Power system optimization scheduling method and device considering transmission margin, and storage medium

Abstract

The present application relates to a kind of power system optimization scheduling method, equipment and storage medium considering transmission margin, the method includes the following steps: step S1, considering the fluctuation and uncertainty of new energy and load, quantifies node flexibility demand and its line distribution amount;Step S2, based on resource flexibility margin and line flexibility transmission margin, determine system flexibility deficiency evaluation index;Step S3, system flexibility deficiency evaluation index is used as opportunity constraint condition, constructs with total operation cost minimum as optimization goal, considers the double margin of resource and line Multi-source day-ahead optimization scheduling model, obtains optimization scheduling result by solving.Compared with prior art, the present application has the advantages of considering system flexibility in many aspects, reducing line congestion, reducing load shedding, improving new energy consumption.

Description

Technical Field

[0001] This invention relates to the field of power system flexibility assessment and optimized scheduling, and in particular to a power system optimized scheduling method, equipment and storage medium that takes into account transmission margin. Background Technology

[0002] As gradually increasing renewable energy generation is a key measure in building a new power system, renewable energy generation, represented by wind and solar power, has developed rapidly in recent years. While wind and solar power bring huge environmental benefits, their randomness, volatility, and uncertainty have led to increasingly complex operation of power systems that rely primarily on hydropower and thermal power. Curtailment of wind and solar power is commonplace, and power rationing occurs frequently. Ultimately, this stems from the power system's lack of flexible adjustment capabilities.

[0003] Therefore, a comprehensive assessment of the flexibility of new energy power systems and optimized scheduling that takes into account both economic efficiency and flexibility are of great significance for reducing wind and solar curtailment and load shedding losses, and for promoting the clean energy transition.

[0004] Current power system flexibility assessment and optimized dispatch primarily focus on balancing flexibility supply and demand, rarely considering the impact of line transmission capacity limitations on demand fulfillment during flexibility supply and demand transmission. With the gradual increase in terminal electrification rates, transmission congestion will become apparent during power system dispatch and operation. The volatility and uncertainty of new energy sources and loads will not only generate flexibility demands but also cause fluctuations in line power flow. Therefore, good system flexibility depends on both a balance between flexibility supply and demand and sufficient line transmission margin. Since insufficient line transmission margin may lead to line congestion and unavailability of flexibility supply in actual operation, it is necessary to assess line flexibility transmission margin and conduct day-ahead dispatch based on this assessment to avoid insufficient flexibility issues such as wind and solar curtailment and load shedding. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art by providing a power system optimization scheduling method, equipment and storage medium that takes into account transmission margin, which takes into account the flexibility of the power system, reduces line congestion, reduces load shedding and improves the capacity for renewable energy consumption.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] According to a first aspect of the present invention, a method for optimal scheduling of a power system taking into account transmission margin is provided, the method comprising the following steps:

[0008] Step S1: Taking into account the volatility and uncertainty of new energy sources and loads, quantify the node flexibility requirements and their line allocation.

[0009] Step S2: Based on resource flexibility margin and line flexibility transmission margin, determine the evaluation index for insufficient system flexibility.

[0010] Step S3: Using the system flexibility insufficiency assessment index as an opportunity constraint, construct a multi-source day-ahead optimization scheduling model with the minimum total operating cost as the optimization objective and considering both resource and line margins, and solve to obtain the optimized scheduling result.

[0011] Preferably, the node flexibility requirement in step S1 includes the fluctuation of node net load within a set time scale, as well as the prediction error of new energy sources and loads, expressed as:

[0012]

[0013] P net,i,t =P L,i,t -P res,i,t

[0014] In the formula, F need,i,t P represents the flexibility requirements of node i at time t. net,i,t+1 P net,i,t P represents the net load of node i at times t+1 and t; L,i,t P represents the power of load i at time t. res,i,t Let i be the power of the new energy source at time t; Let be the prediction error of load node i at time t; Let be the prediction error of new energy source i at time t;

[0015] The node flexibility requirement is directional; when F need,i,t >0 represents the node's upward flexibility requirement; when F need,i,t When <0, it represents the node's downward flexibility requirement, which satisfies:

[0016]

[0017]

[0018] In the formula, Let i be the node i's upward flexibility requirement at time t. Let i be the downward flexibility requirement of node i at time t.

[0019] Preferably, the expression for the line allocation amount required for node flexibility is:

[0020]

[0021]

[0022] In the formula, The allocation of upward flexibility requirements at time t on line br; δ represents the allocation of downward flexibility requirements on line br at time t; N is the total number of nodes; br,i Let PTDF be the power transfer distribution factor of the injected power at node i on line br.

[0023] Preferably, the expression for the resource flexibility margin in step S2 is:

[0024]

[0025]

[0026] In the formula, F t + and F t - For the upward and downward flexible supply of the power system at time t; and These represent upward and downward resource flexibility margins, respectively. When the resource flexibility margin is sufficient, that is... and This indicates that the system has sufficient flexibility from the perspective of supply and demand balance.

[0027] Preferably, the line flexibility transmission margin in step S2 is characterized as the line margin after allocating the line amount to meet the node flexibility requirements, reflecting the line's transmission capacity to cope with flexibility, and is expressed as:

[0028]

[0029]

[0030] In the formula, and These represent the upward and downward line flexibility transmission margins, respectively. When the line flexibility transmission margin is sufficient, that is... and This indicates that the system's flexibility requirements can be met from the perspective of line transmission capacity; P br,t and These represent the transmission power of line br at time t and its upper limit, respectively.

[0031] Preferably, the insufficient upward and downward system flexibility takes into account both resource flexibility margin and line flexibility transmission margin, and is expressed as:

[0032]

[0033]

[0034] In the formula, and The system lacks flexibility in both upward and downward directions. and N B This represents the number of branch roads.

[0035] Preferably, the multi-source day-ahead optimization scheduling model in step S3 is specifically as follows:

[0036] Objective function:

[0037] min C Σ =C th +C hy +C pu +C p

[0038] In the formula, C Σ For total operating costs, C th Including the start-up and shutdown costs of thermal power units and fuel consumption costs, C hy For the start-up and shutdown costs of hydropower units, C pu C represents the start-up cost of a pumped-storage hydroelectric power station for generating or pumping water. p The penalty costs for curtailing wind, solar, and hydropower, and for load shedding;

[0039] The system flexibility deficiency assessment index is used as an opportunity constraint:

[0040]

[0041]

[0042] In the formula, pr{·} represents the probability of the random event · occurring; σ1 and σ2 represent the confidence levels that the random event must satisfy; br represents the line, and N... B This is a set of routes.

[0043] Preferably, step S3 further includes processing the chance constraints using a chance constraint deterministic transformation method based on Latin hypercube sampling.

[0044] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement any of the methods described above.

[0045] According to a third aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described herein.

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] The method of this invention takes into account the transmission margin of line flexibility, which can reflect the line's ability to support the realization of flexibility requirements, quantifies the important factors that may cause insufficient system flexibility, and supplements the shortcomings of the current system flexibility assessment. Through optimized scheduling, it can effectively tap the line's carrying capacity for flexibility requirements, alleviate the problem of unavailable flexibility supply, alleviate line congestion during operation, and reduce load shedding and renewable energy reduction. Attached Figure Description

[0048] Figure 1 This is a flowchart of the method of the present invention;

[0049] Figure 2 This refers to the upward line flexibility transmission margin in the embodiment; wherein, Figure 2 a to 2e represent the upward flexibility transmission margins for lines 2-3, 4-4, 6-31, 16-17, and 22-23, respectively.

[0050] Figure 3 This refers to the downline flexibility transmission margin in the embodiment; wherein, Figure 3 a to 3e represent the downward flexibility transmission margins for lines 2-3, 3-4, 3-18, 15-16, and 16-17, respectively.

[0051] Figure 4 This refers to the power output of unit 1 in the embodiment.

[0052] Figure 5 This refers to the power output of unit 2 in the embodiment.

[0053] Figure 6 The power flow of lines 22-23 in the example is shown. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0055] This invention provides a power system optimal scheduling method that takes into account transmission margin, such as... Figure 1 As shown, the method includes the following steps:

[0056] Step S1: Taking into account the volatility and uncertainty of new energy sources and loads, quantify the node flexibility requirements and their line allocation.

[0057] Step S2: Based on resource flexibility margin and line flexibility transmission margin, determine the evaluation index for insufficient system flexibility.

[0058] Step S3: Using the system flexibility insufficiency assessment index as an opportunity constraint, construct a multi-source day-ahead optimization scheduling model with the minimum total operating cost as the optimization objective and considering both resource and line margins, and solve to obtain the optimized scheduling result.

[0059] The method of the present invention will now be described in detail.

[0060] 1. Node flexibility requirements:

[0061] Considering the volatility and uncertainty of renewable energy and load, the flexibility requirements of renewable energy power systems mainly stem from net load fluctuations and forecasting errors of renewable energy and load. Nodal flexibility requirements include the fluctuation of nodal net load within a given time scale, as well as forecasting errors of renewable energy and load, expressed as:

[0062]

[0063] P net,i,t =P L,i,t -P res,i,t (2)

[0064] In the formula, F need,i,t P represents the flexibility requirements of node i at time t. net,i,t+1 P net,i,t P represents the net load of node i at times t+1 and t; L,i,t P represents the power of load i at time t. res,i,t Let i be the power of the new energy source at time t; Let be the prediction error of load node i at time t; Let be the prediction error of new energy source i at time t;

[0065] The need for node flexibility is directional; when F need,i,t >0 represents the node's upward flexibility requirement; when F need,i,t When <0, it represents the node's downward flexibility requirement, which satisfies:

[0066]

[0067]

[0068] In the formula, Let i be the node i's upward flexibility requirement at time t. Let i be the downward flexibility requirement of node i at time t.

[0069] 2. Line allocation based on node flexibility requirements

[0070] Line transmission capacity limitations affect the fulfillment of node flexibility requirements, necessitating consideration of the transmission allocation for each line to meet these requirements. The expression for the line allocation for node flexibility requirements is as follows:

[0071]

[0072]

[0073] In the formula, The allocation of upward flexibility requirements at time t on line br; δ represents the allocation of downward flexibility requirements on line br at time t; N is the total number of nodes; br,i The power transfer distribution factor (PTDF) is the power injected at node i onto line br.

[0074] 3. Resource flexibility margin:

[0075] Resource flexibility margin is represented as the difference between the supply of flexible resources and the demand for flexible resources. This invention mainly considers the participation of thermal power (including coal-fired and gas-fired power), hydropower, and pumped storage power stations in flexibility supply and multi-source optimized scheduling. The flexibility supply of each unit is divided into upward flexibility and downward flexibility, expressed as follows:

[0076]

[0077]

[0078] In the formula, Δt is the scheduling time interval; N th N hy and N pu These represent the number of thermal power units, hydropower units, and pumped storage power stations, respectively; F t + and F t - Provides upward and downward flexibility for the system at time t; and To provide upward and downward flexibility for thermal power unit i at time t; u th,i,t P represents the operating state of thermal power unit i at time t, with a value of 0 indicating unit shutdown and a value of 1 indicating unit operation; th,i,t , and These represent the output and upper and lower limits of thermal power unit i at time t, respectively. and The upward and downward climbing ability of thermal power unit i; and Provides upward and downward flexibility for hydropower unit i at time t; P hy,i,t , and These represent the output and upper and lower limits of hydropower unit i at time t; and The upward and downward climbing ability of hydropower unit i; The planned power generation of hydropower unit i during the scheduling cycle; and To provide upward and downward flexibility for pumped storage power station i at time t; P pu,i,t , and These are the output of pumped storage power station i at time t, its maximum power generation, and its maximum pumping power, respectively. and These are the water loss coefficients for pumped storage power station i during power generation and pumping, respectively; E pu,i,t , and Let represent the upper reservoir capacity and its upper and lower limits of pumped storage power station i at time t.

[0079] Therefore, the resource flexibility margin is expressed as:

[0080]

[0081]

[0082] In the formula, and These represent upward and downward resource flexibility margins, respectively. When the resource flexibility margin is sufficient, that is... and This indicates that the system has sufficient flexibility from the perspective of supply and demand balance.

[0083] 4. Line flexibility and transmission margin:

[0084] While the lines themselves do not provide or consume flexibility, they support flexibility requirements and are an indispensable part of flexibility assessment. Line flexibility transmission margin is defined as the line margin after allocating the required number of lines to meet node flexibility needs, reflecting the line's transmission capacity to handle flexibility. The expression for line flexibility transmission margin is:

[0085]

[0086]

[0087] In the formula, and These represent the upward and downward line flexibility transmission margins, respectively. When the line flexibility transmission margin is sufficient, that is... and This indicates that the system's flexibility requirements can be met from the perspective of line transmission capacity; P br,t and These represent the transmission power of line br at time t and its upper limit, respectively.

[0088] 5. Insufficient system flexibility

[0089] When resource flexibility margin or line flexibility transmission margin is insufficient, the system needs to maintain dynamic balance by reducing renewable energy output or shedding loads. The upstream and downstream system flexibility insufficiency, taking into account both resource flexibility margin and line flexibility transmission margin, is expressed as:

[0090]

[0091]

[0092] In the formula, and The system lacks flexibility in both upward and downward directions. and N B This represents the number of branch roads.

[0093] 6. Day-ahead optimal scheduling model considering resource-line dual margins

[0094] Objective function:

[0095] min C Σ =C th +C hy +C pu +C p

[0096] In the formula, C Σ For total operating costs, C th Including the start-up and shutdown costs of thermal power units and fuel consumption costs, C hy For the start-up and shutdown costs of hydropower units, C pu C represents the start-up cost of a pumped-storage hydroelectric power station for generating or pumping water. p The penalty costs for curtailing wind, solar, and hydropower, and for load shedding;

[0097] The system flexibility deficiency assessment index is used as an opportunity constraint:

[0098]

[0099]

[0100] In the formula, pr{·} represents the probability of the random event · occurring; σ1 and σ2 represent the confidence levels that the random event must satisfy; br represents the line, and N... B This is a set of routes.

[0101] To simplify the model solution, the model is linearized. The flexibility constraint is simplified using a chance constraint deterministic transformation method based on Latin hypercube sampling. Equations (16) and (17) can be transformed into:

[0102]

[0103]

[0104] In the formula, ε is a sufficiently small negative number; N sa d represents the number of samples; the upper right corner 'sa' indicates the value of the sa-th sample; d 1,t (sa) and d 2,t (sa) is a 0-1 variable.

[0105] To further simplify the model and reduce optimization variables, the sampled data can be sorted according to the degree to which the constraint on the chance of success holds, excluding extreme scenarios and better reflecting the model's economy. For equation (18), Sort in ascending order for the [N]th... sa [×σ1] sampled values ​​satisfy the constraint. For equation (19), [the following will be used]. Sort in descending order for the [N]th... sa [×σ²] sampled values ​​satisfy the constraint. [·] represents rounding up, i.e., the smallest integer greater than or equal to ·, thus reducing d. 1,t (sa) and d 2,t (sa) are two 0-1 variables.

[0106] Case Analysis

[0107] To verify the effectiveness of the proposed power system optimization scheduling method that takes into account transmission margin, a simulation analysis was conducted using an improved IEEE 39-bus system. Based on the original IEEE 39-bus system, a 600MW and a 1200MW large-scale wind farm were connected to nodes 6 and 16 respectively, a 900MW large-scale photovoltaic power station was connected to node 18, a 300MW hydropower station was connected to node 26, the thermal power unit at node 30 was replaced with a 120MW pumped storage power station, and node 37 was set up with a gas turbine unit.

[0108] To verify the effectiveness of the power system optimization scheduling method that takes into account transmission margin proposed in this invention, the following two scheduling schemes are compared and analyzed.

[0109] Option 1: An optimized scheduling method that takes into account resource flexibility margins.

[0110] Solution 2: The optimized scheduling method proposed in this invention considers both resource and line margins.

[0111] Analysis of flexibility assessment results:

[0112] Figure 2 and Figure 3 These refer to the upward and downward line flexibility transmission margins under two scheduling schemes. Figure 2 It can be seen that five lines in Scheme 1 exhibit insufficient upward transmission flexibility margin: lines 2-3, 4-5, 6-31, 16-17, and 22-23. For line 2-3, the insufficient upward transmission flexibility margin occurs at time point 4-18. The main reason is that at this time, the wind power output at node 16 decreases, creating upward flexibility demand, and its injected power has a negative PTDF for line 2-3, resulting in a positive line allocation to line 2-3. However, line 2-3 is already operating at full load at this time, thus resulting in insufficient upward transmission flexibility. Figure 3 Analysis revealed that five lines in Scheme 1 exhibited insufficient downward transmission flexibility: lines 2-3, 3-4, 3-18, 15-16, and 16-17. For line 3-18, its downward transmission flexibility margin was located between time points 42 and 50. This was primarily due to the increased photovoltaic output at node 18, creating downward flexibility demand. Furthermore, the injected power had a significant impact on the PTDF of line 3-18, resulting in a larger allocation of downward flexibility demand across line 3-18 and consequently, insufficient downward transmission flexibility margin. The analysis of other lines was similar and will not be elaborated further. It is evident that lines with insufficient transmission flexibility margin may face congestion in actual operation. Even with sufficient resource flexibility margin, the limitation of line transmission capacity may lead to the reduction of renewable energy and load shedding. The scheduling method proposed in this invention incorporates the flexibility evaluation index of line transmission capacity into the constraints. By adjusting the start-up and shutdown of units and the output, the power flow of lines with insufficient transmission margin is changed, so that the transmission flexibility margin of lines in day-ahead scheduling is greater than or equal to zero. This ensures that more flexibility needs are met in actual operation, and reduces wind curtailment, solar curtailment and load shedding.

[0113] Table 1 below shows the system's lack of flexibility.

[0114] Table 1

[0115]

[0116] Analysis of recent dispatch results:

[0117] Figure 4 and Figure 5The unit start-up, shutdown, and output of two scheduling schemes at the top of the hour are presented. Due to the poor economic efficiency of the thermal power unit at node 32, Scheme 1, while ensuring resource flexibility, did not start this unit. Scheme 2, considering line flexibility and transmission margin, started the thermal power unit at node 32 between 9:00-13:00 and 21:00-24:00, but did not start the thermal power unit at node 33 between 21:00-24:00. The unit output during this period is analyzed. Figure 3 It can be seen that under Scheme 1, the downward flexibility transmission margin of line 15-16 is insufficient during this period. The PTDF of the injected power at nodes 32 and 33 to line 15-16 are 0.04 and -0.5441, respectively. During this period, the transmission capacity of this line is negative, and the allocation of downward flexibility demand on this line is also negative. Therefore, Scheme 2 shuts down the thermal power unit at node 33, reducing the transmission capacity of line 15-16, thereby satisfying the downward flexibility transmission margin of this line.

[0118] Flexible supply availability analysis

[0119] To verify the effectiveness of the proposed scheduling method in alleviating line congestion, improving renewable energy absorption, and reducing load shedding, the flexibility and supply availability of the scheduling plans obtained from the day-ahead scheduling were tested through intraday actual scheduling, based on the optimization results obtained from the day-ahead scheduling. The objective function of the actual scheduling is the same as that of the day-ahead scheduling, but the start-up and shutdown of thermal power units are not considered. At the same time, slack variables are added to reflect line overruns, and corresponding penalty terms are added to the objective function.

[0120] Table 2 presents a comparison of the operating costs and penalties under the two schemes, where "line over-limit" refers to the sum of the absolute values ​​of the power flow of all lines exceeding their transmission capacity limits in a single day.

[0121] Table 2

[0122] Option 1 Option 2 Current operating costs (in ten thousand yuan) 4316.903 4334.664 Actual operating cost (ten thousand yuan) 4387.217 4369.627 New energy reduction (MWh) 21.095 10.297 Load shedding capacity (MWh) 68.318 0 Line exceeding capacity (MWh) 306.683 163.882

[0123] Figure 6 The power flow conditions of congested lines 22-23 under two schemes are presented. It can be seen that the method proposed in this invention can alleviate or even eliminate line congestion at certain times, and the line transmission limit is not exceeded for a long time. Since the thermal power unit at node 32 has poor economic efficiency, and Scheme 2 starts the unit to meet the line flexibility transmission margin, the day-ahead operating cost of Scheme 2 is 0.411% higher than that of Scheme 1. However, in actual operation, since the day-ahead scheduling of Scheme 2 takes into account the impact of line transmission capacity, the amount of renewable energy reduction, load shedding, and line over-limit are all smaller than those of Scheme 1, so the actual operating cost is lower than that of Scheme 1. It can be seen that the scheduling method of this invention is effective in alleviating line congestion, improving renewable energy absorption, and reducing load shedding, and the supply capacity of flexible resources is better utilized.

[0124] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0125] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0126] The processing unit executes the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 by any other suitable means (e.g., by means of firmware).

[0127] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0128] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on 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 foregoing.

[0130] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A power system optimal scheduling method considering transmission margin, characterized in that, The method includes the following steps: Step S1: Taking into account the volatility and uncertainty of new energy sources and loads, quantify the node flexibility requirements and their on-line allocation. Step S2: Based on resource flexibility margin and line flexibility transmission margin, determine the evaluation index for insufficient system flexibility. Step S3: Using the system flexibility insufficiency assessment index as an opportunity constraint, construct a multi-source day-ahead optimization scheduling model with the minimum total operating cost as the optimization objective and considering both resource and line margins, and solve it to obtain the optimization scheduling result; The node flexibility requirement in step S1 includes the fluctuation of node net load within a set time scale, as well as the prediction error of new energy sources and loads, expressed as: In the formula, For nodes i exist t The need for flexibility at any time; For nodes i exist t +1、 t Net load at any given time; For load i exist t Power at any given moment; For new energy i exist t Power at any given moment; for t Time-based load nodes i The prediction error; for t Shike New Energy i The prediction error; The node flexibility requirement is directional; when When the node needs upward flexibility, The node's downward flexibility requirement must be met, satisfying: In the formula, For nodes i exist t The need for constant upward flexibility For nodes i exist t The need for flexibility in the face of changing circumstances; The expression for the line allocation amount required for node flexibility is: In the formula, for t The need for constant upward flexibility in the line br The amount allocated on; for t Downward flexibility requirements in the line br The amount allocated on; N This represents the total number of nodes; For nodes i The injected power on the line br The power transfer distribution factor (PTDF); The expression for the resource flexibility margin in step S2 is: In the formula, and for t The power system provides flexible supply both upward and downward at all times; and These represent upward and downward resource flexibility margins, respectively. When the resource flexibility margin is sufficient, that is... and This indicates that the system has sufficient flexibility from the perspective of balancing supply and demand. The line flexibility transmission margin in step S2 is characterized as the line margin after allocating the line quantity to meet the node flexibility requirements, reflecting the line's transmission capacity to cope with flexibility. Its expression is: In the formula, and These represent the upward and downward line flexibility transmission margins, respectively. When the line flexibility transmission margin is sufficient, that is... and This indicates that the system's flexibility requirements can be met from the perspective of line transmission capacity. and The lines are respectively br exist t Transmission power at any given time and its upper limit.

2. The power system optimal scheduling method considering transmission margin according to claim 1, characterized in that, The insufficient upward and downward system flexibility, taking into account both resource flexibility margin and line flexibility transmission margin, is expressed as: In the formula, and The system lacks flexibility in both upward and downward directions. and ; N B This represents the number of branch roads.

3. The power system optimization scheduling method considering transmission margin according to claim 2, characterized in that, The multi-source day-ahead optimization scheduling model in step S3 is specifically as follows: Objective function: In the formula, Total operating cost, C th This includes the costs of starting and stopping thermal power units and fuel consumption. C hy For the start-up and shutdown costs of hydropower units, C pu The startup cost of a pumped-storage hydroelectric power station for generating or pumping water. C p The penalty costs for curtailing wind, solar, and hydropower, and for load shedding; The system flexibility deficiency assessment index is used as an opportunity constraint: In the formula, pr{·} represents the probability of the random event · occurring; and The confidence level that a random event must satisfy; For the line, This is a set of routes.

4. The power system optimization scheduling method considering transmission margin according to claim 3, characterized in that, Step S3 further includes processing the chance constraints using a chance constraint deterministic transformation method based on Latin hypercube sampling.

5. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

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