Demand response resource dynamic allocation method and device for virtual power plants

Abstract

The present invention provides a method and device for dynamically allocating demand response resources for virtual power plants, wherein the method includes the following steps: collecting load regulation characteristic parameters of various types of users in the virtual power plant; establishing an objective function of the user's comprehensive regulation cost and corresponding constraints based on the load regulation characteristic parameters; constructing a mixed integer linear programming problem based on the objective function and the constraints, and solving it using a branch and bound algorithm to obtain a dynamic allocation scheme for demand response resources. The method for dynamically allocating demand response resources for virtual power plants according to the present invention can not only improve the fairness and economy of the allocation scheme, but also effectively improve the implementation effect of demand response.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant operation optimization, and in particular to a method for dynamically allocating demand response resources for virtual power plants and a device for dynamically allocating demand response resources for virtual power plants. Background Art

[0002] Demand-side response is one of the main forms of virtual power plants. Its basic function is to use advanced information communication and intelligent sensing technologies to aggregate the load resources of various industrial and commercial users to form a unified load resource pool for power grid dispatch, thereby improving the operational flexibility of the power grid and achieving safe and economical operation of the system.

[0003] In related technologies, when responding to grid regulation needs, virtual power plants mainly determine the load resources participating in the response based on simple screening rules. This not only fails to guarantee the fairness and economy of the response plan, but also makes it difficult to take into account complex operating constraints, resulting in an overly extensive demand response implementation plan, which is not conducive to the long-term and healthy development of virtual power plants. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for dynamic allocation of demand response resources for virtual power plants, which can not only improve the fairness and economy of the allocation scheme, but also effectively improve the implementation effect of demand response.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for dynamic allocation of demand response resources for virtual power plants comprises the following steps: collecting load regulation characteristic parameters of various users in the virtual power plant; establishing an objective function of the user's comprehensive regulation cost and corresponding constraints according to the load regulation characteristic parameters; constructing a mixed integer linear programming problem according to the objective function and the constraints, and solving it using a branch and bound algorithm to obtain a dynamic allocation plan for demand response resources.

[0007] The load regulation characteristic parameters include: the regulation time and regulation capacity range allowed by the user, the unit regulation cost, the load regulation duration and recovery time, the credit rating score, and the current operating status of the load.

[0008] The objective function is:

[0009]

[0010] in, The upward adjustment capacity for the nth user in time period t; The downward adjustment capacity for the nth user in time period t; The unit adjustment cost for adjusting upward for the nth user; The unit adjustment cost for adjusting downward for the nth user; c n is the credit rating score of the nth user; Δt is the duration of each time period; T is the total number of optimized time periods; N is the number of users participating in the demand response calculation.

[0011] The constraint condition includes a user-adjustable capacity range, wherein the user-adjustable capacity range satisfies the following formula:

[0012]

[0013] in, The minimum adjustment capacity when adjusting upward for the nth user; The maximum adjustment capacity when adjusting upward for the nth user; The minimum adjustment capacity when adjusting downward for the nth user; The maximum adjustment capacity when adjusting downward for the nth user; is the state variable adjusted upward by the nth user in the tth period, wherein if the nth user adjusts upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 1, and if the nth user does not adjust upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 0; is the state variable adjusted downward by the nth user in the tth period, wherein if the nth user adjusts downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 1, and if the nth user does not adjust downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 0.

[0014] The constraint condition also includes that the user adjustment capacity is maintained at a constant value within a duration, wherein the user adjustment power satisfies the following formula:

[0015]

[0016] Where M is a positive number. is the upward adjustment auxiliary variable introduced, is an auxiliary variable introduced for downward adjustment.

[0017] The constraint condition also includes that the user adjustment duration is within an allowable range, wherein the user adjustment duration satisfies the following formula:

[0018]

[0019] in, is the minimum upward adjustment duration, is the maximum upward adjustment duration,

[0020] is the minimum down adjustment duration, is the maximum downward adjustment duration.

[0021] The constraint condition also includes the recovery time after the user completes the adjustment instruction, wherein the recovery time satisfies the following formula:

[0022]

[0023] in, The recovery time adjusted upward for the nth user, The recovery time adjusted downward for the nth user.

[0024] The constraint condition also includes that the sum of the adjustment capacities of the users participating in the response meets the minimum adjustment requirement of the system, wherein the sum of the adjustment capacities of the users participating in the response satisfies the following formula:

[0025]

[0026] Among them, η n is the sensitivity coefficient of the nth user; They are respectively the upward and downward adjustment indicators for period t issued by the dispatcher.

[0027] The constraint condition also includes a user-adjusted starting state, wherein the user-adjusted starting state satisfies the following formula:

[0028]

[0029] in, is the upward adjustment state sequence of the nth user in time period t, where t=1, 2, ..., T.

[0030] A device for dynamically allocating demand response resources for a virtual power plant comprises: a collection module, which is used to collect load regulation characteristic parameters of various users in the virtual power plant; an establishment module, which is used to establish an objective function of the user's comprehensive regulation cost and corresponding constraints according to the load regulation characteristic parameters; and an acquisition module, which is used to construct a mixed integer linear programming problem according to the objective function and the constraints, and solve it using a branch and bound algorithm to obtain a dynamic allocation plan for demand response resources.

[0031] The beneficial effects of the present invention are as follows: the present invention can not only improve the fairness and economy of the allocation scheme, but also effectively improve the implementation effect of demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1A flowchart of a method for dynamically allocating demand response resources for a virtual power plant according to an embodiment of the present invention;

[0033] Figure 2 A block diagram of a demand response resource dynamic allocation device for a virtual power plant according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Figure 1 It is a flowchart of a method for dynamic allocation of demand response resources for a virtual power plant according to an embodiment of the present invention.

[0036] Specifically, Figure 1 As shown, the method for dynamic allocation of demand response resources for virtual power plants according to an embodiment of the present invention may include the following steps:

[0037] S1, collects the load regulation characteristic parameters of various users in the virtual power plant.

[0038] The load regulation characteristic parameters may include: the regulation time and regulation capacity range allowed by the user (for example, the range of regulation power), unit regulation cost, load regulation duration and recovery time, credit rating score, and current load operation status. The credit rating score can be divided into five levels from 1 to 5, with the higher the level, the better the credit.

[0039] S2, establish the objective function of the user's comprehensive regulation cost and the corresponding constraints according to the load regulation characteristic parameters.

[0040] According to one embodiment of the present invention, the objective function is:

[0041]

[0042] in, The upward adjustment capacity for the nth user in time period t; The downward adjustment capacity for the nth user in time period t; The unit adjustment cost for adjusting upward for the nth user; The unit adjustment cost for adjusting downward for the nth user; c n is the credit rating score of the nth user; Δt is the duration of each time period; T is the total number of optimized time periods; N is the number of users participating in the demand response calculation.

[0043] According to one embodiment of the present invention, the constraint condition includes a user-adjustable capacity range, wherein the user-adjustable capacity range satisfies the following formula:

[0044]

[0045] in, The minimum adjustment capacity when adjusting upward for the nth user; The maximum adjustment capacity when adjusting upward for the nth user; The minimum adjustment capacity when adjusting downward for the nth user; The maximum adjustment capacity when adjusting downward for the nth user; is the state variable adjusted upward by the nth user in the tth period, wherein if the nth user adjusts upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 1, and if the nth user does not adjust upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 0; is the state variable adjusted downward by the nth user in the tth period, wherein if the nth user adjusts downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 1, and if the nth user does not adjust downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 0.

[0046] That is to say, the user's adjustable capacity should be within the range of its minimum and maximum adjustable capacity, and at most only one of the upward adjustment and downward adjustment states is allowed to be established.

[0047] According to an embodiment of the present invention, the constraint condition further includes that the user adjustment capacity is maintained at a constant value within a duration, wherein the user adjustment power satisfies the following formula:

[0048]

[0049]

[0050] Among them, M is a positive number (M is a large positive number), is the upward adjustment auxiliary variable introduced, is an auxiliary variable introduced for downward adjustment.

[0051] Specifically, formula (2), formula (5) and formula (6) ensure that the adjustment amount is a constant when the adjustment index is assigned to the nth user, that is, the adjustment capacity of all adjustment periods is one value (0 and a positive value), and multiple values ​​of the adjustment capacity are not allowed. For example, the adjustment capacity allocated to the nth user in the first period is 10. Assuming that the nth user is still in the upward adjustment state in the second and third periods, the adjustment capacity in the second and third periods is also 10. The reason for introducing the above constraints is that it is impractical for the nth user to frequently change its load or output by tracking the adjustment instructions. The best strategy is to allocate only one adjustment capacity within a period of time, which can significantly reduce the adjustment difficulty of the nth user and improve its enthusiasm and subjective willingness. Formula (3), formula (7) and formula (8) have similar functions and will not be repeated here.

[0052] According to an embodiment of the present invention, the constraint condition further includes that the user adjustment duration is within an allowable range, wherein the user adjustment duration satisfies the following formula:

[0053]

[0054] in, is the minimum upward adjustment duration, is the maximum upward adjustment duration,

[0055] is the minimum down adjustment duration, is the maximum downward adjustment duration.

[0056] According to an embodiment of the present invention, the constraint condition further includes a recovery time after the user completes the adjustment instruction, wherein the recovery time satisfies the following formula:

[0057]

[0058] in, The recovery time adjusted upward for the nth user, The recovery time adjusted downward for the nth user.

[0059] That is to say, after the user completes the adjustment instruction, it takes a while to recover before it is allowed to be called again.

[0060] According to an embodiment of the present invention, the constraint condition further includes that the sum of the adjustment capacities of the users participating in the response meets the minimum adjustment requirement of the system, wherein the sum of the adjustment capacities of the users participating in the response satisfies the following formula:

[0061]

[0062] Among them, η n is the sensitivity coefficient of the nth user; They are respectively the upward and downward adjustment indicators for period t issued by the dispatcher.

[0063] According to one embodiment of the present invention, the constraint condition further includes a starting state adjusted by the user, wherein the starting state adjusted by the user satisfies the following formula:

[0064]

[0065] in, is the upward adjustment state sequence of the nth user in time period t, where t=1, 2, ..., T. The upward adjustment state sequence of the nth user in time period t can be determined in advance according to factors such as the initial state, climbing time, and recovery time of the nth user.

[0066] S3, construct a mixed integer linear programming problem based on the objective function and constraints, and solve it using the branch and bound algorithm to obtain a dynamic allocation scheme for demand response resources.

[0067] Specifically, the objective function (i.e., formula (1)) and the constraints (i.e., formula (2)-formula (18)) are combined to construct a mixed integer linear programming problem, and the branch and bound algorithm is used to solve it to obtain the adjustment plan for each user, i.e.

[0068] Therefore, based on the regulation characteristics (including regulation cost, response time, credit rating, etc.) of different users in the load resource pool, the present invention takes the minimum comprehensive response cost as the optimization goal, considers the constraints such as regulation demand, recovery time, duration, and regulation range, forms a mixed integer linear optimization problem and solves it, thereby obtaining the optimal load distribution scheme that meets the regulation demand of the power grid. It not only improves the fairness and economy of the load distribution scheme, but also avoids the subjective arbitrariness in the distribution process, and is conducive to improving the implementation effect of demand response.

[0069] In summary, according to the method for dynamic allocation of demand response resources for virtual power plants in an embodiment of the present invention, the load regulation characteristic parameters of various users in the virtual power plant are collected, and the objective function of the comprehensive regulation cost of the user and the corresponding constraints are established according to the load regulation characteristic parameters, and a mixed integer linear programming problem is constructed according to the objective function and the constraints, and the branch and bound algorithm is used to solve it to obtain a dynamic allocation scheme for demand response resources. In this way, not only the fairness and economy of the allocation scheme can be improved, but also the implementation effect of demand response can be effectively improved.

[0070] Corresponding to the method for dynamic allocation of demand response resources for virtual power plants in the above-mentioned embodiment, the present invention also proposes a device for dynamic allocation of demand response resources for virtual power plants.

[0071] like Figure 2 As shown, the demand response resource dynamic allocation device for virtual power plants according to an embodiment of the present invention may include: a collection module 100 , an establishment module 200 and an acquisition module 300 .

[0072] Among them, the collection module 100 is used to collect the load regulation characteristic parameters of various users in the virtual power plant; the establishment module 200 is used to establish the objective function of the user's comprehensive regulation cost and the corresponding constraints according to the load regulation characteristic parameters; the acquisition module 300 is used to construct a mixed integer linear programming problem according to the objective function and the constraints, and use the branch and bound algorithm to solve it, so as to obtain the dynamic allocation plan of demand response resources.

[0073] According to one embodiment of the present invention, the load regulation characteristic parameters include: the regulation time and regulation capacity range allowed by the user, the unit regulation cost, the load regulation duration and recovery time, the credit rating score, and the current operating status of the load.

[0074] According to one embodiment of the present invention, the objective function is:

[0075]

[0076] in, The upward adjustment capacity for the nth user in time period t; The downward adjustment capacity for the nth user in time period t; The unit adjustment cost for adjusting upward for the nth user; The unit adjustment cost for adjusting downward for the nth user; c n is the credit rating score of the nth user; Δt is the duration of each time period; T is the total number of optimized time periods; N is the number of users participating in the demand response calculation.

[0077] According to one embodiment of the present invention, the constraint condition includes a user-adjustable capacity range, wherein the user-adjustable capacity range satisfies the following formula:

[0078]

[0079] in, The minimum adjustment capacity when adjusting upward for the nth user; The maximum adjustment capacity when adjusting upward for the nth user; The minimum adjustment capacity when adjusting downward for the nth user; The maximum adjustment capacity when adjusting downward for the nth user; is the state variable adjusted upward by the nth user in the tth period, wherein if the nth user adjusts upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 1, and if the nth user does not adjust upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 0; is the state variable adjusted downward by the nth user in the tth period, wherein if the nth user adjusts downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 1, and if the nth user does not adjust downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 0.

[0080] According to an embodiment of the present invention, the constraint condition further includes that the user adjustment capacity is maintained at a constant value within a duration, wherein the user adjustment power satisfies the following formula:

[0081]

[0082] Where M is a positive number. is the upward adjustment auxiliary variable introduced, is an auxiliary variable introduced for downward adjustment.

[0083] According to an embodiment of the present invention, the constraint condition further includes that the user adjustment duration is within an allowable range, wherein the user adjustment duration satisfies the following formula:

[0084]

[0085] in, is the minimum upward adjustment duration, is the maximum upward adjustment duration,

[0086] is the minimum down adjustment duration, is the maximum downward adjustment duration.

[0087] According to an embodiment of the present invention, the constraint condition further includes a recovery time after the user completes the adjustment instruction, wherein the recovery time satisfies the following formula:

[0088]

[0089]

[0090] in, The recovery time adjusted upward for the nth user, The recovery time adjusted downward for the nth user.

[0091] According to an embodiment of the present invention, the constraint condition further includes that the sum of the adjustment capacities of the users participating in the response meets the minimum adjustment requirement of the system, wherein the sum of the adjustment capacities of the users participating in the response satisfies the following formula:

[0092]

[0093] Among them, η n is the sensitivity coefficient of the nth user; They are respectively the upward and downward adjustment indicators for period t issued by the dispatcher.

[0094] According to one embodiment of the present invention, the constraint condition further includes a starting state adjusted by the user, wherein the starting state adjusted by the user satisfies the following formula:

[0095]

[0096] in, is the upward adjustment state sequence of the nth user in time period t, where t=1, 2, ..., T.

[0097] It should be noted that a more specific implementation of the demand response resource dynamic allocation device for a virtual power plant in an embodiment of the present invention can refer to the above-mentioned embodiment of the demand response resource dynamic allocation method for a virtual power plant, which will not be repeated here.

[0098] According to the dynamic allocation device for demand response resources for virtual power plants in the embodiment of the present invention, the load regulation characteristic parameters of various users in the virtual power plant are collected through the collection module, and the objective function and corresponding constraints of the comprehensive regulation cost of the users are established according to the load regulation characteristic parameters through the establishment module, and the mixed integer linear programming problem is constructed according to the objective function and the constraints through the acquisition module, and the branch and bound algorithm is used to solve it to obtain the dynamic allocation plan of demand response resources. In this way, not only the fairness and economy of the allocation plan can be improved, but also the implementation effect of demand response can be effectively improved.

[0099] In the description of the present invention, 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 the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0100] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0101] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0102] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0103] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0104] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0105] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0106] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0107] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0108] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A dynamic allocation method of demand response resources for virtual power plants. It is characterized in that The following steps are involved: Collecting load regulation characteristic parameters of various users in the virtual power plant; The load regulation characteristic parameters include: the regulation time and regulation capacity range allowed by the user, unit regulation cost, load regulation duration and recovery time, credit rating score, and current operating status of the load; The objective function of the user's comprehensive regulation cost and the corresponding constraint conditions are established according to the load regulation characteristic parameters; wherein the objective function is: in, The upward adjustment capacity for the nth user in time period t; The downward adjustment capacity for the nth user in time period t; The unit adjustment cost for adjusting upward for the nth user; The unit adjustment cost for adjusting downward for the nth user; c n is the credit rating score of the nth user; Δt is the duration of each period; T is the total number of optimized periods; N is the number of users participating in the demand response calculation; where, The constraint condition includes a user-adjustable capacity range, wherein the user-adjustable capacity range satisfies the following formula: in, The minimum adjustment capacity when adjusting upward for the nth user; The maximum adjustment capacity when adjusting upward for the nth user; The minimum adjustment capacity when adjusting downward for the nth user; The maximum adjustment capacity when adjusting downward for the nth user; is the state variable adjusted upward by the nth user in the tth period, wherein if the nth user adjusts upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 1, and if the nth user does not adjust upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 0; is the state variable adjusted downward by the nth user in the tth period, wherein if the nth user adjusts downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 1, and if the nth user does not adjust downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 0; the constraint condition also includes that the user adjustment capacity is maintained at a constant value within the duration, wherein the user adjustment power satisfies the following formula: Where M is a positive number. is the upward adjustment auxiliary variable introduced, is an introduced downward adjustment auxiliary variable; the constraint condition also includes that the user adjustment duration is within the allowable range, wherein the user adjustment duration satisfies the following formula: in, is the minimum upward adjustment duration, is the maximum upward adjustment duration, is the minimum down adjustment duration, is the maximum downward adjustment duration; the constraint condition also includes the recovery time after the user completes the adjustment instruction, wherein the recovery time satisfies the following formula: in, The recovery time adjusted upward for the nth user, is the recovery time of the nth user's downward adjustment; the constraint condition also includes that the sum of the adjustment capacities of the users participating in the response meets the minimum adjustment requirement of the system, wherein the sum of the adjustment capacities of the responding users satisfies the following formula: Among them, η n is the sensitivity coefficient of the nth user; are respectively the upward and downward adjustment indicators of the t period issued by the scheduler; the constraint condition also includes the starting state of the user adjustment, wherein the starting state of the user adjustment satisfies the following formula: in, is the upward adjustment state sequence of the nth user in time period t, where t = 1, 2, ..., T; A mixed integer linear programming problem is constructed according to the objective function and the constraint conditions, and is solved by using a branch and bound algorithm to obtain a dynamic allocation scheme for demand response resources.

2. A dynamic resource allocation device for demand response of virtual power plants, It is characterized in that include: A collection module, the collection module is used to collect load regulation characteristic parameters of various users in the virtual power plant; wherein the load regulation characteristic parameters include: the regulation time and regulation capacity range allowed by the user, the unit regulation cost, the load regulation duration and recovery time, the credit rating score, and the current operating status of the load; An establishment module is used to establish an objective function of the user's comprehensive regulation cost and corresponding constraints according to the load regulation characteristic parameters; wherein the objective function is: in, The upward adjustment capacity for the nth user in time period t; The downward adjustment capacity for the nth user in time period t; The unit adjustment cost for adjusting upward for the nth user; The unit adjustment cost for adjusting downward for the nth user; c n is the credit rating score of the nth user; Δt is the duration of each period; T is the total number of optimized periods; N is the number of users participating in the demand response calculation; where, The constraint condition includes a user-adjustable capacity range, wherein the user-adjustable capacity range satisfies the following formula: in, The minimum adjustment capacity when adjusting upward for the nth user; The maximum adjustment capacity when adjusting upward for the nth user; The minimum adjustment capacity when adjusting downward for the nth user; The maximum adjustment capacity when adjusting downward for the nth user; is the state variable adjusted upward by the nth user in the tth period, wherein if the nth user adjusts upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 1, and if the nth user does not adjust upward in the tth period, the state variable adjusted upward by the nth user in the tth period is 0; is the state variable adjusted downward by the nth user in the tth period, wherein if the nth user adjusts downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 1, and if the nth user does not adjust downward in the tth period, the state variable adjusted downward by the nth user in the tth period is 0; the constraint condition also includes that the user adjustment capacity is maintained at a constant value within the duration, wherein the user adjustment power satisfies the following formula: where M is a positive number, is an introduced upward adjustment auxiliary variable, is an introduced downward adjustment auxiliary variable; the constraint condition further includes that the user adjustment duration is within an allowable range, where the user adjustment duration satisfies the following formula: in, is the minimum upward adjustment duration, is the maximum upward adjustment duration, is the minimum down adjustment duration, is the maximum downward adjustment duration; the constraint condition also includes the recovery time after the user completes the adjustment instruction, wherein the recovery time satisfies the following formula: in, The recovery time adjusted upward for the nth user, is the recovery time of the nth user's downward adjustment; the constraint condition also includes that the sum of the adjustment capacities of the users participating in the response meets the minimum adjustment requirement of the system, wherein the sum of the adjustment capacities of the responding users satisfies the following formula: Among them, η n is the sensitivity coefficient of the nth user; are respectively the upward and downward adjustment indicators of the t period issued by the scheduler; the constraint condition also includes the starting state of the user adjustment, wherein the starting state of the user adjustment satisfies the following formula: in, is the upward adjustment state sequence of the nth user in time period t, where t = 1, 2, ..., T; An acquisition module is used to construct a mixed integer linear programming problem according to the objective function and the constraint conditions, and solve it using a branch and bound algorithm to obtain a demand response resource dynamic allocation solution.

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