A method and device for identifying congestion of a key power transmission section under spot market operation

Abstract

The application discloses a kind of spot market operation under key transmission section congestion identification method and device, belong to electric power system technical field.The method includes: constructing the key transmission section congestion identification model for physical congestion and economic congestion, analyze the congestion formation mechanism of key transmission section, distinguish physical congestion and economic congestion;Under the condition that market subject quotes according to cost, construct the market clearing model considering network constraint, to identify the physical congestion section triggered directly by power grid physical constraint;Construct market participant bidding strategy model, on this basis, establish economic congestion identification model, identify economic congestion section.The method can realize the differentiation and identification of different congestion types of key transmission section under spot market operation, reveal the influence mechanism of market subject behavior on section congestion state, provide decision support for power spot market operation analysis, key transmission section management and related market mechanism optimization.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and apparatus for identifying blockages in critical transmission sections under spot market operation. Background Technology

[0002] In traditional power grid operation analysis, the identification of critical transmission sections is mainly based on the physical operating status of the power grid. This typically involves identifying whether a section has insufficient transmission capacity through power flow calculations, stability checks, and security constraint analysis to ensure the safe and stable operation of the power system. This type of analysis method is well-suited to conditions with low levels of electricity marketization and relatively rigid power generation and consumption plans.

[0003] However, with the continuous advancement of the electricity spot market, the large-scale integration of new energy sources and the diversified participation of market players have significantly changed the congestion mechanism of key transmission sections. Congestion at key transmission sections may not only stem from insufficient physical transmission capacity in the traditional sense, but may also be influenced by factors such as market players' bidding strategies, the exercise of market power, and price differentiation, exhibiting economic congestion characteristics with market attributes. If we continue to use an analytical approach based solely on physical constraints, it will be difficult to fully reflect the formation mechanism of key transmission section congestion in the spot market environment and its impact on market performance.

[0004] Operational results show that blockages at key transmission sections have multiple impacts. Firstly, the transmission capacity of low-cost power sources, especially renewable energy sources, is limited, reducing the system's clean energy utilization level. Secondly, blockages prevent some load areas from prioritizing low-cost power sources, forcing the use of high-cost units to meet demand, thus increasing system operating costs. In a spot market environment, blockages can also cause price differentiation between different regions or nodes, affecting the optimal allocation of market resources and reducing market efficiency.

[0005] Existing research on congestion at critical transmission sections largely focuses on power flow analysis, safety checks, and general market clearing analysis. It lacks a method that can simultaneously distinguish between physical and economic congestion and incorporate strategic bidding behavior of market participants into a unified identification framework. Therefore, it is necessary to propose a method and apparatus for identifying congestion at critical transmission sections under spot market operation. This would enable the differentiation and identification of different congestion types at critical transmission sections and provide technical support for power spot market operation analysis, critical transmission section management, and market mechanism optimization. Summary of the Invention

[0006] The purpose of this invention is to provide a method and apparatus for identifying blockages in critical transmission sections under spot market operation, so as to distinguish and identify different types of blockages in critical transmission sections.

[0007] This invention is implemented as follows: a method and apparatus for identifying blockages in critical transmission sections under spot market operation, comprising:

[0008] The formation mechanism of blockage at critical transmission sections is analyzed, and the blockage at critical transmission sections is divided into physical blockage and economic blockage.

[0009] Under the condition that market participants compete for day-ahead market clearing based on cost bids, a physical congestion identification model is constructed to identify physical congestion directly caused by line capacity constraints, power flow distribution, and the safe operation boundary of the provincial electricity spot market system; the provincial electricity spot market system includes traditional power generators, new energy power generators, load nodes, and key transmission networks;

[0010] A bidding strategy model for market participants is constructed, and a two-layer model is established, which includes an upper-level collusion group strategy bidding model and a lower-level day-ahead market clearing model. This model is used to characterize the interaction between the collusion bidding behavior of market participants and the day-ahead market clearing results, and to provide market clearing results under the strategy bidding scenario for identifying economic congestion.

[0011] Based on the market clearing results under the strategic bidding of market participants, an economic congestion identification model is constructed to identify economic congestion caused by the strategic bidding of market participants.

[0012] By identifying physical and economic congestion separately, the system can distinguish and determine different types of congestion at key transmission sections under spot market operation.

[0013] A further design of the above technical solution is as follows: The physical congestion identification model is based on the provincial spot market clearing process, establishing a day-ahead market optimization clearing model that considers network constraints, and characterizing the market equilibrium result of the system under cost-pricing conditions with the goal of maximizing social welfare. The model is as follows:

[0014] (1)

[0015] (2)

[0016] (3)

[0017] (4)

[0018] (5)

[0019] (6)

[0020] In the formula, The objective function for optimizing the day-ahead market clearing under cost-based pricing conditions; , These are the traditional power producers in the conspiratorial group. and new energy power generators Power output; , These are respectively a collection of traditional power generators and a collection of new energy power generators belonging to the Hemou Group. The total number of members in the collusion group; For load sets; , These are the coefficients of the load-side utility function; For load Demand; For the line The upper limit of transmission capacity; For the market area; for Power injection in the region The power transfer factor on the line is related to the actual grid topology and market zoning. , and These are traditional power generators, renewable energy power generators, and load-in-region power generators. The apportionment coefficient within; Candidate blocked lines; , and This is the market pricing coefficient for generators; at this point, all prices are quoted based on cost. and Traditional power producers Maximum output constraints and minimum output constraints; and Respectively, new energy power generators Maximum output constraints and minimum output constraints; and respectively load Equation (2) represents the maximum power demand constraint and the minimum power demand constraint; Equation (3) represents the power balance constraint on the supply side and the demand side; Equation (4) represents the line... The power flow constraint; Equations (4) and (5) are the output constraints of traditional units and new energy units, respectively; Equation (6) is the power constraint of load demand; For the Lagrange multiplier in the current market clearing model; when the line capacity constraint (3) is triggered, the corresponding line is identified as a critical line with physical blockage.

[0021] A further design of the above technical solution is as follows: the market participants participate in day-ahead market clearing based on cost bids, and use two indicators, line capacity margin and power distribution factor, to assist in the identification of physical congestion;

[0022] Among them, the line capacity margin RAMs are used to reflect the relationship between the actual power flow of the line and its available transmission capacity, and to identify lines whose power flow exceeds the preset ratio threshold of the transmission capacity as physically blocked lines.

[0023] The power distribution factor (PTDF) is used to describe the impact of changes in node injected power on line power flow. When the PTDF of a line is higher than a preset threshold, the line is identified as a critical line that is sensitive to system power transfer and is prone to physical blockage.

[0024] By comprehensively judging the line capacity constraint triggering conditions, line capacity margin, and power distribution factor, the system can identify physically blocked lines in the system.

[0025] A further design of the above technical solution is as follows: the market participant bidding strategy model is based on the Staberg game theory, establishing a two-layer model that includes an upper-level collusion group strategy bidding model and a lower-level day-ahead market clearing model.

[0026] The upper-level model aims to maximize the profits of the collusive group and determines the optimal strategy bidding for traditional power generators and new energy power generators within the group. The upper-level collusive group strategy bidding model is as follows:

[0027] (7)

[0028] In the formula, The objective function of the upper-level collusion group's strategy pricing model; , and All are traditional power producers and new energy power generators The cost coefficients of traditional power generators are quadratic functions of ax² + bx, while the cost of new energy power generators is linear functions of ax. and For traditional power producers and new energy power generators Market area Regional market clearing prices; , The first The conspiratorial group includes both traditional power generation companies and new energy power generation companies. , These are the sets of all traditional power generators and the sets of new energy power generators in the system, respectively.

[0029] The upper-level model obtains the optimal collusion strategy by maximizing the total revenue of traditional power generators and new energy power generators within the collusion group.

[0030] The further design of the above technical solution is as follows: the lower-level day-ahead market clearing model is basically consistent with the physical blockage analysis model corresponding to equations (1)-(6), the difference being that, , and All of these are strategy pricing coefficients given by the upper-level model. The lower-level day-ahead market clearing model is as follows:

[0031] (8)

[0032] (9)

[0033] (10)

[0034] (11)

[0035] (12)

[0036] (13)

[0037] In the formula, The objective function for day-ahead market clearing optimization under strategic pricing conditions; , These are the traditional power producers in the conspiratorial group. and new energy power generators Power output; , These are collections of traditional power generators and new energy power generators belonging to the Hemou Group, among which... The total number of members in the collusion group; For load sets; , These are the coefficients of the load-side utility function; For load Demand; For the line The upper limit of transmission capacity; For the market area; for Power injection in the region The power transfer factor on the line is related to the actual grid topology and market zoning. , and These are traditional power generators, renewable energy power generators, and load-in-region power generators. The apportionment coefficient within; Candidate blocked lines; , and This is the market pricing coefficient for generators; at this point, all prices are quoted based on cost. and Traditional power producers Maximum output constraints and minimum output constraints; and Respectively, new energy power generators Maximum output constraints and minimum output constraints; and respectively load The maximum power demand constraint and minimum power demand constraint; Equation (9) is the power balance constraint on the supply side and the demand side; Equation (10) is the line The power flow constraint; Equations (11) and (12) are the output constraints of traditional units and new energy units, respectively; Equation (13) is the power constraint of load demand; This refers to the Lagrange multiplier in the current market clearing model.

[0038] The further design of the above technical solution is as follows: under the condition of given upper-level collusion strategy parameters, the day-ahead market clearing result is obtained according to equation (8)-equation (13), including the output of traditional units, the output of new energy units and the load demand level;

[0039] By constructing the Lagrange function of the lower-level market clearing model and introducing KKT conditions, the relationship between the market clearing result and the strategic bidding behavior of market participants is established. The Lagrange equation and KKT conditions for the day-ahead (DA) market clearing are shown in equations (14)-(16):

[0040] (14)

[0041] (15)

[0042] (16)

[0043] in, This is the Lagrangian function corresponding to the lower-level day-ahead market clearing model; For the set of candidate blocked lines;

[0044] The impact of collusion on market clearing outcomes and cross-sectional blockage status is analyzed using the Lagrange equation and KKT conditions.

[0045] A further design of the above technical solution is as follows: the economic congestion identification model includes the following identification indicators:

[0046] Market share of the conspiratorial group :

[0047] (17)

[0048] In the formula, For the first A conspiratorial group; For the first The total market share held by the colluding groups.

[0049] Demand share of the conspiratorial group :

[0050] (18)

[0051] In the formula, For the first The demand share held by the colluding group.

[0052] Cost Variance of Conspiracy Groups :

[0053] (19)

[0054] In the formula, For the first Cost variance among collusive groups; and These are the quotation coefficients for traditional power generators and new energy power generators, respectively. For the first The power output of each member of the collusion group; For the first The weighted average price of the colluding groups.

[0055] Key suppliers of the conspiratorial group :

[0056] (20)

[0057] In the formula, For the first The collusion group has several key suppliers. ; Let be the key supplier counting function, representing the number of key suppliers that satisfy the judgment condition of equation (20);

[0058] When a collusive group meets the criteria, it is determined that it has the motivation to create economic obstruction.

[0059] A further design of the above technical solution is as follows: To determine whether economic congestion truly exists in the market, the market operation results under the scenario of strategic bidding by market participants are compared with the market operation results when power generators all bid at cost, and an economic congestion line identification index is constructed, which is expressed as follows:

[0060] ;(twenty one)

[0061] In the formula, A function for identifying economically congested lines; For the line The economically congested lines represent the actual operating conditions. and non-collusive operation Lines with unequal Lagrange multipliers corresponding to the lower line capacity constraints ;

[0062] Lines that meet the criteria of equation (21) are identified as economically congested lines.

[0063] A further design of the above technical solution is as follows: by comprehensively comparing the physical blockage identification results with the economic blockage identification results, the blockage type determination results of the key transmission sections in the system are obtained.

[0064] Among them, blocked lines directly triggered by line capacity constraints, power flow distribution, and system safety operation boundaries are identified as physically blocked lines;

[0065] Lines whose actual operating conditions change due to the strategic bidding behavior of market participants, and whose line constraint status changes from the non-collusive operating conditions, and which meet the criteria for determining economically congested lines, are identified as economically congested lines.

[0066] This enables the differentiation and identification of physical and economic blockages at key transmission sections under spot market conditions.

[0067] Compared with the prior art, the present invention has the following advantages:

[0068] 1. This invention proposes a method and device for identifying congestion at critical transmission sections in a spot market, establishing a unified identification framework that integrates congestion mechanism analysis, physical congestion identification, market participant bidding strategy modeling, and economic congestion identification. This method establishes identification models for two scenarios: cost-based bidding and strategy-based bidding by market participants, enabling the differentiation between physical and economic congestion at critical transmission sections.

[0069] 2. This invention can identify not only physical congestion directly caused by line capacity constraints, power flow distribution, and system safety operation boundaries, but also economic congestion caused by strategic bidding behavior of market participants, thus revealing the impact mechanism of market participant behavior on the congestion status of key transmission sections. The proposed method can provide technical support for the operation analysis of the electricity spot market, the operation monitoring of key transmission sections, and the optimization of market mechanisms, and has good application prospects. Detailed Implementation

[0070] To better understand the technical content of this invention, the technical solutions of this invention are further introduced and explained below with reference to specific embodiments, but are not limited thereto. The technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the embodiments of this invention. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0071] Example 1

[0072] The method for identifying blockages in critical transmission sections under spot market operation in this embodiment includes the following steps:

[0073] Step 1: Analyze the formation mechanism of blockage at critical transmission sections and classify the blockage at critical transmission sections into physical blockage and economic blockage.

[0074] Step two: Under the condition that market participants compete for day-ahead market clearing based on cost bids, a physical congestion identification model is constructed to identify physical congestion directly caused by line capacity constraints, power flow distribution, and system safety operation boundaries.

[0075] In this embodiment, the physical congestion identification model is based on the provincial spot market clearing process, establishing a day-ahead market optimization clearing model that considers network constraints. It aims to maximize social welfare and characterizes the market equilibrium result of the system under cost-pricing conditions. The model is as follows:

[0076] (1)

[0077] (2)

[0078] (3)

[0079] (4)

[0080] (5)

[0081] (6)

[0082] In the formula, The objective function for optimizing the day-ahead market clearing under cost-based pricing conditions; , These are the traditional power producers in the conspiratorial group. and new energy power generators Power output; , These are respectively a collection of traditional power generators and a collection of new energy power generators belonging to the Hemou Group. The total number of members in the collusion group; For load sets; , These are the coefficients of the load-side utility function; For load Demand; For the line The upper limit of transmission capacity; For the market area; for Power injection in the region The power transfer factor on the line is related to the actual grid topology and market zoning. , and These are traditional power generators, renewable energy power generators, and load-in-region power generators. The apportionment coefficient within; Candidate blocked lines; , and This is the market pricing coefficient for generators; at this point, all prices are quoted based on cost. and Traditional power producers Maximum output constraints and minimum output constraints; and Respectively, new energy power generators Maximum output constraints and minimum output constraints; and respectively load Equation (2) represents the maximum power demand constraint and the minimum power demand constraint; Equation (3) represents the power balance constraint on the supply side and the demand side; Equation (4) represents the line... The power flow constraint; Equations (4) and (5) are the output constraints of traditional units and new energy units, respectively; Equation (6) is the power constraint of load demand; For the Lagrange multiplier in the current market clearing model; when the line capacity constraint (3) is triggered, the corresponding line is identified as a critical line with physical blockage.

[0083] When market participants compete for pre-market clearing based on cost bids, two indicators, line capacity margin and power distribution factor, are used to assist in the identification of physical congestion.

[0084] Among them, the line capacity margin RAMs are used to reflect the relationship between the actual power flow of a line and its available transmission capacity. When the power flow of a line exceeds a preset proportional threshold of its transmission capacity, the line is identified as a physically blocked line.

[0085] The power distribution factor (PTDF) is used to describe the impact of changes in node injected power on line power flow. When the PTDF of a line is higher than a preset threshold, the line is identified as a critical line that is more sensitive to system power transfer and is prone to physical blockage.

[0086] By comprehensively judging the line capacity constraint triggering conditions, line capacity margin, and power distribution factor, the system can identify physically blocked lines in the system.

[0087] Step 3: Based on this, construct a bidding strategy model for market participants and establish a two-layer model that includes an upper-level collusion group strategy bidding model and a lower-level day-ahead market clearing model.

[0088] The market participant bidding strategy model is based on the Staberg game theory and is a two-layer model that includes an upper-level collusion group strategy bidding model and a lower-level day-ahead market clearing model.

[0089] The upper-level model aims to maximize the profits of the collusive group and determines the optimal strategy bidding for traditional and new energy power generators within the group. The upper-level collusive group strategy bidding model is as follows:

[0090] (7)

[0091] In the formula, The objective function of the upper-level collusion group's strategy pricing model; , and All are traditional power producers and new energy power generators The cost coefficients of traditional power generators are quadratic functions of ax² + bx, while the cost of new energy power generators is linear functions of ax. and For traditional power producers and new energy power generators Market area Regional market clearing prices; , The first The conspiratorial group includes both traditional power generation companies and new energy power generation companies. , These are the sets of all traditional power generators and the sets of new energy power generators in the system, respectively.

[0092] The upper-level model obtains the optimal collusion strategy by maximizing the total revenue of traditional power generators and new energy power generators within the collusion group.

[0093] The lower-level day-ahead market clearing model is basically the same in form as the physical congestion analysis model corresponding to equations (1)-(6), except that... , and All of these are strategy pricing coefficients given by the upper-level model. The lower-level day-ahead market clearing model is as follows:

[0094] (8)

[0095] (9)

[0096] (10)

[0097] (11)

[0098] (12)

[0099] (13)

[0100] In the formula, The objective function for day-ahead market clearing optimization under strategic pricing conditions; , These are the traditional power producers in the conspiratorial group. and new energy power generators Power output; , These are collections of traditional power generators and new energy power generators belonging to the Hemou Group, among which... The total number of members in the collusion group; For load sets; , These are the coefficients of the load-side utility function; For load Demand; For the line The upper limit of transmission capacity; For the market area; for Power injection in the region The power transfer factor on the line is related to the actual grid topology and market zoning. , and These are traditional power generators, renewable energy power generators, and load-in-region power generators. The apportionment coefficient within; Candidate blocked lines; , and This is the market pricing coefficient for generators; at this point, all prices are quoted based on cost. and Traditional power producers Maximum output constraints and minimum output constraints; and Respectively, new energy power generators Maximum output constraints and minimum output constraints; and respectively load The maximum power demand constraint and minimum power demand constraint; Equation (9) is the power balance constraint on the supply side and the demand side; Equation (10) is the line The power flow constraint; Equations (11) and (12) are the output constraints of traditional units and new energy units, respectively; Equation (13) is the power constraint of load demand; This refers to the Lagrange multiplier in the current market clearing model.

[0101] Given the parameters of the upper-level collusion strategy, the day-ahead market clearing results are obtained according to Equations (8)-(13), including the output of traditional units, the output of new energy units and the load demand level.

[0102] By constructing the Lagrange function of the lower-level market clearing model and introducing KKT conditions, the relationship between the market clearing result and the strategic bidding behavior of market participants is established. The Lagrange equation and KKT conditions for the day-ahead (DA) market clearing are shown in equations (14)-(16):

[0103] (14)

[0104] (15)

[0105] (16)

[0106] in, This is the Lagrangian function corresponding to the lower-level day-ahead market clearing model; For the set of candidate blocked lines;

[0107] The impact of collusion on market clearing outcomes and cross-sectional blockage status is analyzed using the Lagrange equation and KKT conditions.

[0108] Step four: Based on the market clearing results under the strategic bidding behavior of market participants, construct an economic congestion identification model to identify economic congestion caused by strategic bidding by market participants.

[0109] The economic congestion identification model includes the following identification indicators:

[0110] Market share of the conspiratorial group :

[0111] (17)

[0112] In the formula, For the first A conspiratorial group; For the first The total market share held by the colluding groups.

[0113] Demand share of the conspiratorial group :

[0114] (18)

[0115] In the formula, For the first The demand share held by the colluding group.

[0116] Cost Variance of Conspiracy Groups :

[0117] (19)

[0118] In the formula, For the first Cost variance among collusive groups; and These are the quotation coefficients for traditional power generators and new energy power generators, respectively. For the first The power output of each member of the collusion group; For the first The weighted average price of the colluding groups.

[0119] Key suppliers of the conspiratorial group :

[0120] (20)

[0121] In the formula, For the first The collusion group has several key suppliers. ; Let be the key supplier counting function, representing the number of key suppliers that satisfy the judgment condition of equation (20);

[0122] In this embodiment, a collusive group is determined to have the motive to create economic obstruction when it meets the following criteria:

[0123] .

[0124] Step 5: By identifying physical and economic congestion respectively, the different types of congestion at key transmission sections under spot market operation can be distinguished and determined.

[0125] To determine whether economic congestion truly exists in the market, the market performance under the scenario of strategic bidding by market participants is further compared with the market performance when all power generators bid at cost. An economic congestion line identification index is then constructed, expressed as follows:

[0126] ;(twenty one)

[0127] In the formula, A function for identifying economically congested lines; For the line The economically congested lines represent the actual operating conditions. and non-collusive operation Lines with unequal Lagrange multipliers corresponding to the lower line capacity constraints ;

[0128] Lines that meet the criteria of equation (21) are identified as economically congested lines.

[0129] By comprehensively comparing the results of physical congestion identification with those of economic congestion identification, the congestion type determination results of key transmission sections in the system are obtained. Specifically, congested lines directly triggered by line capacity constraints, power flow distribution, and system safety operation boundaries are identified as physically congested lines; lines whose line constraint states change due to market participants' strategic bidding behavior compared to non-collusive operation, and which meet the criteria for economically congested lines, are identified as economically congested lines. This enables the differentiation and identification of physical and economic congestion at key transmission sections under spot market operation.

[0130] Example 2

[0131] This embodiment provides a key transmission section congestion identification device under spot market operation, including a memory, a processor, and a computer program. The computer program is stored in the memory and configured to be executed by the processor to implement the key transmission section congestion identification method under spot market operation, including: analyzing the congestion formation mechanism of key transmission sections, constructing a physical congestion identification model under the condition that market participants bid at cost, constructing an economic congestion identification model under the condition that market participants bid strategically, and combining a two-level game model to distinguish and identify physical and economic congestion of key transmission sections.

[0132] Example 3

[0133] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it is used to implement the above-mentioned method for identifying blockages in key transmission sections under spot market operation. The method includes performing blockage mechanism analysis, constructing a physical blockage identification model, modeling market participant bidding strategies, and constructing an economic blockage identification model, and outputting the blockage type determination result of the key transmission section.

[0134] The embodiments described above are only some embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention. Parts not covered in the present invention are the same as or can be implemented using existing technology.

Claims

1. A method for identifying blockages in key transmission sections under spot market operation, characterized in that, The method for identifying blockages in key transmission sections under spot market operation includes the following: The formation mechanism of blockage at critical transmission sections is analyzed, and the blockage at critical transmission sections is divided into physical blockage and economic blockage. Under the condition that market participants compete for day-ahead market clearing based on cost bids, a physical congestion identification model is constructed to identify physical congestion directly caused by line capacity constraints, power flow distribution, and the safe operation boundary of the provincial electricity spot market system; the provincial electricity spot market system includes traditional power generators, new energy power generators, load nodes, and key transmission networks; A bidding strategy model for market participants is constructed, and a two-layer model is established, which includes an upper-level collusion group strategy bidding model and a lower-level day-ahead market clearing model. This model is used to characterize the interaction between the collusion bidding behavior of market participants and the day-ahead market clearing results, and to provide market clearing results under the strategy bidding scenario for identifying economic congestion. Based on the market clearing results under the strategic bidding of market participants, an economic congestion identification model is constructed to identify economic congestion caused by the strategic bidding of market participants. By identifying physical congestion and economic congestion separately, the system can distinguish and determine different types of congestion on key transmission sections under spot market operation. Specifically, congested lines directly triggered by line capacity constraints, power flow distribution, and system safety operation boundaries are identified as physically congested lines. Lines whose line constraint status changes due to the bidding behavior of market participants under actual operating conditions and non-collusive operating conditions, and which meet the criteria for economically congested lines, are identified as economically congested lines. This enables the distinction and identification of physical and economic congestion on key transmission sections under spot market operation.

2. The method for identifying blockages in key transmission sections under spot market operation as described in claim 1, characterized in that: The physical congestion identification model is based on the provincial spot market clearing process, establishing a day-ahead market optimization clearing model that considers network constraints. It aims to maximize social welfare and characterizes the market equilibrium outcome under cost-pricing conditions. The model is as follows: ；(1) ；(2) ；(3) ；(4) ；(5) ； (6) In the formula, The objective function for optimizing the day-ahead market clearing under cost-based pricing conditions; , These are the traditional power producers in the conspiratorial group. and new energy power generators Power output; , These are respectively a collection of traditional power generators and a collection of new energy power generators belonging to the Hemou Group. The total number of members in the collusion group; For load sets; , These are the coefficients of the load-side utility function; For load Demand; For the line The upper limit of transmission capacity; For the market area; for Power injection in the region The power transfer factor on the line is related to the actual grid topology and market zoning. , and These are traditional power generators, renewable energy power generators, and load-in-region power generators. The apportionment coefficient within; Candidate blocked lines; , and This is the market pricing coefficient for generators; at this point, all prices are quoted based on cost. and Traditional power producers Maximum output constraints and minimum output constraints; and Respectively, new energy power generators Maximum output constraints and minimum output constraints; and respectively load Equation (2) represents the maximum power demand constraint and the minimum power demand constraint; Equation (3) represents the power balance constraint on the supply side and the demand side; Equation (4) represents the line... The power flow constraint; Equations (4) and (5) are the output constraints of traditional units and new energy units, respectively; Equation (6) is the power constraint of load demand; For the Lagrange multiplier in the current market clearing model; when the line capacity constraint (3) is triggered, the corresponding line is identified as a critical line with physical blockage.

3. The method for identifying blockages in critical transmission sections under spot market operation as described in claim 2, characterized in that: The market participants participate in day-ahead market clearing based on cost bids, and use two indicators, line capacity margin and power distribution factor, to assist in the identification of physical congestion; Among them, the line capacity margin RAMs are used to reflect the relationship between the actual power flow of the line and its available transmission capacity, and to identify lines whose power flow exceeds the preset ratio threshold of the transmission capacity as physically blocked lines. The power distribution factor (PTDF) is used to describe the impact of changes in node injected power on line power flow. When the PTDF of a line is higher than a preset threshold, the line is identified as a critical line that is sensitive to system power transfer and is prone to physical blockage. By comprehensively judging the line capacity constraint triggering conditions, line capacity margin, and power distribution factor, the system can identify physically blocked lines in the system.

4. The method for identifying blockages in critical transmission sections under spot market operation as described in claim 3, characterized in that: The market participant bidding strategy model is based on the Staberg game theory and consists of a two-layer model, including an upper-level collusion group strategy bidding model and a lower-level day-ahead market clearing model. The upper-level model aims to maximize the profits of the collusive group and determines the optimal strategy bidding for traditional power generators and new energy power generators within the group. The upper-level collusive group strategy bidding model is as follows: ； (7) In the formula, The objective function of the upper-level collusion group's strategy pricing model; , and All are traditional power producers and new energy power generators The cost coefficients of traditional power generators are quadratic functions of ax² + bx, while the cost of new energy power generators is linear functions of ax. and For traditional power producers and new energy power generators Market area Regional market clearing prices; , The first The conspiratorial group includes both traditional power generation companies and new energy power generation companies. , These are the sets of all traditional power generators and the sets of new energy power generators in the system, respectively. The upper-level model obtains the optimal collusion strategy by maximizing the total revenue of traditional power generators and new energy power generators within the collusion group.

5. The method for identifying blockages in key transmission sections under spot market operation as described in claim 4, characterized in that: The lower-level day-ahead market clearing model is basically the same in form as the physical congestion analysis model corresponding to equations (1)-(6), except that... , and All of these are strategy pricing coefficients given by the upper-level model. The lower-level day-ahead market clearing model is as follows: ；(8) ；(9) ；(10) ；(11) ；(12) ； (13) In the formula, The objective function for day-ahead market clearing optimization under strategic pricing conditions; , These are the traditional power producers in the conspiratorial group. and new energy power generators Power output; , These are collections of traditional power generators and new energy power generators belonging to the Hemou Group, among which... The total number of members in the collusion group; For load sets; , These are the coefficients of the load-side utility function; For load Demand; For the line The upper limit of transmission capacity; For the market area; for Power injection in the region The power transfer factor on the line is related to the actual grid topology and market zoning. , and These are traditional power generators, renewable energy power generators, and load-in-region power generators. The apportionment coefficient within; Candidate blocked lines; , and This is the market pricing coefficient for generators; at this point, all prices are quoted based on cost. and Traditional power producers Maximum output constraints and minimum output constraints; and Respectively, new energy power generators Maximum output constraints and minimum output constraints; and respectively load The maximum power demand constraint and minimum power demand constraint; Equation (9) is the power balance constraint on the supply side and the demand side; Equation (10) is the line The power flow constraint; Equations (11) and (12) are the output constraints of traditional units and new energy units, respectively; Equation (13) is the power constraint of load demand; This refers to the Lagrange multiplier in the current market clearing model.

6. The method for identifying blockages in critical transmission sections under spot market operation as described in claim 5, characterized in that: Given the parameters of the upper-level collusion strategy, the day-ahead market clearing results are obtained according to Equations (8)-(13), including the output of traditional units, the output of new energy units and the load demand level. By constructing the Lagrange function of the lower-level market clearing model and introducing KKT conditions, the relationship between the market clearing result and the strategic pricing behavior of market participants is established. The Lagrange equation and KKT conditions for the day-ahead market clearing are shown in equations (14)-(16): ；(14) ；(15) ；(16) in, This is the Lagrangian function corresponding to the lower-level day-ahead market clearing model; For the set of candidate blocked lines; The impact of collusion on market clearing outcomes and cross-sectional blockage status is analyzed using the Lagrange equation and KKT conditions.

7. The method for identifying blockages in critical transmission sections under spot market operation as described in claim 6, characterized in that: The economic congestion identification model includes the following identification indicators: Market share of the conspiratorial group : ； (17) In the formula, For the first A conspiratorial group; For the first The total market share held by the colluding groups; Demand share of the conspiratorial group : ； (18) In the formula, For the first The share of demand held by a conspiratorial group; Cost Variance of Conspiracy Groups : ； (19) In the formula, For the first Cost variance among collusive groups; and These are the quotation coefficients for traditional power generators and new energy power generators, respectively. For the first The power output of each member of the collusion group; For the first Weighted average price of the colluding groups; Key suppliers of the conspiratorial group : ； (20) In the formula, For the first The collusion group has several key suppliers. ; Let be the key supplier counting function, representing the number of key suppliers that satisfy the judgment condition of equation (20); When a collusive group meets the criteria, it is determined that it has the motivation to create economic obstruction.

8. The method for identifying blockages in critical transmission sections under spot market operation as described in claim 7, characterized in that: To determine whether economic congestion truly exists in the market, the market performance under the scenario of strategic bidding by market participants is further compared with the market performance when all power generators bid at cost. An economic congestion line identification index is then constructed, expressed as follows: ； (21) In the formula, A function for identifying economically congested lines; For the line The economically congested lines represent the actual operating conditions. and non-collusive operation Lines with unequal Lagrange multipliers corresponding to the lower line capacity constraints ; Lines that meet the criteria of equation (21) are identified as economically congested lines.

9. A device for identifying blockages in critical transmission sections under spot market operation, characterized in that: A method for identifying blockages in critical transmission sections under any of the spot market operations as described in claims 1-8 includes a memory, a processor, and a computer program, wherein the computer program is stored in the memory and configured to be executed by the processor.

10. A computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement a method for identifying blockages in critical transmission sections under any of the spot market operations as described in claims 1-8.

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