An in-province-inter-province deliverable reserve optimization decision-making method considering elastic transmission capacity of power transmission line in extreme scenario

Abstract

The application discloses a kind of extreme scene under the optimization decision method of in-province-province-interchangeable deliverable reserve considering transmission line elastic transmission capacity, comprising the following steps: 1) constructing the unified optimization decision model of regional power grid reserve considering system deliverable scene;2) constructing the unified optimization decision model of regional power grid reserve considering transmission line elastic transmission capacity;3) solving the unified optimization decision model of regional power grid reserve considering system deliverable scene, obtains the transmission power bound condition of each transmission line under different time periods in different scenes;4) according to transmission power bound condition, the integer variable in the unified optimization decision model of regional power grid reserve considering transmission line elastic transmission capacity is pre-fixed;5) solving the unified optimization decision model of regional power grid reserve considering transmission line elastic transmission capacity after fixing integer variable, obtains final optimization decision result.The application can more fully exert power transmission line power transmission capacity.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation, specifically to an intra-provincial / inter-provincial deliverable backup optimization decision-making method that takes into account the elastic transmission capacity of transmission lines under extreme scenarios. Background Technology

[0002] In the industrial sector, regions such as Southern, Central, and Northwest China have issued regional reserve market rules. When provincial flexibility resources cannot meet their own reserve capacity reservation needs, these rules aim to achieve regional sharing of flexibility resources through market mechanisms, thereby improving the overall flexibility and regulation capabilities of the regional power grid. However, the delivery (i.e., actual deployment) of reserve resources is limited by the transmission capacity of power grid transmission lines, a factor that current regional reserve market rules do not consider. This could lead to reserved reserve capacity being unable to be actually deployed due to line congestion in extreme scenarios. Specifically, the Southern regional power grid determines inter-provincial reserve capacity based on the tradable generation space of generating units, without considering transmission line capacity. The Central China regional power grid proposes that when line congestion prevents generating units from executing market transactions, the power dispatching agency will prioritize arranging other generating units to bear the deviation. This only considers post-congestion response measures and does not take into account the impact of line transmission capacity during the reserve market clearing process. This makes it difficult to alleviate or avoid situations where reserve resources cannot be actually delivered due to line congestion. Furthermore, post-event adjustments will undermine the optimality of the regional reserve resource market allocation scheme.

[0003] In academia, numerous scholars have researched the optimization decision-making for intra-provincial and inter-provincial reserves. Some studies have proposed a regional power grid reserve reservation mechanism for shared reserves across the entire network. Others have embedded inter-provincial reserve constraints into dispatch decision models, achieving mutual assistance in inter-provincial flexibility resource decisions, but without considering the impact of line congestion on the actual deliverability of reserves. To address this, some studies have embedded inter-provincial reserve decision quantities into tie-line transmission power constraints, ensuring sufficient transmission capacity on tie lines to achieve full delivery of inter-provincial reserves, but without considering the impact of intra-provincial transmission line transmission capacity on reserve deliverability. Therefore, some studies have embedded reserve decision quantities into both intra-provincial transmission line constraints and inter-provincial tie-line constraints to improve reserve deliverability. However, the above methods only consider the scenario where generator units deliver all reserves (i.e., increasing or decreasing output), without considering load and renewable energy power changes in the reserve delivery scenario, making it difficult to accurately characterize transmission line power transmission. To address this issue, some studies have embedded the maximum prediction error of each market participant under extreme scenarios into line power flow constraints, thus taking into account the impact of load and renewable energy power changes on line power flow. However, system extreme scenarios are not simply a superposition of individual extreme scenarios, and the above approach still cannot reflect the actual backup delivery scenarios of the system.

[0004] In summary, while existing research has addressed the impact of line congestion on reserve deliverability, it still falls short of reflecting actual system reserve delivery scenarios. Furthermore, due to the time-dependent heat accumulation caused by line overload and current exceeding limits, the power grid can typically withstand short-term power flow exceeding limits. However, existing methods do not consider the elastic transmission capacity of transmission lines under extreme reserve delivery scenarios, failing to fully utilize the power transmission capacity of transmission lines. This leads to both the inability to actually deliver reserves, impacting system operational safety, and also affects the effectiveness of reserve decision-making schemes. In addition, existing reserve optimization decision-making modeling methods only consider the total reserve reserves within the province, the total reserve reserves between provinces, and the total reserve reserves of individual generating units, failing to differentiate the supply situation of individual generating units for intra-provincial and inter-provincial reserves. This lack of differentiation fails to provide a basis for the settlement of intra-provincial and inter-provincial reserves in a market environment. Summary of the Invention

[0005] The purpose of this invention is to provide an optimization decision-making method for intra-provincial and inter-provincial deliverable backup considering the resilient transmission capacity of transmission lines under extreme scenarios, comprising the following steps:

[0006] 1) Construct a unified optimization decision-making model for regional power grid backup, taking into account system delivery scenarios;

[0007] 2) Optimize the unified optimization decision-making model for regional power grid reserves, thereby constructing a unified optimization decision-making model for regional power grid reserves that takes into account the elastic transmission capacity of transmission lines;

[0008] 3) Solve the unified optimization decision model for regional power grid backup considering system delivery scenarios, determine whether the line power flow calculation results exceed their transmission capacity limits, and thus obtain the transmission power limit status of each transmission line in different scenarios and time periods without considering the elastic transmission capacity of transmission lines.

[0009] 4) Based on the transmission power limit, the integer variables in the unified reserve optimization decision model of the regional power grid that takes into account the elastic transmission capacity of transmission lines are pre-fixed;

[0010] 5) Solve the unified optimization decision model for regional power grid reserves, taking into account the elastic transmission capacity of transmission lines, after fixing integer variables, and obtain the final optimization decision results.

[0011] Furthermore, the unified optimization decision model for regional power grid backup, which takes into account the system delivery scenario, aims to minimize the regional power grid energy and overall backup operating costs.

[0012] Furthermore, the objective function of the regional power grid reserve unified optimization decision model, which takes into account the system delivery scenario, is as follows:

[0013] (1)

[0014] In the formula, 'a' represents the provincial power grid number within the region; For the provincial power grid set within the region; t is the time period number; T is the time period set; g a For unit number in province A; G a For example, b represents the set of generating units in province a; b represents the number of provincial power grids outside province a; k a K is the number for the alternative delivery scenario. a A collection of alternative delivery scenarios; Energy cost; For reserve costs within the province; For inter-provincial backup costs; The penalty term for inter-provincial backup provided by province a to province b is embedded to enable the model to prioritize the use of intra-provincial backup capacity in backup delivery scenarios. Price energy; To provide power to the generator unit; , For backup quotes within the province; , Provincial-level backup / standby for the generating unit; , Quotations for inter-provincial standby / backup services provided by generating units in province A to province B; , For inter-provincial backup / standby power supply from unit A to unit B; This is the penalty coefficient for inter-provincial backup calls, and it is a large constant. , This refers to the amount of calls made between provinces for backup delivery scenarios.

[0015] Furthermore, the constraints of the regional power grid reserve unified optimization decision model considering system delivery scenarios include inter-provincial tie line transmission power constraints, power balance constraints, unit output upper and lower limit constraints, unit reserve constraints, unit ramping constraints, unit reserve delivery quantity constraints, total reserve delivery quantity constraints, system operation reserve demand constraints, and line power flow constraints.

[0016] Furthermore, the power constraints for inter-provincial tie lines are as follows:

[0017] (2)

[0018] In the formula, l T For the contact line number; n a Number the nodes; w a For wind turbine generator number; W a A collection of wind turbine units; , These are the upper and lower limits of the transmission power of the tie line; For node n in province a aTo inter-provincial connecting lines T The power transfer distribution factor; , These represent the distances from thermal power units and wind power units to node n, respectively. a The elements of the correlation matrix; , This refers to the amount of thermal power units that can be called up within the province for backup delivery scenarios. , These are the predicted power values ​​for wind turbines and loads; , To reduce the prediction error of wind turbines and loads under the sampled backup delivery scenarios, this invention uses the Latin hypercube sampling method to efficiently generate backup delivery scenarios, thereby avoiding excessive model solving burden due to too many scenarios. To transmit power to the tie line;

[0019] The power balance constraints are as follows:

[0020] (3)

[0021] (4)

[0022] In the formula, L represents the power between provinces a and b. T,ab This represents the set of connecting lines between provinces a and b. The power generation transfer distribution factor;

[0023] The upper and lower limits of the unit's output are constrained as follows:

[0024] (5)

[0025] In the formula, , These represent the upper and lower limits of the output of thermal power units, respectively.

[0026] The unit's standby constraints are as follows:

[0027] (6)

[0028] In the formula, , These refer to the uphill / downhill climbing capabilities of thermal power units.

[0029] The unit's ramp-up constraints are as follows:

[0030] (7)

[0031] The unit standby delivery constraints are as follows:

[0032] (8)

[0033] The total available delivery capacity constraints are as follows:

[0034] (9)

[0035] (10)

[0036] The system's standby requirements are constrained as follows:

[0037] (11)

[0038] In the formula, , The system requires backup power on and off separately.

[0039] The power flow constraints for the lines are as follows:

[0040] (12)

[0041] In the formula, l is the number of the route within the province; , P represents the upper and lower limits of transmission power for line l within the province; l For line transmission power; For node n in province a a The power transfer distribution factor to line l.

[0042] Furthermore, the objective function of the unified optimization decision model for regional power grid reserves, which takes into account the flexible transmission capacity of transmission lines, is the same as that of the unified optimization decision model for regional power grid reserves, which takes into account the system delivery scenario.

[0043] The constraints of the regional power grid reserve unified optimization decision model that takes into account the flexible transmission capacity of transmission lines include formulas (2)-(11) and (13)-(17).

[0044] (13)

[0045] (14)

[0046] (15)

[0047] (16)

[0048] (17)

[0049] In the formula, It is a constant with a very large value; , These are integer variables used to determine whether the line power flow exceeds the lower and upper limits of the transmission capacity, respectively; if the line power flow exceeds the lower limit of the transmission capacity... ,but =0, otherwise, =1. m is the short-term elastic capacity expansion factor for line transmission capacity.

[0050] Furthermore, when solving the unified optimization decision model for regional power grid backup considering system delivery scenarios, constraints (9)-(10) are relaxed, and the relaxation amount is embedded as a penalty term in the objective function;

[0051] The relaxed constraints are shown below:

[0052] (18)

[0053] (19)

[0054] In the formula, , In the context of upper / lower standby delivery scenarios, this represents the slack amount that characterizes the standby delivery shortfall.

[0055] The relaxed objective function is shown below:

[0056] (20)

[0057] Furthermore, in step 4), the step of pre-fixing the integer variables in the unified reserve optimization decision model for the regional power grid, which takes into account the elastic transmission capacity of transmission lines, based on the transmission power reaching the limit is as follows:

[0058] For lines that have not reached their limits, the corresponding integer variables will be... , Fixed at 1;

[0059] For the boundary-reaching path, let integer variables... , The initial values ​​and integer variables in the regional power grid reserve unified optimization decision model that take into account system delivery scenarios , The values ​​are consistent, and the integer variable is used. , As a decision variable.

[0060] The technical effects of this invention are undeniable. This invention proposes an optimized decision-making method for intra-provincial and inter-provincial deliverable reserve capacity under extreme scenarios, taking into account the flexible transmission capacity of transmission lines. First, a unified optimization decision-making model for regional power grid reserve considering system delivery scenarios is constructed. Unit reserve delivery constraints are established under load and renewable energy power change scenarios, and based on this, line power flow constraints considering reserve delivery scenarios are constructed to ensure the actual deliverability of unit reserve. Second, line power flow constraints considering flexible transmission capacity are constructed to fully utilize line transmission capacity. Then, for the large-scale integer variables introduced in the modeling of flexible transmission capacity, an accelerated calculation method based on pre-fixed integer variables is proposed. The method proposed in this invention can accurately reflect the impact of power changes on both generation and consumption sides on line power flow under reserve delivery scenarios, thereby significantly improving the actual deliverability of reserve capacity. Simultaneously, it can more fully utilize the power transmission capacity of power grid transmission lines, effectively addressing the power grid reserve and delivery needs under extreme scenarios while considering the economic efficiency of power grid operation. Furthermore, it significantly and accurately reduces the scale of integer variables in the model, maintaining high computational accuracy while significantly improving model solution efficiency. Attached Figure Description

[0061] Figure 1 An optimization decision-making framework for intra-provincial and inter-provincial deliverable backup considering the resilient transmission capacity of transmission lines under extreme scenarios;

[0062] Figure 2 To accelerate the calculation process for unified optimization decision-making on regional power grid reserves based on pre-fixed integer variables;

[0063] Figure 3 The reserve of provincial resources is less than the emergency reserve requirement and the net load reserve requirement;

[0064] Figure 4 The relative error convergence curves during the solution process for M1 and M2.

[0065] Figure 5 This document describes the power transmission status of line 386 and the standby delivery status of thermal power unit C-G10 during a certain period. Detailed Implementation

[0066] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0067] Example 1:

[0068] See Figures 1 to 5An optimization decision-making method for intra-provincial and inter-provincial deliverable backup considering the elastic transmission capacity of transmission lines under extreme scenarios includes the following steps:

[0069] 1) Construct a unified optimization decision-making model for regional power grid backup, taking into account system delivery scenarios;

[0070] 2) Optimize the unified optimization decision-making model for regional power grid reserves, thereby constructing a unified optimization decision-making model for regional power grid reserves that takes into account the elastic transmission capacity of transmission lines;

[0071] 3) Solve the unified optimization decision model for regional power grid backup considering system delivery scenarios, determine whether the line power flow calculation results exceed their transmission capacity limits, and thus obtain the transmission power limit status of each transmission line in different scenarios and time periods without considering the elastic transmission capacity of transmission lines.

[0072] Specifically, in the regional power grid reserve unified optimization decision model, the unit output and the reserved reserve are the unknowns for optimization. The calculation results of line power flow are judged by equations (13)-(17) to determine whether they exceed the transmission capacity limit.

[0073] 4) Based on the transmission power limit, the integer variables in the unified reserve optimization decision model of the regional power grid that takes into account the elastic transmission capacity of transmission lines are pre-fixed;

[0074] 5) Solve the unified optimization decision model for regional power grid reserves, taking into account the elastic transmission capacity of transmission lines, after fixing integer variables, and obtain the final optimization decision results.

[0075] The regional power grid backup unified optimization decision model, which takes into account the system delivery scenario, aims to minimize the regional power grid energy and overall backup operating costs.

[0076] The objective function of the regional power grid reserve unified optimization decision model considering system delivery scenarios is shown below:

[0077] (1)

[0078] In the formula, 'a' represents the provincial power grid number within the region; For the provincial power grid set within the region; t is the time period number; T is the time period set; g a For unit number in province A; G a For example, b represents the set of generating units in province a; b represents the number of provincial power grids outside province a; k a K is the number for the alternative delivery scenario. a A collection of alternative delivery scenarios; Energy cost; For reserve costs within the province; For inter-provincial backup costs; The penalty term for inter-provincial backup provided by province a to province b is embedded to enable the model to prioritize the use of intra-provincial backup capacity in backup delivery scenarios. Price energy; To provide power to the generator unit; , For backup quotes within the province; , Provincial-level backup / standby for the generating unit; , Quotations for inter-provincial standby / backup services provided by generating units in province A to province B; , For inter-provincial backup / standby power supply from unit A to unit B; This is the penalty coefficient for inter-provincial backup calls, and it is a large constant. , This refers to the amount of calls made between provinces for backup delivery scenarios.

[0079] The constraints of the regional power grid reserve unified optimization decision model considering system delivery scenarios include inter-provincial tie line transmission power constraints, power balance constraints, unit output upper and lower limit constraints, unit reserve constraints, unit ramping constraints, unit reserve delivery quantity constraints, total reserve delivery quantity constraints, system operation reserve demand constraints, and line power flow constraints.

[0080] The power constraints for inter-provincial tie lines are as follows:

[0081] (2)

[0082] In the formula, l T For the contact line number; n a Number the nodes; w a For wind turbine generator number; W a A collection of wind turbine units; , These are the upper and lower limits of the transmission power of the tie line; For node n in province a a To inter-provincial connecting lines T The power transfer distribution factor; , These represent the distances from thermal power units and wind power units to node n, respectively. a The elements of the correlation matrix; , This refers to the amount of thermal power units that can be called up within the province for backup delivery scenarios. , These are the predicted power values ​​for wind turbines and loads; , To reduce the prediction error of wind turbines and loads under the sampled backup delivery scenarios, this invention uses the Latin hypercube sampling method to efficiently generate backup delivery scenarios, thereby avoiding excessive model solving burden due to too many scenarios. To transmit power to the tie line;

[0083] The power balance constraints are as follows:

[0084] (3)

[0085] (4)

[0086] In the formula, L represents the power between provinces a and b. T,ab This represents the set of connecting lines between provinces a and b. The power generation transfer distribution factor;

[0087] The upper and lower limits of the unit's output are constrained as follows:

[0088] (5)

[0089] In the formula, , These represent the upper and lower limits of the output of thermal power units, respectively.

[0090] The unit's standby constraints are as follows:

[0091] (6)

[0092] In the formula, , These refer to the uphill / downhill climbing capabilities of thermal power units.

[0093] The unit's ramp-up constraints are as follows:

[0094] (7)

[0095] The unit standby delivery constraints are as follows:

[0096] (8)

[0097] The total available delivery capacity constraints are as follows:

[0098] (9)

[0099] (10)

[0100] The system's standby requirements are constrained as follows:

[0101] (11)

[0102] In the formula, , The system requires backup power on and off separately.

[0103] The power flow constraints for the lines are as follows:

[0104] (12)

[0105] In the formula, l is the number of the route within the province; , P represents the upper and lower limits of transmission power for line l within the province; l For line transmission power; For node n in province a a The power transfer distribution factor to line l.

[0106] The objective function of the unified optimization decision model for regional power grid reserves that takes into account the flexible transmission capacity of transmission lines is the same as that of the unified optimization decision model for regional power grid reserves that takes into account the system delivery scenario.

[0107] The constraints of the regional power grid reserve unified optimization decision model that takes into account the flexible transmission capacity of transmission lines include formulas (2)-(11) and (13)-(17).

[0108] (13)

[0109] (14)

[0110] (15)

[0111] (16)

[0112] (17)

[0113] In the formula, It is a constant with a very large value; , These are integer variables used to determine whether the line power flow exceeds the lower and upper limits of the transmission capacity, respectively; if the line power flow exceeds the lower limit of the transmission capacity... ,but =0, otherwise, =1. m is the short-term elastic capacity expansion factor for line transmission capacity.

[0114] When solving the unified optimization decision model for regional power grid backup considering system delivery scenarios, the constraints (9)-(10) are relaxed, and the relaxation amount is embedded as a penalty term in the objective function;

[0115] The relaxed constraints are shown below:

[0116] (18)

[0117] (19)

[0118] In the formula, , In the context of upper / lower standby delivery scenarios, this represents the slack amount that characterizes the standby delivery shortfall.

[0119] The relaxed objective function is shown below:

[0120] (20)

[0121] In step 4), the step of pre-fixing the integer variables in the unified reserve optimization decision model for the regional power grid, which takes into account the elastic transmission capacity of transmission lines, based on the transmission power reaching the limit is as follows:

[0122] For lines that have not reached their limits, the corresponding integer variables will be... , Fixed at 1;

[0123] For the boundary-reaching path, let integer variables... , The initial values ​​and integer variables in the regional power grid reserve unified optimization decision model that take into account system delivery scenarios , The values ​​are consistent, and the integer variable is used. , As a decision variable.

[0124] Example 2:

[0125] An optimization decision-making method for intra-provincial and inter-provincial deliverable backup considering the resilient transmission capacity of transmission lines under extreme scenarios includes the following steps:

[0126] 1) Construct a unified optimization decision-making model for regional power grid backup, taking into account system delivery scenarios;

[0127] 2) Optimize the unified optimization decision-making model for regional power grid reserves, thereby constructing a unified optimization decision-making model for regional power grid reserves that takes into account the elastic transmission capacity of transmission lines;

[0128] 3) Solve the unified optimization decision model for regional power grid backup considering system delivery scenarios, determine whether the line power flow calculation results exceed their transmission capacity limits, and thus obtain the transmission power limit status of each transmission line in different scenarios and time periods without considering the elastic transmission capacity of transmission lines.

[0129] 4) Based on the transmission power limit, the integer variables in the unified reserve optimization decision model of the regional power grid that takes into account the elastic transmission capacity of transmission lines are pre-fixed;

[0130] 5) Solve the unified optimization decision model for regional power grid reserves, taking into account the elastic transmission capacity of transmission lines, after fixing integer variables, and obtain the final optimization decision results.

[0131] Example 3:

[0132] An optimization decision-making method for intra-provincial and inter-provincial deliverable backup considering the elastic transmission capacity of transmission lines under extreme scenarios, with the same technical content as in Embodiment 2, further includes a unified optimization decision-making model for regional power grid backup considering system delivery scenarios, with the goal of minimizing regional power grid energy and overall backup operating costs.

[0133] Example 4:

[0134] An optimization decision-making method for intra-provincial and inter-provincial deliverable reserve considering the elastic transmission capacity of transmission lines under extreme scenarios, with the same technical content as any one of Embodiments 2-3. Furthermore, the objective function of the unified optimization decision-making model for regional power grid reserve considering system delivery scenarios is as follows:

[0135] (1)

[0136] In the formula, 'a' represents the provincial power grid number within the region; For the provincial power grid set within the region; t is the time period number; T is the time period set; g a For unit number in province A; G a For example, b represents the set of generating units in province a; b represents the number of provincial power grids outside province a; k a K is the number for the alternative delivery scenario. a A collection of alternative delivery scenarios; Energy cost; For reserve costs within the province; For inter-provincial backup costs; The penalty term for inter-provincial backup provided by province a to province b is embedded to enable the model to prioritize the use of intra-provincial backup capacity in backup delivery scenarios. Price energy; To provide power to the generator unit; , For backup quotes within the province; , Provincial-level backup / standby for the generating unit; , Quotations for inter-provincial standby / backup services provided by generating units in province A to province B; , For inter-provincial backup / standby power supply from unit A to unit B; This is the penalty coefficient for inter-provincial backup calls, and it is a large constant. , This refers to the amount of calls made between provinces for backup delivery scenarios.

[0137] Example 5:

[0138] An optimization decision-making method for intra-provincial and inter-provincial deliverable reserve considering the elastic transmission capacity of transmission lines under extreme scenarios, with the same technical content as any one of embodiments 2-4. Further, the constraints of the unified optimization decision-making model for regional power grid reserve considering the system delivery scenario include inter-provincial tie line transmission power constraints, power balance constraints, upper and lower limits of unit output constraints, unit reserve constraints, unit ramping constraints, unit reserve delivery quantity constraints, total reserve delivery quantity constraints, system operation reserve demand constraints, and line power flow constraints.

[0139] Example 6:

[0140] An optimization decision-making method for intra-provincial / inter-provincial deliverable backup considering the elastic transmission capacity of transmission lines under extreme scenarios, with the same technical content as any one of embodiments 2-5, further wherein the transmission power constraint of inter-provincial tie lines is as follows:

[0141]

[0142] In the formula, l T For the contact line number; n a Number the nodes; w a For wind turbine generator number; W a A collection of wind turbine units; , These are the upper and lower limits of the transmission power of the tie line; For node n in province a a To inter-provincial connecting lines T The power transfer distribution factor; , These represent the distances from thermal power units and wind power units to node n, respectively. a The elements of the correlation matrix; , This refers to the amount of thermal power units that can be called up within the province for backup delivery scenarios. , These are the predicted power values ​​for wind turbines and loads; , To reduce the prediction error of wind turbines and loads under the sampled backup delivery scenarios, this invention uses the Latin hypercube sampling method to efficiently generate backup delivery scenarios, thereby avoiding excessive model solving burden due to too many scenarios. To transmit power to the tie line;

[0143] The power balance constraints are as follows:

[0144] (3)

[0145] (4)

[0146] In the formula, L represents the power between provinces a and b. T,ab This represents the set of connecting lines between provinces a and b.

[0147] The upper and lower limits of the unit's output are constrained as follows:

[0148] (5)

[0149] In the formula, , These represent the upper and lower limits of the output of thermal power units, respectively.

[0150] The unit's standby constraints are as follows:

[0151] (6)

[0152] In the formula, , These refer to the uphill / downhill climbing capabilities of thermal power units.

[0153] The unit's ramp-up constraints are as follows:

[0154] (7)

[0155] The unit standby delivery constraints are as follows:

[0156] (8)

[0157] The total available delivery capacity constraints are as follows:

[0158] (9)

[0159] (10)

[0160] The system's standby requirements are constrained as follows:

[0161] (11)

[0162] In the formula, , The system requires backup power on and off separately.

[0163] The power flow constraints for the lines are as follows:

[0164] (12)

[0165] In the formula, l is the number of the route within the province; , P represents the upper and lower limits of transmission power for line l within the province; l For line transmission power; For node n in province a a The power transfer distribution factor to line l.

[0166] Example 7:

[0167] An optimization decision-making method for intra-provincial and inter-provincial deliverable backup considering the flexible transmission capacity of transmission lines under extreme scenarios, with the same technical content as any one of embodiments 2-6. Furthermore, the objective function of the unified optimization decision-making model for regional power grid backup considering the flexible transmission capacity of transmission lines is the same as the objective function of the unified optimization decision-making model for regional power grid backup considering the system delivery scenario.

[0168] The constraints of the regional power grid reserve unified optimization decision model that takes into account the flexible transmission capacity of transmission lines include formulas (2)-(11) and (13)-(17).

[0169] (13)

[0170] (14)

[0171] (15)

[0172] (16)

[0173] (17)

[0174] In the formula, It is a constant with a very large value; , These are integer variables used to determine whether the line power flow exceeds the lower and upper limits of the transmission capacity, respectively; if the line power flow exceeds the lower limit of the transmission capacity... ,but =0, otherwise, =1. m is the short-term elastic capacity expansion factor for line transmission capacity.

[0175] Example 8:

[0176] An optimization decision-making method for intra-provincial and inter-provincial deliverable backup considering the elastic transmission capacity of transmission lines under extreme scenarios, with the same technical content as any one of embodiments 2-7. Furthermore, when solving the unified optimization decision-making model for regional power grid backup considering system delivery scenarios, constraints (9)-(10) are relaxed, and the relaxation amount is embedded as a penalty term into the objective function.

[0177] The relaxed constraints are shown below:

[0178] (18)

[0179] (19)

[0180] In the formula, , In the context of upper / lower standby delivery scenarios, this represents the slack amount that characterizes the standby delivery shortfall.

[0181] The relaxed objective function is shown below:

[0182] (20)

[0183] Example 9:

[0184] A method for optimizing intra-provincial and inter-provincial deliverable reserve under extreme scenarios, taking into account the flexible transmission capacity of transmission lines, is technically the same as any one of embodiments 2-8. Further, in step 4), the step of pre-fixing the integer variables in the unified reserve optimization decision model of the regional power grid considering the flexible transmission capacity of transmission lines according to the transmission power threshold is as follows:

[0185] For lines that have not reached their limits, the corresponding integer variables will be... , Fixed at 1;

[0186] For the boundary-reaching path, let integer variables... , The initial values ​​and integer variables in the regional power grid reserve unified optimization decision model that take into account system delivery scenarios , The values ​​are consistent, and the integer variable is used. , As a decision variable.

[0187] Example 10:

[0188] An optimization decision-making method for intra-provincial and inter-provincial deliverable backup considering the resilient transmission capacity of transmission lines under extreme scenarios is as follows:

[0189] (1) Optimization decision framework for intra-provincial and inter-provincial deliverable backup considering the elastic transmission capacity of transmission lines under extreme scenarios

[0190] The provincial-interprovincial deliverable backup optimization decision framework proposed in this invention, which takes into account the resilient transmission capacity of transmission lines under extreme scenarios, is as follows: Figure 1 As shown.

[0191] Significant differences in power resource endowments exist across provinces in my country. Relying on regional and provincial reserve markets to promote inter-provincial mutual assistance of reserve resources is an effective means to improve the flexibility of the power grid across a wide area and help promote consumption and ensure supply. However, current intra-provincial and inter-provincial reserve optimization decision-making methods have two problems in ensuring the deliverability of reserve capacity: 1) The system reserve delivery scenarios are not fully considered, making it difficult to accurately characterize the impact of reserve delivery on line power flow. Situations where reserve capacity cannot be actually delivered due to line congestion may still occur. Furthermore, existing modeling methods do not distinguish between intra-provincial and inter-provincial reserve supply, and there is a lack of basis for settlement of intra-provincial and inter-provincial reserves in a market environment; 2) Current reserve optimization decision-making methods do not consider the elastic transmission capacity of transmission lines. In reality, situations where reserve capacity cannot be actually delivered due to line congestion are often short-lived extreme scenarios. If the short-term elastic transmission capacity of transmission lines can be fully utilized, the power transmission capacity of the power grid transmission lines can be better utilized, ensuring the reserve delivery capacity under extreme scenarios, effectively maintaining system power balance, and further improving the effectiveness of reserve decision-making schemes.

[0192] To address the aforementioned problems, this invention proposes a province-to-province deliverable reserve optimization decision-making method considering the elastic transmission capacity of transmission lines under extreme scenarios. The research approach is briefly described below:

[0193] To address Problem 1, this invention constructs a unified optimization decision-making model for regional power grid reserves that considers system delivery scenarios. This model separately models intra-provincial and inter-provincial reserve supply, taking into account their respective pricing scenarios. The model's objective function includes energy cost, intra-provincial reserve reservation cost, and inter-provincial reserve reservation cost. To ensure the deliverability of unit reserve reserve capacity, the model efficiently generates reserve delivery scenarios caused by load and renewable energy power changes through Latin hypercube sampling. For each scenario, a unit reserve reserve capacity delivery constraint is constructed, requiring that the reserve delivered by the unit equals the load and renewable energy power changes to ensure system power balance. Based on this, the unit reserve delivery situation is embedded in the intra-provincial and inter-provincial transmission line capacity, thereby ensuring that the unit reserve capacity decision scheme can be actually delivered. Simultaneously, considering that intra-provincial reserve resources should be prioritized when there is a power imbalance in the system, and inter-provincial reserve resources should only be used when intra-provincial reserve resources are insufficient, this invention embeds an inter-provincial reserve delivery penalty term into the model's objective function to ensure the priority use of intra-provincial reserve resources. The proposed unified optimization decision model for regional power grid backup in the system delivery scenario will be described in detail in Part (2).

[0194] To address problem 2, this invention proposes a unified optimization and efficient decision-making method for regional power grid reserves that considers the flexible transmission capacity of transmission lines. Based on the aforementioned reserve optimization decision-making model, this invention further embeds relevant constraints related to the flexible transmission capacity of transmission lines. Specifically, considering the characteristic that transmission line capacity can only be increased temporarily, this invention first introduces integer variables to determine whether there is a short-term capacity increase requirement for the line after considering reserve delivery scenarios. Subsequently, based on the integer variables, a line power flow constraint considering the flexible transmission capacity is constructed. For lines without capacity increase requirements, the power flow constraint still considers the conventional line transmission capacity; for lines with capacity increase requirements, the power flow constraint considers the increased line transmission capacity, while avoiding capacity increase processing for the same line in two consecutive time periods to ensure that line safety is not affected by excessively long capacity increase times. Considering the flexible transmission capacity of lines can more effectively address extreme reserve delivery scenarios; however, the introduction of integer variables significantly increases the computational burden of the model. Therefore, this invention further proposes an accelerated calculation method based on pre-fixed integer variables. This method first solves the reserve optimization decision-making model without considering the flexible transmission capacity of transmission lines and obtains the power limit conditions of each transmission line under different scenarios. For lines that have not reached the limit, it indicates that the line has no flexible capacity expansion requirement, so the corresponding integer variables are fixed to the value of no capacity expansion; for lines that have reached the limit, it indicates that the line may have flexible capacity expansion requirement, so the corresponding integer variables are not pre-fixed. Thus, the model size can be greatly reduced by pre-fixing the integer variables, thereby effectively improving the model solution efficiency. Although the proposed acceleration method cannot guarantee the optimality of the calculation results, the proposed method can maintain high calculation accuracy while greatly improving the calculation efficiency. Compared with the current decision-making method that does not consider the flexible transmission capacity of transmission lines, it can still more effectively utilize the power transmission capacity of the power grid transmission lines and improve the rationality of the backup decision scheme. The proposed efficient calculation method for unified optimization decision of regional power grid backup that considers the flexible transmission capacity of transmission lines will be introduced in detail in Part (3).

[0195] (2) Cost allocation methods for various types of operating reserves considering the superimposed uncertainty of market participants

[0196] To accurately consider the impact of line transmission capacity on standby deliverability in standby delivery scenarios, this section constructs a unified optimization decision model for regional power grid standby that takes into account system delivery scenarios. At the same time, models are built separately for intra-provincial standby supply and inter-provincial standby supply, which can provide a basis for relevant market settlement.

[0197] (a) Objective function

[0198] The model takes the minimum regional power grid energy and overall operating cost of reserves as the objective function, as shown in equation (1).

[0199] (1)

[0200] In the formula, 'a' represents the provincial power grid number within the region; For the provincial power grid set within the region; t is the time period number; T is the time period set; g a For unit number in province A; G a For example, b represents the set of generating units in province a; b represents the number of provincial power grids outside province a; k a K is the number for the alternative delivery scenario. a A collection of alternative delivery scenarios; Energy cost; For reserve costs within the province; For inter-provincial backup costs; The penalty term for inter-provincial backup provided by province a to province b is embedded to enable the model to prioritize the use of intra-provincial backup capacity in backup delivery scenarios. Price energy; To provide power to the generator unit; , For backup quotes within the province; , Provincial-level backup / standby for the generating unit; , Quotations for inter-provincial standby / backup services provided by generating units in province A to province B; , For inter-provincial backup / standby power supply from unit A to unit B; This is the penalty coefficient for inter-provincial backup calls, and it is a large constant. , This refers to the amount of calls made between provinces for backup delivery scenarios.

[0201] (a) Operational constraints

[0202] (1) Transmission power constraints of inter-provincial tie lines

[0203] The transmission power of inter-provincial connection lines should not exceed the upper and lower limits of their transmission capacity, as shown in equation (2).

[0204] (2)

[0205] In the formula, l T For the contact line number; n a Number the nodes; w a For wind turbine generator number; W a A collection of wind turbine units; , These are the upper and lower limits of the transmission power of the tie line; For node n in province a a To inter-provincial connecting lines T The power transfer distribution factor; , These represent the distances from thermal power units and wind power units to node n, respectively. a The elements of the correlation matrix; , This refers to the amount of thermal power units that can be called up within the province for backup delivery scenarios. , These are the predicted power values ​​for wind turbines and loads; , To reduce the prediction error of wind turbines and loads under the sampled backup delivery scenarios, this invention uses the Latin hypercube sampling method to efficiently generate backup delivery scenarios, thereby avoiding excessive model solving burden due to too many scenarios.

[0206] (2) Intra-provincial operational constraints

[0207] 1) Power balance constraints

[0208] Under normal circumstances (i.e., without considering backup delivery), the power generation / consumption of each province should be kept in balance, as shown in equation (3).

[0209] (3)

[0210] In the formula, This represents the power flow between provinces a and b. A positive value indicates that power flows from province a to province b, while a negative value indicates that power flows from province b to province a. The calculation method is shown below.

[0211] (4)

[0212] In the formula, This represents the set of connecting lines between provinces a and b.

[0213] 2) Upper and lower limits of unit output constraints

[0214] To provide backup power within and between provinces, the output of thermal power units will not be able to reach their upper and lower limits, as shown in equation (5).

[0215] (5)

[0216] In the formula, , These represent the upper and lower limits of the output of thermal power units, respectively.

[0217] 3) Unit standby constraints

[0218] The total amount of provincial and inter-provincial reserves provided by the unit should not exceed its ramping capacity, as shown in equation (6).

[0219] (6)

[0220] In the formula, , These refer to the uphill / downhill climbing capabilities of thermal power units.

[0221] 4) Unit ramp-up constraints

[0222] The unit output must meet the ramping constraint, as shown in equation (7).

[0223]

[0224] 5) Unit standby delivery constraints

[0225] The reserve delivery capacity of the unit within and between provinces should not exceed the reserve capacity provided by the unit itself, as shown in formula (8).

[0226] (8)

[0227] 6) Total Constraints on Standby Delivery

[0228] The sum of the reserve delivery volume within the province and the reserve delivery volume supported by the province should be equal to the power changes of the load and new energy sources (i.e., be able to cover the system net load prediction error), thereby ensuring the power balance of the system under each reserve delivery scenario. If the overall net load prediction error of the system is greater than 0, it indicates that the upper reserve needs to be called, and the total reserve delivery volume constraint is shown in Equation (9); if the overall net load prediction error of the system is less than 0, it indicates that the lower reserve needs to be called, and the total reserve delivery volume constraint is shown in Equation (10).

[0229] (9)

[0230] (10)

[0231] 7) System operational backup requirements constraints

[0232] The sum of the reserves provided by the province itself and the reserves provided by other provinces must meet the total system operation reserve requirements of the provincial power grid itself, as shown in equation (11).

[0233] (11)

[0234] In the formula, , The system requires backup power on and off separately.

[0235] 8) Power flow constraints of the line

[0236] The power flow of the line within the province should not exceed the upper and lower limits of the line transmission capacity, as shown in equation (12).

[0237] (12)

[0238] In the formula, l is the number of the route within the province; , P represents the upper and lower limits of transmission power for line l within the province; l For line transmission power; For node n in province a a The power transfer distribution factor to line l.

[0239] Thus, a unified optimization decision model for regional power grid reserve considering system delivery scenarios was constructed. This model considers system reserve delivery scenarios caused by load and renewable energy power changes, and embeds the unit reserve delivery amount and load and renewable energy power changes into line power flow constraints to ensure that reserve delivery is not impossible due to line congestion. However, in extreme scenarios, the required system reserve capacity and reserve delivery amount are large, and the transmission power of the power grid transmission lines may not be able to meet the reserve delivery requirements. In this case, the constructed reserve decision model will have no solution. To address this, this invention will further construct a unified optimization decision model for regional power grid reserve considering the elastic transmission capacity of transmission lines, so as to fully utilize the transmission power of the power grid transmission lines and effectively cope with extreme power grid scenarios. At the same time, to address the computational burden on the model caused by considering the elastic transmission capacity of transmission lines, an accelerated calculation method is proposed.

[0240] (3) A unified optimization and efficient decision-making method for regional power grid reserves considering the elastic transmission capacity of transmission lines under extreme scenarios

[0241] This section first constructs line power flow constraints that take into account resilient transmission capacity, enabling full utilization of the power grid's transmission capacity and thus more effectively addressing reserve and delivery requirements under extreme scenarios. Subsequently, to address the additional computational burden caused by resilient transmission modeling, an accelerated computation method based on pre-fixed integer variables is proposed to meet the model's computational efficiency requirements.

[0242] (a) Line flow constraints taking into account flexible transmission capacity

[0243] First, use equations (13)-(16) to determine whether the line power flow calculation result exceeds its transmission capacity limit (i.e., determine whether the line needs to be temporarily increased in capacity).

[0244] (13)

[0245] (14)

[0246] (15)

[0247] (16)

[0248] In the formula, It is a constant with a very large value; , These are integer variables used to determine whether the line power flow exceeds the lower and upper limits of the transmission capacity, respectively. For example, if the line power flow exceeds the lower limit of transmission capacity... ,but It will be limited to 0, otherwise, It will be limited to 1.

[0249] Therefore, it can be based on and The value of determines whether the line has undergone short-term capacity expansion (a value of 0 indicates short-term capacity expansion). Furthermore, considering that transmission lines can only undergo short-term capacity expansion, the line cannot undergo capacity expansion in two consecutive time periods. Therefore, the line power flow constraint is finally established as shown in equation (17). The second term in equation (17) ensures that there is no situation where capacity expansion occurs in two consecutive time periods.

[0250] (17)

[0251] In the formula, m is the short-term elastic capacity expansion coefficient of the line transmission capacity.

[0252] Taking the lower limit of transmission capacity as an example, it can be seen from equation (17) that it is subject to the constraint that the sum of the integer variable values ​​of two consecutive time periods is greater than 0. and Table 1 shows all possible values ​​and the transmission line expansion results calculated by equation (17).

[0253] Table 1. Values ​​of integer variables and corresponding transmission line expansion in two consecutive time periods.

[0254]

[0255] As shown in Table 1, for Case 1, a short-term capacity increase was performed in time period t-1, but no short-term capacity increase will be performed in time period t. Although the transmission line in Equation (17) has been expanded, it can be seen from Equation (13) that the power flow of the line will not exceed the conventional transmission capacity at this time. Therefore, even if the transmission line capacity is expanded, it will not actually have an effect; for scenario 2, a short-term capacity increase will be carried out during time period t, and the transmission capacity of the line will be expanded to For case 3, the line does not undergo short-term capacity expansion in time period t-1 and time period t. Similar to case 1, although the transmission line in equation (17) has been expanded, it does not actually have any effect.

[0256] In summary, the line power flow constraint shown in equation (17) can realize short-term capacity expansion of transmission lines and avoid the impact on line operation safety if the transmission lines are expanded in two consecutive time periods. After considering the flexible transmission capacity of transmission lines, replacing the line power flow constraint equation (12) in the standby decision model described in Section 2 with equations (13)-(17) will yield the unified optimization decision model for regional power grid standby that takes into account the flexible transmission capacity of transmission lines.

[0257] (b) Accelerated Calculation Method for Regional Power Grid Reserve Unified Optimization Decision Based on Pre-fixed Integer Variables

[0258] To construct line flow constraints that take into account elastic transmission capacity, two integer variables need to be introduced for each line in each scenario and time period. and This will significantly increase the computational burden on the model. To improve the model's computational efficiency and meet the computational requirements of the backup unified optimization decision, this section proposes an accelerated computation method based on pre-fixed integer variables, the process of which is as follows: Figure 2 As shown.

[0259] Although the model introduces integer variables for each line in each scenario and time period, the actual extreme cases requiring short-term line capacity expansion are extremely rare. A large number of integer variables are used for ineffective cases where the elastic transmission capacity of the transmission lines does not need to be considered. Based on this consideration, the proposed acceleration method first estimates the cases requiring short-term line capacity expansion, and then fixes the integer variables corresponding to the cases where short-term capacity expansion is not required, thereby significantly reducing the model size and improving the model's solution efficiency. The specific steps of the proposed method are described below:

[0260] 1) Solve the unified optimization decision model for regional power grid backup considering the system delivery scenario described in Section 2. Since the elastic transmission capacity of transmission lines is not considered, the model may have no solution in extreme scenarios. To obtain the model solution results in this case, this section relaxes the constraints (9) and (10), as shown in equations (18)-(19), and embeds the relaxation amount as a penalty term into the objective function.

[0261] (18)

[0262] (19)

[0263] In the formula, , In the context of upper / lower standby delivery scenarios, this represents the slack amount that characterizes the standby delivery shortfall.

[0264] 2) Based on the solution results in 1), obtain the transmission power limit of each transmission line under different scenarios and time periods without considering the elastic transmission capacity of the transmission line.

[0265] 3) Based on the line boundary status, the integer variables in the regional power grid unified reserve optimization decision model that takes into account the flexible transmission capacity of transmission lines are pre-fixed: For lines that have not reached the boundary, it indicates that the line does not need to be elastically expanded, and the value of the corresponding integer variable in the model described in Section (a) of this part is fixed to 1; for lines that have reached the boundary, it indicates that the line may need to be elastically expanded, and the corresponding integer variable in the model described in Section (a) of this part is retained, and the value of the integer variable (i.e. whether to carry out short-term capacity expansion) is determined by subsequent optimization decisions.

[0266] 4) Solve the unified optimization decision model for regional power grid reserves, taking into account the elastic transmission capacity of transmission lines, after fixing integer variables, and obtain the final optimization decision results.

[0267] As can be seen from the above process, the acceleration method proposed in this invention significantly reduces the model size by pre-fixing integer variables of lines that are unlikely to require short-term capacity expansion, thereby effectively improving the model's computational efficiency. Although fixing the integer variables will narrow the model's feasible region and cannot guarantee the optimality of the final solution, the boundary conditions of the lines without considering elastic transmission capacity can effectively reflect the short-term capacity expansion needs of transmission lines in extreme scenarios. Therefore, the proposed acceleration method can maintain high computational accuracy while significantly improving computational efficiency. Compared with decision-making methods that do not consider the elastic transmission capacity of transmission lines, it can still improve the rationality of backup decision schemes and more effectively address the backup capacity reservation and delivery needs in extreme scenarios.

[0268] In summary, this invention constructs a unified optimization decision-making model for regional power grid reserves that considers system delivery scenarios. Load and renewable energy power variation scenarios were generated through Latin hypercube sampling, and system power balance constraints under different scenarios were then constructed. Based on this, the line transmission power in the system reserve delivery scenario was plotted, ensuring the actual deliverability of the unit's reserved reserve capacity. Simultaneously, modeling was performed separately for intra-provincial and inter-provincial reserve supply, clarifying the supply situation of intra-provincial and inter-provincial reserves in the regional power grid.

[0269] This invention proposes a unified optimization and efficient decision-making method for regional power grid reserve that considers the flexible transmission capacity of transmission lines. Based on conditional constraint modeling, flexible transmission capacity constraints are constructed for lines with short-term capacity expansion needs to effectively address reserve delivery problems in extreme scenarios. Subsequently, for the integer variables introduced in the flexible transmission capacity modeling, an accelerated calculation method based on pre-fixed integer variables is proposed. By estimating whether the transmission line needs short-term capacity expansion under the condition of power reaching the limit without considering the flexible transmission capacity, the relevant integer variables are fixed accordingly, thereby effectively reducing the model size and improving the model solution efficiency.

[0270] This invention takes the unified optimization decision-making of regional power grid reserves as an example, but the relevant modeling and solution techniques can also be used in scenarios such as intra-provincial reserve decision-making and inter-provincial-intra-provincial reserve sequential decision-making.

[0271] Example 11:

[0272] The verification of an intra-provincial / inter-provincial deliverable backup optimization decision-making method considering the resilient transmission capacity of transmission lines under extreme scenarios is as follows:

[0273] Simulation results on a regional power grid structure consisting of three IEEE 118-node systems demonstrate that the method proposed in this invention can achieve more reasonable intra-provincial and inter-provincial deliverable reserve optimization decisions. The specific method steps are as follows:

[0274] ① Construct a unified optimization decision model for regional power grid reserves considering system delivery scenarios, establish unit reserve delivery constraints under load and renewable energy power change scenarios, and construct line power flow constraints considering reserve delivery scenarios to ensure the actual deliverability of unit reserve reserves; ② Construct line power flow constraints considering elastic transmission capacity to determine whether there is short-term capacity expansion of transmission lines, and avoid the impact on line operation safety when transmission lines expand capacity in two consecutive time periods; ③ Propose an accelerated calculation method for unified optimization decision-making of regional power grid reserves based on pre-fixed integer variables, thereby significantly reducing the model size and improving the model solution efficiency.

[0275] (1) Example description

[0276] This invention is based on three IEEE 118-node systems and constructs a simulation system for a regional power grid interconnected by three provinces. A schematic diagram is shown below. Figure 3 As shown, the regional power grid comprises 57 thermal power units, 6 wind power units, 354 nodes, and 561 lines. The unified reserve optimization decision-making model for the regional power grid includes 96 scheduling periods. The load and renewable energy data collected in the simulation were obtained from a provincial power grid in my country. The historical prediction error data for load and renewable energy were scaled to form three sets of data with different error magnitudes, resulting in different operational reserve requirements for the three provincial power grids (Province C has the largest prediction error, and Province B has the smallest prediction error). Based on this, the operational reserve requirements of each province were first calculated. Simultaneously, referencing the elastic transmission capacity of the New England power grid (ISO-NE), the short-term elastic capacity expansion factor m for line power flow was set to 0.2. In the simulation, based on the historical prediction error distribution of the main load and renewable energy sources, 500 load and renewable energy power change scenarios were generated using the Latin hypercube sampling method as system reserve delivery scenarios.

[0277] In the simulation, the following methods will be compared:

[0278] M1: The regional power grid reserve unified optimization decision-making method proposed in this invention, which takes into account the elastic transmission capacity of transmission lines, does not consider the accelerated calculation method based on pre-fixed integer variables;

[0279] M2: Same as M1, but further considers accelerated calculation methods based on pre-fixed integer variables;

[0280] M3: A unified optimization decision-making method for regional power grid backup that takes into account system delivery scenarios, but does not consider the elastic transmission capacity of transmission lines;

[0281] M4: Same as M3, but without considering system backup delivery scenarios.

[0282] This invention will verify the effectiveness of the proposed model acceleration method by comparing M1 and M2; and verify the effectiveness of the proposed regional power grid reserve unified optimization efficient decision-making method that takes into account the elastic transmission capacity of transmission lines by comparing M2-M4.

[0283] To compare the rationality of reserve decision schemes obtained by different methods, this invention uses the reserve decision schemes obtained by each method as input conditions (i.e., fixing the reserve reserve capacity of each unit), and re-solves the unified optimization decision model for regional power grid reserve that relaxes the total reserve delivery constraint and takes into account the system reserve delivery scenarios (for M1 and M2, the line power flow constraint additionally considers the elastic transmission capacity of transmission lines). The relaxation value (i.e., reserve delivery deficit) of the total reserve delivery constraint is used to evaluate whether different reserve decision schemes can effectively cope with all reserve delivery scenarios. A relaxation value of 0 indicates that the reserve decision scheme can effectively cope with all reserve delivery scenarios, and no system power imbalance occurs; otherwise, it indicates that the reserve decision scheme cannot effectively cope with all reserve delivery scenarios, and the system has a security risk of not being able to maintain power grid balance. The larger the relaxation value, the greater the power grid security risk.

[0284] The computer hardware environment used in this invention is an AMD Ryzen 7 5800H with Radeon Graphics 3.20GHz and 16.0 GB RAM.

[0285] (2) Validation of the effectiveness of the accelerated calculation method for regional power grid reserve unified optimization decision based on pre-fixed integer variables

[0286] The computational scale and performance of methods M1 and M2 are shown in Table 2. Here, dispatch cost refers to the sum of energy cost and intra-provincial and inter-provincial reserve costs; the average total reserve delivery deficit refers to the average total reserve delivery deficit of the power grid (converted to lost electricity) under various scenarios.

[0287] Table 2 Computational scale and performance of M1 and M2

[0288]

[0289] As shown in Table 2, compared to M1, M2 significantly reduces the size of the integer variables to be solved by pre-fixing the integer variables (fixing 99.89% of the integer variables), thus greatly improving the model's solution efficiency (reducing the solution time by 97.72%). The convergence curves of the relative error of the optimization decision model under the two methods M1 and M2 during the solution process are shown in Table 2. Figure 4 As shown.

[0290] Since the boundary conditions of the transmission line without considering the elastic transmission capacity can effectively reflect the short-term capacity expansion needs of the transmission line in extreme scenarios, M2 can fix a large number of integer variables with relatively high accuracy (compared to the solution of M1, the number of correctly fixed integer variables accounts for 99.84%). Therefore, from Figure 4 It is evident that M2 can quickly locate a high-quality initial feasible solution, thereby significantly reducing computation time (the solution can be completed within 3 minutes). In contrast, the integer variable size of M1 is too large, requiring a significant amount of time to find the initial feasible solution, and also necessitating a much longer iteration process to complete the model solution. Ultimately, this results in a model solution time of 6991.21 seconds for M1, which is insufficient to meet practical computational requirements.

[0291] In addition, M2 loses some computational accuracy because fixing integer variables will narrow the feasible region of the model. However, since the accuracy of fixing integer variables is high, the alternative decision schemes obtained by M2 do not generate additional alternative delivery gaps compared to M1, and the overall scheduling cost only increases by 2.33%.

[0292] In summary, the accelerated calculation method for regional power grid reserve unified optimization decision based on pre-fixed integer variables proposed in this invention can significantly improve the solution efficiency while maintaining high calculation accuracy, thus verifying the effectiveness of the accelerated calculation method.

[0293] (2) Validation of the effectiveness of the intra-provincial and inter-provincial available reserve optimization decision-making method that takes into account the flexible transmission capacity of transmission lines.

[0294] (a) Comparative analysis of the overall effectiveness of alternative decision-making schemes

[0295] The average operational reserve requirements of each province at different time periods in this example are shown in Table 3.

[0296] Table 3. Average operational reserve requirements of each province

[0297]

[0298] As shown in Table 3, the operational reserve requirement of Province C is significantly higher than that of Provinces A and B. Therefore, in this example, the inter-provincial mutual assistance pattern of reserve resources is that Provinces A and B provide reserve support to Province C.

[0299] Table 4 shows the overall scheduling cost of the backup decision schemes obtained by the three methods M2-M4 and the average total amount of backup delivery shortfall in each scenario.

[0300] Table 4. Average total scheduling cost and reserve delivery shortfall for M2-M4

[0301]

[0302] As shown in Table 4, the method (M2) proposed in this invention takes into account the flexible transmission capacity of transmission lines, thus enabling more effective utilization of the transmission capacity of power grid transmission lines and improving the rationality of the backup decision scheme. On the one hand, it reduces the impact of line congestion on the backup delivery capacity of low-priced generator units, thereby reducing the power grid dispatch cost. On the other hand, it can more effectively cope with the backup reserve and delivery needs of the power grid under extreme scenarios without generating a backup delivery shortfall.

[0303] In contrast, method M3 does not consider the flexible transmission capacity of transmission lines. In extreme scenarios with significant changes in load and renewable energy power, there are situations where the reserve capacity of generating units cannot be delivered due to line congestion, resulting in a reserve delivery shortfall in the power grid and affecting the operational security of the grid. In addition, due to line congestion, the operational reserve reserved by low-bid generating units may not be delivered in some scenarios. Therefore, high-bid generating units ultimately have to reserve a certain amount of operational reserve, resulting in an increase in grid dispatch costs compared to the proposed method M2.

[0304] M4 does not consider system reserve delivery scenarios, but only determines the operational reserve reservation based on the remaining generation capacity of the units. Since the impact of line congestion is not taken into account, M4 can enable lower-bid units to reserve more reserves, thus achieving the lowest dispatch cost. However, the trade-off is that the operational reserves reserved by the units cannot be actually delivered in actual operation scenarios due to line congestion, resulting in a large reserve delivery shortfall in the power grid, which seriously affects the operational security of the power grid.

[0305] In summary, the method proposed in this invention can balance the economy and security of alternative decision-making schemes, thus verifying the effectiveness of the method.

[0306] (b) Elucidation of the effectiveness of transmission line resilient transmission capacity modeling using a specific scenario as an example.

[0307] To further clarify the effectiveness of the modeling of the flexible transmission capacity of transmission lines, the following section takes a certain scheduling period of a backup delivery scenario as an example to elaborate on the advantages of the proposed method (M2) compared with the method (M3) that does not consider the flexible transmission capacity.

[0308] During this scheduling period, the proposed method M2 did not generate a reserve delivery deficit, while M3 had a reserve delivery deficit of 70.23 MWh. The following section combines the power transmission situation of line 386 (located in Province C) in the regional power grid with the reserve delivery situation of the relevant thermal power unit (the 10th thermal power unit in Province C, designated C-G10) (e.g.) Figure 5 As shown in the figure, the reasons for the difference in standby delivery between M2 and M3 are explained in detail.

[0309] Depend on Figure 5 As can be seen, both M2 and M3 thermal power units have reserved 208 MW of reserve capacity. However, the actual deliverable reserve capacity of these units is limited by the transmission capacity of line 386. In M3, the transmission power of line 386 has reached its conventional transmission capacity. Due to line congestion, thermal power unit C-G10 cannot fully deliver its reserved reserve capacity (only 179 MW was actually delivered), ultimately resulting in a 70.23 MWh reserve delivery shortfall in the grid, affecting grid operation security. In contrast, the proposed method M2, in this scenario, takes into account the flexible transmission capacity of line 386, allowing the line transmission power to exceed its conventional transmission capacity limit, thus supporting thermal power unit C-G10 to deliver more reserve capacity (actual delivery reached 204 MW, an increase of 13.97% compared to M3). Thanks to this, M2 ultimately did not result in a reserve delivery shortfall, ensuring grid operation security.

[0310] In summary, taking into account the flexible transmission capacity of transmission lines can significantly improve the deliverability of the reserve capacity of generating units, thereby more effectively ensuring the safety of power grid operation.

[0311] This invention proposes an optimized decision-making method for intra-provincial and inter-provincial deliverable reserve capacity under extreme scenarios, taking into account the flexible transmission capacity of transmission lines. First, a unified optimization decision-making model for regional power grid reserve considering system delivery scenarios is constructed. Unit reserve delivery constraints are established under load and renewable energy power change scenarios, and based on this, line power flow constraints considering reserve delivery scenarios are constructed to ensure the actual deliverability of unit reserve. Second, line power flow constraints considering flexible transmission capacity are constructed to fully utilize line transmission capacity. Then, for the large-scale integer variables introduced in the modeling of flexible transmission capacity, an accelerated calculation method based on pre-fixed integer variables is proposed. The proposed method can accurately reflect the impact of power changes on both generation and consumption sides on line power flow under reserve delivery scenarios, thereby significantly improving the actual deliverability of reserve capacity. Simultaneously, it can more fully utilize the power transmission capacity of power grid transmission lines, effectively addressing the power grid reserve and delivery needs under extreme scenarios while considering the economic efficiency of power grid operation. Furthermore, it significantly and accurately reduces the scale of integer variables in the model, maintaining high computational accuracy while significantly improving model solution efficiency.

Claims

1. A method for optimizing intra-provincial and inter-provincial deliverable backup based on the elastic transmission capacity of transmission lines under extreme scenarios, characterized in that, Includes the following steps: Step 1) Construct a unified optimization decision model for regional power grid backup considering system delivery scenarios; Step 2) Optimize the unified optimization decision model for regional power grid reserves to construct a unified optimization decision model for regional power grid reserves that takes into account the elastic transmission capacity of transmission lines; Step 3) Solve the unified optimization decision model for regional power grid backup considering the system delivery scenario, and determine whether the line power flow calculation results exceed their transmission capacity limits, so as to obtain the transmission power limit of each transmission line in different scenarios and time periods without considering the elastic transmission capacity of the transmission line. Step 4) Based on the transmission power limit, pre-fix the integer variables in the unified reserve optimization decision model of the regional power grid that takes into account the elastic transmission capacity of transmission lines; Step 5) Solve the unified optimization decision model for regional power grid reserves, taking into account the elastic transmission capacity of transmission lines, after fixing integer variables, and obtain the final optimization decision results; In step 4), the step of pre-fixing the integer variables in the unified reserve optimization decision model for the regional power grid, which takes into account the elastic transmission capacity of transmission lines, based on the transmission power reaching the limit is as follows: For lines that have not reached their limits, the corresponding integer variables will be... , Fixed at 1; , These are integer variables used to determine whether the line power flow exceeds the lower and upper limits of the transmission capacity, respectively. For the boundary-reaching path, let integer variables... , The initial values ​​and integer variables in the regional power grid reserve unified optimization decision model that take into account system delivery scenarios , The values ​​are consistent, and the integer variable is used. , As a decision variable.

2. The method for optimizing intra-provincial / inter-provincial deliverable backup under extreme scenarios, considering the elastic transmission capacity of transmission lines, as described in claim 1, is characterized in that... The regional power grid backup unified optimization decision model, which takes into account the system delivery scenario, aims to minimize the regional power grid energy and overall backup operating costs.

3. The method for optimizing intra-provincial / inter-provincial deliverable backup under extreme scenarios, considering the elastic transmission capacity of transmission lines, as described in claim 2, is characterized in that... The objective function of the regional power grid reserve unified optimization decision model considering system delivery scenarios is shown below: （1） In the formula, 'a' represents the provincial power grid number within the region; For the provincial power grid set within the region; t is the time period number; T is the time period set; g a For unit number in province A; G a For example, b represents the set of generating units in province a; b represents the number of provincial power grids outside province a; k a K is the number for the alternative delivery scenario. a A collection of alternative delivery scenarios; Energy cost; For reserve costs within the province; For inter-provincial backup costs; Penalty item for inter-provincial backup calls provided by province A to province B; Price energy; To provide power to the generator unit; , For backup quotes within the province; , Provincial-level backup / standby for the generating unit; , Quotations for inter-provincial standby / backup services provided by generating units in province A to province B; , For inter-provincial backup / standby power supply from unit A to unit B; The penalty coefficient for inter-provincial backup calls; , This refers to the amount of calls made between provinces for backup delivery scenarios.

4. The method for optimizing intra-provincial / inter-provincial deliverable backup under extreme scenarios, considering the elastic transmission capacity of transmission lines, as described in claim 1, is characterized in that... The constraints of the regional power grid reserve unified optimization decision model considering system delivery scenarios include inter-provincial tie line transmission power constraints, power balance constraints, unit output upper and lower limit constraints, unit reserve constraints, unit ramping constraints, unit reserve delivery quantity constraints, total reserve delivery quantity constraints, system operation reserve demand constraints, and line power flow constraints.

5. The method for optimizing intra-provincial / inter-provincial deliverable backup under extreme scenarios, considering the elastic transmission capacity of transmission lines, as described in claim 4, is characterized in that... The power constraints for inter-provincial tie lines are as follows: In the formula, l T For the contact line number; n a Number the nodes; w a For wind turbine generator number; W a A collection of wind turbine units; , These are the upper and lower limits of the transmission power of the tie line; For node n in province a a To inter-provincial connecting lines T The power transfer distribution factor; , These represent the distances from thermal power units and wind power units to node n, respectively. a The elements of the correlation matrix; , This refers to the amount of thermal power units that can be deployed within the province for backup in a backup delivery scenario. , These are the predicted power values ​​for wind turbines and loads; , The prediction error for wind turbine units and loads under the sampled backup delivery scenario; To transmit power to the tie line; The power balance constraints are as follows: （3） （4） In the formula, L represents the power between provinces a and b. T,ab This represents the set of connecting lines between provinces a and b. The power generation transfer distribution factor; The upper and lower limits of the unit's output are constrained as follows: （5） In the formula, , These are the upper and lower limits of the output of thermal power units, respectively. The unit's standby constraints are as follows: （6） In the formula, , These refer to the uphill / downhill climbing capabilities of thermal power units; The unit's ramp-up constraints are as follows: （7） The unit standby delivery constraints are as follows: （8） The total available delivery capacity constraints are as follows: （9） （10） The system's standby requirements are constrained as follows: （11） In the formula, , Separate system backup / disk backup requirements; The power flow constraints for the lines are as follows: （12） In the formula, l is the number of the route within the province; , These are the lower and upper limits of the transmission power of line l within the province; P l For line transmission power; For node n in province a a The power transfer distribution factor to line l.

6. The method for optimizing intra-provincial / inter-provincial deliverable backup under extreme scenarios, considering the elastic transmission capacity of transmission lines, as described in claim 5, is characterized in that... The objective function of the unified optimization decision model for regional power grid reserves that takes into account the flexible transmission capacity of transmission lines is the same as that of the unified optimization decision model for regional power grid reserves that takes into account the system delivery scenario. The constraints of the regional power grid reserve unified optimization decision model that takes into account the flexible transmission capacity of transmission lines include formulas (2)-(11) and (13)-(17). （13） （14） （15） （16） （17） In the formula, It is a constant; , These are integer variables used to determine whether the line power flow exceeds the lower and upper limits of the transmission capacity, respectively; if the line power flow exceeds the lower limit of the transmission capacity... ,but =0, otherwise, =1; m is the short-term elastic capacity expansion factor for line transmission capacity.

7. The method for optimizing intra-provincial / inter-provincial deliverable backup under extreme scenarios, considering the elastic transmission capacity of transmission lines, as described in claim 5, is characterized in that... When solving the unified optimization decision model for regional power grid backup considering system delivery scenarios, the constraints (9)-(10) are relaxed, and the relaxation amount is embedded as a penalty term in the objective function; The relaxed constraints are shown below: （18） （19） In the formula, , In the context of upper / lower standby delivery scenarios, the slack amount represents the standby delivery shortfall; The relaxed objective function is shown below: （20）。

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