Heterogeneous flexible load aggregation processing method and device and virtual power plant platform

Abstract

The disclosed embodiment provides a heterogeneous flexible load aggregation processing method and its device and a virtual power plant platform, the method includes: constructing state evolution models according to the time domain state evolution characteristics of various flexible load devices in the heterogeneous flexible load device cluster; the types of various flexible load devices include temperature control load devices, electric vehicles and energy storage devices; based on the time correspondence relationship of the state evolution models of various flexible load devices in the time domain, aggregating each state evolution model to obtain a flexible load aggregation model; based on the flexible load aggregation model and the constraints of each flexible load device, predicting the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain. By establishing time domain evolution models according to the respective characteristics of different types of flexible load devices and forming an aggregation of a unified time scale, the problem of inconsistent time scales between system and device control in related technologies is solved, and the accuracy of regulation of adjustable resources is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of virtual power plants, and in particular to a method and device for aggregating heterogeneous flexible loads and a virtual power plant platform. Background Art

[0002] Virtual Power Plants (VPP) is a power coordination management system that uses advanced information and communication technologies and software systems to achieve coordinated optimization of distributed energy, so that it can participate in the power market and power grid operation as a special power plant. A virtual power plant can participate in power market transactions as a special power plant and optimize resource allocation based on market price data and supply and demand conditions.

[0003] The current virtual power plants cannot fully exploit the flexible and adjustable potential of flexible loads in the time and space dimensions. Therefore, how to tap the adjustable capacity of flexible loads in the time and space dimensions is a key issue that needs to be addressed.

[0004] The aggregation and control strategy of heterogeneous flexible loads actually involves the coordinated optimization between multiple dimensions of objectives and constraints, including user comfort, energy cost, and control effectiveness. However, in the current virtual power plant, the overall system time scale and the device-level time scale of various heterogeneous flexible load devices are inconsistent, which makes it difficult to effectively aggregate and control various heterogeneous flexible load devices, and the control accuracy is poor. Summary of the invention

[0005] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a heterogeneous flexible load aggregation processing method and device and a virtual power plant platform to solve the problems in the related technology.

[0006] The first aspect of the present disclosure provides a method for aggregating and processing heterogeneous flexible loads, comprising: constructing state evolution models according to the time domain state evolution characteristics of each of the multiple flexible load devices in the heterogeneous flexible load device cluster; the multiple types of the flexible load devices include temperature control load devices, electric vehicles and energy storage devices; based on the moment correspondence between the state evolution models of the various flexible load devices in the time domain, aggregating the state evolution models to obtain a flexible load aggregation model; based on the flexible load aggregation model and the constraints of each flexible load device, predicting the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain.

[0007] In an embodiment of the first aspect, the state evolution models are respectively constructed according to the time domain state evolution characteristics of the various flexible load devices in the heterogeneous flexible load device cluster, including:

[0008] Construct a first-order or multi-order equivalent thermal parameter evolution model of the corresponding temperature control load device in the time domain; the equivalent thermal parameter evolution model describes the indoor temperature at the current moment changing to the indoor temperature at the next moment under the influence of each temperature control load device starting / stopping according to the user's wishes and the outdoor temperature;

[0009] Constructing a first state of charge evolution model of the corresponding electric vehicle in the time domain; the first state of charge evolution model describes the change of the first state of charge of each electric vehicle from the current moment to the next moment due to the charge amount; the charge amount is the amount of electricity charged in a certain period of time according to the charging power and charging efficiency of the electric vehicle;

[0010] Construct a second state of charge evolution model of the corresponding energy storage device in the time domain; the second state of charge evolution model describes the change of the second state of charge of each of the energy storage devices from the current moment to the next moment due to the charge / discharge amount; the charge / discharge amount is: the amount of electricity charged in a certain period of time according to the charging power and charging efficiency of the energy storage device, or the amount of electricity released in a certain period of time according to the discharge power and discharge efficiency of the energy storage device.

[0011] In an embodiment of the first aspect, the equivalent thermal parameter evolution model is expressed as:

[0012] ;

[0013] in, is the outdoor temperature at time t+1; and are the indoor temperatures at time t and time t+1 respectively; Q is the equivalent cooling / heating power of the temperature control load equipment; C is the equivalent heat capacity; R is the equivalent thermal resistance; is the start / stop state variable of the temperature control load device, 1 represents the on state, and 0 represents the off state; Δt is the time slot from t to t+1.

[0014] In an embodiment of the first aspect, the first state of charge evolution model is expressed as:

[0015] ;

[0016] in, and are the first charge states of the electric vehicle at time t and time t+1 respectively; Charging efficiency for electric vehicles; is the rated charging power of the electric vehicle; It is the rated on-board battery capacity of the electric vehicle.

[0017] In an embodiment of the first aspect, the second state of charge evolution model is expressed as:

[0018] ;

[0019] ;

[0020] in, and are the second charge states of the energy storage device at time t and time t+1 respectively; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the rated charging power of the energy storage device; is the rated energy storage capacity of the energy storage device.

[0021] In an embodiment of the first aspect, based on the time correspondence relationship of the state evolution model of each flexible load device in the time domain, aggregating each state evolution model and combining the number of devices of each flexible load device to obtain a flexible load aggregation model includes:

[0022] The equivalent thermal parameter evolution model, the first state of charge evolution model and the second state of charge evolution model are combined to obtain a flexible load aggregation model; the flexible load aggregation model describes the matrix operation relationship from the indoor temperature, the first state of charge of the electric vehicle and the second state of charge of the energy storage device at the current moment to the indoor temperature, the first state of charge and the second state of charge at the next moment.

[0023] In an embodiment of the first aspect, the flexible load aggregation model is expressed as: ;

[0024] Among them, ; ; ;

[0025] ;

[0026] in, is the outdoor temperature at time t+1; and are the indoor temperatures at time t and time t+1 respectively; Q is the equivalent cooling / heating power of the temperature control load equipment; C is the equivalent heat capacity; R is the equivalent thermal resistance; is the start / stop state variable of the temperature control load device, 1 represents the on state, and 0 represents the off state; Δt is the time slot from t to t+1;

[0027] and are the first charge states of the electric vehicle at time t and time t+1 respectively; Charging efficiency for electric vehicles; is the rated charging power of the electric vehicle; is the rated onboard battery capacity of the electric vehicle;

[0028] and are the second charge states of the energy storage device at time t and time t+1 respectively; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the rated charging power of the energy storage device; is the rated energy storage capacity of the energy storage device.

[0029] In an embodiment of the first aspect, the number of temperature-controlled load devices is , the number of electric vehicles is , the number of energy storage devices is ; The total dimension of the indoor temperature, the first state of charge and the second state of charge is .

[0030] In an embodiment of the first aspect, the constraints of each flexible load device include at least one of the following: 1) The constraints of the temperature-controlled load device include: an upper threshold of the indoor temperature that triggers the user to turn off / on the temperature-controlled load device, and a lower threshold of the indoor temperature that triggers the user to turn on / off the temperature-controlled load device; the upper temperature threshold and the lower temperature threshold are determined by a temperature setting value that is offset upward and downward by a user-intentioned temperature offset value, respectively; 2) The constraints of the electric vehicle include: the current moment of the electric vehicle is not a moment when the battery is fully charged; the first state of charge is in the range of [0, 1]; 3) The constraints of the energy storage device include: the second state of charge is in the range of [0, 1].

[0031] In an embodiment of the first aspect, the constraints of each flexible load device include at least one of the following:

[0032] 1) The constraints of the temperature control load equipment include:

[0033] ;

[0034] Where δ is the user's acceptance of the temperature offset, is the indoor temperature, is the set temperature value, and They are the upper and lower thresholds of the indoor temperature for triggering the closing / opening and opening / closing of the temperature control load device respectively;

[0035] 2) The constraints on electric vehicles and energy storage equipment include:

[0036] ;

[0037] in, Expected charging time for electric vehicles; The state of charge of the energy storage device; , They are respectively a collection of electric vehicles and energy storage devices.

[0038] In an embodiment of the first aspect, the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain based on the flexible load aggregation model and the constraints of each flexible load device includes: predicted adjustable increase capacity and predicted adjustable decrease capacity; the predicted adjustable decrease capacity is defined as: the total operating power of the heterogeneous flexible load device cluster determined by weighted calculation of the switch state of each flexible load device and the operating power; the predicted adjustable increase capacity is defined as: the total operating power of the heterogeneous flexible load device cluster determined by weighted calculation of the opposite state of the switch state of each flexible load device and the operating power; wherein the switch states of the temperature-controlled load device, the electric vehicle and the energy storage device are affected by the boundaries of their respective constraints; the boundaries of the constraints of the temperature-controlled load device include: the upper limit threshold of the indoor temperature that triggers the user to turn off / on the temperature-controlled load device, and the lower limit threshold of the indoor temperature that triggers the user to turn on / off the temperature-controlled load device; the upper temperature threshold and the lower temperature threshold are determined by a temperature setting value offset upward and downward by a user-willing offset temperature value respectively; the constraint boundaries of the electric vehicle include: the full charge moment that triggers the disconnection of charging.

[0039] In an embodiment of the first aspect, the predicted adjustable capacity is expressed as:

[0040] ;

[0041] in,

[0042] , They are respectively the predicted adjustable capacity increase and the predicted adjustable capacity decrease of the heterogeneous flexible load equipment cluster; , , They are the switch status of temperature control load equipment, electric vehicles and energy storage equipment, 0 represents off and 1 represents on; , , are the operating powers of temperature control load equipment, electric vehicles, and energy storage equipment respectively; ; Q is the equivalent cooling / heating power of the temperature control load equipment; The heating or cooling efficiency of the temperature-controlled load equipment; , , They are the number of temperature control load equipment, electric vehicles, and energy storage equipment respectively.

[0043] In an embodiment of the first aspect, when the outdoor temperature change does not exceed a preset range, the corresponding parameters in the flexible load aggregation model are determined to remain unchanged; when the outdoor temperature change exceeds the preset range, each time period with similar temperature changes is divided to determine the representative outdoor temperature of each time period, and the corresponding parameters in the flexible load aggregation model are updated in the corresponding time period according to the representative outdoor temperatures of different time periods.

[0044] The second aspect of the present disclosure provides a heterogeneous flexible load aggregation processing device applied to a virtual power plant, including: a model construction module, which is used to construct state evolution models according to the time domain state evolution characteristics of each of the multiple flexible load devices in the heterogeneous flexible load device cluster; the types of the multiple flexible load devices include temperature control load devices, electric vehicles and energy storage devices; an aggregation model module, which is used to aggregate each of the state evolution models of various flexible load devices based on the moment correspondence in the time domain to obtain a flexible load aggregation model; an adjustable capacity prediction module, which is used to predict the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain based on the flexible load aggregation model and the constraints of each flexible load device.

[0045] The third aspect of the present disclosure provides a virtual power plant platform, comprising: at least one computer device, including: a processor and a memory; the memory stores a computer program or instructions; the processor is used to run the computer program or instructions to execute the heterogeneous flexible load aggregation processing method as described in any one of the first aspects.

[0046] A fourth aspect of the present disclosure provides a computer-readable storage medium, characterized in that a computer program or instruction is stored therein, and the computer program or instruction is run to execute the heterogeneous flexible load aggregation processing method as described in any one of the first aspects.

[0047] A fifth aspect of the present disclosure provides a computer program product, characterized in that it includes: a computer program or instruction for executing the heterogeneous flexible load aggregation processing method as described in any one of the first aspects.

[0048] As described above, the disclosed embodiment provides a heterogeneous flexible load aggregation processing method and its device and a virtual power plant platform, the method comprising: constructing state evolution models respectively according to the time domain state evolution characteristics of the various flexible load devices in the heterogeneous flexible load device cluster; the types of the various flexible load devices include temperature control load devices, electric vehicles and energy storage devices; based on the time correspondence between the state evolution models of various flexible load devices in the time domain, aggregating the state evolution models to obtain a flexible load aggregation model; based on the flexible load aggregation model and the constraints of each flexible load device, predicting the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain. By considering the respective characteristics of different types of flexible load devices, respectively establishing time domain evolution models and forming an aggregation of a unified time scale, the problem of inconsistent time scales between system and device control in the related technology is solved, and the accuracy of regulation of adjustable resources is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram showing a flow chart of a heterogeneous flexible load aggregation processing method in one embodiment of the present disclosure.

[0050] Figure 2 A specific flow chart of step S101 in an embodiment of the present disclosure is shown.

[0051] Figure 3 A schematic diagram showing the principle of a first-order equivalent thermal parameter evolution model in an embodiment of the present disclosure.

[0052] Figure 4 Displays the thermoelectric coupling relationship diagram of the heating type temperature control load.

[0053] Figure 5 A diagram showing the energy coupling characteristics of an electric vehicle when charging.

[0054] Figure 6 A diagram showing the energy coupling characteristics of energy storage devices when discharging.

[0055] Figure 7 A module schematic diagram of a heterogeneous flexible load aggregation processing device applied to a virtual power plant in one embodiment of the present disclosure is shown.

[0056] Figure 8 A schematic diagram of a communication system for implementing a virtual power plant in one embodiment of the present disclosure is shown.

[0057] Fig. 9 A schematic diagram showing the structure of a computer device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0058] The following is an explanation of the embodiments of the present disclosure by specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the information disclosed by the present disclosure. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in the present disclosure can also be modified or changed in various ways according to different viewpoints and application modules without departing from the spirit of the present disclosure. It should be noted that the embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0059] The following is a detailed description of the embodiments of the present disclosure with reference to the accompanying drawings so that those skilled in the art can easily implement the present disclosure. The present disclosure can be embodied in many different forms and is not limited to the embodiments described herein.

[0060] In the representations of the present disclosure, the reference terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" and the like mean that the specific features, structures, materials or characteristics represented in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. Moreover, the specific features, structures, materials or characteristics represented may be combined in any one or a group of embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples represented in the present disclosure and the features of different embodiments or examples, unless they are mutually contradictory.

[0061] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the representation of the present disclosure, "a group" means two or more, unless otherwise clearly and specifically defined.

[0062] In order to clearly describe the present disclosure, components not related to the description are omitted, and the same reference numerals are given to the same or similar components throughout the specification.

[0063] Throughout the specification, when a device is said to be "connected" to another device, this includes not only the case of "direct connection" but also the case of "indirect connection" by placing other elements therebetween. In addition, when a device is said to "include" a certain component, unless otherwise stated, it does not exclude other components, but means that other components may be included.

[0064] Although the terms first, second, etc. are used to represent various elements in this article in some examples, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, the first interface and the second interface, etc. are represented. Moreover, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising" and "including" indicate that there are the described features, steps, operations, elements, modules, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or a group of other features, steps, operations, elements, modules, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Therefore, "A, B or C" or "A, B and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C". Only when the combination of elements, functions, steps or operations is inherently mutually exclusive in some way, will there be an exception to this definition.

[0065] The technical terms used herein are only used to refer to specific embodiments and are not intended to limit the present disclosure. The singular form used herein also includes the plural form unless the sentence clearly indicates the contrary meaning. The meaning of "including" used in the specification is to specify specific characteristics, regions, integers, steps, operations, elements and / or components, and does not exclude the existence or addition of other characteristics, regions, integers, steps, operations, elements and / or components.

[0066] Although not defined differently, all terms, including technical and scientific terms used herein, have the same meaning as those generally understood by those skilled in the art to which the present disclosure belongs. Terms defined in commonly used dictionaries are additionally interpreted as having meanings consistent with relevant technical literature and the message of the present disclosure, and shall not be overly interpreted as ideal or very formal meanings unless defined.

[0067] A virtual power plant is an intelligent, advanced integrated energy system that integrates distributed energy. However, current virtual power plants find it difficult to fully exploit the flexible and adjustable potential of flexible loads in the spatial and temporal dimensions. Therefore, how to tap into the adjustable capabilities of flexible loads from the spatial and temporal dimensions is a key issue that needs to be addressed. The aggregation and regulation strategy of heterogeneous flexible loads actually involves the coordinated optimization of objectives and constraints in multiple dimensions, including user comfort, energy costs, and regulation effectiveness. However, in current virtual power plants, there is a discrepancy between the overall system time scale and the device-level time scale of various heterogeneous flexible load devices, which makes it difficult to effectively aggregate and regulate various heterogeneous flexible load devices, and the regulation accuracy is poor.

[0068] In view of this, the embodiments of the present disclosure can provide a method for aggregating heterogeneous flexible loads of a virtual power plant, a device thereof, and a virtual power plant platform. By considering the respective time domain evolution characteristics of various heterogeneous flexible load devices, modeling them separately and performing aggregation on a unified time scale, the problems in related technologies can be solved.

[0069] Embodiment 1:

[0070] like Figure 1 As shown, a schematic diagram of the process of heterogeneous flexible load aggregation processing method in one embodiment of the present disclosure is presented.

[0071] exist Figure 1 In the method, the method comprises the following steps:

[0072] Step S101: constructing state evolution models respectively according to the time domain state evolution characteristics of the various flexible load devices in the heterogeneous flexible load device cluster.

[0073] In this embodiment, the types of the multiple flexible load devices included in the heterogeneous load device cluster may include temperature-controlled load devices, electric vehicles, and energy storage devices. It should be noted that in the following embodiments, when modeling and aggregating temperature-controlled load devices, electric vehicles, and energy storage devices, respectively, the three types of flexible load devices are modeled and aggregated in a generalized manner, and each type may include one or more load devices, rather than modeling and aggregating only a single temperature-controlled load device, electric vehicle, and energy storage device.

[0074] like Figure 2 As shown, step S101 may further include:

[0075] Step S201: constructing a first-order or multi-order equivalent thermal parameter evolution model of the corresponding temperature control load equipment in the time domain.

[0076] Let's first introduce the definition of "temperature controlled load equipment". Temperature controlled loads (TCLs) equipment can include electric heat pumps, water heaters, and air conditioners. They are a type of load with similar models but heterogeneous parameters. The description of this type of load tends to be unified. Take the heating type electric heat pump load as an example. When the indoor temperature Reaching the upper limit of user willingness , the electric heat pump is turned off; when the indoor temperature reaches the lower limit of the user's wishes , the electric heat pump starts. When the indoor temperature is maintained within an acceptable range, , the electric heat pump maintains the state of the previous moment. Optionally, the determination of the upper temperature limit and the lower temperature limit desired by the user can be determined by setting a set temperature value , and the upper and lower temperature limits can be set to as well as .

[0077] Similarly, in another embodiment, taking the cooling type air conditioning load as an example, when the indoor temperature reaches the lower limit of the user's preference, the air conditioning cooling is turned off; when the indoor temperature reaches the upper limit of the user's preference, the air conditioning cooling is turned on; when the indoor temperature is maintained within an acceptable range, the air conditioning cooling maintains the previous state. This will not be elaborated here.

[0078] In some embodiments, the modeling of the temperature control load device in the time domain can be described as the time-varying relationship between the indoor temperature, the outdoor temperature and the heating / cooling capacity of the temperature control load device. Specifically, the equivalent thermal parameter evolution model describes the indoor temperature at the current moment changing to the indoor temperature at the next moment under the influence of each temperature control load device starting / stopping according to the user's will and the outdoor temperature.

[0079] In a further example, the first-order differential equation of the first-order equivalent thermal parameter evolution model is shown in the following equation (1). Figure 3 The equivalent circuit model of the circuit is as follows: Equation (1) means that the sum of the currents in the two branches is equal to the current in the total circuit.

[0080] (1)

[0081] in, is the indoor temperature at time t; R is the equivalent thermal resistance; C is the equivalent thermal capacity; s is the switch state of the temperature control load device at time t; is the outdoor temperature at time t; Q is the equivalent cooling / heating power, that is, the cooling / heating power in the time slot from t to t+1.

[0082] Solving equation (1) can obtain the state evolution model of the temperature control load equipment, that is, the equivalent thermal parameter evolution model:

[0083] (2)

[0084] In order to unify the form of temperature control load, electric vehicle and distributed energy storage state model, the equivalent thermal parameter evolution model can be rewritten as:

[0085] (3)

[0086] in, is the outdoor temperature at time t+1; and are the indoor temperatures at time t and time t+1 respectively; Q is the equivalent cooling / heating power of the temperature control load equipment; C is the equivalent heat capacity; R is the equivalent thermal resistance; is the start / stop state variable of the temperature control load device, 1 represents the on state, and 0 represents the off state; Δt is the time slot from t to t+1.

[0087] Step S202: constructing a first state of charge evolution model corresponding to the electric vehicle in the time domain.

[0088] Let me first introduce the definition of "electric vehicle". Electric vehicle (EV) refers to a vehicle that uses a battery as a power source, such as electric mopeds, electric cars, etc. Electric vehicles participate in regulation mainly by relying on on-board batteries. The energy characteristics of electric vehicles during charging are similar to the charging and discharging characteristics of batteries (such as lithium batteries). During the charging process of electric vehicles at a fixed power, its state of charge (i.e., the percentage of power SOC) can be defined as the "first state of charge", showing an approximately linear growth. When the battery is fully charged, that is, the state of charge reaches the maximum value S OC max, stop charging.

[0089] For example, common battery types used in electric vehicles include ternary lithium batteries, lithium iron phosphate batteries, nickel metal hydride batteries, and lithium cobalt oxide batteries. For electric vehicles or electric-assisted bicycles connected to the grid, their main demand is to be charged, and they do not consider discharging to the grid unless in extreme circumstances.

[0090] Therefore, in some embodiments, the first state of charge evolution model can be described as: the change of the first state of charge of each electric vehicle from the current moment to the next moment due to the charge amount. The charge amount is the amount of electricity charged in a certain period of time according to the charging power and charging efficiency of the electric vehicle.

[0091] According to this principle, in a further example, the state evolution model of the electric vehicle, that is, the first state of charge evolution model, can be expressed as follows:

[0092] (4)

[0093] in, and are the first charge states of the electric vehicle at time t and time t+1 respectively; Charging efficiency for electric vehicles; is the rated charging power of the electric vehicle; is the rated on-board battery capacity of the electric vehicle; Δt is the time slot from t to t+1.

[0094] Step S203: constructing a second state of charge evolution model of the corresponding energy storage device in the time domain.

[0095] Let me first introduce the definition of "energy storage equipment". Energy storage equipment (ES) refers to a device that can store energy in the form of electricity and other forms, and release it when needed to supply the power system or other equipment for use. The role of energy storage equipment is to balance the difference between electricity supply and demand and improve the stability and reliability of the power system. They can store excess energy from renewable energy power generation equipment so that it can be released during peak energy demand periods, or serve as a backup power source to provide electricity in emergencies. There are various types of energy storage devices, and the types of energy storage devices that store electrical energy include batteries, supercapacitor systems, etc. Among them, batteries are one of the most common and widely used energy storage devices, which can convert electrical energy into chemical energy and convert it back into electrical energy when needed. There are many types of batteries, including lead-acid batteries, lithium-ion batteries, nickel-metal hydride batteries, etc.

[0096] Energy storage devices can also use the state of charge (defined as the "second state of charge") to indicate their current state, but the purpose of energy storage determines that it must have a discharge function. Therefore, from the perspective of the charging / discharging process, the charging process of energy storage devices is similar to that of electric vehicles, and the discharge process is the opposite of the charging process.

[0097] Therefore, in some embodiments, the second state of charge evolution model describes the change of the second state of charge of each of the energy storage devices from the current moment to the next moment due to the charge / discharge amount; the charge / discharge amount is: the amount of electricity charged in a certain period of time according to the charging power and charging efficiency of the energy storage device, or the amount of electricity released in a certain period of time according to the discharge power and discharge efficiency of the energy storage device.

[0098] According to this principle, in a further example, the state evolution model of the energy storage device, that is, the second state of charge evolution model, can be expressed as:

[0099] (5)

[0100] (6)

[0101] in, and are the second charge states of the energy storage device at time t and time t+1 respectively; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the rated charging power of the energy storage device; is the rated energy storage capacity of the energy storage device.

[0102] Back to Figure 1, step S102: based on the moment correspondence between the state evolution models of various flexible load devices in the time domain, aggregate the state evolution models to obtain a flexible load aggregation model.

[0103] In some embodiments, the equivalent thermal parameter evolution model, the first state of charge evolution model and the second state of charge evolution model can be combined to obtain a flexible load aggregation model. The flexible load aggregation model describes the matrix operation relationship from the indoor temperature, the first state of charge of the electric vehicle and the second state of charge of the energy storage device at the current moment to the indoor temperature, the first state of charge and the second state of charge at the next moment. That is, the operating states of various heterogeneous flexible load devices can be uniformly serialized in the time domain through the association relationship between the moments.

[0104] As an example, the flexible load aggregation model is expressed as: (7)

[0105] Formula (7) can be defined based on formula (3), formula (4)~(5), and formula (6) and expressed as a matrix relationship.

[0106] Among them, ; ; ;

[0107] ;

[0108] in, is the outdoor temperature at time t+1; and are the indoor temperatures at time t and time t+1 respectively; Q is the equivalent cooling / heating power of the temperature control load equipment; C is the equivalent heat capacity; R is the equivalent thermal resistance; is the start / stop state variable of the temperature control load device, 1 represents the on state, and 0 represents the off state; Δt is the time slot from t to t+1;

[0109] and are the first charge states of the electric vehicle at time t and time t+1 respectively; Charging efficiency for electric vehicles; is the rated charging power of the electric vehicle; is the rated onboard battery capacity of the electric vehicle;

[0110] and are the second charge states of the energy storage device at time t and time t+1 respectively; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the rated charging power of the energy storage device; is the rated energy storage capacity of the energy storage device.

[0111] Therefore, equation (7) can be written as equation (8):

[0112]

[0113] It can be understood that formula (8) is only a generalized formula. In fact, the number of temperature control load equipment, electric vehicles, and energy storage equipment may be more than one. , , The dimensions of A and B will change accordingly based on the number of devices of each type.

[0114] For example, the number of temperature control load devices is , the number of electric vehicles is , the number of energy storage devices is ,but The dimension of , The dimension of , The dimension of , so The dimension of (i.e., the total dimension of indoor temperature, first state of charge, and second state of charge) is , and the dimensions of the A and B matrices used in the matrix operation also change accordingly to be consistent.

[0115] According to the above formula (8), the matrix will introduce unique parameters of each temperature control load device, electric vehicle, and energy storage device, such as different R, C, , Q, P EV , P ES wait.

[0116] It can be understood that each parameter in item B can be determined as a constant value, and item B is used as a constant item that does not contain variables. Among them, since the outdoor temperature is used, in order to determine that the outdoor temperature is a constant, the following processing can be performed:

[0117] When the outdoor temperature change does not exceed the preset range, the corresponding parameters in the flexible load aggregation model are determined to be unchanged. For example, between t and t+n, the temperature is relatively close and within the preset range [A-1, A+1], then the temperature between t and t+n can be determined to be a constant temperature A.

[0118] When the outdoor temperature changes beyond the preset range, the time periods with similar temperature changes are divided to determine the representative outdoor temperature of each time period, and the corresponding parameters in the flexible load aggregation model are updated in the corresponding time period by the representative outdoor temperatures of different time periods. For example, between t~t+n, t~t+3 fluctuates between the temperature m±1, and after t+3, the fluctuation exceeds ±1, and t+3~t+6 fluctuates between the temperature n±1; after t+6, the fluctuation exceeds ±1 relative to t+6, and t+6~t+10 fluctuates between the temperature p±1. Therefore, the outdoor temperature from t~t+3 can be set to m, the outdoor temperature from t+3~t+6 can be set to n, and the outdoor temperature from t+6~t+10 can be set to p, and the matrix B can be updated accordingly.

[0119] Step S103: predicting the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain based on the flexible load aggregation model and the constraints of each flexible load device.

[0120] For more information, please refer to the constraints of temperature control load equipment, electric vehicle constraints, and energy storage equipment constraints. Figure 4~Figure 6 shown.

[0121] In some embodiments, the constraints of the temperature-controlled load device include: an upper threshold value of the indoor temperature that triggers the user to turn off / on the temperature-controlled load device, and a lower threshold value of the indoor temperature that triggers the user to turn on / off the temperature-controlled load device; the upper temperature threshold and the lower temperature threshold are determined by a temperature setting value offset upward and downward by a user-intentioned offset temperature value, respectively.

[0122] In the specific examples, please refer to Figure 4 , Figure 4 Display the thermoelectric coupling relationship diagram of the heating type temperature control load. The constraints of the temperature control load equipment include:

[0123] (9)

[0124] Where δ is the user's acceptance of the temperature offset, is the indoor temperature, is the set temperature value, and They are the upper and lower thresholds of the indoor temperature that trigger the temperature control load device to be turned off / on and on / off respectively.

[0125] In some embodiments, the constraints of the electric vehicle include: the current moment of the electric vehicle is not yet fully charged; and the first state of charge is within the range of [0, 1].

[0126] In some embodiments, the constraint of the energy storage device includes: the second state of charge is in the range of [0, 1].

[0127] For reference Figure 5 and Figure 6 As shown, Figure 5 A diagram showing the energy coupling characteristics of an electric vehicle when charging. Figure 6 The energy coupling characteristic diagram of the energy storage device during discharge is shown, and it can be seen that the power of the second state of charge decreases with discharge. It can be seen that in the specific example, the constraints of the electric vehicle and the energy storage device include:

[0128] (10)

[0129] in, The time when the electric vehicle is expected to be fully charged, that is, the charging can be turned off when the electric vehicle is fully charged, so that the energy of the power grid will no longer be consumed and the power regulation will not be involved. The state of charge of the energy storage device; , They are respectively a collection of electric vehicles and energy storage devices. It can be seen that energy storage devices can flexibly switch between charging and discharging modes.

[0130] It should be noted that the grid dispatching scenario has both increasing demand and decreasing demand. Therefore, in the embodiment of the present disclosure, the predicted adjustable capacity in step S103 may include predicted adjustable increasing capacity and predicted adjustable decreasing capacity. It should also be noted that according to the constraints of the temperature control load device, δ is set to trigger the user to switch the temperature control load device on and off. and The constraint parameters are used to predict the indoor temperature at each moment in the future through the equivalent thermal parameter evolution model. When the predicted indoor temperature at a certain future moment reaches or When the temperature rises, the on / off action may be triggered, which can be predicted based on the predicted indoor temperature. In addition to the switching action that meets the user's comfort needs, the switching action for the purpose of the virtual power plant control strategy can be implemented.

[0131] In addition, for temperature-controlled loads and electric vehicles, the power cannot be reduced in the off state, but can be reduced by shutting down for a short time in the on state. Energy storage equipment can flexibly switch between charging and discharging modes, and can provide active power that can be increased or decreased according to the grid regulation requirements.

[0132] In some embodiments, the predicted adjustable capacity reduction is defined as: a total operating power of a heterogeneous flexible load device cluster determined by weighted calculation of a switch state and an operating power of each flexible load device.

[0133] In some embodiments, the predicted adjustable incremental capacity is defined as: a total operating power of a heterogeneous flexible load device cluster determined by weighted calculation of the opposite states of the switch states of each flexible load device and the operating power.

[0134] Among them, when the indoor temperature predicted by the flexible load aggregation model reaches the boundary of the temperature control load device constraint, a trigger is formed for the switch state change of the load device. The boundary of the temperature control load device constraint includes: the upper limit threshold of the indoor temperature that triggers the user to turn off / on the temperature control load device, and the lower limit threshold of the indoor temperature that triggers the user to turn on / off the temperature control load device; the upper limit threshold and the lower limit threshold of the temperature are determined by a temperature setting value offset upward and downward by a user's desired offset temperature value. Specifically, taking the heating type electric heat pump load as an example, when the indoor temperature Reaching the upper limit of user willingness , the user's intention is to turn off the electric heat pump; when the indoor temperature Reach the lower limit of user willingness , the user's intention is to turn on the heat pump when the indoor temperature Maintain within the user acceptable range[ , ], the user's intention is not to turn the electric heat pump on or off, and the electric heat pump maintains the state of the previous moment. That is, if the electric heat pump was on at the previous moment, the electric heat pump remains on at this moment; if the electric heat pump was off at the previous moment, the electric heat pump remains off at this moment.

[0135] Similarly, taking the cooling type air conditioning load as an example, when the indoor temperature reaches the lower limit of the user's preference, the user's preference is to turn off the air conditioning cooling. When the indoor temperature reaches the upper limit of the user's preference, the user's preference is to turn on the air conditioning cooling. When the indoor temperature is maintained within an acceptable range, the air conditioning cooling maintains the state of the previous moment, and the switch state remains unchanged. That is, if the air conditioning cooling is turned on at the previous moment, the air conditioning cooling remains on at this moment; if the air conditioning cooling is turned off at the previous moment, the air conditioning cooling remains off at this moment.

[0136] Therefore, in some embodiments, the predicted adjustable capacity under heterogeneous flexible load aggregation can be expressed as:

[0137] (11)

[0138] in,

[0139] , They are respectively the predicted adjustable capacity increase and the predicted adjustable capacity decrease of the heterogeneous flexible load equipment cluster; , , They are the switch status of temperature control load equipment, electric vehicles and energy storage equipment, 0 represents off and 1 represents on; , , They are the operating power of temperature control load equipment, electric vehicles, and energy storage equipment, respectively. The operating power can be exemplified as actual power. s=0 means that the initial state of each switch is 0, that is, closed.

[0140] in, ; Q is the equivalent cooling / heating power of the temperature control load equipment; The heating or cooling efficiency of the temperature-controlled load equipment; , , They are the number of temperature control load equipment, electric vehicles, and energy storage equipment respectively.

[0141] Optionally, the method may further include step S104: implementing a control strategy based on the predicted adjustable capacity of the heterogeneous flexible load device cluster.

[0142] In some embodiments, the control strategy may be one or more of controlling the on / off of flexible load equipment according to grid demand (which may include turning the equipment on / off, or "using power" / "disconnecting power" from the grid), power consumption regulation, discharge power regulation, etc.

[0143] Embodiment 2:

[0144] like Figure 7 As shown, a heterogeneous flexible load aggregation processing device applied to a virtual power plant in an embodiment of the present disclosure is shown. It should be noted that the principle and technical implementation of the heterogeneous flexible load aggregation processing device applied to a virtual power plant can refer to the heterogeneous flexible load aggregation processing method in the previous embodiment, so it will not be repeated in this embodiment.

[0145] exist Figure 7 In the embodiment, the heterogeneous flexible load aggregation processing device 700 includes: a model building module 701, an aggregation model module 702 and an adjustable capacity prediction module 703.

[0146] The model building module 701 is used to build state evolution models according to the time domain state evolution characteristics of various flexible load devices in the heterogeneous flexible load device cluster; the types of the various flexible load devices include temperature control load devices, electric vehicles and energy storage devices.

[0147] The aggregation model module 702 is used to aggregate the state evolution models of various flexible load devices based on the time correspondence relationship in the time domain to obtain a flexible load aggregation model.

[0148] The adjustable capacity prediction module 703 is used to predict the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain based on the flexible load aggregation model and the constraints of each flexible load device.

[0149] In some embodiments, the state evolution model is constructed according to the time domain state evolution characteristics of each of the multiple flexible load devices in the heterogeneous flexible load device cluster, including:

[0150] Construct a first-order or multi-order equivalent thermal parameter evolution model of the corresponding temperature control load device in the time domain; the equivalent thermal parameter evolution model describes the indoor temperature at the current moment changing to the indoor temperature at the next moment under the influence of each temperature control load device starting / stopping according to the user's wishes and the outdoor temperature;

[0151] Constructing a first state of charge evolution model of the corresponding electric vehicle in the time domain; the first state of charge evolution model describes the change of the first state of charge of each electric vehicle from the current moment to the next moment due to the charge amount; the charge amount is the amount of electricity charged in a certain period of time according to the charging power and charging efficiency of the electric vehicle;

[0152] Construct a second state of charge evolution model of the corresponding energy storage device in the time domain; the second state of charge evolution model describes the change of the second state of charge of each of the energy storage devices from the current moment to the next moment due to the charge / discharge amount; the charge / discharge amount is: the amount of electricity charged in a certain period of time according to the charging power and charging efficiency of the energy storage device, or the amount of electricity released in a certain period of time according to the discharge power and discharge efficiency of the energy storage device.

[0153] In some embodiments, the equivalent thermal parameter evolution model is expressed as:

[0154] ;

[0155] in, is the outdoor temperature at time t+1; and are the indoor temperatures at time t and time t+1 respectively; Q is the equivalent cooling / heating power of the temperature control load equipment; C is the equivalent heat capacity; R is the equivalent thermal resistance; is the start / stop state variable of the temperature control load device, 1 represents the on state, and 0 represents the off state; Δt is the time slot from t to t+1.

[0156] In some embodiments, the first state of charge evolution model is expressed as:

[0157] ;

[0158] in, and are the first charge states of the electric vehicle at time t and time t+1 respectively; Charging efficiency for electric vehicles; is the rated charging power of the electric vehicle; It is the rated onboard battery capacity of the electric vehicle.

[0159] In some embodiments, the second state of charge evolution model is expressed as:

[0160] ;

[0161] ;

[0162] in, and are the second charge states of the energy storage device at time t and time t+1 respectively; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the rated charging power of the energy storage device; is the rated energy storage capacity of the energy storage device.

[0163] In some embodiments, based on the time correspondence relationship of the state evolution models of the flexible load devices in the time domain, aggregating the state evolution models and combining the number of devices of each flexible load device to obtain a flexible load aggregation model includes:

[0164] The equivalent thermal parameter evolution model, the first state of charge evolution model and the second state of charge evolution model are combined to obtain a flexible load aggregation model; the flexible load aggregation model describes the matrix operation relationship from the indoor temperature, the first state of charge of the electric vehicle and the second state of charge of the energy storage device at the current moment to the indoor temperature, the first state of charge and the second state of charge at the next moment.

[0165] In some embodiments, the flexible load aggregation model is expressed as: ;

[0166] Among them, ; ; ;

[0167] ;

[0168] in, is the outdoor temperature at time t+1; and are the indoor temperatures at time t and time t+1 respectively; Q is the equivalent cooling / heating power of the temperature control load equipment; C is the equivalent heat capacity; R is the equivalent thermal resistance; is the start / stop state variable of the temperature control load device, 1 represents the on state, and 0 represents the off state; Δt is the time slot from t to t+1;

[0169] and are the first charge states of the electric vehicle at time t and time t+1 respectively; Charging efficiency for electric vehicles; is the rated charging power of the electric vehicle; is the rated onboard battery capacity of the electric vehicle;

[0170] and are the second charge states of the energy storage device at time t and time t+1 respectively; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the rated charging power of the energy storage device; is the rated energy storage capacity of the energy storage device.

[0171] In some embodiments, the number of temperature-controlled load devices is , the number of electric vehicles is , the number of energy storage devices is ; The total dimension of the indoor temperature, the first state of charge and the second state of charge is .

[0172] In some embodiments, the constraints of each flexible load device include at least one of the following: 1) The constraints of the temperature-controlled load device include: an upper threshold of the indoor temperature that triggers the user to turn off / on the temperature-controlled load device, and a lower threshold of the indoor temperature that triggers the user to turn on / off the temperature-controlled load device; the upper temperature threshold and the lower temperature threshold are determined by a temperature setting value that is offset upward and downward by a user-intentioned temperature value, respectively; 2) The constraints of the electric vehicle include: the current moment of the electric vehicle is not a moment when the battery is fully charged; the first state of charge is in the range of [0, 1]; 3) The constraints of the energy storage device include: the second state of charge is in the range of [0, 1].

[0173] In some embodiments, the constraints of each flexible load device include at least one of the following:

[0174] 1) The constraints of the temperature control load equipment include:

[0175] ;

[0176] Where δ is the user's acceptance of the temperature offset, is the indoor temperature, is the set temperature value, and They are the upper and lower thresholds of the indoor temperature for triggering the closing / opening and opening / closing of the temperature control load device respectively;

[0177] 2) The constraints on electric vehicles and energy storage equipment include:

[0178] ;

[0179] in, Expected charging time for electric vehicles; The state of charge of the energy storage device; , They are respectively a collection of electric vehicles and energy storage devices.

[0180] In some embodiments, the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain based on the flexible load aggregation model and the constraints of each flexible load device includes: predicted adjustable increase capacity and predicted adjustable decrease capacity; the predicted adjustable decrease capacity is defined as: the total operating power of the heterogeneous flexible load device cluster determined by weighted calculation of the switch state of each flexible load device and the operating power; the predicted adjustable increase capacity is defined as: the total operating power of the heterogeneous flexible load device cluster determined by weighted calculation of the opposite state of the switch state of each flexible load device and the operating power; wherein the switch states of the temperature-controlled load device, the electric vehicle and the energy storage device are affected by the boundaries of their respective constraints; the constraints of the temperature-controlled load device include: the upper limit threshold of the indoor temperature that triggers the user to turn off / on the temperature-controlled load device, and the lower limit threshold of the indoor temperature that triggers the user to turn on / off the temperature-controlled load device; the upper temperature threshold and the lower temperature threshold are determined by a temperature setting value offset upward and downward by a user-willing offset temperature value respectively; the constraints of the electric vehicle include: the full charge moment that triggers the disconnection of charging.

[0181] In some embodiments, the predicted adjustable capacity is expressed as:

[0182] ;

[0183] in,

[0184] , They are respectively the predicted adjustable capacity increase and the predicted adjustable capacity decrease of the heterogeneous flexible load equipment cluster; , , They are the switch status of temperature control load equipment, electric vehicles and energy storage equipment, 0 represents off and 1 represents on; , , are the operating powers of temperature control load equipment, electric vehicles, and energy storage equipment respectively; ; Q is the equivalent cooling / heating power of the temperature control load equipment; The heating or cooling efficiency of the temperature-controlled load equipment; , , They are the number of temperature control load equipment, electric vehicles, and energy storage equipment respectively.

[0185] In some embodiments, when the outdoor temperature change does not exceed a preset range, the corresponding parameters in the flexible load aggregation model are determined to remain unchanged; when the outdoor temperature change exceeds a preset range, each time period with similar temperature changes is divided to determine the representative outdoor temperature of each time period, and the corresponding parameters in the flexible load aggregation model are updated in the corresponding time period according to the representative outdoor temperatures of different time periods.

[0186] It should be noted that in Figure 7 Each functional module in the embodiment can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program or instruction product. The computer program or instruction product includes one or a group of computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function according to the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer program or instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0187] and, Figure 7 The device disclosed in the embodiment can be implemented by other module division methods. The device embodiments shown above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as a group of modules or modules can be combined or dynamically added to another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical or other forms.

[0188] in addition, Figure 7Each functional module and submodule in the embodiment can be dynamically in a processing component, or each module can exist physically separately, or two or more modules can be dynamically in one component. The above-mentioned dynamic components can be implemented in the form of hardware or in the form of software functional modules. If the above-mentioned dynamic components are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0189] It should be particularly noted that the process or method represented by the flowchart of the above embodiment of the present disclosure can be understood as representing a module, fragment or part of a code including one or more sets of executable instructions configured to implement the steps of a specific logical function or process. And the scope of the preferred embodiment of the present disclosure includes other implementations, in which the functions may not be performed in the order shown or discussed, including performing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0190] For example, Figure 1 , Figure 2 The order of the steps in the method embodiments may be changed in specific scenarios and is not limited to the above.

[0191] Embodiment 3:

[0192] like Figure 8 As shown, a schematic diagram of a communication system for implementing a virtual power plant in one embodiment of the present disclosure is shown.

[0193] exist Figure 8 In the figure, the communication system for realizing the virtual power plant includes a virtual power plant platform 801, an aggregator terminal 802, a collection and control terminal 803 and a heterogeneous flexible load device cluster 804.

[0194] The heterogeneous flexible load device cluster 804 includes a plurality of heterogeneous flexible load devices, such as a temperature control load device 841 , an electric vehicle 842 , and an energy storage device 843 .

[0195] The virtual power plant platform 801 is communicatively connected to (or integrated with) an aggregator terminal 802, and the aggregator terminal 802 is communicatively connected to respective acquisition and control terminals 803, and the acquisition and control terminals 803 are set corresponding to the respective flexible load devices. As an example, the communication connection may be a wired and / or wireless local and / or wide area network connection.

[0196] On the one hand, the acquisition and control terminal 803 senses the load status data of various flexible load devices, such as the load status data of temperature control load devices 841, electric vehicles 842 and energy storage devices 843. It is uploaded to the virtual power plant platform 801 through the aggregator terminal 802 for analysis and determination of the control strategy. In some embodiments, each acquisition and control terminal 803 can be set to sense the same / different types of flexible load devices in one, a group or a park. On the other hand, the control instructions issued from the aggregator terminal 802 are also executed to realize resource allocation. In some embodiments, the acquisition and control terminal 803 can be implemented based on a composite device of sensors and controllers.

[0197] The aggregator terminal 802 is responsible for managing the flexible load equipment under each aggregator, so as to carry out two-way communication between the virtual power plant platform 801 and the collection and control terminal 803 related to the flexible load equipment under its jurisdiction, so as to realize the uplink of load status data and the downlink of control instructions.

[0198] The virtual power plant platform 801 can execute the heterogeneous flexible load aggregation processing method in the embodiment of the present disclosure to process the load state data and obtain the predicted adjustable capacity. Further optionally, a corresponding control strategy can be implemented based on the predicted adjustable capacity, and then a corresponding control instruction can be issued to the acquisition and control terminal 803.

[0199] It should be noted that Figure 8 The communication system architecture in the embodiment is only an example and can be changed based on needs in actual application scenarios, but is not limited to this.

[0200] like Fig. 9 As shown, a schematic diagram of the structure of a computer device in an embodiment of the present disclosure is shown. The virtual power plant platform may include at least one computer device 900. In some embodiments, the virtual power plant platform may be implemented as a cloud platform, and the computer device may be implemented as a physical server or a virtual server. Alternatively, in other embodiments, the computer device may also be implemented as a desktop, a laptop, a tablet computer, a smart phone, etc., or a cluster thereof.

[0201] The computer device 900 includes a bus 901, a processor 902, and a memory 903. The processor 902 and the memory 903 can communicate with each other through the bus 901. The memory 903 can store a computer program or instruction. The processor 902 implements the functions of the signal processing unit or the execution control device in the previous embodiment by running the computer program or instruction in the memory 903.

[0202] The bus 901 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, although only one thick line is used in the figure, it does not mean that there is only one bus or one type of bus.

[0203] In some embodiments, the processor 902 may be implemented as a central processing unit (CPU), a microcontroller unit (MCU), a system on chip (System On Chip), or a field programmable logic array (FPGA). The memory 903 may include a volatile memory (Volatile Memory) for temporary storage of data when running a program, such as a random access memory (Random Access Memory, RAM).

[0204] The memory 903 may also include a non-volatile memory (non-volatile memory) for data storage, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state disk (SSD).

[0205] In some embodiments, the computer unit 900 may further include a communicator 904. The communicator 904 is used to communicate with the outside. In a specific example, the communicator 904 may include one or a group of wired and / or wireless communication circuit modules. For example, the communicator 904 may include one or more of a wired network card, a USB module, a serial interface module, etc. The wireless communication protocol followed by the wireless communication module includes: for example, near field communication (NFC) technology, infrared (IR) technology, Global System for Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Bluetooth (BT), Global Navigation Satellite System (GNSS), etc. One or more.

[0206] In an embodiment of the present disclosure, a computer-readable storage medium may be provided, storing a computer program or instruction. When the computer program or instruction is executed, the heterogeneous flexible load aggregation processing method or its steps in the previous embodiment are implemented.

[0207] That is, the method steps in the above embodiments are implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or are implemented as computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded via a network and to be stored in a local recording medium, so that the method represented herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA).

[0208] In the embodiments of the present disclosure, a computer program product may be provided, one or more computer programs or instructions, which, when executed, fully or partially execute the heterogeneous flexible load aggregation processing method or its steps in the previous embodiments. The computer program product includes one or more computer programs or instructions.

[0209] The computer program or instructions may be stored in a readable storage medium or transmitted from one readable storage medium to another readable storage medium, for example, the computer program or instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means. The readable storage medium may be any available medium that can be accessed or a data storage device such as a server, data center, etc. that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it may also be an optical medium, such as a digital video disk; it may also be a semiconductor medium, such as a solid state drive. The computer readable storage medium may be a volatile or non-volatile storage medium, or may include both volatile and non-volatile types of storage media.

[0210] In summary, the disclosed embodiments provide a heterogeneous flexible load aggregation processing method and device and a virtual power plant platform, the method comprising: constructing state evolution models according to the time domain state evolution characteristics of each of the various flexible load devices in the heterogeneous flexible load device cluster; the types of the various flexible load devices include temperature control load devices, electric vehicles and energy storage devices; based on the time correspondence between the state evolution models of various flexible load devices in the time domain, aggregating the state evolution models to obtain a flexible load aggregation model; based on the flexible load aggregation model and the constraints of each flexible load device, predicting the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain. By establishing time domain evolution models according to the respective characteristics of different types of flexible load devices and forming an aggregation of a unified time scale, the problem of inconsistent time scales between system and device control in the related technology is solved, and the accuracy of regulation of adjustable resources is improved.

[0211] The above embodiments are merely illustrative of the principles and effects of the present disclosure, and are not intended to limit the present disclosure. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present disclosure. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed in the present disclosure shall still be covered by the protection scope of the present disclosure.

Claims

1. A method for processing heterogeneous flexible load aggregation, characterized in that: include: State evolution models are constructed based on the time domain state evolution characteristics of various flexible load devices in a heterogeneous flexible load device cluster. The types of the various flexible load devices include temperature-controlled load devices, electric vehicles and energy storage devices, including: constructing a first-order or multi-order equivalent thermal parameter evolution model of the corresponding temperature-controlled load device in the time domain; the equivalent thermal parameter evolution model describes the indoor temperature at the current moment changes to the indoor temperature at the next moment under the influence of each temperature-controlled load device starting / stopping according to the user's wishes and the outdoor temperature; constructing a first charge state evolution model of the corresponding electric vehicle in the time domain; the first charge state evolution model describes each The first state of charge of each of the electric vehicles changes from the current moment to the next moment due to the charge amount; the charge amount is the amount of electricity charged in a certain period of time according to the charging power and charging efficiency of the electric vehicle; a second state of charge evolution model of the corresponding energy storage device in the time domain is constructed; the second state of charge evolution model describes the change of the second state of charge of each of the energy storage devices from the current moment to the next moment due to the charge / discharge amount; the charge / discharge amount is: the amount of electricity charged in a certain period of time according to the charging power and charging efficiency of the energy storage device, or the amount of electricity released in a certain period of time according to the discharge power and discharge efficiency of the energy storage device; The equivalent thermal parameter evolution model is expressed as: ； in, for t +1 The outdoor temperature at time; and They are t Moment and t +1 Indoor temperature at time; Q Is the equivalent cooling / heating power of the temperature control load equipment; C is the equivalent heat capacity; R is the equivalent thermal resistance; is the start / stop state variable of the temperature control load device, 1 indicates the on state, and 0 indicates the off state; Δ t for t to t+1 time slot; The first state of charge evolution model is expressed as: ； in, and are the first charge states of the electric vehicle at time t and time t+1 respectively; Charging efficiency for electric vehicles; is the rated charging power of the electric vehicle; is the rated on-board battery capacity of the electric vehicle; the second state of charge evolution model is expressed as: ； ； in, and are the second charge states of the energy storage device at time t and time t+1 respectively; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the rated charging power of the energy storage device; is the rated energy storage capacity of the energy storage device; Based on the time correspondence relationship between the state evolution models of various flexible load devices in the time domain, aggregating the state evolution models to obtain a flexible load aggregation model; The flexible load aggregation model is expressed as: ； Among them, ； ； ； ； in, for t +1 The outdoor temperature at time; and They are t Moment and t +1 Indoor temperature at time; Q Is the equivalent cooling / heating power of the temperature control load equipment; C is the equivalent heat capacity; R is the equivalent thermal resistance; is the start / stop state variable of the temperature control load device, 1 indicates the on state, and 0 indicates the off state; Δ t for t to t+1 time slot; and are the first charge states of the electric vehicle at time t and time t+1 respectively; Charging efficiency for electric vehicles; is the rated charging power of the electric vehicle; is the rated onboard battery capacity of the electric vehicle; and are the second charge states of the energy storage device at time t and time t+1 respectively; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the rated charging power of the energy storage device; is the rated energy storage capacity of the energy storage device; The predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain is predicted based on the flexible load aggregation model and the constraints of each flexible load device.

2. The heterogeneous flexible load aggregation processing method according to claim 1 is characterized in that: Based on the time correspondence relationship of the state evolution models of the flexible load devices in the time domain, the state evolution models are aggregated and combined with the number of devices of each flexible load device to obtain a flexible load aggregation model, including: The equivalent thermal parameter evolution model, the first state of charge evolution model and the second state of charge evolution model are combined to obtain a flexible load aggregation model; the flexible load aggregation model describes the matrix operation relationship from the indoor temperature, the first state of charge of the electric vehicle and the second state of charge of the energy storage device at the current moment to the indoor temperature, the first state of charge and the second state of charge at the next moment.

3. The heterogeneous flexible load aggregation processing method according to claim 1 is characterized in that: The number of temperature control load devices is , the number of electric vehicles is , the number of energy storage devices is ; The total dimension of the indoor temperature, the first state of charge and the second state of charge is .

4. The heterogeneous flexible load aggregation processing method according to claim 1 is characterized in that: The constraints of each flexible load device include at least one of the following: 1) The constraints of the temperature control load device include: an upper threshold of the indoor temperature that triggers the user to turn off / on the temperature control load device, and a lower threshold of the indoor temperature that triggers the user to turn on / off the temperature control load device; the upper temperature threshold and the lower temperature threshold are determined by a temperature setting value offset upward and downward by a user's desired offset temperature value respectively; 2) The constraints of the electric vehicle include: the current moment of the electric vehicle is not yet fully charged; the first state of charge is within the range of [0, 1]; 3) The constraints of the energy storage device include: the second state of charge is in the range of [0, 1].

5. The heterogeneous flexible load aggregation processing method according to claim 1 is characterized in that: The constraints of each flexible load device include at least one of the following: 1) The constraints of the temperature control load equipment include: ； Where δ is the user's acceptance of the temperature offset, is the indoor temperature, is the set temperature value, and They are the upper and lower thresholds of the indoor temperature for triggering the closing / opening and opening / closing of the temperature control load device respectively; 2) The constraints on electric vehicles and energy storage equipment include: ； in, Expected charging time for electric vehicles; The state of charge of the energy storage device; , They are respectively a collection of electric vehicles and energy storage devices.

6. The heterogeneous flexible load aggregation processing method according to claim 1 is characterized in that: The predicting of the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain based on the flexible load aggregation model and the constraints of each flexible load device includes: Predicted adjustable capacity increase and predicted adjustable capacity reduction; The predicted adjustable capacity reduction is defined as: the total operating power of the heterogeneous flexible load device cluster determined by weighted calculation of the switch state and operating power of each flexible load device; The predicted adjustable capacity increase is defined as: the total operating power of the heterogeneous flexible load device cluster determined by weighted calculation of the opposite states of the switch states of each flexible load device and the same operating power; Among them, the switching states of the temperature-controlled load device, the electric vehicle and the energy storage device are affected by their respective constrained boundaries; the constrained boundaries of the temperature-controlled load device include: the upper limit threshold of the indoor temperature that triggers the user to turn off / on the temperature-controlled load device, and the lower limit threshold of the indoor temperature that triggers the user to turn on / off the temperature-controlled load device; the upper temperature limit threshold and the lower temperature limit threshold are determined by a temperature setting value offset upward and downward by a user-intentioned offset temperature value respectively; the constraint boundaries of the electric vehicle include: the full charge moment that triggers the disconnection of charging.

7. The heterogeneous flexible load aggregation processing method according to claim 1 or 6, characterized in that: The predicted adjustable capacity is expressed as: ； in, , They are respectively the predicted adjustable capacity increase and the predicted adjustable capacity decrease of the heterogeneous flexible load equipment cluster; , , They are the switch states of temperature control load equipment, electric vehicles and energy storage equipment, 0 represents off, 1 represents on; s=0 means that the initial state of each switch is off; , , are the operating powers of temperature control load equipment, electric vehicles, and energy storage equipment respectively; among them, ; Q is the equivalent cooling / heating power of the temperature control load equipment; The heating or cooling efficiency of the temperature-controlled load equipment; , , They are the number of temperature control load equipment, electric vehicles, and energy storage equipment respectively.

8. The heterogeneous flexible load aggregation processing method according to claim 1 or 2, characterized in that: When the outdoor temperature change does not exceed the preset range, it is determined that the corresponding parameters in the flexible load aggregation model remain unchanged; When the outdoor temperature changes beyond the preset range, each time period with similar temperature changes is divided into time periods to determine the representative outdoor temperature of each time period, and the corresponding parameters in the flexible load aggregation model are updated in the corresponding time period according to the representative outdoor temperatures of different time periods.

9. A heterogeneous flexible load aggregation processing device applied to a virtual power plant, characterized in that: For executing the method according to any one of claims 1 to 8, the heterogeneous flexible load aggregation processing device comprises: A model building module, for building state evolution models according to the time domain state evolution characteristics of various flexible load devices in a heterogeneous flexible load device cluster; the types of the various flexible load devices include temperature control load devices, electric vehicles and energy storage devices; An aggregation model module, used for aggregating the state evolution models of various flexible load devices based on the time correspondence relationship in the time domain to obtain a flexible load aggregation model; The adjustable capacity prediction module is used to predict the predicted adjustable capacity of the heterogeneous flexible load device cluster in the time domain based on the flexible load aggregation model and the constraints of each flexible load device.

10. A virtual power plant platform, characterized in that: include: At least one computer device, comprising: a processor and a memory; The memory stores computer programs or instructions; The processor is used to run the computer program or instructions to execute the heterogeneous flexible load aggregation processing method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: A computer program or instruction is stored, and the computer program or instruction is executed to execute the heterogeneous flexible load aggregation processing method as described in any one of claims 1 to 8.

12. A computer program product, characterized in that include: A computer program or instruction for executing the heterogeneous flexible load aggregation processing method as described in any one of claims 1 to 8.

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