Adjustment capacity demand determination and classification calculation method and medium

Abstract

The invention provides an adjustment capacity demand determination and classification calculation method and a medium, and the method comprises the following steps: obtaining the related data of a power grid, and generating a load and new energy output curve; a daily net load climbing curve of the system is calculated, the maximum net load climbing and the maximum secondary net load climbing of each month are counted, and the monthly capacity adjusting requirement is obtained; determining the proportion of the secondary net load climbing in the seasonal average regulation capacity demand; the capacity demand proportion is adjusted on the basis of the secondary net load climbing proportion, the secondary net load climbing proportion is reduced by 95% of the adjustment capacity demand as the peak demand proportion, the total proportion is reduced by the basic proportion and the peak proportion as the over-peak proportion, and the adjustment capacity demand classification method in different seasons is obtained. The problem that the adjusting capacity of a high-proportion new energy power system is increased sharply is solved, the system is helped to reasonably determine the adjusting capacity requirement, and a classification mode is provided according to the actual operation requirement of a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and specifically to a method and medium for determining and classifying the regulation capacity requirements and calculating them. Background Art

[0002] With the increase in new energy installed capacity, the "steepness" of the net load curve gradually increases during two periods: downward in the morning and upward in the evening. Especially during the period from the afternoon trough to the evening peak, the net demand during the day decreases and increases sharply at sunset. Therefore, the system urgently needs to have sufficient medium- and long-term regulation capacity to meet the climbing capacity requirements brought by the increasingly sinking net load curve. However, in the actual power grid, the volatility and instability problems of new energy and load are relatively prominent and are easily affected by seasonal changes. If the classification method for determining the regulation capacity requirements is not reasonable, the differences in regulation capacity requirements caused by seasonal factors in the system cannot be solved. Therefore, it is necessary to explore how to consider the seasonal characteristics of new energy and load and reasonably determine the classification method for medium- and long-term regulation capacity requirements, which helps to enhance the system's regulation ability and has important practical significance. Summary of the Invention

[0003] Object of the Invention: Aiming at the deficiencies of the prior art, the present invention provides a method and medium for determining and classifying the regulation capacity requirements and calculating them to solve the problems raised in the above background art.

[0004] The technical solution adopted by the present invention is as follows:

[0005] To achieve the above object, the present invention is realized through the following technical solutions:

[0006] A method for determining and classifying the regulation capacity requirements and calculating them includes the following steps:

[0007] Step 1: Obtain the relevant data of the power grid and generate the output curves of the load and new energy;

[0008] Step 2: Calculate the daily net load climbing curve of the system, and count the maximum net load climb and the maximum secondary net load climb per month to obtain the monthly regulation capacity requirements;

[0009] Step 3: Calculate the average values of the maximum three-hour net load climb and the secondary net load climb in different seasons, and determine the proportion of the secondary net load climb in the seasonal average regulation capacity requirements;

[0010] Step 4: Based on the proportion of the secondary net load climb as the proportion of the basic regulation capacity requirements, the proportion of the peak demand is 95% of the regulation capacity requirements minus the proportion of the secondary net load climb, and the proportion of the super-peak demand is the total proportion minus the basic proportion and the peak proportion, so as to obtain the classification of the regulation capacity requirements in different seasons.

[0011] In step one, the grid data includes historical data of load and new energy, as well as future peak load of electricity consumption and new energy installed capacity data.

[0012] In step two, calculate the daily net load ramp curve of the system, and calculate the maximum net load ramp and maximum secondary net load ramp of each month. The calculation formulas are as follows:

[0013] Netload t =load t -wind t -solar t

[0014] Ramp t =Netload t -Netload t-2

[0015]

[0016] In the formula: load t is the electricity consumption of the load at time t, wind t is the wind power output at time t, solar t is the photovoltaic power output at time t, Netload t is the net load at time t; Netload t-2 is the net load at time t - 2, Ramp t is the 3-hour net load ramp at time t; is the net load ramp at time t on the j-th day of the i-th month, is the maximum net load ramp on the j-th day of the i-th month; is the maximum net load ramp of the i-th month, is the secondary net load ramp on the j-th day of the i-th month; is the maximum secondary net load ramp of the i-th month.

[0017] In step two, calculate the monthly regulation capacity demand. The calculation formula is as follows:

[0018]

[0019] In the formula: Need i is the regulation capacity demand of the i-th month, is the maximum net load ramp of the i-th month.

[0020] In step three, calculate the average values of the maximum three-hour net load ramp and secondary net load ramp in different seasons. The calculation formula is as follows:

[0021]

[0022] In the formula: is the maximum net load ramp in the τ-th month of the k-th season, is the maximum secondary net load ramp in the τ-th month of the k-th season, is the average maximum net load ramp in the k-th season, is the average maximum secondary net load ramp in the k-th season.

[0023] In step three, determine the proportion of the secondary net load ramp in the seasonal average regulation capacity demand. The calculation formula is:

[0024]

[0025] In the formula: Base k is the proportion of the basic regulation capacity demand in the k-th season, is the average maximum secondary net load ramp in the k-th season, is the average regulation capacity demand in the k-th season.

[0026] In step four, based on the proportion of the secondary net load ramp as the proportion of the basic regulation capacity demand, 95% of the regulation capacity demand minus the proportion of the secondary net load ramp is the proportion of the peak demand. The calculation formula is:

[0027]

[0028] In the formula: Peak k is the proportion of the peak demand, is the average maximum net load ramp in the k-th season, is the average maximum secondary net load ramp in the k-th season, is the average regulation capacity demand in the k-th season.

[0029] In step four, the total proportion minus the basic proportion and the peak proportion is the super-peak proportion. Obtain the classification of the regulation capacity demand in different seasons. The calculation formula is:

[0030] SuperPeak k = 100% - Base k - Peak k

[0031] In the formula: SuperPeak k is the proportion of the super-peak demand, Base k is the proportion of the basic regulation capacity demand in the k-th season, Peak k is the proportion of the peak demand. The present invention also provides a computer-readable storage medium storing program codes, and when the program codes are executed by a processor, the above method for determining and classifying the regulation capacity demand is implemented.

[0032] The beneficial effects of the present invention are as follows: An adjustment capacity demand determination and classification calculation method and medium are invented. A reasonable medium- and long-term adjustment capacity demand determination and classification method is determined in view of the influence of seasonal factors, solving the difference in adjustment capacity demand caused by seasonal factors, rationally allocating adjustment capacity resources, and enhancing the system adjustment ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is the general flowchart of the method of the present invention;

[0035] Figure 2 It is the system diagram simulated by the example of the present invention;

[0036] Figure 3 It is the grid-related data of a city with low new energy penetration rate in the first embodiment of the present invention, including load data, wind power data, and photovoltaic data;

[0037] Figure 4 It is the maximum net load ramp-up data per hour for 24 hours each month in a city with low new energy penetration rate in the first embodiment of the present invention;

[0038] Figure 5 It is the grid-related data of a city with medium new energy penetration rate in the second embodiment of the present invention, including load data, wind power data, and photovoltaic data;

[0039] Figure 6 It is the maximum net load ramp-up data per hour for 24 hours each month in a city with medium new energy penetration rate in the second embodiment of the present invention;

[0040] Figure 7 It is the grid-related data of a city with high new energy penetration rate in the third embodiment of the present invention, including load data, wind power data, and photovoltaic data;

[0041] Figure 8 It is the maximum net load ramp-up data per hour for 24 hours each month in a city with high new energy penetration rate in the third embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Please refer to Figure 1 As shown in the figure, this embodiment provides a method for determining and classifying the regulation capacity demand, including the following steps:

[0044] The present invention provides a method for determining and classifying the regulation capacity demand, and the steps are as follows:

[0045] Step 1: Obtain the power grid-related data and generate the output curves of the load and new energy.

[0046] Step 2: Calculate the daily net load ramp-up curve of the system, and count the maximum net load ramp-up and the maximum secondary net load ramp-up per month to obtain the monthly regulation capacity demand.

[0047] Step 3: Calculate the average values of the maximum three-hour net load ramp-up and the secondary net load ramp-up in different seasons, and determine the proportion of the secondary net load ramp-up in the seasonal average regulation capacity demand.

[0048] Step 4: Based on the proportion of the secondary net load ramp-up in the regulation capacity demand, the proportion of the regulation capacity demand minus the proportion of the secondary net load ramp-up is the peak demand proportion, and the total proportion minus the base proportion and the peak proportion is the super-peak proportion, so as to obtain the classification of the regulation capacity demand in different seasons.

[0049] Embodiment 1

[0050] As Figure 2 shown, based on a simulated simplified system, the calculation steps of the example of the present disclosure are illustrated. Among them, the power grids of three different cities with low, medium, and high new energy penetration rates (25.1%, 34.7%, 47%) are equivalent to a two-node system of a1 and a2. The a1 node is connected to n1 conventional units G1,..., Gn1, the a2 node is connected to n2 conventional units g1,..., gn2, and n3 new energy farms W1,..., Wn3 are connected to the a2 node.

[0051] Step 1: Obtain the power grid-related data of different cities, including load data, wind power data, and photovoltaic data, and generate the output curves of the load and new energy. For example, the city with low new energy penetration rate is as Figure 3 shown;

[0052] Step 2: Calculate the daily net load ramp-up curves of different urban power grids, and count the maximum net load ramp-up and the maximum secondary net load ramp-up per month to obtain the monthly regulation capacity requirements for different cities. First, calculate the three-hour net load ramp-up for each time period of each day, and then count the ramp-up magnitudes for each time period within a month to obtain the maximum net load ramp-up data for each hour of the 24 hours of each month. The calculation results for cities with low new energy penetration rates are as Figure 4 shown, which are the maximum net load ramp-up data for each hour of the 24 hours of each month;

[0053] Step 3: Calculate the average values of the maximum three-hour net load ramp-up and the secondary net load ramp-up for different seasons in different cities, and determine the proportion of the secondary net load ramp-up in the seasonal average regulation capacity requirements. The monthly regulation capacity requirements and secondary net load ramp-up for cities with low new energy penetration rates are shown in Table 1, and the average values of the maximum three-hour net load ramp-up and the secondary net load ramp-up for different seasons in cities with low new energy penetration rates are shown in Table 2;

[0054] Table 1 Monthly regulation capacity requirements and secondary net load ramp-up for cities with low new energy penetration rates

[0055]

[0056] Table 2 Average values of the maximum three-hour net load ramp-up and the secondary net load ramp-up for different seasons in cities with low new energy penetration rates

[0057]

[0058]

[0059] Step 4: Based on the proportion of the secondary net load ramp-up as the proportion of the regulation capacity requirements, subtract the proportion of the secondary net load ramp-up from 95% of the regulation capacity requirements to obtain the peak demand proportion, and subtract the basic proportion and the peak proportion from the total proportion to obtain the super-peak proportion, so as to obtain the classification of the regulation capacity requirements for the power grids of cities with low new energy penetration rates in different seasons. Finally, the proportions of each demand classification are obtained as shown in Table 3.

[0060] Table 3 Classification of regulation capacity requirements for different seasons in cities with low new energy penetration rates

[0061]

[0062] Example 2

[0063] As Figure 2As shown, based on the simulation-based simplified system, the example calculation steps of the present disclosure are illustrated. Among them, the power grids of three different cities with low, medium, and high new energy penetration rates (25.1%, 34.7%, 47%) are equivalent to a two-node system of a1 and a2. Node a1 is connected to n1 conventional units G1, …, Gn1, node a2 is connected to n2 conventional units g1, …, gn2, and n3 new energy farms W1, …, Wn3 are connected to node a2.

[0064] Step 1: Obtain the relevant data of different urban power grids, including load data, wind power data, and photovoltaic data, and generate the output curves of the load and new energy. A city with medium new energy penetration rate is as Figure 5 shown;

[0065] Step 2: Calculate the daily net load ramp-up curves of different urban power grids, and count the maximum net load ramp-up and the maximum secondary net load ramp-up per month to obtain the monthly regulation capacity requirements of different cities. First, calculate the three-hour net load ramp-up for each time period of each day, and then count the ramp-up magnitudes of each time period within a month to obtain the maximum net load ramp-up data for each hour of the 24 hours of each month. The calculation results for a city with medium new energy penetration rate are as Figure 6 shown, which are the maximum net load ramp-up data for each hour of the 24 hours of each month;

[0066] Step 3: Calculate the average values of the maximum three-hour net load ramp-up and the secondary net load ramp-up in different seasons of different cities, and determine the proportion of the secondary net load ramp-up in the seasonal average regulation capacity requirements. Among them, the monthly regulation capacity requirements and the secondary net load ramp-up of a city with medium new energy penetration rate are shown in Table 4, and the average values of the maximum three-hour net load ramp-up and the secondary net load ramp-up in different seasons of a city with low new energy penetration rate are shown in Table 5;

[0067] Table 4 Monthly regulation capacity requirements and secondary net load ramp-up of cities with medium new energy penetration rate

[0068]

[0069] Table 5 Average values of the maximum three-hour net load ramp-up and the secondary net load ramp-up in different seasons of cities with medium new energy penetration rate

[0070]

[0071] Step 4: Based on the proportion of the secondary net load ramp-up in the regulation capacity requirements, the proportion of the peak demand is obtained by subtracting the proportion of the secondary net load ramp-up from 95% of the regulation capacity requirements, and the proportion of the super-peak demand is obtained by subtracting the basic proportion and the peak proportion from the total proportion, so as to obtain the classification of the regulation capacity requirements of the urban power grid with medium new energy penetration rate in different seasons. Finally, the proportions of each demand classification are obtained as shown in Table 6.

[0072] Table 6 Classification of Regulation Capacity Requirements in Different Seasons for Cities with Medium New Energy Penetration

[0073]

[0074]

[0075] Example 3

[0076] As Figure 2 shown, based on a simplified simulated system, the calculation steps of the examples of the present disclosure are illustrated. Among them, the power grids of three different cities with low, medium, and high new energy penetrations (25.1%, 34.7%, 47%) are equivalent to a two-node system of a1 and a2. The a1 node is connected to n1 conventional units G1,…,Gn1, the a2 node is connected to n2 conventional units g1,…,gn2, and n3 new energy farms W1,…,Wn3 are connected to the a2 node.

[0077] Step 1: Obtain relevant data of different city power grids, including load data, wind power data, and photovoltaic data, and generate the output curves of load and new energy. For cities with high new energy penetration, as Figure 7 shown;

[0078] Step 2: Calculate the daily net load ramp-up curves of different city power grids, and count the maximum net load ramp-up and the maximum secondary net load ramp-up per month to obtain the monthly regulation capacity requirements of different cities. First, calculate the three-hour net load ramp-up for each time period of each day, and then count the ramp-up magnitudes of each time period within a month to obtain the maximum net load ramp-up data for each hour of the 24 hours of each month. The calculation results for cities with high new energy penetration are as Figure 8 shown, which are the maximum net load ramp-up data for each hour of the 24 hours of each month;

[0079] Step 3: Calculate the average values of the maximum three-hour net load ramp-up and the secondary net load ramp-up in different seasons of different cities, and determine the proportion of the secondary net load ramp-up in the seasonal average regulation capacity requirements. Among them, the monthly regulation capacity requirements and the secondary net load ramp-up of cities with high new energy penetration are shown in Table 7, and the average values of the maximum three-hour net load ramp-up and the secondary net load ramp-up in different seasons of cities with high new energy penetration are shown in Table 8;

[0080] Table 7 Monthly Regulation Capacity Requirements and Secondary Net Load Ramp-up of Cities with High New Energy Penetration

[0081]

[0082] Table 8 Average Values of the Maximum Three-hour Net Load Ramp-up and the Secondary Net Load Ramp-up in Different Seasons of Cities with High New Energy Penetration

[0083]

[0084] Step 4: Adjust the proportion of capacity demand based on the proportion of secondary net load ramp-up. The proportion of peak demand is 95% of the adjusted capacity demand minus the proportion of secondary net load ramp-up. The proportion of super-peak demand is the total proportion minus the basic proportion and the peak proportion. Obtain the classification of the adjusted capacity demand for the urban power grid with a high new energy penetration rate in different seasons, and finally obtain the proportion of each demand classification as shown in Table 9.

[0085] Table 9 Classification of Adjusted Capacity Demand for Cities with High New Energy Penetration Rate in Different Seasons

[0086]

[0087] Example 4 The present invention also provides a computer-readable storage medium, a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the method described in any one of the embodiments when executed by a computer processor.

[0088] The computer storage medium of the embodiments of the present invention can be any combination of one or more computer-readable media. The computer-readable media can be computer-readable signal media or computer-readable storage media. The computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage media can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or component.

[0089] The computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0090] Certainly, for a storage medium containing computer-executable instructions provided by an embodiment of the present invention, the computer-executable instructions are not limited to the above method operations, and can also execute relevant operations in the methods provided by any embodiment of the present invention.

[0091] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0092] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement without departing from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method and medium for determining and classifying regulation capacity requirements, characterized in that: The following steps are involved: Step 1: Obtain grid data and generate output curves of load and renewable energy; Step 2: Calculate the daily net load ramp curve of the system, and count the maximum net load ramp and the maximum secondary net load ramp of each month to obtain the monthly regulation capacity demand; Step 3: Calculate the average of the maximum three-hour net load ramp and secondary net load ramp in different seasons, and determine the proportion of secondary net load ramp in the seasonal average regulation capacity demand; Step 4: The capacity demand ratio is adjusted based on the secondary net load ramp ratio. 95% of the capacity demand minus the secondary net load ramp ratio is the peak demand ratio. The total ratio minus the basic ratio and the peak ratio is the super peak ratio. The classification of capacity demand in different seasons is obtained.

2. The method and medium for determining and classifying the regulation capacity demand according to claim 1, characterized in that: The power grid data includes historical data of load and new energy and future load power consumption peak and new energy installed capacity data.

3. The method and medium for determining and classifying the regulation capacity demand according to claim 1, characterized in that: Calculate the system's daily net load ramp curve, and calculate the monthly maximum net load ramp and maximum secondary net load ramp. The calculation formula is: Netload t =load t -wind t -solar t Ramp t =Netload t -Netload t-2 Where: load t is the load power consumption at time t, wind t is the wind power output at time t, solar t is the photovoltaic output at time t, Netload t Netload is the net load at time t; t-2 is the net load at time t-2, Ramp t is the 3-hour net load ramp at time t; is the net load ramp at time t on the jth day of the i-th month, is the maximum net load ramp on the jth day of the i-th month; is the maximum net load ramp in month i, is the secondary net load ramp on the jth day of the i-th month; is the maximum secondary net load ramp in month i.

4. The method and medium for determining and classifying the regulation capacity demand according to claim 1, characterized in that: Calculate the monthly regulation capacity requirement using the following formula: In the formula: Need i is the regulation capacity demand in month i, is the maximum net load ramp in month i.

5. The method and medium for determining and classifying the regulation capacity demand according to claim 1, characterized in that: Calculate the average of the maximum three-hour net load ramp and secondary net load ramp in different seasons using the following formula: Where: is the maximum net load ramp in month τ of the kth season, is the maximum secondary net load ramp in month τ of the kth season, is the average maximum net load ramp in the kth season, is the average maximum secondary net load ramp in the kth season.

6. The method and medium for determining and classifying the regulation capacity demand according to claim 1, characterized in that: Determine the proportion of secondary net load ramping in the seasonal average regulation capacity demand using the following calculation formula: Where: Base k is the proportion of basic regulation capacity demand in season k, is the average maximum secondary net load ramp in the kth season, is the average regulation capacity demand in the kth season.

7. The method and medium for determining and classifying the regulation capacity demand according to claim 1, characterized in that: The capacity demand ratio is adjusted based on the secondary net load ramp ratio. 95% of the capacity demand minus the secondary net load ramp ratio is the peak demand ratio. The calculation formula is: Where: Peak k is the peak demand ratio, is the average maximum net load ramp in the kth season, is the average maximum secondary net load ramp in the kth season, is the average regulation capacity demand in the kth season.

8. The method and medium for determining and classifying the regulation capacity demand according to claim 1, characterized in that: The total proportion minus the basic proportion and the peak proportion is the super peak proportion, and the regulation capacity demand classification in different seasons is obtained. The calculation formula is: SuperPeak k =100%-Base k -Peak k Where: SuperPeak k Base is the proportion of super peak demand. k is the proportion of basic regulation capacity demand in season k, Peak k is the percentage of peak demand.

9. A computer-readable storage medium storing program codes, wherein when the program codes are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

Images