Distributed resource adjustment capability quantification method and device, storage medium and product

Abstract

The invention discloses a distributed resource adjustment capability quantification method and device, equipment, a storage medium and a product. The method comprises the following steps: determining a feasible region of the monomer adjustment capability of the distributed resources according to a monomer operation state of the distributed resources and a preset constraint condition of the distributed resources; polymerizing the feasible region of the monomer regulation capability of the distributed resources by utilizing a Minkowski method to obtain a feasible region of the group regulation capability of the distributed resources; and determining a group regulation capability value of the distributed resources according to the feasible region of the group regulation capability of the distributed resources. According to the invention, the regulation capability of the distributed resources can be accurately quantified, and the regulation effect can be fully exerted when short-time supply and demand imbalance occurs in a power system.

Description

Technical Field

[0001] This invention relates to the field of power system operation and regulation technology, and in particular to methods, devices, storage media and products for quantifying distributed resource regulation capabilities. Background Technology

[0002] With the large-scale integration of new energy sources such as wind and solar power, power fluctuations in the power system on timescales ranging from seconds to minutes have increased significantly, raising the risk of short-term supply-demand imbalances. Traditional solutions rely on large generating units for regulation, but these methods are slow to respond, have high start-up and shutdown costs, and limited available capacity under conditions of high proportion of new energy sources.

[0003] Distributed flexible adjustment resources are widely distributed and numerous, and have rapid response capabilities. However, the physical characteristics of distributed resources are complex and the application scenarios are different. Existing methods cannot achieve efficient and accurate quantitative assessment of the adjustment capabilities of distributed resources, making it difficult to fully utilize the adjustment capabilities of distributed resources. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, storage medium, and product for quantifying the regulation capacity of distributed resources, so as to accurately quantify the regulation capacity of distributed resources and fully leverage the regulation role of distributed resources when short-term supply and demand imbalances occur in the power system.

[0005] According to one aspect of the present invention, a method for quantifying the decentralized resource adjustment capability is provided, comprising:

[0006] Based on the individual operating status of the distributed resources and the preset constraints of the distributed resources, the feasible domain of the individual adjustment capability of the distributed resources is determined.

[0007] The feasible region of the individual regulation capability of the distributed resources is aggregated using the Minkowski method to obtain the feasible region of the group regulation capability of the distributed resources.

[0008] The group adjustment capability value of the distributed resource is determined based on the feasible region of the group adjustment capability of the distributed resource.

[0009] According to another aspect of the present invention, a distributed resource regulation capability quantification device is provided, comprising:

[0010] The single-unit adjustment feasible region determination module is used to determine the feasible region of the single-unit adjustment capability of the distributed resource based on the single-unit operating status of the distributed resource and the preset constraints of the distributed resource.

[0011] The group adjustment feasible region determination module is used to aggregate the feasible regions of the individual adjustment capabilities of the distributed resources using the Minkowski method to obtain the feasible region of the group adjustment capability of the distributed resources.

[0012] The group adjustment capability value determination module is used to determine the group adjustment capability value of the distributed resource based on the feasible domain of the group adjustment capability of the distributed resource.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the distributed resource regulation capability quantification method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the distributed resource regulation capability quantification method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the distributed resource regulation capability quantification method according to any embodiment of the present invention.

[0019] The technical solution of this invention, by combining the individual operating states of distributed resources with their preset constraints, ensures a more accurate feasible domain for the individual adjustment capabilities of distributed resources. By aggregating the feasible domains of the individual adjustment capabilities of the distributed resources using the Minkowski method, a feasible domain for the collective adjustment capabilities of the distributed resources is obtained. Since the Minkowski method retains the operating limitations of each individual resource when aggregating the feasible domains of the individual adjustment capabilities, it ensures that the obtained feasible domain for the collective adjustment capabilities does not exceed the actual adjustment capability range, making the feasible domain for the collective adjustment capabilities of the distributed resources more accurate. By determining the collective adjustment capability value of the distributed resources based on the feasible domain of their collective adjustment capabilities, compared to the traditional method of obtaining the adjustment capability value by adding rated power, the collective adjustment capability value of the distributed resources obtained by this invention is more accurate and reliable.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for quantifying the decentralized resource regulation capability according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for quantifying the decentralized resource regulation capability according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a flowchart of a method for quantifying the decentralized resource regulation capability according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of a distributed resource regulation capability quantification device provided in Embodiment 4 of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the distributed resource regulation capability quantification method of Embodiment 5 of the present invention. Detailed Implementation

[0027] 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.

[0028] 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.

[0029] Example 1

[0030] Figure 1 This is a flowchart illustrating a method for quantifying the distributed resource regulation capability according to Embodiment 1 of the present invention. This embodiment is applicable to situations where the regulation capability of regulated resources needs to be quantified. This method can be executed by a distributed resource regulation capability quantification device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S101. Based on the individual operating status of the distributed resources and the preset constraints of the distributed resources, determine the feasible domain of the individual adjustment capability of the distributed resources.

[0032] In this embodiment, distributed resources can be understood as flexible adjustment resources capable of participating in the dynamic regulation of the power system, ensuring the stability of key parameters such as voltage, frequency, and power. Distributed resources are characterized by wide distribution, large quantity, and rapid response, enabling them to guarantee short-term supply and demand balance in the power system. Distributed resources may include electric vehicles (the electric vehicles involved in this invention can be understood as electric vehicles in a charging state), air conditioners (the air conditioners involved in this invention can be understood as air conditioners capable of participating in power system regulation), and distributed energy storage devices, etc. Individual operating status can be understood as the operating status of a single distributed resource, such as the load status of a certain air conditioner or the charging status of a certain electric vehicle that is being charged. For electric vehicles, their individual operating status may include: initial state of charge (i.e., the state of charge of the battery when the electric vehicle starts charging), battery charging efficiency, and battery discharging efficiency, etc. For air conditioners, their individual operating status may include: operating power, operating voltage, and cooling efficiency, etc. For distributed energy storage devices, their individual operating status may include: stored energy value, charging power, and discharging power, etc. The preset constraints of distributed resources can be understood as conditions that limit the regulation capabilities of distributed resources. Each type of distributed resource includes one or more preset constraints. The feasible domain of individual regulation capability can be understood as the range of power regulation that a single distributed resource can provide to the power system under the corresponding operating state and preset constraints. For example, the range of power regulation that each electric vehicle can provide to the power system, the range of power regulation that each air conditioner can provide to the power system, and the range of power regulation that each distributed energy storage device can provide to the power system.

[0033] For example, taking the determination of the feasible domain of the individual regulation capability of a distributed energy storage device as an example, if the current stored energy value of a certain distributed energy storage device is 90% of the maximum stored energy value, and the theoretical maximum charging and discharging power is ±100kW, the preset constraints are: when the stored energy value is within 20% of the maximum stored energy value, the maximum charging power is 100kW and the maximum discharging power is 20kW; when the stored energy value is in the range of 20%-80% of the maximum stored energy value, the maximum charging power and the maximum discharging power are both 100kW; when the stored energy value is in the range of 80%-100% of the maximum stored energy value, the maximum charging power is 20kW and the maximum discharging power is 100kW; then the feasible domain of the regulation capability of the distributed energy storage device can be determined to be [-20kW, +100kW]. Since this application quantifies the regulation capability of distributed resources on the power system, a negative sign "-" is used to represent the distributed energy storage device using the power system for charging, and a positive sign "+" is used to represent the distributed energy storage device discharging to the power system.

[0034] S102. Using the Minkowski method, the feasible region of the individual regulation capability of distributed resources is aggregated to obtain the feasible region of the group regulation capability of distributed resources.

[0035] In this embodiment, the feasible region of the collective regulation capability of distributed resources can be understood as the power regulation range that distributed resources of the same type can provide to the power system. For example, for electric vehicles, the feasible region of the collective regulation capability can be obtained by aggregating the feasible region of each electric vehicle using the Minkowski method. For different individual distributed resources, due to their different operating states, the feasible region of individual regulation capability determined according to preset constraints is also different. Since the Minkowski method can add the feasible regions of each individual regulation capability in the vector space, it can ensure that the obtained feasible region of the collective regulation capability does not exceed the actual regulation capability range, making the feasible region of the collective regulation capability of distributed resources more accurate.

[0036] S103. Determine the value of the group adjustment capability of distributed resources based on the feasible region of the group adjustment capability of distributed resources.

[0037] In this embodiment, the group regulation capability value of distributed resources can be the maximum regulation capability value that distributed resources of the same type can provide to the power system. The group regulation capability value of distributed resources can include the maximum upward regulation capability value and the maximum downward regulation capability value of distributed resources. The maximum upward regulation capability value can be understood as the maximum power that distributed resources of the same type can discharge to the power system, and the maximum downward regulation capability value can be understood as the maximum power that distributed resources of the same type can use to charge the power system. By discharging distributed resources to the power system or using distributed resources to charge the power system, the power stability of the power system can be maintained, avoiding large fluctuations in the power system's output.

[0038] For example, taking the determination of the group regulation capacity of distributed energy storage devices as an example, the feasible region of the individual regulation capacity of distributed energy storage device A is [-20kW, +70kW], and the feasible region of the individual regulation capacity of distributed energy storage device B is [-30kW, +50kW]. Calculating the Minkowski sum of the feasible regions of the two devices, the feasible region of the group regulation capacity of the distributed energy storage devices is obtained as [-50kW, +120kW]. Therefore, the maximum upward regulation capacity of the distributed resources group can be determined as 120kW, and the maximum downward regulation capacity of the distributed resources group can be determined as 50kW.

[0039] This invention provides a method for quantifying the regulation capability of distributed resources. By combining the individual operating status of distributed resources with preset constraints, the feasible region of the individual regulation capability of distributed resources can be more accurate. The feasible region of the collective regulation capability of distributed resources is obtained by aggregating the feasible regions of the individual regulation capabilities using the Minkowski method. Since the Minkowski method retains the operating limitations of each individual resource when aggregating the feasible regions of the individual regulation capabilities, it ensures that the obtained feasible region of the collective regulation capability does not exceed the actual regulation capability range, making the feasible region of the collective regulation capability of distributed resources more accurate. By determining the collective regulation capability value of distributed resources based on the feasible region of the collective regulation capability, compared with the traditional method of obtaining the regulation capability value of distributed resources by adding rated power, the collective regulation capability value of distributed resources obtained by this invention is more accurate and reliable.

[0040] Example 2

[0041] Figure 2This is a flowchart of a method for quantifying the regulation capability of distributed resources according to Embodiment 2 of the present invention. This embodiment is a refinement based on the above embodiment. Distributed resources in this application include at least one of electric vehicles, air conditioners, and distributed energy storage devices. This embodiment processes the above three types of distributed resources to obtain the group regulation capability value of each type of distributed resource, such as... Figure 2 As shown, the method includes:

[0042] S201. Based on the individual operating status of electric vehicles, air conditioners, and distributed energy storage devices and the corresponding preset constraints, determine the feasible domain of the corresponding individual adjustment capacity.

[0043] In this embodiment, the feasible range of individual regulation capabilities of electric vehicles, air conditioners, and distributed energy storage devices can be determined according to the following steps A1-A3.

[0044] A1. Determine the feasible domain of the individual unit adjustment capability of the electric vehicle based on the current state of charge of the electric vehicle and the first preset constraint condition; wherein, the first preset constraint condition includes at least one of the following: charging and discharging power constraint condition and charge boundary constraint condition.

[0045] In this embodiment, the current State of Charge (SOC) can be understood as the current SOC value of the battery during the charging process of the electric vehicle. Optionally, the charge / discharge power constraint is determined based on the maximum and minimum charge / discharge power of the battery. The charge boundary constraint is determined based on the minimum and maximum SOC values ​​of the battery. The maximum and minimum charge / discharge power, minimum and maximum SOC values ​​can be determined in conjunction with battery performance.

[0046] For example, the first preset constraints include charging and discharging power constraints and charge boundary constraints. When the first preset constraints are met, the feasible power of each electric vehicle at the current moment is solved based on its current state of charge. The solution result is used as the feasible region of the individual vehicle's adjustment capability. For example, the first constraint can be solved based on the following constraints: electric vehicles Feasible power at time:

[0047] ;

[0048] in, The feasible region representing the individual's regulatory capacity; Represents feasible power. , Represents the battery discharge power. Represents battery charging power. A positive value indicates that the electric vehicle is discharging into the power system. A negative value indicates that the electric vehicle is being charged using an electric power system; This represents the charging and discharging power constraint condition. This represents the minimum power of battery charging and discharging. This represents the maximum charging and discharging power of the battery; This represents the boundary constraints for charged components. Represents the minimum SOC of the battery. Represents the maximum SOC of the battery. Representing the electric vehicles The state of charge at any given moment.

[0049] Optionally, the current state of charge of the electric vehicle is calculated based on the initial state of charge of the electric vehicle using a preset energy balance formula.

[0050] In this embodiment, the initial state of charge (SOC) can be understood as the SOC value of the battery when the electric vehicle starts charging. Considering the uncertain charging behavior of electric vehicles, to avoid overload damage to the battery, only electric vehicles with an initial SOC value between the first and second SOC values ​​are classified as distributed resources that can participate in power system regulation. The first and second SOC values ​​can be determined based on the probability density distribution determined by historical statistical data. For example, it can be assumed that the probability density distribution of the initial SOC of an electric vehicle conforms to a normal distribution. Therefore, the difference between the mean and twice the standard deviation can be determined as the first SOC value, and the sum of the mean and twice the standard deviation can be determined as the second SOC value. In practical applications, the first and second SOC values ​​can also be set in other ways. The preset energy balance formula can determine the current SOC of the electric vehicle based on the initial SOC and the charging / discharging status. The charging status can be determined based on the battery charging efficiency, battery charging power, battery rated capacity, and charging time. The discharging status can be determined based on the battery discharging efficiency, battery discharging power, battery rated capacity, and discharging time.

[0051] For example, the preset energy balance formula can be expressed by the following expression:

[0052] ;

[0053] in, Representing the The current state of charge of the electric vehicle; Representing the electric vehicles The state of charge at time t, when When the value is 0, Represents the initial state of charge; This represents the index value used to characterize the charging status, where represents Battery charging efficiency, Represents battery charging power. Represents the battery's rated capacity. Represents charging time; These represent index values ​​used to characterize the discharge condition, where... This represents the battery discharge efficiency. This represents the battery discharge power. To ensure that the state of charge (SBC) of the electric vehicle at any given time does not exceed the battery's rated SBC, preset energy balance constraints can be added to the preset energy balance formula. These preset energy balance constraints include at least one of a first SBC constraint, a first charging power constraint, a first discharging power constraint, and a first charge-discharge mutual exclusion constraint. Specifically, the first SBC constraint ensures that the electric vehicle's SBC at any given time does not exceed the battery's rated SBC value; the first charging power constraint ensures that the electric vehicle's charging power at any given time does not exceed the battery's maximum charging power; the first discharging power constraint ensures that the electric vehicle's discharging power at any given time does not exceed the battery's maximum discharging power; and the first charge-discharge mutual exclusion constraint ensures that the electric vehicle can only be in a charging or discharging state at any given time. For example, regarding the first... electric vehicles At any given time, its preset energy balance constraint can be expressed by the following expression:

[0054] ;

[0055] in, This represents the constraint condition for the first state of charge. Represents the rated minimum state of charge value. Represents the rated maximum state of charge value; This represents the first charging power constraint condition. This represents the maximum charging power of the battery. Represents the charging control variable; This represents the first discharge power constraint condition. This represents the battery's maximum discharge power. Represents the discharge control variable; This represents the first charge-discharge mutual exclusion constraint condition. and The value can only be 0 or 1, ensuring that the electric vehicle can only be in a charging or discharging state at any given time.

[0056] Through the above steps, when determining the current state of charge of each electric vehicle, its initial state of charge and the current charging and discharging status are considered, ensuring that the calculated state of charge at each moment conforms to the actual charging and discharging status, thereby ensuring that the feasible domain of the individual electric vehicle's adjustment capability is more accurate.

[0057] A2. Determine the feasible range of the individual adjustment capability of the air conditioner based on the current operating power of the air conditioner and the second preset constraint; wherein, the second preset constraint includes at least one of the following: user comfort constraint and cooling power constraint.

[0058] In this embodiment, the current operating power of the air conditioner includes its current cooling power. User comfort constraints can be understood as constraints on indoor temperature, which can be determined based on the air conditioner's set temperature and temperature fluctuation range. Cooling power constraints can be determined based on the air conditioner's rated cooling power. Taking the second preset constraint condition, which includes both user comfort and cooling power constraints, as an example, under the condition that the second preset constraint condition is met, the preset indoor temperature dynamic balance equation is solved to obtain the adjustable range of the air conditioner's current cooling power, and this range is used as the feasible domain of the air conditioner's individual adjustment capability. The preset indoor temperature dynamic balance equation is determined based on indoor temperature, outdoor temperature, indoor thermal resistance, indoor air heat capacity, air conditioner cooling power, air conditioner cooling efficiency, and cooling duration.

[0059] Optionally, user comfort constraints are determined based on the air conditioner set temperature, indoor temperature, and preset temperature fluctuation range.

[0060] For example, regarding the first For an air conditioner, the user comfort constraint is expressed by the following expression:

[0061] ;

[0062] in, This represents the air conditioner's set temperature; represent Real-time indoor temperature; This represents the width of the temperature dead zone, and its value can be set according to the actual application scenario, usually 1-2℃. This represents the lower limit of the preset temperature fluctuation range. This represents the upper limit of the preset temperature fluctuation range.

[0063] For example, regarding the first Based on user comfort constraints, the following expression for the preset indoor temperature dynamic balance equation is solved to obtain the adjustable range of the air conditioner's cooling power:

[0064] ;

[0065] in, This represents the current indoor temperature. represent Real-time indoor temperature; Represents cooling time; Represents indoor thermal resistance; Represents the heat capacity of indoor air; represent outdoor temperature at all times; Represents the air conditioner's cooling efficiency; represent The cooling capacity of the air conditioner at all times.

[0066] By taking the steps described above to determine the user comfort constraints, the air conditioner's set temperature, indoor temperature, and preset temperature fluctuation range were considered. This ensures that when the air conditioner participates in power system regulation, changes in cooling power will not cause significant fluctuations in room temperature, thus guaranteeing user comfort. Furthermore, when determining the feasible region of the individual air conditioner's adjustment capability, user comfort constraints were introduced. This ensures that the air conditioner's cooling power at any given time is not only limited by its rated cooling power but also meets the user comfort constraints. This allows the feasible region of the individual air conditioner's adjustment capability to fully consider the actual operating conditions of each air conditioner, thereby improving the accuracy of the quantification results.

[0067] A3. Determine the feasible domain of the individual adjustment capability of the distributed energy storage device based on the current stored energy of the distributed energy storage device and the third preset constraint condition; wherein, the third preset constraint condition includes at least one of the following: storage energy constraint condition, charging power constraint condition, discharging power constraint condition, and power ramping constraint condition.

[0068] In this embodiment, taking the third preset constraint condition, which includes storage energy constraint condition, charging power constraint condition, discharging power constraint condition, and power ramping constraint condition, as an example, the actual charging power and actual discharging power of the distributed energy storage device are obtained by solving the energy balance formula of the distributed energy storage device. The charging power constraint condition and discharging power constraint condition are then determined based on the relationship between the actual charging power and actual discharging power and the rated charging power and rated discharging power.

[0069] The energy balance formula for distributed energy storage devices includes the current stored energy, charging / discharging efficiency, and charging / discharging power. The boundary values ​​of the stored energy constraint can be determined based on the maximum and minimum stored energy of the distributed energy storage device; the upper limit of the charging power constraint can be determined based on the minimum of the rated charging power and the actual charging power; the upper limit of the discharging power constraint can be determined based on the minimum of the rated discharging power and the actual discharging power; and the power ramp-up constraint can be determined based on the actual net power of the distributed energy storage device and the upper limit of the net power ramp-up.

[0070] For example, the energy balance formula for distributed energy storage devices can be expressed by the following expression:

[0071] ;

[0072] Among them, for the first Taiwan distributed energy storage device; Represents the current stored energy; represent Storing energy in a given moment; This represents the duration of continuous charging or discharging of the distributed energy storage device to the power system. Represents charging efficiency. Represents discharge efficiency; represent Constant charging power, represent Discharge power at any given time.

[0073] For example, the third preset constraint includes the following constraint: when the third preset constraint is met, the power adjustable range of the distributed energy storage device is determined based on the difference between the discharge power and the charging power of the distributed energy storage device, and this difference is taken as the feasible domain of the individual adjustable capability of the distributed energy storage device.

[0074] ;

[0075] The above expression represents the charging power constraint condition. Represents the rated charging power. This represents the actual charging power.

[0076] ;

[0077] The above expression represents the discharge power constraint condition. Represents the rated discharge power. This represents the actual discharge power.

[0078] ;

[0079] The above expression represents the storage energy constraint. Represents the minimum value of stored energy. Represents the maximum value of stored energy. and It can be determined based on the performance of the distributed energy storage equipment.

[0080] ;

[0081] ;

[0082] in, Representative distributed energy storage devices The formula for calculating net power at a given time. represent The actual net power during the time period This represents the upper limit of net power ramp-up.

[0083] Optionally, the discharge power constraint is calculated based on the rated discharge power of the distributed energy storage device using a preset health correction formula; wherein the preset health correction formula is determined based on the depth of discharge and battery temperature of the distributed energy storage device.

[0084] In this embodiment, the rated discharge power of the distributed energy storage device is corrected using a preset health correction formula. The maximum value between the correction result and 0 (to ensure that the effective maximum discharge power is not negative) is taken as the effective maximum discharge power and used as the upper limit value in the discharge power constraint condition. The preset health correction formula is determined based on the discharge depth and battery temperature of the distributed energy storage device, ensuring that the set discharge power constraint condition can fully consider the impact of the working environment and service life of the distributed energy storage device on the discharge power, thereby further limiting the power adjustable range of the distributed energy storage device and ensuring that the feasible domain of the individual adjustment capability of the distributed energy storage device is more accurate.

[0085] For example, regarding the first For a distributed energy storage device, the effective maximum discharge power can be determined using the following expression, which is then used as the upper limit value in the discharge power constraint:

[0086] ;

[0087] in, represent The effective maximum discharge power at any given moment; Represents the rated discharge power; The degradation coefficient represents the degree of impact of health degradation on rated discharge power, and its value can be set based on empirical values. represent The health correction value at any given time is calculated based on a preset health correction formula. Representative distributed energy storage devices in Discharge depth at time, Representative distributed energy storage devices in Battery temperature at any given time.

[0088] Optionally, the preset health correction formula is based on an exponential function and determined according to the depth of discharge and battery temperature. The health correction value calculated according to the preset health correction formula is used to characterize the degree of health decline, and the health correction value is positively correlated with the depth of discharge and battery temperature. The advantage of this setting is that it can accurately correlate the depth of discharge and battery temperature with the degree of health decline, ensuring that as the depth of discharge and battery temperature increase, the corresponding degree of health decline increases. This makes the result of correcting the rated discharge power of the distributed energy storage device using the preset health correction formula more consistent with the actual operating state of the distributed energy storage device.

[0089] For example, the preset health correction formula is expressed by the following expression:

[0090] ;

[0091] in, Represents the health level correction value; This represents the calibration coefficient, which is calibrated based on actual data in practical applications. This represents the depth of discharge when the distributed energy storage device is operating. The reference depth of discharge for distributed energy storage devices can be determined based on the performance parameters of the distributed energy storage devices. The depth of discharge index is used to characterize the sensitivity of the depth of discharge to the decline in health. It represents activation energy, which is related to the chemical reaction characteristics inside the batteries of distributed energy storage devices; Represents the gas constant; The reference battery temperature representing a distributed energy storage device can be determined based on the battery performance parameters of the distributed energy storage device. This represents the battery temperature when the distributed energy storage device is operating.

[0092] The above expression ensures that as the depth of discharge increases and the battery temperature rises, the corresponding health correction value increases. This results in the effective maximum discharge power being lower than the rated discharge power when the rated discharge power of the distributed energy storage device is corrected using the preset health correction formula.

[0093] Accordingly, the upper limit of the discharge power constraint is determined based on the minimum value between the effective maximum discharge power and the actual discharge power, and the discharge power constraint is expressed by the following expression:

[0094] ;

[0095] Using the above discharge power constraints, the power adjustable range of distributed energy storage devices can be further limited based on the health status of the distributed energy storage devices, thereby improving the accuracy of the feasible domain of the individual adjustment capability of distributed energy storage devices.

[0096] S202. For each type of distributed resource, the feasible region of the individual adjustment capability of the current distributed resource is aggregated using the Minkowski method to obtain the feasible region of the group adjustment capability of the current distributed resource.

[0097] In this embodiment, for each type of distributed resource, since the actual operating states of different individuals are different, the feasible region of the adjustment capability of each individual can be aggregated using the Minkowski method, thereby obtaining the feasible region of the collective adjustment capability of the current distributed resources.

[0098] For example, taking the feasible region for determining the group regulation capability of electric vehicles as an example, if the number of electric vehicles participating in power system regulation is... Then, the feasible region of the adjustment capability of each electric vehicle is aggregated using the Minkowski method, thereby obtaining... The feasible domain of the group regulation capability of electric vehicles:

[0099] ;

[0100] in, represent The feasible domain of the group adjustment capability of electric vehicles; represent electric vehicles The feasible power at time t, which is calculated using the Minkowski method. electric vehicles The feasible power at each time step is aggregated to obtain the result; The number representing the electric vehicle, with a value range of [value range missing]. ; Representing the electric vehicles Feasible power at any given time; Representing the The feasible range of individual adjustment capabilities for electric vehicles; This represents the Minkowski summation operation.

[0101] The feasible region of the group regulation capability of air conditioning and distributed energy storage devices is obtained by aggregating the feasible region of the regulation capability of each individual unit using the Minkowski method.

[0102] S203. For each distributed resource, determine the group regulation capability value of the current distributed resource based on the difference between the boundary value of the feasible region of the current distributed resource's group regulation capability and the first power value of the current distributed resource.

[0103] The first power value is determined based on the average power value of the corresponding distributed resource under unregulated conditions.

[0104] In this embodiment, for each type of distributed resource, the boundary value of the feasible region of the group regulation capability may include an upper limit and a lower limit of the feasible region of the group regulation capability; the first power value can be understood as the average power value of the distributed resource when it does not participate in power system regulation, for example, for For each electric vehicle, the first power value can be obtained by calculating the average power value of each electric vehicle when it is not participating in power system regulation. The group regulation capability value of distributed resources can include the maximum upward regulation capability value and the maximum downward regulation capability value of distributed resources; wherein, the maximum upward regulation capability value of distributed resources is determined based on the difference between the first power value and the lower limit of the feasible region of the group regulation capability; the maximum downward regulation capability value of distributed resources is determined based on the difference between the upper limit of the feasible region of the group regulation capability and the first power value.

[0105] For example, take electric vehicles as an example. The upper limit of the feasible region for the group adjustment capability of electric vehicles is represented by the sum of the maximum feasible power of each electric vehicle. The lower bound of the feasible region of the group adjustment capability of electric vehicles is represented by the sum of the minimum feasible power of each electric vehicle. electric vehicles The maximum upward adjustment capacity of the group at time t and electric vehicles The maximum downregulation capability of the group at time t is expressed by the following expression:

[0106] ;

[0107] in, The maximum upward adjustment capacity of the group; Represents the first power value; The lower bound of the feasible region representing the group's adjustment capacity; The maximum downward adjustment capacity of the representative group; The upper limit of the feasible domain representing the group's regulatory capacity.

[0108] In determining the feasible domain of the individual adjustable capability of an electric vehicle, this invention introduces a state of charge constraint, taking into account the initial state of charge of the electric vehicle, thus quantifying its adjustable capability based on the actual state of each vehicle. Similarly, in determining the feasible domain of the individual adjustable capability of an air conditioner, a user comfort constraint is introduced to ensure that the actual adjustable capability of the air conditioner is determined without affecting the user experience. Furthermore, in determining the feasible domain of the individual adjustable capability of a distributed energy storage device, a preset health correction formula is used to correct the rated discharge power of the distributed energy storage device, thereby improving the quantification accuracy of its adjustable capability. This preset health correction formula also considers the depth of discharge and battery temperature, ensuring that the distributed energy storage device does not sacrifice its health when participating in regulation.

[0109] Example 3

[0110] Figure 3 This is a flowchart of a method for quantifying the regulation capability of distributed resources according to Embodiment 3 of the present invention. This embodiment refines the above embodiments by using the Minkowski method to aggregate the group regulation capability values ​​of each distributed resource to obtain the overall regulation capability value of the distributed resources. Figure 3 As shown, the method includes:

[0111] S301. Based on the individual operating status of the distributed resources and the preset constraints of the distributed resources, determine the feasible domain of the individual adjustment capability of the distributed resources.

[0112] S302. Using the Minkowski method, the feasible region of the individual regulation capability of distributed resources is aggregated to obtain the feasible region of the group regulation capability of distributed resources.

[0113] S303. Determine the value of the group adjustment capability of distributed resources based on the feasible region of the group adjustment capability of distributed resources.

[0114] S304. Using the Minkowski method, aggregate the group regulation capability values ​​of each type of distributed resource at each moment within a preset time period to obtain the overall regulation capability value of the distributed resources at each moment within the preset time period.

[0115] In this embodiment, the distributed resources as a whole can be understood as a collection of electric vehicles, air conditioners, and distributed energy storage devices capable of participating in power system regulation. The regulation capability value of the distributed resources as a whole can be understood as the regulation capability value of the distributed resources as a whole on the power system obtained by aggregating the group regulation capability values ​​of each distributed resource. For example, for the regulation capability value of the distributed resources as a whole at a certain moment within a preset time period, the Minkowski method can be used to aggregate the group regulation capability values ​​of electric vehicles, air conditioners, and distributed energy storage devices at that moment.

[0116] Optionally, the overall adjustment capability value of distributed resources includes the overall upward adjustment capability value and the overall downward adjustment capability value of distributed resources.

[0117] In this embodiment, the overall upward adjustment capability of distributed resources can be understood as the maximum power that distributed resources can discharge to the power system at a certain moment, and the overall downward adjustment capability of distributed resources can be understood as the maximum power that distributed resources can use the power system to charge at a certain moment.

[0118] S305. Based on the overall adjustment capability value of the distributed resources at each moment within a preset time period, determine the overall comprehensive adjustment capability value of the distributed resources within the preset time period.

[0119] In this embodiment, the overall regulation capability value includes an overall upward regulation capability value and an overall downward regulation capability value. The overall upward regulation capability value can be understood as the maximum power that the distributed resources as a whole can discharge to the power system within a preset time period, and the overall downward regulation capability value can be understood as the maximum power that the distributed resources as a whole can use the power system to charge within a preset time period.

[0120] Optionally, this step specifically includes: determining the maximum value among the minimum upward adjustment capabilities of the distributed resources at each moment within a preset time as the overall upward adjustment capability value of the distributed resources within the preset time; determining the minimum value among the maximum downward adjustment capabilities of the distributed resources at each moment within the preset time as the overall downward adjustment capability value of the distributed resources within the preset time; and determining the overall adjustment capability value of the distributed resources within the preset time based on the overall upward adjustment capability value and the overall downward adjustment capability value of the distributed resources within the preset time. The advantage of this setting is that by selecting the maximum value among the minimum upward adjustment capabilities of distributed resources at each moment within a preset time period as the overall upward adjustment capability value of distributed resources within the preset time period, the lower limit of the upward adjustment capability that distributed resources can provide to the power system at each moment can be taken into account, and the maximum value is selected to ensure that the overall adjustment capability of distributed resources is not underestimated. By selecting the minimum value among the maximum downward adjustment capabilities of distributed resources at each moment within a preset time period as the overall downward adjustment capability value of distributed resources within the preset time period, the most conservative case of the downward adjustment capability provided to the power system by distributed resources at each moment can be taken into account, avoiding overestimation of its overall downward adjustment capability. The above method avoids the problem of overestimation of the adjustment capability of distributed resources in traditional methods that only rely on the simple addition of rated power.

[0121] Example 4

[0122] Figure 4 This is a schematic diagram of a distributed resource regulation capability quantification device provided in Embodiment 4 of the present invention. Figure 4 As shown, the device includes: a single-unit adjustment feasible region determination module 401, a group adjustment feasible region determination module 402, and a group adjustment capability value determination module 403.

[0123] The single-unit adjustment feasible region determination module is used to determine the feasible region of the single-unit adjustment capability of the distributed resource based on the single-unit operating status of the distributed resource and the preset constraints of the distributed resource.

[0124] The group adjustment feasible region determination module is used to aggregate the feasible regions of the individual adjustment capabilities of the distributed resources using the Minkowski method to obtain the feasible region of the group adjustment capability of the distributed resources.

[0125] The group adjustment capability value determination module is used to determine the group adjustment capability value of the distributed resource based on the feasible domain of the group adjustment capability of the distributed resource.

[0126] This invention provides a device for quantifying the regulation capability of distributed resources. By combining the individual operating status of distributed resources with preset constraints, the feasible region of the individual regulation capability of distributed resources can be more accurate. The Minkowski method is used to aggregate the feasible regions of the individual regulation capabilities of distributed resources to obtain the feasible region of the group regulation capability of distributed resources. Because the Minkowski method retains the operating limitations of each individual resource when aggregating the feasible regions of the individual regulation capabilities, it ensures that the obtained feasible region of the group regulation capability does not exceed the actual regulation capability range, making the feasible region of the group regulation capability of distributed resources more accurate. By determining the group regulation capability value of distributed resources based on the feasible region of the group regulation capability, compared with the traditional method of obtaining the regulation capability value of distributed resources by adding rated power, the group regulation capability value of distributed resources obtained by this invention is more accurate and reliable.

[0127] Optionally, the distributed resources include at least one of electric vehicles, air conditioners, and distributed energy storage devices; the single-unit adjustment feasible region determination module includes at least one of the following: electric vehicle single-unit feasible region determination unit, air conditioner single-unit feasible region determination unit, and energy storage device single-unit feasible region determination unit:

[0128] The electric vehicle single-unit feasible domain determination unit is used to determine the feasible domain of the single-unit adjustment capability of the electric vehicle based on the current state of charge of the electric vehicle and a first preset constraint condition; wherein, the first preset constraint condition includes at least one of the following: charging and discharging power constraint condition and charge boundary constraint condition.

[0129] An air conditioner unit feasible domain determination unit is used to determine the feasible domain of the air conditioner's unit adjustment capability based on the air conditioner's current operating power and a second preset constraint condition; wherein, the second preset constraint condition includes at least one of: user comfort constraint condition and cooling power constraint condition.

[0130] A single feasible domain determination unit for energy storage devices is used to determine the feasible domain of the single-unit adjustment capability of the distributed energy storage device based on the current stored energy of the distributed energy storage device and a third preset constraint condition; wherein, the third preset constraint condition includes at least one of: storage energy constraint condition, charging power constraint condition, discharging power constraint condition, and power ramping constraint condition.

[0131] The current state of charge of the electric vehicle is calculated based on the initial state of charge of the electric vehicle using a preset energy balance formula; the user comfort constraint is determined based on the air conditioning set temperature, indoor temperature, and a preset temperature fluctuation range; the discharge power constraint is calculated based on the rated discharge power of the distributed energy storage device using a preset health correction formula; wherein the preset health correction formula is determined based on the depth of discharge and battery temperature of the distributed energy storage device.

[0132] Optional, the group regulation capacity value determination module is specifically used for:

[0133] For each distributed resource, the group regulation capability value of the current distributed resource is determined based on the difference between the boundary value of the feasible region of the current distributed resource's group regulation capability and the first power value of the current distributed resource; wherein, the first power value is determined based on the average power value of the corresponding distributed resource when it does not participate in regulation.

[0134] Optionally, the device further includes:

[0135] The overall regulation capability value determination module is used to aggregate the group regulation capability value of each type of distributed resource at each moment within a preset time using the Minkowski method, so as to obtain the overall regulation capability value of the distributed resources at each moment within the preset time.

[0136] The comprehensive adjustment capability value determination module is used to determine the overall comprehensive adjustment capability value of the distributed resources within the preset time period based on the overall adjustment capability value of the distributed resources at each moment within the preset time period.

[0137] Optionally, the overall adjustment capability value of the distributed resources includes the overall upward adjustment capability value and the overall downward adjustment capability value of the distributed resources. The comprehensive adjustment capability value determination module includes:

[0138] The comprehensive upward adjustment capability value determination unit is used to determine the maximum value among the minimum upward adjustment capability values ​​of the distributed resources as a whole at each moment within a preset time as the comprehensive upward adjustment capability value of the distributed resources as a whole within the preset time.

[0139] The comprehensive downward adjustment capability value determination unit is used to determine the minimum value among the maximum downward adjustment capability values ​​of the distributed resources at each moment within a preset time as the comprehensive downward adjustment capability value of the distributed resources within the preset time.

[0140] The comprehensive adjustment capability value determination unit is used to determine the comprehensive adjustment capability value of the distributed resources as a whole within the preset time period based on the comprehensive upward adjustment capability value and the comprehensive downward adjustment capability value of the distributed resources as a whole within the preset time period.

[0141] The distributed resource regulation capability quantification device provided in the embodiments of the present invention can execute the distributed resource regulation capability quantification method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0142] Example 5

[0143] Figure 5 A schematic diagram of an electronic device 500 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0144] like Figure 5 As shown, the electronic device 500 includes at least one processor 501 and a memory, such as a read-only memory (ROM) 502 and a random access memory (RAM) 503, communicatively connected to the at least one processor 501. The memory stores computer programs executable by the at least one processor. The processor 501 can perform various appropriate actions and processes based on the computer program stored in the ROM 502 or loaded into the RAM 503 from storage unit 508. The RAM 503 can also store various programs and data required for the operation of the electronic device 500. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0145] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0146] Processor 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 501 performs the various methods and processes described above, such as distributed resource regulation capability quantization methods.

[0147] In some embodiments, the distributed resource scalability quantification method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by processor 501, one or more steps of the distributed resource scalability quantification method described above can be performed. Alternatively, in other embodiments, processor 501 can be configured to perform the distributed resource scalability quantification method by any other suitable means (e.g., by means of firmware).

[0148] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0149] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0150] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0151] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0152] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0153] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0154] This disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the distributed resource regulation capability quantification method provided in the above embodiments.

[0155] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0156] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for quantifying the decentralized resource regulation capability, characterized in that, include: Based on the individual operating status of the distributed resources and the preset constraints of the distributed resources, the feasible domain of the individual adjustment capability of the distributed resources is determined. The feasible region of the individual regulation capability of the distributed resources is aggregated using the Minkowski method to obtain the feasible region of the group regulation capability of the distributed resources. The group adjustment capability value of the distributed resource is determined based on the feasible region of the group adjustment capability of the distributed resource.

2. The method for quantifying the decentralized resource regulation capability according to claim 1, characterized in that, The distributed resources include at least one of: electric vehicles, air conditioners, and distributed energy storage devices; The step of determining the feasible region of the individual adjustment capability of the distributed resource based on the individual operating status of the distributed resource and the preset constraints of the distributed resource includes at least one of the following: The feasible domain of the individual unit adjustment capability of the electric vehicle is determined based on the current state of charge of the electric vehicle and the first preset constraint condition; wherein, the first preset constraint condition includes at least one of the following: charging and discharging power constraint condition and charge boundary constraint condition. The feasible range of the individual adjustment capability of the air conditioner is determined based on the current operating power of the air conditioner and the second preset constraint; wherein, the second preset constraint includes at least one of the following: user comfort constraint and cooling power constraint. The feasible domain of the individual regulation capability of the distributed energy storage device is determined based on the current stored energy of the distributed energy storage device and a third preset constraint condition; wherein, the third preset constraint condition includes at least one of the following: storage energy constraint condition, charging power constraint condition, discharging power constraint condition, and power ramping constraint condition.

3. The method for quantifying the decentralized resource regulation capability according to claim 2, characterized in that, The current state of charge of the electric vehicle is calculated based on the initial state of charge of the electric vehicle and a preset energy balance formula. The user comfort constraints are determined based on the air conditioner set temperature, indoor temperature, and preset temperature fluctuation range. The discharge power constraint is calculated based on the rated discharge power of the distributed energy storage device and a preset health correction formula; wherein the preset health correction formula is determined based on the discharge depth and battery temperature of the distributed energy storage device.

4. The method for quantifying the decentralized resource regulation capability according to claim 1, characterized in that, Determining the group adjustment capability value of the distributed resource based on the feasible region of the group adjustment capability of the distributed resource includes: For each distributed resource, the group regulation capability value of the current distributed resource is determined based on the difference between the boundary value of the feasible region of the current distributed resource's group regulation capability and the first power value of the current distributed resource; wherein, the first power value is determined based on the average power value of the corresponding distributed resource when it does not participate in regulation.

5. The method for quantifying the decentralized resource regulation capability according to claim 4 further includes: The Minkowski method is used to aggregate the group regulation capacity values ​​of each type of distributed resource at each moment within a preset time period, thereby obtaining the overall regulation capacity value of the distributed resources at each moment within the preset time period. Based on the overall adjustment capability value of the distributed resources at each moment within the preset time period, the overall adjustment capability value of the distributed resources within the preset time period is determined.

6. The method for quantifying the decentralized resource regulation capability according to claim 5, characterized in that, The overall adjustment capability value of the distributed resources includes the overall upward adjustment capability value and the overall downward adjustment capability value of the distributed resources. The determination of the overall adjustment capability value of the distributed resources within the preset time period, based on the overall adjustment capability value of the distributed resources at each moment within the preset time period, includes: The maximum value among the minimum upward adjustment capabilities of the distributed resources at each moment within a preset time period is determined as the overall upward adjustment capability of the distributed resources within the preset time period. The minimum value among the maximum downward adjustment capabilities of the distributed resources at each moment within a preset time period is determined as the overall downward adjustment capability value of the distributed resources within the preset time period. The overall adjustment capability value of the distributed resources within the preset time period is determined based on the overall upward adjustment capability value and the overall downward adjustment capability value of the distributed resources within the preset time period.

7. A distributed resource regulation capacity quantification device, characterized in that, include: The single-unit adjustment feasible region determination module is used to determine the feasible region of the single-unit adjustment capability of the distributed resource based on the single-unit operating status of the distributed resource and the preset constraints of the distributed resource. The group adjustment feasible region determination module is used to aggregate the feasible regions of the individual adjustment capabilities of the distributed resources using the Minkowski method to obtain the feasible region of the group adjustment capability of the distributed resources. The group adjustment capability value determination module is used to determine the group adjustment capability value of the distributed resource based on the feasible domain of the group adjustment capability of the distributed resource.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the distributed resource regulation capability quantification method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the distributed resource regulation capability quantification method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the distributed resource regulation capability quantification method according to any one of claims 1-6.

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