A high-frequency demand-side response method and system

Abstract

The present invention provides a high-frequency demand-side response method, and the power system state assessment method includes the following steps: S1: Module one, calculation of the maximum active power capacity available for dispatch of the controlled high-voltage nodes of the power grid; S2: Module two, a quotation curve formulated based on the power generation cost; S3: Module three, an algorithm for calculating the climbing ability corresponding to different power generation Q of the controlled high-voltage nodes; S4: Module four, output allocation instructions for each unit at the low-voltage node based on a given high-voltage node power generation instruction. The present invention provides a high-frequency demand-side response method and system, by constructing a more flexible and high-frequency demand-side response mechanism, the aggregator can more effectively manage and dispatch its aggregated decentralized resources, such as adjustable loads, energy storage systems, etc., and can solve the challenges of insufficient flexibility and high-frequency response capabilities faced in demand-side response.

Description

Technical Field

[0001] The present invention belongs to the technical field of power demand side management, and in particular relates to a high-frequency demand side response method and system. Background Art

[0002] The existing demand-side response strategy covers multiple dimensions, including demand-side analysis and resource aggregation, demand response implementation, market-based mechanisms, and policy support. In terms of demand-side analysis and resource aggregation, the power grid system relies on the smart grid monitoring system to capture user power demand information in real time, conduct in-depth analysis and research on user power consumption behavior, and analyze detailed data such as power consumption time periods and power consumption, aiming to classify and model the power consumption characteristics of different user groups, and then predict future trends in power demand.

[0003] At the same time, by integrating scattered user-adjustable load resources, such as smart buildings, distributed wind and solar power generation, energy storage systems, and vehicle-grid interaction, an adjustable load resource pool is built to achieve efficient local consumption and coordinated control of distributed energy.

[0004] The implementation of demand-side response focuses on flexibly adjusting the output of user adjustable load resources according to the actual needs of the power grid, including reducing load during peak hours and increasing load during off-peak hours to maintain the balance of power supply and demand. In addition, by connecting users' smart appliances and power control devices, flexible power demand management is implemented, and the equipment operation mode and power consumption period are flexibly adjusted, which not only improves the utilization rate of power equipment, but also reduces the power supply cost.

[0005] In terms of market mechanism and policy support, the power grid system has the ability to directly participate in the power auxiliary service market and power trading. With the acceleration of the marketization of power trading, virtual power plants are gradually transforming into trading power plants with spot trading as the main profit model. At the same time, the government has introduced a number of policy measures, such as encouraging the layout of power supply side and grid side energy storage systems in combination with the peak and frequency regulation needs of the power grid, and carrying out source-grid-load-storage integration and multi-energy complementary pilot projects, which provide solid policy support for the development of demand-side response strategies. Summary of the invention

[0006] In order to solve the above problems, the present invention provides a high-frequency demand-side response method and system. By constructing a more flexible and high-frequency demand-side response mechanism, the aggregator can more effectively manage and schedule its aggregated decentralized resources, such as adjustable loads, energy storage systems, etc., and can solve the challenges of insufficient flexibility and high-frequency response capabilities faced in demand-side response.

[0007] The present invention is specifically a high-frequency demand side response method, and the power system state assessment method comprises the following steps:

[0008] S1: Module 1, calculation of the maximum active power capacity available for dispatch at the controlled high-voltage nodes of the power grid:

[0009] S2: Module 2, quotation curve based on power generation cost;

[0010] S3: Module three, an algorithm for calculating the ramping capability corresponding to different power generation Q of the controlled high-voltage node;

[0011] S4: Module four, based on the given high-voltage node power generation command, output distribution instructions for each unit at the low-voltage node.

[0012] The calculation of the maximum active power capacity available for dispatch of a pair of controlled high-voltage nodes of the power grid in module S1 includes:

[0013] S11: Input the upper limit of active output Pg of low-voltage nodes in N low-voltage networks imax ;

[0014] S12: Maximum active power capacity Pg available for dispatch at the controlled high-voltage node K of the output grid Kmax .

[0016] The maximum active power capacity Pg available for dispatch at the controlled high-voltage node K of the output grid in S12 Kmax The specific algorithm is:

[0017] Mathematical model construction

[0018] Assuming that the upper limit of the active output of the low-voltage node in the low-voltage network is known, the maximum capacity calculation method of the corresponding controlled high-voltage node K is as follows (assuming that one controlled high-voltage node corresponds to N low-voltage nodes that provide active output):

[0019]

[0020] The variables in formula (1) are described as follows:

[0021] Pg imax : The upper limit of active output of low-voltage nodes in the low-voltage network, i = 1, 2...N;

[0022] Pg Kmax : The maximum active power capacity available for dispatch at the controlled high-voltage node K of the power grid.

[0023] The specific method of the quotation curve formulated by S2 module 2 based on power generation cost is as follows:

[0024] S21: Construct a power generation cost calculation model;

[0025] S22: Construct a quotation calculation model.

[0026] The specific method of constructing the power generation cost calculation model in S21 is as follows:

[0027]

[0028] The variables in formula (2) are described as follows:

[0029] (1)TotalCost: power generation cost;

[0030] (2) Pg i : Active output value of low voltage nodes in low voltage network;

[0031] (3)C i : Active power output price of low voltage nodes in low voltage network;

[0032] Total power generation constraint:

[0033]

[0034] The variables in formula (3) are described as follows:

[0035] (1) Pg i : Active output value of low voltage nodes in low voltage network;

[0036] (2) Q: total power generation of the controlled high-voltage node;

[0037] Upper limit constraint on power generation:

[0038]

[0039] The power generation of each unit Pg i It must be within its output upper limit Pg max and 0.

[0040] The algorithm for solving the power generation cost calculation model is:

[0041] S211: Sorting:

[0042] First, the units are sorted in ascending order according to the power generation cost Ci.

[0043] S212: Allocate power generation:

[0044] S2121: Starting from the first unit in the sorted unit list, check each unit in turn;

[0045] S2122: If the winning quantity (total power generation power Q of the controlled high-voltage node) is less than or equal to the current unit Pg i The upper limit of output Pg max , then all are allocated to the current unit and the allocation process ends;

[0046] S2123: Otherwise, the output of the current unit Pg maxAfter that, the remaining winning bid quantity is allocated to the second unit in the list, and so on, gradually allocating the winning bid quantity to each unit until all allocations are completed;

[0047] The specific method of constructing the quotation calculation model in S22 is:

[0048] Y=TotalCost×(1+P) (5)

[0049] The variables in formula (5) are described as follows:

[0050] (1) Y: quotation;

[0051] (2)TotalCost: power generation cost;

[0052] (3) P: profit rate.

[0053] The algorithm of S3 module three for calculating the climbing capability corresponding to different power generation Q of controlled high-voltage nodes specifically includes: sorting, comparison and selection.

[0054] S4 module 4 is based on the given high-voltage node power generation instruction, and distributes the output instructions to each unit at the low-voltage node, including: input power generation quality Q, optimization allocation algorithm, output value Pg of each unit i .

[0055] The present invention proposes a high-frequency demand-side response system, including module one, module two, module three, and module four.

[0056] Module 1: Calculation of the maximum active power capacity available for dispatch at the controlled high-voltage nodes of the power grid;

[0057] Module 2: Quotation curve based on power generation cost;

[0058] Module three, an algorithm for calculating the ramping capability corresponding to different power generation Q of the controlled high-voltage node;

[0059] Module 4 distributes the output instructions to each unit at the low-voltage node based on the given high-voltage node power generation instructions.

[0060] Compared with the prior art, the high-frequency demand-side response method and system of the present invention improves the response speed and accuracy of aggregators to changes in the power market, and also enhances their competitiveness and profitability in the power market. At the same time, by optimizing resource scheduling and user participation incentive mechanisms, aggregators can further improve user experience, enhance user stickiness, and lay a solid foundation for their long-term development. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is the feedback diagram of low-voltage node and high-voltage node;

[0062] Figure 2 It is a general diagram of the technical solution of a high-frequency demand-side response method and system of the present invention;

[0063] Figure 3 This is a general flow chart of a high-frequency demand-side response method of the present invention;

[0064] Figure 4 A flow chart of a module for calculating the maximum active power capacity available for dispatch at a controlled high-voltage node of a power grid (considering only active power);

[0065] Figure 5 Flow chart of the model for calculating the cost of electricity generation;

[0066] Figure 6 Flowchart of algorithm for solving the generation cost calculation model;

[0067] Figure 7 Flowchart of the quotation calculation model;

[0068] Figure 8 A technical solution diagram for calculating the climbing capability corresponding to different power generation Q of the controlled high-voltage node;

[0069] Fig. 9 An algorithm flow chart for calculating the ramping capability corresponding to different power generation Q of the controlled high voltage node;

[0070] Fig.10 It is a technical solution diagram for distributing output instructions to each unit at a low-voltage node based on a given high-voltage node power generation instruction;

[0071] Fig.11 It is a flow chart of the algorithm for distributing the output instructions to each unit at the low-voltage node based on the given high-voltage node power generation instruction;

[0072] Fig.12 It is the output cost price curve diagram in the embodiment;

[0073] Fig.13 It is a quotation curve diagram in the embodiment. DETAILED DESCRIPTION

[0074] The specific implementation of a high-frequency demand-side response method and system of the present invention is described in detail below with reference to the accompanying drawings.

[0075] In the present invention, when the upper limit of the output of a low-voltage node is given in the power grid, the aggregator can inform the power grid company of: 1. the maximum power that can be dispatched at the high-voltage node at a certain moment; 2. the comprehensive price of controlling all downstream units; 3. different prices correspond to the climbing capabilities of different units and when the high-voltage node power generation instruction is given, the instruction is split into multiple sub-instructions and distributed to each unit of the low-voltage node.

[0076] Among them, the feedback diagram of the low voltage node and the high voltage node of the present invention is as follows Figure 1 shown.

[0077] like Figure 2 and 3 As shown, a high-frequency demand side response method of the present invention comprises the following steps:

[0078] S1: Module 1, calculation of the maximum active power capacity available for dispatch at the controlled high-voltage nodes of the power grid:

[0079] S2: Module 2, quotation curve based on power generation cost;

[0080] S3: Module three, an algorithm for calculating the ramping capability corresponding to different power generation Q of the controlled high-voltage node;

[0081] S4: Module four, based on the given high-voltage node power generation command, output distribution instructions for each unit at the low-voltage node.

[0082] like Figure 4 As shown, the calculation of the maximum active power capacity available for dispatch of a controlled high-voltage node of a power grid in module S1 specifically includes:

[0083] S11: Input the upper limit of active output Pg of low-voltage nodes in N low-voltage networks imax ;

[0084] S12: Maximum active power capacity Pg available for dispatch at the controlled high-voltage node K of the output grid Kmax .

[0086] Definitions of related terms:

[0087] Controlled high-voltage node: a high-voltage node in the power grid architecture that is directly controlled by the power grid;

[0088] Low-voltage node: Under the high-voltage node that is directly controlled by the power grid in the power grid architecture, there is a complex medium-voltage network. Each medium-voltage network has a corresponding low-voltage network. There is a cascade power supply relationship between the three, that is, the medium-voltage network is controlled by the high-voltage node, and the low-voltage network is controlled by the medium-voltage network. The low-voltage node refers to the corresponding node in the low-voltage network;

[0089] High-voltage node power generation instruction: the total power generation power of the high-voltage nodes in the power grid architecture that are directly controlled by the power grid, defined as Q in this patent.

[0090] The maximum active power capacity Pg available for dispatch at the controlled high-voltage node K of the output grid in S12 Kmax The specific algorithm is:

[0091] Mathematical model construction

[0092] Assuming that the upper limit of the active output of the low-voltage node in the low-voltage network is known, the maximum capacity calculation method of the corresponding controlled high-voltage node K is as follows (assuming that one controlled high-voltage node corresponds to N low-voltage nodes that provide active output):

[0093]

[0094] The variables in formula (1) are described as follows:

[0095] Pg imax : The upper limit of active output of low-voltage nodes in the low-voltage network, i = 1, 2...N;

[0096] Pg Kmax : The maximum active power capacity available for dispatch at the controlled high-voltage node K of the power grid;

[0097] like Figure 5 As shown, the flow chart of module two.

[0098] The specific method of the quotation curve formulated by S2 module 2 based on power generation cost is as follows:

[0099] S21: Construct a power generation cost calculation model;

[0100] S22: Construct a quotation calculation model.

[0101] The specific method of constructing the power generation cost calculation model in S21 is as follows:

[0102]

[0103] The variables in formula (2) are described as follows:

[0104] (1)TotalCost: power generation cost;

[0105] (2) Pg i : Active output value of low voltage nodes in low voltage network;

[0106] (3)C i : Active power output price of low voltage nodes in low voltage network;

[0107] Total power generation constraint:

[0108]

[0109] The variables in formula (3) are described as follows:

[0110] (1) Pg i : Active output value of low voltage nodes in low voltage network;

[0111] (2) Q: total power generation of the controlled high-voltage node;

[0112] Upper limit constraint on power generation:

[0113]

[0114] The power generation of each unit Pg i It must be within its output upper limit Pg max and 0.

[0115] like Figure 6 As shown in the figure, the algorithm for solving the power generation cost calculation model is:

[0116] S211: Sorting:

[0117] First, the units are sorted in ascending order according to the power generation cost Ci.

[0118] S212: Allocate power generation:

[0119] S2121: Starting from the first unit in the sorted unit list, check each unit in turn;

[0120] S2122: If the winning quantity (total power generation power Q of the controlled high-voltage node) is less than or equal to the current unit Pg i The upper limit of output Pg max , then all are allocated to the current unit and the allocation process ends;

[0121] S2123: Otherwise, the output of the current unit Pg max Finally, the remaining winning bid will be allocated to the second unit in the list, and so on, gradually allocating the winning bid to each unit until all allocations are completed.

[0122] like Figure 7 As shown, the specific method of constructing the quotation calculation model in S22 is:

[0123] Y=TotalCost×(1+P) (5)

[0124] The variables in formula (5) are described as follows:

[0125] (1) Y: quotation;

[0126] (2)TotalCost: power generation cost;

[0127] (3) P: profit rate.

[0128] like Figure 8 As shown, the algorithm for calculating the climbing capability corresponding to different power generation Q of the controlled high-voltage node in module S3 specifically includes: sorting, comparison and selection.

[0129] like Fig. 9As shown in FIG. 1 , the algorithm flow chart for calculating the climbing capability corresponding to different power generation Q of the controlled high-voltage node is as follows:

[0130] S31 initialization:

[0131] S311 input: the power generation value Q of the controlled high-voltage node and the active output price Ci of each low-voltage node in the low-voltage network.

[0132] S312 output: corresponding climbing capability when the controlled high-voltage node has a given power generation of Q.

[0133] S32 sorting:

[0134] S321 sorts all dispatchable low-voltage nodes in ascending order according to their bids Ci. It is assumed here that each low-voltage node has a known maximum ramping capability and a corresponding bid Ci.

[0135] S33 comparison and selection:

[0136] S331 starts from the sorted list and checks each low-voltage node in turn until the total power generation demand Q is met.

[0137] During this process, S332 records the last selected low-voltage node and its climbing capability.

[0138] S34 determines gradeability:

[0139] S341 When the total power generation reaches the required power generation Q, the selection process is stopped, and the climbing capacity of the last selected low-voltage node is selected as the climbing capacity of the entire controlled high-voltage node under the power generation Q.

[0140] S35 output results:

[0141] S351 outputs the climbing capability of the controlled high voltage node for a given power generation Q.

[0142] like Fig.10 As shown in the figure, module S4 allocates output instructions to each unit at the low-voltage node based on the given high-voltage node power generation instruction, specifically including: input power generation quality Q, optimization allocation algorithm, output value Pg of each unit i .

[0143] like Fig.11 As shown, the optimization allocation algorithm is as follows:

[0144] S41 initialization:

[0145] S411 input: the total power generation value Q of the controlled high-voltage node.

[0146] S412 output: active output Pgi of each unit at each low-voltage node in the low-voltage network.

[0147] S42 sorting:

[0148] All dispatchable low-voltage nodes are sorted in ascending order according to their power generation bids Ci. It is assumed here that each low-voltage node has a known maximum power generation capacity Pgimax and a corresponding power generation cost Ci.

[0149] S43 distributes power generation:

[0150] Starting from the sorted list, check each low-voltage node in turn. According to the sorted list, while ensuring that the maximum power generation capacity limit of a single node is not exceeded, select the nodes with bids from low to high in turn for power generation until the total power generation demand Q is met.

[0151] S44 output results:

[0152] Output the active output Pgi of each unit at each low-voltage node in the low-voltage network.

[0153] like Figure 2 As shown, the present invention proposes a high-frequency demand-side response system, including module one, module two, module three, and module four.

[0154] Module 1: Calculation of the maximum active power capacity available for dispatch at the controlled high-voltage nodes of the power grid;

[0155] Module 2: Quotation curve based on power generation cost;

[0156] Module three, an algorithm for calculating the ramping capability corresponding to different power generation Q of the controlled high-voltage node;

[0157] Module 4 distributes the output instructions to each unit at the low-voltage node based on the given high-voltage node power generation instructions.

[0158] Module 1 input and output:

[0159] Input quantity: Active output upper limit Pg of low-voltage nodes in low-voltage network imax ;

[0160] Output: The maximum active power capacity Pg available for dispatch at the controlled high-voltage node K of the power grid Kmax ;

[0161] Module 2 input and output:

[0162] Input:

[0163] (1) The total power generation value of the controlled high-voltage node is Q;

[0164] (2) Active power output price Ci of low-voltage nodes in the low-voltage network;

[0165] (3) Profit rate P;

[0166] Output: Quote;

[0167] Module 3 Input and Output

[0168] Input:

[0169] (1) Active output upper limit Pg of low-voltage nodes in low-voltage network imax ;

[0170] (2) The power generation value of the controlled high-voltage node is Q;

[0171] (3) Active power output price Ci of low-voltage nodes in the low-voltage network;

[0172] Output:

[0173] (1) When the controlled high-voltage node has a given power generation Q, the corresponding climbing capacity

[0174] Module 4 input and output:

[0175] Input quantity: the total power generation value Q of the controlled high-voltage node;

[0176] Output: The active output Pgi of each unit at the low-voltage node in the low-voltage network.

[0177] Best practices for specific applications

[0178] Module 1, an embodiment of calculating the maximum active power capacity available for dispatch at a controlled high-voltage node of a power grid:

[0179] Assume that a controlled high-voltage node in the power grid architecture is connected to K nodes in the cascade low-voltage network. Among these K nodes, there are N low-voltage nodes that can provide active output, and each active output upper limit Pg imax If both are 10MW, then:

[0180] From the following formula (1):

[0181]

[0182] Module input quantity Pg imax =10MW

[0183] Module output Pg Kmax =(N*10)MW

[0184] Module 2: Example of quotation curve based on power generation cost:

[0185] Based on module 1, it is assumed that there are 5 low-voltage nodes in the low-voltage network that can provide active output, that is, N = 5, and they are named nodes a, b, c, d, and e respectively; the active output upper limit of the node Pg imax The values ​​are all 5MW, and the output quotation unit price is shown in Table 1:

[0186] Table 1

[0187] Node Number Output quotation unit price (10,000 yuan / MW) a 2 b 3 c 5 d 7 e 9

[0188] Assuming that the power generation of the controlled high-voltage node is Q = 18MW, the quotation curve based on power generation cost is prepared according to the following formula and steps:

[0189] Step 1: Solve the algorithm based on the power generation cost calculation model and sort the units in ascending order according to the power generation cost Ci. In this example, they are nodes a, b, c, d, and e.

[0190] Step 2: Allocate Q to each node in the above order. In this example, the allocation result of Q is: a = 5; b = 5; c = 5; d = 3; e = 0. According to the formula Calculate the output cost and make the output cost price curve as follows Fig.12 As shown:

[0191] Chart analysis:

[0192] (1) Horizontal axis:

[0193] 0-5: Node a output 5MW;

[0194] 5-10: Node b output 5MW;

[0195] 10-15: Node c output 5MW;

[0196] 15-18: Node d output 3MW;

[0197] (2) Vertical axis: output cost;

[0198] (3) Slope: unit price of node output quotation;

[0199] Ka=2: The output cost of node a is RMB 20,000 / MW;

[0200] Kb=3: the output cost of node b is RMB 30,000 / MW;

[0201] Kc=5: the unit price of node c output cost is 50,000 yuan / MW;

[0202] Kd=7: the unit price of node d output cost is RMB 70,000 / MW;

[0203] Step 3: According to the formula Y = TotalCost × (1 + P), the quotation curve is obtained from the output cost price curve; in this example, it is assumed that the profit rate P = 0.2; then the quotation curve is as follows Fig.13 As shown;

[0204] Chart analysis:

[0205] (4) Horizontal axis:

[0206] 0-5: Node a output 5MW;

[0207] 5-10: Node b output 5MW;

[0208] 10-15: Node c output 5MW;

[0209] 15-18: Node d output 3MW;

[0210] (5) Vertical axis: output quotation;

[0211] (6) Slope: unit price of node output quotation;

[0212] Ka=2.4: The output quotation unit price of node a is 24,000 yuan / MW;

[0213] Kb=3.6: Node b output quotation unit price is 36,000 yuan / MW;

[0214] Kc=6: The output quotation unit price of node c is RMB 60,000 / MW;

[0215] Kd=8.4: Node d output quotation unit price 84,000 yuan / MW

[0216] Module 3: Algorithm for calculating the ramping capability corresponding to different power generation Q of controlled high-voltage nodes

[0217] Based on module 2, the climbing capacity of the last output node unit selected by the search algorithm is the climbing capacity corresponding to the power generation Q. In this example, when Q=18MW, the last selected node is node d. Then the climbing capacity corresponding to the power generation Q=18MW of the controlled high-voltage node = the climbing capacity of the unit on node d.

[0218] Module 4: Based on the given high-voltage node power generation instructions, output distribution instructions for each unit at the low-voltage node.

[0219] Assume that the conditions given in the example are consistent with those in Module 2:

[0220] Step 1: Solve the algorithm based on the power generation cost calculation model and sort the units in ascending order according to the power generation cost Ci. In this example, they are nodes a, b, c, d, and e.

[0221] Step 2: Allocate Q to each node in the above order. In this example, the allocation result of Q is: a = 5; b = 5; c = 5; d = 3; e = 0;

[0222] In summary, if the high-voltage node power generation instruction Q=18MW is given, the output distribution instructions of each unit at the low-voltage node are a=5; b=5; c=5; d=3; e=0.

[0223] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit the present invention. A person skilled in the art should understand that the specific implementation of the present invention can be modified or replaced by equivalents, but these modifications or changes are within the scope of protection of the pending claims.

Claims

1. A high-frequency demand-side response method, characterized in that: The power system state assessment method comprises the following steps: S1: Module 1, calculation of the maximum active power capacity available for dispatch at the controlled high-voltage nodes of the power grid: S2: Module 2, quotation curve based on power generation cost; S3: Module three, an algorithm for calculating the ramping capability corresponding to different power generation Q of the controlled high-voltage node; S4: Module four, based on the given high-voltage node power generation command, output distribution instructions for each unit at the low-voltage node.

2. A high-frequency demand side response method according to claim 1, characterized in that: The calculation of the maximum active power capacity available for dispatch at the controlled high-voltage node of the power grid in S1 specifically includes: S11: Input the upper limit of active output Pg of low-voltage nodes in N low-voltage networks imax ; S12: Maximum active power capacity Pg available for dispatch at the controlled high-voltage node K of the output grid Kmax .

3. A high-frequency demand side response method according to claim 2, characterized in that: The maximum active power capacity Pg available for dispatch at the controlled high-voltage node K of the output grid in S12 Kmax The specific algorithm is: Mathematical model construction Assuming that the upper limit of the active output of the low-voltage node in the low-voltage network is known, and assuming that one controlled high-voltage node corresponds to N low-voltage nodes that provide active output, the maximum capacity calculation method of the corresponding controlled high-voltage node K is as follows: The variables in formula (1) are described as follows: Pg imax : The upper limit of active output of low-voltage nodes in the low-voltage network, i = 1, 2...N; Pg Kmax : The maximum active power capacity available for dispatch at the controlled high-voltage node K of the power grid.

4. A high-frequency demand side response method according to claim 3, characterized in that: The specific method of the quotation curve formulated by S2 module 2 based on power generation cost is as follows: S21: Construct a power generation cost calculation model; S22: Construct a quotation calculation model.

5. A high-frequency demand side response method according to claim 4, characterized in that: S21 The specific method of constructing the power generation cost calculation model is: The variables in formula (2) are described as follows: (1)TotalCost: power generation cost; (2) Pg i : Active output value of low voltage nodes in low voltage network; (3)C i : Active power output price of low voltage nodes in low voltage network; Total power generation constraint: The variables in formula (3) are described as follows: (1) Pg i : Active output value of low voltage nodes in low voltage network; (2) Q: total power generation of the controlled high-voltage node; Upper limit constraint on power generation: The power generation of each unit Pg i It must be within its output upper limit Pg max and 0.

6. A high-frequency demand side response method according to claim 5, characterized in that: The algorithm for solving the power generation cost calculation model described in S21 is: S211: Sorting: First, the units are sorted in ascending order according to the power generation cost Ci. S212: Allocate power generation: S2121: Starting from the first unit in the sorted unit list, check each unit in turn; S2122: If the total power generation power Q of the controlled high-voltage node of the winning bid is less than or equal to the current unit Pg i The upper limit of output Pg max , then all are allocated to the current unit and the allocation process ends; S2123: Otherwise, the output of the current unit Pg max Finally, the remaining winning bid will be allocated to the second unit in the list, and so on, gradually allocating the winning bid to each unit until all allocations are completed.

7. A high-frequency demand-side response method according to claim 6, characterized in that: The specific method of constructing the quotation calculation model in S22 is: Y=TotalCost×(1+P) (5) The variables in formula (5) are described as follows: (1) Y: quotation; (2)TotalCost: power generation cost; (3) P: profit rate.

8. A high-frequency demand-side response method according to claim 7, characterized in that: The algorithm of S3 module three for calculating the climbing capability corresponding to different power generation Q of controlled high-voltage nodes specifically includes: sorting, comparison and selection.

9. A high-frequency demand-side response method according to claim 8, characterized in that: S4 module 4 is based on the given high-voltage node power generation instruction, and distributes the output instructions to each unit at the low-voltage node, including: input power generation quality Q, optimization allocation algorithm, output value Pg of each unit i .

10. A high-frequency demand-side response system, characterized in that: It includes Module 1, Module 2, Module 3 and Module 4. The module 1 is to calculate the maximum active power capacity available for dispatch at the controlled high-voltage node of the power grid; The second module is a quotation curve based on power generation cost; The module three is an algorithm for calculating the climbing capability corresponding to different power generation amounts Q of the controlled high-voltage nodes; The module four distributes output instructions to each unit at the low-voltage node based on a given high-voltage node power generation instruction.

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