example, a step of 10 to 15 minutes. In system simulation, EnergyPlus software automatically sets shorter step sizes (e.g., several seconds to 1 hour). The calculation modules include: shading module, natural lighting module, natural ventilation module, HVAC Template module, and HVAC air conditioning system module. Based on these calculation modules, an accurate thermodynamic model of the building can be established according to the building's physical structure and air conditioning system composition, and then used to simulate the building's heating, air conditioning, lighting, ventilation, and water usage processes.

[0074] To achieve modeling, the EnergyPlus thermodynamic simulation tool primarily inputs two types of data: building model data (.idf) and meteorological data (.epw). The building model data is a detailed description of the building's physical structure. Modules used to process this data include: auxiliary units, time units, material component units, design index units, internal load units, HVAC system units, and result output units. Meteorological data can utilize the International Weather for Energy Calculation (IWEC) data, which is suitable for hourly energy consumption simulation and can be used to simulate a building's annual hourly energy consumption.

[0075] A thermodynamic simulation model was used to simulate and solve the electricity load curve of a typical weather day for commercial users, and the results were compared with the historical load curve of a typical weather day for commercial users. The thermodynamic simulation model was then adjusted based on the comparison results. The thermodynamic simulation model is explained in detail below.

[0076] In one embodiment, the thermodynamic model is:

[0077]

[0078] Among them, C Z For the thermal capacity of commercial buildings, for commercial users For all N in commercial user buildings si The sum of the cooling power of each load; For all N in commercial user buildings surface The sum of heat conducted through each outer surface, h j A is the surface convective heat transfer coefficient of the building's exterior surface. jLet T be the area of ​​the j-th outer surface of the commercial user building. sup The temperature of the exterior surface of a commercial building, where m inf C p (T si -T Z (m) represents the heat exchange rate between the load and the internal environment of a commercial building per unit time. inf C is the air heat exchange coefficient. p For the heat capacity of air in commercial buildings, m inf C p (T sup -T Z T represents the heat exchange rate between the internal and external environments of a commercial building per unit time. Z The system temperature setpoint T for the HVAC system of the commercial user. sup T represents the temperature of the exterior surface of the commercial user building. si The temperature refers to the internal ambient temperature of the commercial user building, i.e., the temperature of the air inside the commercial user building.

[0079] It is important to understand that the aforementioned load cooling power can be used to distinguish the heat changes of the load itself caused by heat exchange and heat conduction between different parts of the thermodynamic model. The load cooling power is the power consumed by the load when it is in operation or working condition.

[0080] Based on the thermodynamic simulation model in this optional embodiment, and combined with meteorological and social factors of typical weather days for users, simulation can be achieved. The simulation results are compared with historical load curves. Based on the comparison results, the factors causing the differences between the simulation results and historical load curves are identified. Then, parameters such as the heat capacity of commercial user buildings and the heat capacity of air in commercial user buildings in the thermodynamic simulation model are adjusted to ensure that the differences between the simulation results obtained from the thermodynamic simulation model and the historical load curves meet preset conditions, thereby optimizing the thermodynamic simulation model.

[0081] When acquiring historical load curves for different system set temperatures, the predetermined initial set value can be adjusted upwards by 1°C, 2°C, or 3°C, or downwards by 1°C, 2°C, or 3°C, or other temperature adjustments can be made as needed. In one optional embodiment, the predetermined initial set value for the system temperature is 24°C.

[0082] In this optional embodiment, the initial setpoint can be adjusted by raising or lowering the temperature by a certain amount to regulate the system temperature setpoint. In practical applications, if an event requiring demand response occurs in the power grid, the willingness of one or more commercial users to participate in load regulation is determined based on historical demand response data or statistical data on willingness to participate. In one embodiment, if a user's willingness to participate in load regulation is strong, it can be assumed that the user can tolerate a fluctuation of approximately three degrees above or below the system temperature setpoint, i.e., a higher temperature range of regulation is acceptable. If the user's willingness to participate in load regulation is weak, it can be assumed that the user only accepts a temperature adjustment of approximately one degree above or below the setpoint.

[0083] In order to enable the subsequent calculation process to accurately predict the adjustment and response potential according to different user intentions, this optional embodiment pre-designs different temperature setpoint adjustment levels, thereby predicting the load under different adjustment levels, and then predicting the response potential based on the adjustment level.

[0084] Specifically, the system temperature setpoint T for HVAC systems used by commercial users. Z Adjustments are made, and the power consumption changes of commercial users on typical weather days are obtained through simulation. Based on the power consumption changes of commercial users on typical weather days, the response potential for typical weather days is generated.

[0085] Figure 4 This is a schematic diagram illustrating the prediction results of a user's electricity load response potential according to an embodiment of the present invention. (Refer to...) Figure 4 As shown, on a typical summer weather day (a workday), the HVAC system temperature setpoint was adjusted upwards and downwards to varying degrees. Based on a thermodynamic simulation model, the predicted changes in electrical load corresponding to different setpoint temperatures were obtained. Figure 4 When the temperature setpoint is increased, the load power decreases between 6:00 AM and 10:00 PM. The degree of decrease varies slightly depending on the extent of the temperature setpoint increase. Conversely, when the temperature setpoint is decreased, the load power increases significantly, with higher setpoint increases resulting in greater power increases. The calculation of temperature increases is included to illustrate the typical weather conditions on a winter day.

[0086] Figure 5 This is a schematic diagram illustrating the predicted response potential of an electrical load at different time periods according to an embodiment of the present invention. Figure 6 This is a schematic diagram illustrating the response potential prediction results of another electrical load at different time periods according to an embodiment of the present invention; Figure 7This is a schematic diagram illustrating the predicted response potential of an electrical load at different time periods according to another embodiment of the present invention. It should be understood that the response potential can be obtained based on the difference in predicted power before and after temperature setpoint adjustment. (Continue referring to...) Figure 5 As shown, when the temperature setpoint is increased by about 1 degree, the adjustment potential varies every 15 minutes. For example, between 8:00 and 8:15, the adjustment potential for current commercial users on a typical weather day is about 8%, while between 15:00 and 15:15, the adjustment potential is about 13%. (Continue to refer to...) Figure 6 and Figure 7 As shown, the adjustment potential varies depending on the degree of temperature setting adjustment.

[0087] Figure 5 , Figure 6 and Figure 7 This is a schematic diagram showing the distribution of the adjustment potential of a medium-sized office building during the summer peak load period. (Continue referring to...) Figure 5 , Figure 6 and Figure 7 As shown, during peak summer load periods, increasing the HVAC system temperature setpoint allows for a regulation potential of approximately 10% in a medium-sized office building, indicating significant controllable potential. However, the regulation potential varies depending on external temperature and other factors. When the HVAC system temperature setpoint is increased by 2°C and 3°C, the regulation potential of the office building is not significantly different from that when the setpoint is increased by 1°C, due to the saturation of the air conditioning cooling capacity. Limited by the air conditioning cooling capacity, even with further increases in the temperature control setpoint, the air conditioning has no further room for adjustment. Similarly, decreasing the HVAC system temperature setpoint by -2°C and -3°C also results in similar regulation potential for the office building, due to the saturation of the air conditioning heating capacity. Table 2 shows the response potential under different setpoint temperature variations, obtained by analyzing data from multiple typical weather days.

[0088] Table 2

[0089]

[0090] Step 2: Based on the solution results in Step 1, obtain the correlation between different system temperature setpoints and their corresponding response potentials, and fit the regression equation between temperature and response potential based on the correlation of multiple typical meteorological days.

[0091] Based on the results of the historical data collected and analyzed in step 1, a simplified regression relationship between the response potential of commercial users and the ambient temperature is fitted at different time periods. This regression relationship can accelerate the assessment of the response potential of large-scale building clusters. In one embodiment, commercial users include buildings.

[0092] In an optional embodiment, the current system temperature setpoint T Z The response potential of all typical weather days under the given conditions is extracted, and a regression function is obtained by fitting the temperature and response potential values ​​of typical weather days (equivalent to the calculation method in the aforementioned embodiment).

[0093] Taking a medium-sized office building as an example, a regression analysis was conducted on the building's response potential during the entire summer (July-September) using two peak load periods: 10:00-11:00 AM and 2:00-3:00 PM. It's important to note that during hot summer weather, the HVAC system normally operates in cooling mode. If the ambient temperature is low (e.g., below the air conditioner's set temperature), the air conditioner will shut down or remain in standby mode. In this state, the air conditioner consumes very little power and has no adjustment potential. Therefore, when the ambient temperature is low in summer, the air conditioner load will not participate in demand response. Increasing the temperature setpoint at this time would cause the HVAC system to switch to heating, which is neither energy-efficient nor reasonable. Therefore, this unreasonable data needs to be removed before regression analysis.

[0094] Figure 8 This is a schematic diagram illustrating the relationship between ambient temperature and response potential according to an embodiment of the present invention; Figure 9 This is a schematic diagram illustrating another relationship between ambient temperature and response potential according to an embodiment of the present invention; Figure 10 This is a schematic diagram illustrating another relationship between ambient temperature and response potential according to an embodiment of the present invention; Figure 11 This is a schematic diagram illustrating another relationship between ambient temperature and response potential according to an embodiment of the present invention. Figure 8 and Figure 9 When the system set temperature is increased by 1℃, regression functions are fitted during the morning peak electricity consumption period of 10:00-11:00 and the afternoon peak electricity consumption period of 14:00-15:00. The obtained regression functions are: Among them, P ot For the adjustment potential (i.e., response potential) of business users, θ a The outside temperature. Figure 10 and Figure 11 The regression function is obtained by fitting the peak electricity consumption periods of 10:00-11:00 AM and 14:00-15:00 PM respectively when the system's electricity load is increased by 2 kWh. This regression function is... The goodness of fit for both time periods was above 0.8, indicating that the fitting effect was good.

[0095] Step 3: Solve the regression equation based on the current temperature, and obtain the response potential of commercial users based on the number and scale of commercial users in the current region.

[0096] Based on demand response events within the region, temperature parameters are obtained to identify the response willingness and potential of each business user within the region, thereby calculating the region's response potential.

[0097] In this optional implementation, multiple buildings of the same type that may have the same regression equation in the entire area can have their aggregate response power value calculated based on the proportion of electricity consumption of a typical building of that type in the entire area after obtaining the regression equation. This method greatly speeds up the assessment of the aggregate response potential of large-scale building clusters.

[0098] Preferably, the system temperature setpoint T of the business user is obtained based on the user's willingness to respond. Z The adjustment method; and, obtain the system temperature setpoint T. Z The adjustment method corresponds to the response potential of business users.

[0099] In this optional embodiment, the temperature setpoint is adjusted based on the user's desired response. This aspect can be achieved through various methods in related technologies to collect user feedback. For example, a power system can provide different electricity packages to users.

[0100] In this optional implementation, historical load curves and historical temperature curves of commercial users on typical meteorological days are collected. A system temperature is set, and a thermodynamic simulation model is used to obtain the response potential of individual commercial users. Furthermore, a fitting algorithm is used to model the correlation between response potential and temperature, thereby obtaining the response potential of multiple aggregated commercial users within a certain area. This optional implementation provides a simple and accurate method that can be effectively used to mine the response potential of commercial users within a region, while adhering to their willingness to reduce electricity consumption.

[0101] Example 2

[0102] According to an embodiment of this optional implementation, an electrical load response potential prediction device is also provided. Figure 12 This is a framework diagram of an optional load response potential prediction device according to an embodiment of this optional implementation. (Refer to...) Figure 12 As shown, the device includes a first determining module 1202, a first acquiring module 1204, a second determining module 1206, a third determining module 1208, and a second acquiring module 1210.

[0103] The first determining module 1202 is used to determine multiple initial system temperature settings of the power system of the target object; the first acquiring module 1204, connected to the first determining module 1202, is used to acquire multiple calculation methods corresponding to each initial system temperature setting among the multiple initial system temperature settings of the power system, wherein the multiple calculation methods are multiple ways to calculate the power load response potential based on the ambient temperature, and the multiple calculation methods correspond one-to-one with multiple predetermined temperature adjustment values, and the predetermined temperature adjustment values ​​are the temperature values ​​used to adjust the initial system temperature settings; the second determining module 1206, connected to the first acquiring module 1204, is used to determine The target system temperature initial setpoint, target temperature adjustment value, and target ambient temperature of the power system are defined, wherein the target system temperature initial setpoint is one of multiple system temperature initial setpoints, and the target temperature adjustment value is one of multiple predetermined temperature adjustment values; a third determining module 1208, connected to the second determining module 1206, is used to determine the target calculation method based on the target system temperature initial setpoint and target temperature adjustment value, and multiple calculation methods corresponding to the multiple system temperature initial setpoints; a second obtaining module 1210, connected to the third determining module 1208, is used to obtain the power load response potential of the target object based on the target ambient temperature and the target calculation method.

[0104] It should be noted that the first determining module 1202, the first acquiring module 1204, the second determining module 1206, the third determining module 1208, and the second acquiring module 1210 correspond to steps S102 to S110 in Embodiment 1. These modules and their corresponding steps implement the same examples and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should also be noted that these modules, as part of the device, can run on the computer terminal 10 provided in Embodiment 1.

[0105] Embodiments of the present invention also provide a computer-readable storage medium. Optionally, in this embodiment, the computer-readable storage medium can be used to store the program code executed by the power load response potential prediction method provided in Embodiment 1.

[0106] Optionally, in this embodiment, the computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0107] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining multiple initial system temperature settings of the power system of the target object; for each of the multiple initial system temperature settings of the power system: obtaining multiple calculation methods corresponding to the initial system temperature settings, wherein the multiple calculation methods are multiple methods for calculating the power load response potential based on the ambient temperature, the multiple calculation methods correspond one-to-one with multiple predetermined temperature adjustment values, and the predetermined temperature adjustment values ​​are temperature values ​​for adjusting the initial system temperature settings; determining the target initial system temperature settings, the target temperature adjustment values, and the target ambient temperature of the power system of the target object, wherein the target initial system temperature settings are one of the multiple initial system temperature settings, and the target temperature adjustment values ​​are one of the multiple predetermined temperature adjustment values; determining the target calculation method based on the target initial system temperature settings and the target temperature adjustment values, and the multiple calculation methods corresponding to the multiple initial system temperature settings; and obtaining the power load response potential of the target object based on the target ambient temperature and the target calculation method.

[0108] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining multiple initial system temperature settings of the power system of the target object on a typical weather day; for each of the multiple initial system temperature settings: obtaining a first load curve corresponding to the initial system temperature setting, and multiple second load curves corresponding one-to-one with multiple predetermined temperature adjustment values; wherein, the first load curve is obtained based on the power load of the power system at multiple predetermined times on a typical weather day when the set temperature of the power system is the initial system temperature setting; each of the multiple second load curves is obtained based on the power load of the power system at multiple predetermined times on a typical weather day when the set temperature of the power system is the sum of the initial system temperature setting and the corresponding predetermined temperature adjustment value; and obtaining multiple calculation methods corresponding to the initial system temperature setting based on the first load curve corresponding to the initial system temperature setting, the multiple second load curves corresponding one-to-one with the multiple predetermined temperature adjustment values, and the temperature curve of the typical weather day.

[0109] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining, respectively, multiple historical load curves and multiple historical temperature curves corresponding to multiple typical weather days, where the set temperature of the target object's power system is multiple initial system temperature setpoints; multiple typical weather days are obtained by dividing multiple dates based on meteorological data and social factors within a predetermined historical time period, where the meteorological data includes ambient temperature and the social factors include whether the dates are working days or non-working days; optimizing the correspondence model between power load and the set temperature of the power system based on the multiple historical load curves and multiple historical temperature curves of the multiple typical weather days to obtain a thermodynamic simulation model; the historical load curves are obtained by fitting the power load of the power system at multiple times on the corresponding typical weather days, and the historical temperature curves are obtained by fitting the ambient temperature at multiple times on the corresponding typical weather days; obtaining, based on the thermodynamic simulation model, a first load curve corresponding to the initial system temperature setpoint, and multiple second load curves corresponding one-to-one with multiple predetermined temperature adjustment values.

[0110] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: for each typical weather day of multiple typical weather days for the target object, the set temperature of the power system is each of the multiple initial system temperature set values; the power load of the power system at multiple predetermined times on the typical weather day is collected; and a historical load curve is fitted based on the multiple power loads and the corresponding times; for each typical weather day of multiple typical weather days for the target object, the ambient temperature at multiple predetermined times on the typical weather day is collected; and a historical temperature curve is fitted based on the multiple ambient temperatures and the corresponding times.

[0111] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: for each typical weather day among a plurality of typical weather days, obtaining the historical temperature curve corresponding to the typical weather day; based on the correspondence model between the historical temperature curve and the power load and the set temperature of the power system, obtaining the predicted load curve of the target object on the typical weather day, under the condition that the system set temperature is the initial set value of the system temperature; and optimizing the correspondence model between the power load and the set temperature of the power system based on the predicted load curve and the corresponding historical load curve to obtain a thermodynamic simulation model.

[0112] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: based on the heat capacity of the target building, the system temperature setpoint of the target electrical system, the cooling power of multiple loads in the target electrical system, the sum of the heat conduction of all external surfaces in the target building, the surface convective heat transfer coefficients of multiple external surfaces in the target building, the area of ​​multiple external surfaces in the target building, the temperature of the external surfaces of the target building, the internal ambient temperature and air heat exchange coefficient of the target building, and the heat capacity of the air in the target building, constructing a correspondence model between the electrical load and the set temperature of the electrical system.

[0113] Optionally, the correspondence model between electrical load and the set temperature of the electrical system includes:

[0114]

[0115] Among them, C Z For the heat capacity of the target building, T Z Q represents the system temperature setpoint of the target HVAC system, where t is time; i Let i be the cooling power of the i-th load. For all N in the target object's buildings si The sum of the cooling power of each load; For all N in the target object's buildings surface The sum of heat conducted through each outer surface, h j Let A be the surface convective heat transfer coefficient of the j-th outer surface of the target building. j Let T be the area of ​​the j-th outer surface of the target building. sup T represents the temperature of the building's exterior surface. si The internal ambient temperature of the target building, m inf C p (T si -T Z (m) represents the heat exchange rate between the load and the internal environment of the target building per unit time. inf C is the air heat exchange coefficient. p m is the heat capacity of the air in the building of the target object. inf C p (T sup -T Z ) represents the heat exchange rate between the internal and external environments of the target building per unit time.

[0116] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining a target temperature regulation value for a target object, including at least one of the following: acquiring historical behavioral data of the target object participating in demand response, and determining a target temperature regulation value corresponding to the target object based on the historical behavioral data; acquiring survey data of the target object participating in demand response, and determining a target temperature regulation value corresponding to the target object based on the survey data.

[0117] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0118] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0122] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0123] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting the response potential of electrical load, characterized in that, include: Determine the initial temperature settings for multiple systems in the target object's power system; For each of the multiple initial system temperature settings of the power system: obtain multiple calculation methods corresponding to the initial system temperature setting, wherein the multiple calculation methods are multiple ways to calculate the power load response potential based on the ambient temperature, the multiple calculation methods correspond one-to-one with multiple predetermined temperature adjustment values, and the predetermined temperature adjustment values ​​are temperature values ​​that are used to adjust the initial system temperature setting. The target system temperature initial setpoint, target temperature adjustment value, and target ambient temperature of the power system are determined, wherein the target system temperature initial setpoint is one of the plurality of system temperature initial setpoints, and the target temperature adjustment value is one of the plurality of predetermined temperature adjustment values; The target calculation method is determined based on the initial set value of the target system temperature and the target temperature adjustment value, as well as multiple calculation methods corresponding to multiple initial set values ​​of system temperature. Based on the target ambient temperature and the target calculation method, the electrical load response potential of the target object is obtained; Specifically, for each of the multiple initial system temperature settings of the power system, multiple calculation methods are obtained corresponding to the initial system temperature settings, including: determining multiple initial system temperature settings of the target power system on a typical weather day; for each of the multiple initial system temperature settings: obtaining a first load curve corresponding to the initial system temperature setting, and multiple second load curves corresponding one-to-one with the multiple predetermined temperature adjustment values; wherein, the first load curve is based on the power system when the set temperature is the initial system temperature setting. The load is obtained from the electricity consumption at multiple predetermined times during the typical weather day; each of the multiple second load curves is obtained from the electricity consumption of the power system at the multiple predetermined times during the typical weather day, assuming the set temperature of the power system is the sum of the initial set value of the system temperature and the corresponding predetermined temperature adjustment value; based on the first load curve corresponding to the initial set value of the system temperature, the multiple second load curves corresponding one-to-one with the multiple predetermined temperature adjustment values, and the temperature curve of the typical weather day, multiple calculation methods corresponding to the initial set value of the system temperature are obtained, wherein the multiple calculation methods are regression equations; The acquisition of a first load curve corresponding to the initial system temperature setting and a plurality of second load curves corresponding one-to-one with the plurality of predetermined temperature adjustment values ​​includes: acquiring, respectively, a plurality of historical load curves and a plurality of historical temperature curves corresponding to the plurality of typical weather days, where the set temperature of the target object's power system is the plurality of initial system temperature setting values; the plurality of typical weather days are obtained by dividing the plurality of dates into multiple dates based on meteorological data and social factors within a predetermined historical time period, wherein the meteorological data includes ambient temperature and the social factors include whether the plurality of dates are working days or non-working days; optimizing the correspondence model between power load and power system set temperature based on the plurality of historical load curves and the plurality of historical temperature curves of the plurality of typical weather days to obtain a thermodynamic simulation model; the historical load curves are obtained by fitting the power load of the power system at multiple times on the corresponding typical weather days, and the historical temperature curves are obtained by fitting the ambient temperature at multiple times on the corresponding typical weather days; and acquiring, according to the thermodynamic simulation model, a first load curve corresponding to the initial system temperature setting and a plurality of second load curves corresponding one-to-one with the plurality of predetermined temperature adjustment values. The method further includes: constructing a correspondence model between electrical load and electrical system set temperature based on the heat capacity of the target building, the system temperature setpoint of the target electrical system, the cooling power of multiple loads in the target electrical system, the sum of the heat conduction of all external surfaces of the target building, the surface convective heat transfer coefficients of multiple external surfaces of the target building, the area of ​​multiple external surfaces of the target building, the temperature of the external surfaces of the target building, the internal ambient temperature and air heat exchange coefficient of the target building, and the heat capacity of the air in the target building; The correspondence model between the electrical load and the set temperature of the electrical system includes: ， in, The heat capacity of the building of the target object. The system temperature setpoint for the HVAC system of the target object. For time; For the first The cooling power of the load, All of the buildings of the target object The sum of the cooling power of each load; In the building of the target object The sum of heat conducted through each outer surface, For the building of the target object The surface convective heat transfer coefficient of the outer surface For the building of the target object The surface area of ​​the outer surface, The temperature of the building's outer surface of the target object. The internal ambient temperature of the target object's building. This refers to the heat exchange rate between the load and the internal environment of the building containing the target object per unit time. The air heat exchange coefficient, The heat capacity of the air in the building containing the target object. The heat exchange rate between the internal and external environments of the target building per unit time; The step of determining the target temperature adjustment value for the target object includes: acquiring historical behavioral data of the target object's participation in demand response, and determining the target temperature adjustment value corresponding to the target object based on the historical behavioral data; or, acquiring survey data of the target object's participation in demand response, and determining the target temperature adjustment value corresponding to the target object based on the survey data.

2. The method according to claim 1, characterized in that, The step of acquiring, respectively, multiple historical load curves and multiple historical temperature curves corresponding to multiple typical weather days, assuming the set temperature of the target object's power system is at multiple initial system temperature setpoints, includes: For each typical weather day of the target object in the plurality of typical weather days, the set temperature of the power system is each of the plurality of initial system temperature set values. The power load of the power system at the plurality of predetermined times in the typical weather days is collected. Based on the plurality of power loads and the corresponding times, the historical load curve is fitted. For each typical weather day of the target object: collect the ambient temperature at the multiple predetermined times on the typical weather day, and fit the historical temperature curve based on the multiple ambient temperatures and the corresponding times.

3. The method according to claim 1, characterized in that, The process involves optimizing the correspondence model between electricity load and the set temperature of the power system based on multiple historical load curves and multiple historical temperature curves from multiple typical meteorological days, to obtain a thermodynamic simulation model, including: For each typical meteorological day among the plurality of typical meteorological days, obtain the historical temperature curve corresponding to the typical meteorological day; Based on the historical temperature curve and the correspondence model between the power load and the set temperature of the power system, the predicted load curve of the target object is obtained on the typical meteorological day when the system set temperature is the initial set value of the system temperature. Based on the predicted load curve and the corresponding historical load curve, the correspondence model between the power load and the set temperature of the power system is optimized to obtain a thermodynamic simulation model.

4. A device for predicting the potential response of electrical load, characterized in that, The method applied to claim 1 includes: The first determining module is used to determine the initial set values ​​of multiple system temperatures of the target object's power system; The first acquisition module is used to acquire, for each of the multiple initial system temperature settings of the power system, multiple calculation methods corresponding to the initial system temperature setting, wherein the multiple calculation methods are multiple ways to calculate the power load response potential based on the ambient temperature, the multiple calculation methods correspond one-to-one with multiple predetermined temperature adjustment values, and the predetermined temperature adjustment values ​​are temperature values ​​used to adjust the initial system temperature setting. The second determining module is used to determine the target system temperature initial setting value, the target temperature adjustment value, and the target ambient temperature of the power system, wherein the target system temperature initial setting value is one of the plurality of system temperature initial setting values, and the target temperature adjustment value is one of the plurality of predetermined temperature adjustment values; The third determining module is used to determine the target calculation method based on the initial set value of the target system temperature and the target temperature adjustment value, as well as multiple calculation methods corresponding to multiple initial set values ​​of system temperature. The second acquisition module is used to acquire the power load response potential of the target object based on the target ambient temperature and the target calculation method.

5. A computer-readable storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the method for predicting the electrical load response potential as described in any one of claims 1 to 3.

Images