An Aggregation Method and System Applicable to Different Types of Demand Response Resources

Abstract

The present invention discloses an aggregation method and system applicable to different types of demand response resources. The specific method includes: defining parameter sets and variable sets shared by various types of demand response resources, constructing constraint condition sets reflecting different response characteristics of demand response resources, constructing objective function sets associated with response directions and response time periods in external input information of the aggregation system, and establishing an adjustable capacity evaluation system; screening parameter sets and constraint condition sets in the evaluation system according to the input information of individual demand response resources, and screening the objective function set according to external input information to obtain constraint conditions and objective functions of the adjustable capacity evaluation optimization problem, and solving to obtain the adjustable capacity of individual demand response resources. The present invention is applicable to systems that aggregate different types of demand response resources. The system is simple and effective, and can fully aggregate various types of demand response resources of the power grid, tap their adjustable potential, promote the consumption of new energy, and improve the reliability of power grid operation.

Description

Technical Field

[0001] The present invention relates to the technical field of power demand side management, and in particular, to an aggregation method and system for different types of demand response resources. Background Art

[0002] Demand Response (DR) is one of the important components of power grid demand side management (DSM). In the future energy system with a large amount of renewable energy, demand side management and demand response will play a key role. When the power grid is operating in a state of tight power supply and demand, demand response can make full use of the adjustable resources on the load side to achieve peak shaving and valley filling, thus avoiding the commissioning of expensive peak standby units and improving the economic operation of the power grid. At the same time, demand response can provide flexible resources for power grid dispatching and is one of the important means to promote the consumption of new energy.

[0003] Demand response resources widely exist in the power grid load side. Due to their small scale, large quantity and scattered distribution, it is not conducive to the power grid to observe and control the scattered demand response resources. Therefore, it is necessary to aggregate the demand response resources, and a unified aggregation system is used to regulate and manage the demand response resources. Since the types of demand response resources are diverse, including temperature-controlled loads, washing machines, electric vehicles, etc., the physical processes and electrical characteristics of demand response are different due to different resource types. In the existing technologies, there is still a lack of an aggregation method and system for different types of demand response resources. In the existing aggregation methods, the modeling technology for demand response resources is usually too simplified, only considering some demand response characteristics, such as simple load curtailment and load transfer, etc., without detailed modeling of response time, load transfer time, etc., and is not applicable to aggregating different types of demand response resources. On the other hand, the modeling technology oriented by resource types and relying on the physical processes and electrical characteristics of demand response is too complex, requiring a large number of physical or electrical parameters, and the expressions used to describe demand response characteristics are usually non-linear, which is not suitable for calculating large-scale demand response resources in the aggregation system.

[0004] An important function in the aggregation system is to evaluate the adjustable capacity of each demand response resource based on the improved modeling technology. On the one hand, it can be used to verify the effectiveness of the improved modeling technology, and on the other hand, it can formulate the operation strategy of demand response resources according to the evaluation results. The existing adjustable capacity evaluation methods at home and abroad are mostly based on the historical data of load regulation and the method of elastic electricity price. However, due to the certain randomness of electricity consumption load and the lack of data on electricity price elasticity coefficients of various demand response resources in China, the above methods cannot accurately evaluate the adjustable capacity of demand response resources.

[0005] In summary, there is an urgent need to propose an aggregation system applicable to different types of demand response resources, especially a function for evaluating the adjustable capacity of demand response resources based on improved modeling techniques. Summary of the Invention

[0006] The object of the present invention is to provide an aggregation method and system applicable to different types of demand response resources, so as to solve the technical problem in the prior art that the evaluation of the adjustable capacity of demand response resources cannot be realized due to the lack of modeling techniques applicable to different types of demand response resources.

[0007] The object of the present invention can be achieved through the following technical solutions:

[0008] An aggregation method applicable to different types of demand response resources, which is applied to an aggregation system. The method includes:

[0009] Define a parameter set and a variable set shared by different types of demand response resources, and establish a constraint condition set and an objective function set shared by different types of demand response resources according to the parameter set and the variable set. The objective function set is related to the response direction and response period in the external input information of the aggregation system. The response direction includes a load reduction response direction and a load increase response direction;

[0010] Screen the parameter set and the constraint condition set according to the input information of the individual demand response resources to obtain the constraint conditions for the adjustable capacity evaluation optimization problem; screen the objective function set according to the external input information of the aggregation system to obtain the objective function for the adjustable capacity evaluation optimization problem;

[0011] Solve for the adjustable capacity of the individual demand response resources according to the initial parameters and response modes in the input information of the individual demand response resources, the response direction in the external input information of the aggregation system, and the objective function and constraint conditions of the adjustable capacity evaluation optimization problem.

[0012] Optionally, establishing a constraint condition set shared by different types of demand response resources according to the parameter set and the variable set specifically includes:

[0013] Construct a general operation constraint model for various types of demand response resources according to the parameter set and the variable set, and obtain a constraint condition set shared by various types of demand response resources according to the operation constraint model.

[0014] Optionally, the constraint condition set specifically includes:

[0015] Load reduction constraint conditions and load transfer constraint conditions;

[0016] Among them, the load shedding constraint condition represents the constraint condition when the response mode of the single demand response resource is the load shedding mode or the load increase mode, and the load transfer constraint condition represents the constraint condition when the response mode of the single demand response resource is the load transfer mode.

[0017] Optionally, the load shedding constraint condition specifically includes:

[0018] The power curtailment constraint, the energy curtailment constraint, the response duration constraint, the response time constraint, the response frequency constraint, and the response interval constraint.

[0019] Optionally, the load transfer constraint condition specifically includes:

[0020] The load transfer power constraint, the mutual exclusion constraint, the energy storage constraint, and the load transfer time constraint.

[0021] Optionally, the input information of the single demand response resource specifically includes:

[0022] The initial parameters, operating parameters, resource type, and response mode of the single demand response resource.

[0023] Optionally, screening the parameter set and the constraint condition set according to the input information of the single demand response resource, and the constraint conditions for obtaining the adjustable capacity evaluation and optimization problem specifically include:

[0024] Screening the parameter set according to the operating parameters to obtain the screened parameters;

[0025] Screening the constraint condition set according to the resource type and the response mode, substituting the screened parameters into the constraint conditions, and obtaining the screened constraint conditions;

[0026] Establishing the constraint conditions for the adjustable capacity evaluation and optimization problem according to the screened constraint conditions.

[0027] Optionally, screening the objective function set according to the external input information of the aggregation system, and the objective function for obtaining the adjustable capacity evaluation and optimization problem specifically includes:

[0028] Screening the objective function set according to the response direction and response period in the external input information of the aggregation system to obtain the screened objective function;

[0029] Establishing the objective function for the adjustable capacity evaluation and optimization problem according to the screened objective function.

[0030] Optionally, according to the initial parameters and response methods in the monomer demand response resource input information, aggregating the response directions in the external input information of the system, as well as the objective function and constraint conditions of the adjustable capacity evaluation and optimization problem, the adjustable capacity of the monomer demand response resource obtained by solving includes:

[0031] When the response direction is the load shedding response direction and the response method is the load shedding response method or the load transfer response method, or when the response direction is the load increase response direction and the response method is the load increase response method or the load transfer response method, then it is necessary to solve the optimization problem, call the MILP solver to solve the adjustable capacity evaluation and optimization problem, and the calculation result is the adjustable capacity of the monomer demand response resource. The solution process is as follows:

[0032] First, substitute the initial parameters into the adjustable capacity evaluation and optimization problem;

[0033] Then, according to the different response methods in the monomer resource input information, it is necessary to discuss in different cases:

[0034] When the response method only has the load shedding response method, then output the power that can be shed;

[0035] When the response method only has the load increase response method, then output the power that can be increased;

[0036] When the response method has both the load shedding and load increase response methods and does not contain energy storage parameters, then output the power that can be shed and the power that can be increased at the same time;

[0037] When the response method has both the load shedding and load increase response methods and contains energy storage variables, then output the power that can be shed, the power that can be increased, and the energy storage value;

[0038] When the response direction is the load shedding response direction and the response method is the load increase response method, or when the response direction is the load increase response direction and the response method is the load shedding response method, then there is no need to solve the adjustable capacity evaluation and optimization problem, and the calculation result of the adjustable capacity of the monomer demand response resource is 0.

[0039] The present invention also provides an aggregation system applicable to different types of demand response resources, including:

[0040] A shared set construction module, used to define a parameter set and a variable set shared by different types of demand response resources, and establish a constraint condition set and an objective function set shared by different types of demand response resources according to the parameter set and the variable set. The objective function set is related to the response direction and response period in the external input information of the aggregation system, and the response direction includes the load shedding response direction and the load increase response direction;

[0041] An adjustable capacity evaluation problem construction module, which is used to screen the parameter set and constraint set according to the monomer demand response resource input information to obtain the constraints of the adjustable capacity evaluation optimization problem; and screen the objective function set according to the external input information of the aggregation system to obtain the objective function of the adjustable capacity evaluation optimization problem.

[0042] An adjustable capacity evaluation problem solving module, which is used to solve the adjustable capacity of the monomer demand response resource according to the initial parameters and response methods in the monomer demand response resource input information, the response direction in the external input information of the aggregation system, and the objective function and constraints of the adjustable capacity evaluation optimization problem.

[0043] In view of this, the beneficial effects brought by the present invention are as follows:

[0044] The aggregation method provided by the present invention models the demand response resources in detail, is applicable to aggregating different types of demand response resources, does not depend on complex demand response physical processes and electrical characteristics, does not require a large number of physical or electrical parameters, and is applicable to calculating large-scale demand response resources in the aggregation system; the adjustable capacity of each demand response resource is evaluated based on an improved modeling technology. On the one hand, it can be used to verify the effectiveness of the improved modeling technology, and on the other hand, the operation strategy of the demand response resources can be formulated according to the evaluation results. The present invention provides an aggregation system to aggregate different types of demand response resources, and the unified aggregation system regulates and manages the demand response resources, which is conducive to the power grid to observe and control the dispersed demand response resources, can make full use of the adjustable resources on the load side, promote the balance between power supply and demand in the power grid operation and the consumption of new energy, and improve the environmental benefits. Description of the Drawings

[0045] Figure 1 It is a schematic flow chart of the method of the present invention;

[0046] Figure 2 It is a schematic structural diagram of the method of the present invention;

[0047] Figure 3 It is a schematic diagram of the constraint set of the system of the present invention;

[0048] Figure 4 It is a schematic result diagram of an embodiment under the load shedding response mode of the monomer demand response resource of the present invention Figure 1 ;

[0049] Figure 5 It is a schematic result diagram of an embodiment under the load transfer response mode of the monomer demand response resource of the present invention Figure 2 ;

[0050] Figure 6Schematic diagram of the results of the load aggregator embodiment of the present invention Figure 3 。 Detailed implementation manners

[0051] Please refer to Figure 1 , the following are embodiments of the aggregation method of the present invention applicable to different types of demand response resources, including:

[0052] In step S100, first define a parameter set and a variable set shared by different types of demand response resources, as shown in Table 1, specifically including the symbols and meanings of all parameters and variables. The parameter set and the variable set will be used to establish a constraint condition set and an objective function set shared by different types of demand response resources.

[0053] Table 1 Definition of symbols and meanings of the parameter set and the variable set

[0054]

[0055]

[0056] In a preferred implementation manner, this embodiment adopts an optimization method of Mixed-Integer Linear Programming (MILP) to model a series of constraint conditions and objective functions shared by different types of demand response resources.

[0057] Through research on the prior art, the optimization method still dominates in power and other energy modeling, and Linear Programming (LP) has become the preferred optimization method due to its excellent computational performance. The mixed-integer programming optimization method developed in recent years can also obtain good computational performance through the branch and bound method, and can give the optimal gap between the upper and lower bounds of the optimal solution and evaluate the optimality of the solution.

[0058] In this embodiment, please refer to Figure 2 , establish a series of constraint condition sets shared by different types of demand response resources, specifically including load curtailment constraint conditions and load transfer constraint conditions.

[0059] In this embodiment, the load curtailment constraint conditions specifically include: available curtailment power constraint, available curtailment energy constraint, response duration constraint, response time constraint, response frequency constraint, and response interval constraint.

[0060] In this embodiment, the load transfer constraint conditions specifically include: load transfer constraint, mutual exclusion constraint, energy storage constraint, and load transfer time constraint.

[0061] It should be noted that, in addition to load curtailment and load shifting, the response methods of demand response resources may also include load increase response methods. However, since the constraint conditions of the load increase response method are very similar to those of the load curtailment response method, and the main difference lies only in the response direction of the load, the load increase response method can be regarded as a subset of the load curtailment response method. In this embodiment, the load curtailment constraint conditions will be mainly discussed.

[0062] Among them, the constraint modeling method for the load curtailment constraint conditions is as follows:

[0063] Available curtailment power constraint: The upper and lower limits of the available curtailment power The available curtailment power is constrained, which is represented by formula (1). And a 0 / 1 variable is introduced to represent the response status of the demand response resource, where 1 indicates that a response occurs and 0 indicates that the response is aborted.

[0064]

[0065] Available curtailment energy constraint: The upper and lower limits of the available curtailment energy E max 、E min , which constrains the sum of d t - within the T time range, and is represented by formula (2).

[0066]

[0067] To represent constraint conditions such as response duration, response time, and response frequency, a response trigger indicator variable is introduced to indicate the relationship between and , which is represented by formula (3). When is 1, it indicates that the demand response resource starts to respond; when is -1, it indicates that the demand response resource interrupts the response; when is 0, it indicates that the response status of the demand response resource remains unchanged.

[0068]

[0069] Response duration constraint: The maximum response duration TD max and the minimum response duration TD min , which constrain the response duration of the demand response resource, and are represented by formula (4) and formula (5) respectively. In formula (4), when is 1, within the subsequent TD min time range (including the t period), the demand response resource should be in a responding state (at this time )。The big M method is adopted in Table of Formula (5), and the conditional constraints are modeled with a large constant value M. When is 1, then within the TD max +1 time range (including the t period), the sum of does not exceed TD max .

[0070]

[0071]

[0072] Response time constraint: The maximum response time TR max and the minimum response time TR min constrain the response time of demand response resources, which are represented by Formula (6) and Formula (7) respectively. In Formula (6), when is 1, then within the TR min time range (including the t period), the demand response resources should be in the suspended response state (at this time ). In Formula (7), when is -1, then within the TR max time range (including the t period), there should be a situation where the demand response resources start to respond (at this time ), that is, start to respond.

[0073]

[0074]

[0075] Response frequency constraint: The maximum and minimum response trigger times N max 、N min constrain the sum of the absolute values within the response period T, which is represented by Formula (8).

[0076]

[0077] Response interval constraint: Outside the time set T SE of the response occurrence interval, the demand response resources should be in the suspended response state (at this time δ t =0), which is represented by Formula (9).

[0078]

[0079] The method for modeling the load transfer constraint conditions is as follows:

[0080] First, introduce the incremental power used to characterize the load increase response direction and the 0 / 1 variable

[0081] Load transfer power constraint: The total power reduction energy during the response period is equal to the total power increase energy, which is expressed by formula (10).

[0082]

[0083] Mutual exclusion constraint: Ensure that the load increase and reduction do not occur simultaneously at the same time t, which is expressed by formula (11).

[0084]

[0085] Energy storage constraint: It is necessary to maintain the energy conservation of the energy storage for each unit time, which is expressed by formula (12). The upper and lower limits of the energy storage value ES max and ES min constrain the energy storage variable e t , which is expressed by formula (13).

[0086]

[0087] ES min ≤e t ≤ES max , t = 1, 2,..., T (13)

[0088] Load transfer time constraint: According to the sequence of load transfer, two cases of load reduction first and then increase and load increase first and then reduction are discussed separately.

[0089] For the case of load reduction first and then increase: The maximum load transfer time and the minimum load transfer time constrain the load transfer time of the demand response resources, which are expressed by formula (14) and formula (15) respectively. In formula (14), when is -1, the load reduction direction interrupts the response, and then within the minimum load transfer time (including the t period), the demand response resource load increase response direction is in the suspended response state In formula (15), when is -1, the load reduction direction interrupts the response, and then within the maximum load transfer time (including the t period), there should be a situation where the demand response resource has a response

[0090]

[0091]

[0092]

[0093] For the case where the load first increases and then decreases: the maximum load transfer time and the minimum load transfer time constrain the load transfer time of demand response resources, which are represented by Formula (16) and Formula (17) respectively. In Formula (16), when is -1, the load increase response direction interrupts the response, and then within the minimum load transfer time (including the t period), the load reduction response direction of the demand response resource is in the suspended response state In Formula (17), when is -1, the load increase response direction interrupts the response, and then within the maximum load transfer time (including the t period), there should be a situation where the demand response resource has a response

[0094]

[0095]

[0096] In this embodiment, a set of objective functions shared by a series of different types of demand response resources is established. The objective functions in the set of objective functions are related to the response direction and response period in the external input information of the aggregation system. The response direction specifically includes the load reduction response direction and the load increase response direction.

[0097] It should be noted that the power grid company issues the external input information of the response direction and response period of the aggregation system within a specific time range according to the supply-demand balance state of the power grid operation. Use to represent the response period in the load reduction response direction, and use to represent the response period in the load increase response direction. When the response direction is the load reduction response direction, the objective function is expressed as:

[0098]

[0099] When the response direction is the load increase response direction, the objective function is expressed as:

[0100]

[0101] When the response direction includes both the load reduction response direction and the load increase response direction at the same time, the objective function is expressed as:

[0102]

[0103] In this embodiment, the objective functions in the above three cases are combined into the set of objective functions.

[0104] In step S200, the parameter set and the constraint condition set are screened according to the monomer resource input information to obtain the constraint conditions of the adjustable capacity evaluation and optimization problem; the objective function set is screened according to the external input information of the aggregation system to obtain the objective function of the adjustable capacity evaluation and optimization problem.

[0105] The monomer resource input information includes resource type, initial parameters, operating parameters, and response mode.

[0106] The external input information of the aggregation system includes response direction and response period.

[0107] The specific screening steps are as follows:

[0108] First, the parameter set is screened according to the operating parameters;

[0109] Then, the constraint condition set is screened according to the resource type and response mode;

[0110] Finally, the objective function set is screened according to the response direction and response period.

[0111] The screened parameters are substituted into the constraint conditions. The constraint conditions of the adjustable capacity evaluation and optimization problem are established according to the screened constraint conditions, and the objective function of the adjustable capacity evaluation and optimization problem is established according to the screened objective function.

[0112] In step S300, according to the initial parameters and response mode in the monomer resource input information, the response direction in the external input information of the aggregation system, and the objective function and constraint conditions of the adjustable capacity evaluation and optimization problem, the adjustable capacity of the demand response resource is solved.

[0113] According to the differences in the relationship between the response direction in the external input information of the aggregation system released by the power grid company and the response mode in the monomer resource input information, the following situations need to be discussed:

[0114] (1) When the response direction is the load reduction response direction and the response mode is the load reduction response mode or the load transfer response mode, or when the response direction is the load increase response direction and the response mode is the load increase response mode or the load transfer response mode, the optimization problem needs to be solved, and the calculation result is the adjustable capacity of the demand response resource.

[0115] First, the initial parameters are substituted into the adjustable capacity evaluation and optimization problem;

[0116] Then, according to the differences in the response mode in the monomer resource input information, the following situations need to be discussed:

[0117] When only the load shedding response mode exists in the response mode, the calculable power to be shed is output ; when only the load increase response mode exists in the response mode, the calculable power to be increased is output ; when both the load shedding and load increase response modes exist in the response mode and the energy storage parameter is not included, both and are output; when both the load shedding and load increase response modes exist in the response mode and the energy storage variable is included, and the energy storage values {e1, e2,..., e T} are output

[0118] In a preferred embodiment, this embodiment uses the MATLAB software platform to configure the YALMIP optimization problem modeling toolbox and calls the GUROBI 9.5.0 MILP solver to solve the optimization problem

[0119] (2) When the response direction is the load shedding response direction and the response mode is the load increase response mode, or when the response direction is the load increase response direction and the response mode is the load shedding response mode, there is no need to solve the optimization problem, and the calculable result of the adjustable capacity of the demand response resource is 0

[0120] Please refer to Figure 3 , the following are embodiments of the aggregation system applicable to different types of demand response resources and its components according to the present invention, including

[0121] Embodiments of the demand response resource adjustable capacity evaluation system, including

[0122] Input module 1, which is used to input the monomer resource input information of different types of monomer demand response resources and the external input information of the aggregation system. The monomer resource input information includes resource type, initial parameters, operating parameters, and response mode, and the external input information of the aggregation system includes response direction and response period

[0123] Set establishment and screening module 2, which is used to establish a common parameter set, variable set, constraint condition set, and objective function set for different types of demand response resources, and to screen the parameter set, constraint condition set, and objective function set according to the monomer resource input information and the external input information of the aggregation system. The parameter set and variable set will be used to establish the common constraint condition set and objective function set for different types of demand response resources; screen the parameter set according to the operating parameters; screen the constraint condition set according to the resource type and response mode; substitute the screened parameters into the constraints

[0124] A calculation module 3 is used to solve the adjustable capacity of the demand response resources. Establish the constraint conditions of the adjustable capacity evaluation optimization problem according to the filtered constraint conditions, and establish the objective function of the adjustable capacity evaluation optimization problem according to the filtered objective function; according to the differences between the response directions in the external input information of the aggregation system released by the power grid company and the response methods in the input information of the single resource, it is necessary to discuss in different cases; substitute the initial parameters into the adjustable capacity evaluation optimization problem; call the GUROBI 9.5.0 MILP solver to solve the optimization problem; output the calculation results of the curtailable power, the increasable power and the energy storage value.

[0125] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0126] In the embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0129] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0130] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

[0131] The following is a specific case analysis of the present invention:

[0132] Consider that there are multiple different types of demand response resources in a certain load area, and they are uniformly managed by the regional load aggregator agent. In this embodiment, according to the power grid operation supply-demand balance state, the power grid company issues external input information to the aggregation system of the load aggregator on the day before, requiring load curtailment during the peak load periods from 10:00 to 12:00 and from 16:00 to 19:00 on the same day.

[0133] Then, the aggregation system realizes the function of evaluating the adjustable capacity of demand response resources based on improved modeling techniques. In this embodiment, the response period is taken as T = 24 hours, and the time scale is in units of 1 hour. It is assumed that the initial response state of all individual demand response resources in the aggregation system is suspended response.

[0134] It is worth noting that the reference load parameters of individual demand response resources are often used to evaluate the implementation effect of demand response. Table 2 provides the reference load parameters of individual demand response resources for calculating the upper and lower limits of the power that can be curtailed or increased by individual demand response resources in a single time period.

[0135] Table 2 Reference Load Parameters of Individual Demand Response Resources

[0136]

[0137]

[0138] (1) Load shedding response mode scenario

[0139] Figure 4 is the maximum reducible power calculated for a single demand response resource in the load shedding response mode scenario. The input information of the single resource operation parameters is given at the upper left corner of Figure 4 . In this embodiment, the maximum reducible power at 9 o'clock is 0.54 MW, the maximum reducible power from 10 to 11 o'clock is 0.69 MW, the maximum reducible power from 17 to 19 o'clock is 0.75 MW, and the total maximum reducible energy is 4.17 MWh. Within the response period, the calculation results of the reducible power of the single demand response resource all meet the constraints of reducible power, reducible energy, response duration, and response time.

[0140] (2) Load transfer response mode scenario

[0141] Figure 5 is the maximum reducible power calculated for a single demand response resource in the load transfer response mode scenario. The input information of the single resource operation parameters is given at the upper left corner of Figure 5 . In this embodiment, the maximum reducible power is limited within the response interval of 18 - 22, and the response frequencies in both the load shedding and load increasing response directions are 1 time. This implementation realizes load transfer, and the reducible energy and the increasable energy have the same value, both being 2 MWh. The original load from 18 to 20 o'clock is transferred to start the response from 11 to 13 o'clock. The initial energy storage is 5 MWh. During 11 - 13 o'clock, the energy storage discharges to increase the load, and the energy storage drops to 3 MWh. Subsequently, during 18 - 20 o'clock, the energy storage charges to reduce the load, and the energy storage resumes to 5 MWh.

[0142] (3) Load aggregator response scenario

[0143] Figure 6 is a schematic diagram of the maximum reducible power when the above two single demand response resources become a load aggregator in the aggregation system. The maximum reducible power of the load aggregator is composed of the reducible load and the transferable load. It should be noted that due to the existence of the transfer load, implementing load shedding during the peak load period from 16 to 19 o'clock will affect the maximum reducible power from 10 to 12 o'clock. In this embodiment, the maximum reducible power at 11 o'clock is -0.2 MW. In addition to the maximum reducible power schematic diagram, Figure 6 also shows the change of the energy storage value.

Claims

1. An aggregation method applicable to different types of demand response resources, characterized in that, Applied to an aggregation system, the method includes: Defining a parameter set and a variable set shared by different types of demand response resources, and establishing a constraint condition set and an objective function set shared by different types of demand response resources according to the parameter set and the variable set. The objective function set is related to the response direction and response period in the external input information of the aggregation system. The response direction includes a load reduction response direction and a load increase response direction; Screening the parameter set and the constraint condition set according to the input information of individual demand response resources to obtain the constraint conditions for the adjustable capacity evaluation and optimization problem; screening the objective function set according to the external input information of the aggregation system to obtain the objective function for the adjustable capacity evaluation and optimization problem; Solving to obtain the adjustable capacity of the individual demand response resource according to the initial parameters and response mode in the input information of the individual demand response resource, the response direction in the external input information of the aggregation system, and the objective function and constraint conditions of the adjustable capacity evaluation and optimization problem; Among them, the constraint condition set includes a load reduction constraint condition and a load transfer constraint condition; The load reduction constraint condition represents the constraint condition when the response mode of the individual demand response resource is the load reduction mode or the load increase mode; the load reduction constraint condition includes a cuttable power constraint, a cuttable energy constraint, a response duration constraint, a response time constraint, a response frequency constraint, and a response interval constraint; The load transfer constraint condition represents the constraint condition when the response mode of the individual demand response resource is the load transfer mode; the load transfer constraint condition includes a load transfer power constraint, a mutual exclusion constraint, a energy storage constraint, and a load transfer time constraint.

2. The aggregation method for different types of demand response resources according to claim 1, characterized in that, Specifically, establishing the constraint condition set shared by different types of demand response resources according to the parameter set and the variable set includes: Constructing a general operation constraint model for various types of demand response resources according to the parameter set and the variable set, and obtaining the constraint condition set shared by various types of demand response resources according to the operation constraint model.

3. The aggregation method for different types of demand response resources according to claim 1, characterized in that The input information of the individual demand response resource specifically includes: The initial parameters, operation parameters, resource type, and response mode of the individual demand response resource.

4. The aggregation method for different types of demand response resources according to claim 3, characterized in that, Specifically, screening the parameter set and the constraint condition set according to the input information of the individual demand response resource to obtain the constraint conditions for the adjustable capacity evaluation and optimization problem includes: Screening the parameter set according to the operation parameters to obtain the screened parameters; Screening the constraint condition set according to the resource type and the response mode, and substituting the screened parameters into the constraint conditions to obtain the screened constraint conditions; Establishing the constraint conditions for the adjustable capacity evaluation and optimization problem according to the screened constraint conditions.

5. The aggregation method for different types of demand response resources according to claim 1, characterized in that Specifically, screening the objective function set according to the external input information of the aggregation system to obtain the objective function for the adjustable capacity evaluation and optimization problem includes: Screening the objective function set according to the response direction and response period in the external input information of the aggregation system to obtain the screened objective function; Establishing the objective function for the adjustable capacity evaluation and optimization problem according to the screened objective function.

6. The aggregation method for different types of demand response resources according to claim 1, characterized in that According to the initial parameters and response modes in the input information of the individual demand response resources, aggregating the response directions in the external input information of the system, as well as the objective function and constraint conditions of the adjustable capacity evaluation and optimization problem, the obtained adjustable capacity of the individual demand response resources includes: When the response direction is the load reduction response direction and the response mode is the load reduction response mode or the load transfer response mode, or when the response direction is the load increase response direction and the response mode is the load increase response mode or the load transfer response mode, then it is necessary to solve the optimization problem, call the MILP solver to solve the adjustable capacity evaluation and optimization problem, and the calculation result is the adjustable capacity of the individual demand response resources. The solution process is as follows: First, substitute the initial parameters into the adjustable capacity evaluation and optimization problem; Then, according to the differences in the response modes in the input information of the individual resources, it is necessary to discuss in different cases: When the response mode only has the load reduction response mode, then output the reducible power; When the response mode only has the load increase response mode, then output the increasable power; When the response mode has both the load reduction and load increase response modes and does not contain energy storage parameters, then output both the reducible power and the increasable power; When the response mode has both the load reduction and load increase response modes and contains energy storage variables, then output the reducible power, the increasable power, and the energy storage value; When the response direction is the load reduction response direction and the response mode is the load increase response mode, or when the response direction is the load increase response direction and the response mode is the load reduction response mode, then there is no need to solve the adjustable capacity evaluation and optimization problem, and the calculation result of the adjustable capacity of the individual demand response resources is 0.

7. An aggregation system applicable to different types of demand response resources, characterized in that, Including: A shared set construction module, used to define the parameter set and variable set shared by different types of demand response resources, and establish the constraint condition set and objective function set shared by different types of demand response resources according to the parameter set and variable set. The objective function set is related to the response direction and response period in the external input information of the aggregation system, and the response direction includes the load reduction response direction and the load increase response direction; An adjustable capacity evaluation problem construction module, used to screen the parameter set and constraint condition set according to the input information of the individual demand response resources to obtain the constraint conditions of the adjustable capacity evaluation and optimization problem; Screen the objective function set according to the external input information of the aggregation system to obtain the objective function of the adjustable capacity evaluation and optimization problem; An adjustable capacity evaluation problem solving module, used to solve the adjustable capacity of the individual demand response resources according to the initial parameters and response modes in the input information of the individual demand response resources, the response direction in the external input information of the aggregation system, as well as the objective function and constraint conditions of the adjustable capacity evaluation and optimization problem; Among them, the constraint condition set includes load reduction constraint conditions and load transfer constraint conditions; The load shedding constraint condition represents the constraint condition when the response mode of a single demand response resource is the load shedding mode or the load increase mode; the load shedding constraint condition includes the available shedding power constraint, the available shedding energy constraint, the response duration constraint, the response time constraint, the response frequency constraint, and the response interval constraint; The load transfer constraint condition represents the constraint condition when the response mode of a single demand response resource is the load transfer mode; the load transfer constraint condition includes the load transfer power constraint, the mutual exclusion constraint, the energy storage constraint, and the load transfer time constraint.

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