A zero-carbon park non-convex power flexibility area identification method and system

Abstract

The application provides a zero-carbon park non-convex power flexibility area identification method and system, belonging to the technical field of transmission and distribution collaborative scheduling and flexibility evaluation, comprising the following steps: constructing a zero-carbon park convex operation constraint model containing continuous variables and binary variables, and defining a power flexibility area at a transmission and distribution interface; determining an external search area in an active-reactive power plane according to a transformer apparent power capacity, and selecting a reference point located outside the power flexibility area from the vertexes of the external search area; performing angle scanning within a preset angle range from the reference point; performing line search on each scanning ray to identify a feasible line segment and an infeasible gap on the scanning ray; and collecting the feasible line segments and the infeasible gaps in each scanning direction to obtain a non-convex power flexibility area induced by binary variables.

Description

Technical Field

[0001] This invention belongs to the field of transmission and distribution coordinated scheduling and flexibility assessment technology, and particularly relates to a method and system for identifying non-convex power flexibility areas in zero-carbon industrial parks. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The power flexibility area at the grid connection point of the park can describe the power regulation capability that the zero-carbon park can present to the upper-level power grid under the premise of meeting internal safe operation, energy demand and carbon emission constraints. It is an important basis for the park to participate in demand response, ancillary services and transmission and distribution coordinated dispatch.

[0004] Existing methods for assessing power flexibility regions typically construct approximate regions based on linearized or convex power flow models to obtain conservative and easily computable flexibility boundaries. When considering only continuous variables and meeting certain conditions, network operation and equipment constraints can be effectively approximated through convex relaxation or high-precision linearization. In zero-carbon industrial parks, the states of interconnection switches, network topology reconfiguration, energy storage charging and discharging, orderly charging and discharging of charging piles, capacitor bank switching, and the start-up and shutdown of some flexible loads all have discrete attributes and typically require description using binary variables. These binary variables alter the park's aggregation feasibility, causing the power flexibility region at the grid connection point to exhibit non-convex, void-containing, or even disconnected geometric characteristics.

[0005] For binary variables, existing technologies mainly fall into two categories: one is to relax the binary variables into continuous variables, thereby improving computational efficiency; the other is to enumerate all combinations of binary variables and take the union of the feasible regions corresponding to each combination as the result. The former may introduce pseudo-feasible regions that cannot be realized in actual equipment configurations, leading to an overly optimistic flexibility assessment and security risks; the latter, although more accurate, suffers from unacceptable computation time as the number of binary variables increases and the number of combinations grows dramatically. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a method and system for identifying non-convex power flexibility regions in zero-carbon parks. It is applicable to zero-carbon parks and can quickly identify non-convex power flexibility regions induced by binary variables under convex continuous network constraints. While clearly considering park operation constraints, carbon constraints, and topology reconstruction constraints, it also takes into account geometric accuracy and computational efficiency.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: Firstly, a method for identifying non-convex power flexibility regions in zero-carbon industrial parks is disclosed, including: A convex operation constraint model for zero-carbon industrial parks containing continuous and binary variables is constructed, and the power flexibility region at the power transmission and distribution interface is defined. The external search area in the active-reactive plane is determined based on the apparent power capacity of the transformer, and a reference point located outside the power flexibility area is selected from the vertex of the external search area. An angle scan is performed within a preset angle range starting from the reference point; Perform a line search for each scan ray to identify feasible line segments and infeasible gaps on the scan ray; By summarizing the feasible line segments and infeasible gaps in each scanning direction, the non-convex power flexibility region induced by binary variables is obtained.

[0008] As a further technical solution, an angle scan is performed from the reference point within a preset angle range, including: Starting from the reference point, multiple scanning rays are generated within a preset angle range, and candidate search directions corresponding to each scanning ray are determined sequentially or in parallel. As a further technical solution, identifying feasible line segments and infeasible gaps on the scanning ray includes: for each scanning ray, solving the minimization and maximization subproblems of the distance parameter to obtain the first boundary point and the second boundary point in the scanning direction, and obtaining the binary variable configuration corresponding to the first boundary point and the second boundary point; When the binary variable configurations corresponding to the first boundary point and the second boundary point are the same, the line segment between them is identified as a feasible line segment in the power flexibility region. When the binary variable configurations corresponding to the first boundary point and the second boundary point are different, the corresponding binary variable configurations are fixed to solve the continuous subproblem, determine the endpoints of different feasible segments, and determine whether there are infeasible gaps on the scanning ray according to the preset tolerance.

[0009] As a further technical solution, it also includes: when the infeasible gap exceeds the preset tolerance, continue to solve for the nearest feasible point to the identified feasible endpoint and the farthest feasible point under the same binary variable configuration, iteratively expand the feasible line segment and eliminate infeasible gaps until the accuracy requirements are met.

[0010] As a further technical solution, the continuous variables include the square of the node voltage amplitude, the active power of the branch, the reactive power of the branch, the output of distributed new energy, the charging and discharging power of energy storage, the state of charge of energy storage, the adjustable load adjustment, the charging and discharging power of the charging pile, the reactive power compensation power, the power of the heat pump or electric cooling equipment, and the carbon emission accounting variables. The binary variables include branch switch status, energy storage charging and discharging status, charging pile charging and discharging status, capacitor bank switching status, interruptible load start and stop status, and topology reconfiguration status.

[0011] As a further technical solution, the power flexibility region is defined as the set of active and reactive power domains for which feasible continuous and binary variables exist, such that the convex operation constraints of the zero-carbon park are satisfied.

[0012] As a further technical solution, an outer angle scan is performed within a preset angle range, including: Based on the apparent power capacity of the transformer, a square external search area with the origin as the center and a side length of twice the apparent power capacity of the transformer is constructed in the plane of active and reactive power at the power transmission and distribution interface. Choose one of the four vertices as a reference point. This reference point is physically guaranteed to be located outside the power flexibility region. Starting from this reference point, in Evenly divided within the angle range One scanning direction; For the Each scanning direction corresponds to a search angle of . Any point on the ray can be reached via the distance parameter. Establish a linear relationship with the domains of active and reactive power.

[0013] As a further technical solution, after selecting the scanning direction, an inner line search is performed for each scanning ray: first, the distance parameter is minimized and maximized respectively in that direction. We obtain two boundary points A and B, where the distance parameters corresponding to A and B are respectively... , ; If both optimization problems are infeasible, then there is no feasible point in that direction, and the algorithm moves on to the next scanning angle; if a feasible point exists, it is recorded. , and its corresponding binary variable configuration , .

[0014] As a further technical solution, when determining the continuity of feasible intervals in the same scanning direction, if Since the power flexibility sub-region under the fixed binary configuration is a convex set, the entire line segment between A and B belongs to the power flexibility region, and the line search terminates. like Then fix them respectively and Solving the continuous subproblem yields feasible endpoints C and D close to both ends in the same direction, where the distance parameters for C and D are respectively... , .

[0015] As a further technical solution, when identifying infeasible gaps, if the distance parameters corresponding to C and D satisfy... If two feasible segments overlap or connect, then A and B can be considered as continuous feasible segments. like Less than the preset tolerance Then the small gap between C and D is considered an acceptable approximation error; like Greater than If an infeasible gap exists, the search continues using the following formula to find the nearest feasible point E to C, where the distance parameter corresponding to E is... ; If the distance between E and C If the error still exceeds the tolerance, the line segment between C and E is identified as an infeasible region.

[0016] Secondly, a zero-carbon industrial park non-convex power flexibility area identification system is disclosed, including: The zero-carbon park model building module is configured to: construct a convex operation constraint model of the zero-carbon park containing continuous and binary variables, and define the power flexibility region at the power transmission and distribution interface; The reference point selection module is configured to: determine the external search area in the active-reactive plane based on the transformer's apparent power capacity, and select a reference point located outside the power flexibility region from the vertices of the external search area; The angle scanning module is configured to: generate multiple scanning rays within a preset angle range starting from the reference point, and determine the candidate search direction corresponding to each scanning ray sequentially or in parallel; perform line search for each scanning ray, and identify feasible line segments and infeasible gaps on the scanning ray; The region output module is configured to summarize feasible line segments and infeasible gaps in all scanning directions to obtain a non-convex power flexibility region induced by binary variables.

[0017] As a further technical solution, it also includes: a boundary point search module, configured to: solve the minimization and maximization subproblems of the distance parameter for each scanning ray, obtain the first boundary point and the second boundary point in the scanning direction, and obtain the binary variable configuration corresponding to the first boundary point and the second boundary point; The feasible line segment judgment module is configured to: when the binary variable configurations corresponding to the first boundary point and the second boundary point are the same, identify the line segment between them as a feasible line segment in the power flexibility region; The infeasible gap identification module is configured to: when the binary variable configurations corresponding to the first boundary point and the second boundary point are different, fix the corresponding binary variable configurations to solve the continuous subproblem, determine the endpoints of different feasible segments, and determine whether there is an infeasible gap on the scanning ray according to the preset tolerance.

[0018] As a further technical solution, it also includes: an iterative line search module, which is configured to: when the infeasible gap exceeds the preset tolerance, continue to solve for the nearest feasible point to the identified feasible endpoint and the farthest feasible point under the same binary variable configuration, iteratively expand feasible line segments and eliminate infeasible gaps until the accuracy requirements are met.

[0019] The above one or more technical solutions have the following beneficial effects: This invention integrates the sources, grid, load, storage, charging facilities, and carbon emission constraints in a zero-carbon park into a flexible region identification model. It models topology reconstruction as a mixed-integer linear form containing switch binary variables and radial connectivity constraints, ensuring that the obtained power flexibility results reflect the actual operating boundary of the park to the greatest extent possible. This invention performs angular scanning from an external reference point, without relying on known internal feasible points, thus avoiding the dependence of traditional radial boundary searches on the quality of initial point selection.

[0020] This invention identifies multiple feasible intervals and infeasible gaps along the same scanning direction through inner-line search, enabling the capture of non-convex, void-containing, and disconnected power flexibility regions induced by binary variables. This invention avoids enumerating all combinations of binary variables, and different scanning angles can be solved in parallel, significantly reducing computation time while maintaining high geometric accuracy. This invention also reduces pseudo-feasible regions introduced by binary variable relaxation models, improving the security and reliability of power flexibility assessment results in power transmission and distribution coordinated scheduling.

[0021] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 A schematic diagram of infeasible voids, external reference points, and scan angles in the power flexibility region; Figure 2 The flowchart shows the algorithm for identifying non-convex power flexibility regions. Detailed Implementation

[0024] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0026] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0027] Example 1 This embodiment discloses a method for identifying non-convex power flexibility regions in zero-carbon industrial parks, including: Step 1: Construct a convex operation constraint model for the zero-carbon industrial park containing continuous and binary variables, and define the power flexibility region at the power transmission and distribution interface; Step 2: Determine the external search area in the active-reactive plane based on the transformer's apparent power capacity, and select a reference point located outside the power flexibility region from the vertex of the external search area; Step 3: Starting from the reference point, perform an angle scan within a preset angle range of the active-reactive plane of the park-upper-level network interface; Step 4: Perform a line search for each scan ray to identify feasible line segments and infeasible gaps on the scan ray; Step 5: Summarize the feasible line segments and infeasible gaps in each scanning direction to obtain the non-convex, void-containing or disconnected power flexibility region induced by binary variables.

[0028] This embodiment of the above technical solution focuses on identifying non-convexity caused by binary variables such as topology reconstruction, device switching, and operating state switching, based on the fact that the continuous network constraints have been convex or linearized. It does not take the non-convexity of the power flow equation itself as the main processing object, does not rely on known internal feasible points, and avoids enumerating all combinations of binary variables. It can significantly improve computational efficiency while ensuring high geometric accuracy.

[0029] TDI: Power Transmission and Distribution Interface.

[0030] In one implementation example, specifically in step one, a zero-carbon park power flexibility region model containing binary and continuous variables is established. A mixed-integer linear or mixed-integer convex optimization model is established, incorporating park power grid operation constraints, zero-carbon resource operation constraints, carbon emission constraints, and topology reconfiguration constraints. Park power grid operation constraints include grid connection point power balance constraints, linearized power flow constraints, node voltage constraints, branch capacity constraints, adjustable load constraints, and reactive power regulation equipment constraints. Zero-carbon resource operation constraints include distributed renewable energy output constraints, energy storage operation constraints, and orderly charging and discharging constraints of charging piles. These constraints need to be convexized beforehand to ensure they are convex after fixing the 0-1 variables. The advantage is that it facilitates both discussing the non-convexity of the flexibility domain caused by 0-1 variables and solving the subsequent model.

[0031] set up This represents the vector consisting of active and reactive power at point TDI, where , This indicates the active and reactive power at TDI. , This represents the domain of active power and reactive power. Represents the remaining continuous operating variables of the zero-carbon park. , This indicates the domain of the continuously running variable; Represents binary variables. ,in Indicates the dimension of the variable.

[0032] The above continuously running variables This includes the square of node voltage amplitude, branch active power, branch reactive power, distributed renewable energy output, energy storage charging and discharging power, energy storage state of charge, adjustable load adjustment, charging and discharging power of charging piles, reactive power compensation, power of heat pumps or electric refrigeration equipment, and carbon emission accounting variables.

[0033] The above binary variables This includes branch switch status, energy storage charging and discharging status, charging pile charging and discharging status, capacitor bank switching status, interruptible load start and stop status, and topology reconfiguration status.

[0034] In one implementation example, it is stipulated that the convex operation constraints of the zero-carbon park, including the power balance constraints of the park's grid connection point, the operation constraints of the park's network and equipment, carbon emission constraints, and topology reconfiguration constraints, will be uniformly represented as follows: .

[0035] Power flexibility area Defined as: all feasible and Make of The set, namely:

[0036] Therefore, the power flexibility region is a feasible projection set of the active and reactive power of the interface. This definition projects the continuous variables and discrete operating states within the park onto the p0-q0 plane of TDI, which facilitates direct characterization of the active and reactive power regulation capabilities that the park can provide to the upper-level power grid.

[0037] For a given binary variable configuration If the constraint space of continuous variables is a convex set, then the power flexibility sub-region corresponding to this binary configuration... It is a convex set. However, after taking the union of the convex sub-regions corresponding to different binary variable configurations, the overall power flexibility region... It may contain non-convex boundaries, internal infeasible voids, or multiple disconnected branches. See the detailed explanation of this statement. Figure 1 . Figure 1 In the diagram, Z1, Z2, and Z3 represent three different sets of binary variable configurations, each corresponding to a convex feasible subregion. The union of the three subregions forms the actual power flexibility region, which may enclose infeasible holes. The power flexibility region is abbreviated as PFR. The subregion expression under a fixed integer configuration is: ( )={ |existence ∈ ,make }, = .fixed Since the continuous constraint is a convex set, the sub-region is also a convex set; after taking the union of multiple sub-regions, holes, non-convex boundaries or disconnected branches may be generated.

[0038] Therefore, this example will next identify these geometric features caused by the aforementioned binary variables through outer angle scanning and inner line search.

[0039] In one implementation example, specifically in step two: an external reference point is selected and an outer angle scan is performed. This is based on the transformer's apparent power capacity. ,exist The planar construction is centered at the origin and has a side length of... The square bounded search region. From the four vertices Choose a vertex as a reference point, which is physically guaranteed to be located outside the power flexibility region.

[0040] about Plane: The active-reactive plane at the transmission and distribution interface TDI, that is, a two-dimensional plane with p0 as the horizontal axis and q0 as the vertical axis.

[0041] In addition, the apparent power capacity constraint of the transformer It can be seen that the truly feasible region is located within the radius. Inside the circle, therefore four reference points It is definitely outside the flexible domain and can be chosen as any reference point.

[0042] In one implementation example, specifically in step three: starting from the aforementioned reference point, in Evenly divided within the angle range The scanning direction; for the first scanning direction; Each scanning direction corresponds to a search angle of . Any point on the ray can be reached via the distance parameter. and Establish a linear relationship.

[0043] This represents the non-negative distance parameter between the scan point X and the selected reference point on the m-th scan ray; it is not the distance between any two points. Subsequent steps involve minimizing or maximizing this parameter. Locate the near and far feasible boundaries on the ray, and determine whether there is an infeasible gap based on the distance difference.

[0044] Distance parameters The calculation and selection of reference points and and Relevant. To choose. Taking this as a reference point as an example It can be calculated using the following formula:

[0045] In one implementation example, specifically in step four: a line search is performed for each scan ray to identify feasible line segments and infeasible gaps on the scan ray. This specifically includes: 4-1): See Appendix Figure 2 As shown, after selecting the scanning direction, an inner line search is performed for each scanning ray. First, the distance parameter is minimized and maximized in this direction using the following formulas. We obtain two boundary points A and B, where the distance parameters corresponding to A and B are respectively... , :

[0046] If the optimization problem has no solution, the solver returns infeasible. If both optimization problems are infeasible, then there is no feasible point in that direction, and the algorithm proceeds to the next scanning angle. If the optimization problem has a solution, the solution returned by the solver corresponds to a feasible point; if a feasible point exists, it is recorded. , and its corresponding binary variable configuration , .

[0047] , These are all the binary variable values ​​obtained when solving for the endpoints A and B of the feasible region, used to identify whether the two ends belong to the same discrete operating mode. Different steps are then taken based on the values ​​obtained.

[0048] 4-2): Determine the continuity of feasible intervals along the same scanning direction. If Then, due to the power flexibility sub-region under the fixed binary configuration For a convex set, the entire line segment between A and B belongs to the power flexibility region, and the line search terminates. If Then fix them respectively and Solving the continuous subproblem using the following formula yields feasible endpoints C and D in the same direction, closest to both ends, where the distance parameters for C and D are respectively... , :

[0049] and These represent the convex feasible subregions after fixing the binary configuration at both ends. C is... The furthest feasible point that can be reached along the lower scan direction, D is at The feasible point closest to the reference point along the lower scan direction; and Indicates the distances of C and D from the selected reference point, and compares them. and It can determine whether two sub-regions overlap, connect, or have a gap along the ray.

[0050] 4-3): Identify infeasible gaps. If the distance parameters corresponding to C and D satisfy... If two feasible segments overlap or connect, then A and B can be considered as all feasible line segments. Less than the preset tolerance If the small gap between C and D is considered an acceptable approximation error, then A and B are still considered as all feasible line segments. Greater than If an infeasible gap exists between A and B, then the search continues using the following formula to find the nearest feasible point E to C, where the distance parameter corresponding to E is... :

[0051] If the distance between E and C If the distance still exceeds the tolerance, the line segment between C and E is identified as an infeasible gap.

[0052] 4-4): Expand feasible line segments and iterate. Fix the binary variable configuration corresponding to E. The binary variable configuration with the same value as point E in this direction is solved by equation (6). If the farthest feasible point F is E, then the line segment between E and F is considered feasible, where the distance parameter corresponding to F is... :

[0053] If the distance between F and D Still exceeds the preset tolerance If so, then update C to F and repeat the above steps until the accuracy requirement is met.

[0054] In one implementation example, specifically in step five: summarizing the region. By summarizing the feasible line segments and infeasible gaps obtained from all scanning directions, a controllable approximation of the non-convex power flexibility region can be formed. Since different scanning angles are independent of each other, parallel computation can be used to improve efficiency.

[0055] Example 2 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0056] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0057] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above method.

[0058] Example 4 The purpose of this embodiment is to provide a zero-carbon industrial park non-convex power flexibility area identification system, including: The zero-carbon park model building module is configured to: construct a convex operation constraint model of the zero-carbon park containing continuous and binary variables, and define the power flexibility region at the power transmission and distribution interface; The reference point selection module is configured to: determine the external search area in the active-reactive plane based on the transformer's apparent power capacity, and select a reference point located outside the power flexibility region from the vertices of the external search area; The angle scanning module is configured to: generate multiple scanning rays within a preset angle range starting from the reference point, and determine the candidate search direction corresponding to each scanning ray sequentially or in parallel; perform line search for each scanning ray, and identify feasible line segments and infeasible gaps on the scanning ray; The region output module is configured to summarize feasible line segments and infeasible gaps in all scanning directions to obtain a non-convex power flexibility region induced by binary variables.

[0059] In one implementation example, it also includes: a boundary point search module, configured to: for each scanning ray, solve the minimization and maximization subproblems of the distance parameter to obtain the first boundary point and the second boundary point in the scanning direction, and obtain the binary variable configuration corresponding to the first boundary point and the second boundary point; The feasible line segment judgment module is configured to: when the binary variable configurations corresponding to the first boundary point and the second boundary point are the same, identify the line segment between them as a feasible line segment in the power flexibility region; The infeasible gap identification module is configured to: when the binary variable configurations corresponding to the first boundary point and the second boundary point are different, fix the corresponding binary variable configurations to solve the continuous subproblem, determine the endpoints of different feasible segments, and determine whether there is an infeasible gap on the scanning ray according to the preset tolerance.

[0060] In one implementation example, it also includes an iterative line search module, configured to: when the infeasibility gap exceeds a preset tolerance, continue to solve for the nearest feasible point to the identified feasible endpoint and the farthest feasible point under the same binary variable configuration, iteratively expand feasible line segments and eliminate infeasibility gaps until the accuracy requirements are met.

[0061] Example 5 The purpose of this embodiment is to provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions involved in any of the above embodiments.

[0062] The steps and methods involved in the apparatus of the above embodiments correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0063] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0064] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for identifying non-convex power flexibility regions in zero-carbon industrial parks, characterized by comprising: A convex operation constraint model for zero-carbon industrial parks containing continuous and binary variables is constructed, and the power flexibility region at the power transmission and distribution interface is defined. The external search area in the active-reactive plane is determined based on the apparent power capacity of the transformer, and a reference point located outside the power flexibility area is selected from the vertex of the external search area. An angle scan is performed within a preset angle range starting from the reference point; For each scanning ray, perform a line search to identify feasible line segments and infeasible gaps on the scanning ray, including: for each scanning ray, solve the minimization and maximization subproblems of the distance parameter to obtain the first boundary point and the second boundary point in the scanning direction, and obtain the binary variable configuration corresponding to the first boundary point and the second boundary point; When the binary variable configurations corresponding to the first boundary point and the second boundary point are the same, the line segment between them is identified as a feasible line segment in the power flexibility region. When the binary variable configurations corresponding to the first boundary point and the second boundary point are different, the corresponding binary variable configurations are fixed respectively to solve the continuous subproblem, determine the endpoints of different feasible segments, and determine whether there is an infeasible gap on the scanning ray according to the preset tolerance. When the infeasible gap exceeds the preset tolerance, continue to solve for the nearest feasible point to the identified feasible endpoint and the farthest feasible point under the same binary variable configuration, iteratively expand the feasible line segment and eliminate infeasible gaps until the accuracy requirements are met. By summarizing the feasible line segments and infeasible gaps in each scanning direction, the non-convex power flexibility region induced by binary variables is obtained.

2. The method for identifying non-convex power flexibility regions in a zero-carbon industrial park as described in claim 1, characterized in that, An angle scan is performed from the reference point within a preset angle range, including: Starting from the reference point, multiple scanning rays are generated within a preset angle range, and candidate search directions corresponding to each scanning ray are determined sequentially or in parallel.

3. The method for identifying non-convex power flexibility regions in a zero-carbon industrial park as described in claim 1, characterized in that, The continuous variables include the square of the node voltage amplitude, branch active power, branch reactive power, distributed new energy output, energy storage charging and discharging power, energy storage state of charge, adjustable load adjustment, charging and discharging power of charging piles, reactive power compensation power, power of heat pumps or electric cooling equipment, and carbon emission accounting variables. The binary variables include branch switch status, energy storage charging and discharging status, charging pile charging and discharging status, capacitor bank switching status, interruptible load start and stop status, and topology reconfiguration status.

4. The method for identifying non-convex power flexibility areas in a zero-carbon industrial park as described in claim 1, characterized in that, The power flexibility region is defined as the set of active and reactive power domains for which feasible continuous and binary variables exist, satisfying the convex operation constraints of zero-carbon parks.

5. A zero-carbon industrial park non-convex power flexibility area identification system, characterized in that it includes: The zero-carbon park model building module is configured to: construct a convex operation constraint model of the zero-carbon park containing continuous and binary variables, and define the power flexibility region at the power transmission and distribution interface; The reference point selection module is configured to: determine the external search area in the active-reactive plane based on the transformer's apparent power capacity, and select a reference point located outside the power flexibility region from the vertices of the external search area; The angle scanning module is configured to: generate multiple scanning rays within a preset angle range starting from the reference point, and determine the candidate search direction corresponding to each scanning ray sequentially or in parallel; For each scanning ray, perform a line search to identify feasible line segments and infeasible gaps on the scanning ray, including: for each scanning ray, solve the minimization and maximization subproblems of the distance parameter to obtain the first boundary point and the second boundary point in the scanning direction, and obtain the binary variable configuration corresponding to the first boundary point and the second boundary point; When the binary variable configurations corresponding to the first boundary point and the second boundary point are the same, the line segment between them is identified as a feasible line segment in the power flexibility region. When the binary variable configurations corresponding to the first boundary point and the second boundary point are different, the corresponding binary variable configurations are fixed respectively to solve the continuous subproblem, determine the endpoints of different feasible segments, and determine whether there is an infeasible gap on the scanning ray according to the preset tolerance. When the infeasible gap exceeds the preset tolerance, continue to solve for the nearest feasible point to the identified feasible endpoint and the farthest feasible point under the same binary variable configuration, iteratively expand the feasible line segment and eliminate infeasible gaps until the accuracy requirements are met. The region output module is configured to summarize feasible line segments and infeasible gaps in all scanning directions to obtain a non-convex power flexibility region induced by binary variables.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 4.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method described in any one of claims 1-4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it performs the steps of the method described in any one of claims 1-4 above.

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