Power grid black-start path optimization method and device, electronic equipment and storage medium

Abstract

The invention discloses a power grid black-start path optimization method and device, electronic equipment and a storage medium, and the method takes the maximum total generating capacity of a system as an optimization target, and is specifically improved as follows: simply adding the actual cost and the estimated cost in a conventional A * algorithm, and improving the calculation of a cost function into the introduction of a weight coefficient. The weight coefficient is used for adjusting the ratio of the actual cost to the estimated cost, so that the efficiency and directivity of path search are enhanced, finally, curve fitting is performed on an initial black-start path output by the A * algorithm by adopting an interpolation method, and a smooth black-start path with continuous derivatives and geometric continuity is constructed, so that the problem of overlarge turning among path points is solved, and the path search efficiency and directivity are improved. And potential safety hazards such as voltage fluctuation and protection maloperation caused by path abrupt change are avoided.

Description

Technical Field

[0001] The present invention relates to power grid operation control technology, and in particular to a power grid black start path optimization method, device, electronic equipment and storage medium. Background Art

[0002] With the gradual transformation of the global energy structure, the proportion of new energy (such as wind power, solar energy, etc.) in the power grid is increasing. However, the volatility and intermittency of new energy have brought new challenges to the stability of the power system, especially the recovery problem in the event of large-scale power outages or system failures.

[0003] As a key technology for emergency power restoration, black start plays a crucial role in restoring normal grid operation after a power system failure. Black start technology typically involves restarting the grid system from scratch using specialized equipment and procedures after a complete grid outage, restoring normal power supply. Traditional black start operations typically rely on traditional energy sources such as coal and natural gas-fired power generation equipment. These power generation equipment can start without external power support and provide initial power to the grid. Subsequently, the grid is gradually expanded by starting more generators and connecting more loads, ultimately restoring stable operation of the entire network.

[0004] In practical applications, path planning for a power grid black start is a complex process involving the coordination and scheduling of multiple power generation sources, substations, and transmission lines. Path planning faces even greater challenges, especially when the proportion of renewable energy generation in the grid is high. Traditional black start path planning methods typically optimize for the shortest recovery time or minimum system risk. However, due to the fluctuating nature of renewable energy, single time optimization or risk optimization methods are insufficient when faced with the intermittent and uncertain nature of renewable energy generation. The distribution of renewable energy access points, the volatility of available output, and the complexity of coordinated scheduling with traditional units can all lead to the failure of the original black start path or a significant decrease in execution efficiency. Therefore, there is an urgent need to develop black start path optimization strategies for power grids containing renewable energy to achieve efficient and reliable grid restoration. Summary of the Invention

[0005] Based on this, the present invention aims to propose a power grid black start path optimization method, device, electronic device and storage medium, taking the total power generation of the system as the optimization target, and constructing multi-dimensional constraints. The improved A* algorithm is used to solve the optimization model, thereby improving the black start path quality and path optimization efficiency.

[0006] In a first aspect, the present invention provides a method for optimizing a black start path of a power grid, comprising:

[0007] Construct a path optimization model with the goal of maximizing power generation in the power grid, and determine the constraints of the path optimization model;

[0008] An improved A* algorithm is used to solve the path optimization model. The cost function of the improved A* algorithm is a weighted operation of the actual cost and the estimated cost. The initial black start path is determined based on the solution of the path optimization model by the improved A* algorithm.

[0009] The initial black start path is interpolated to obtain a smooth black start path.

[0010] Furthermore, the path optimization model is constructed with the goal of maximizing the power generation of the power grid, including:

[0011] Establish an initial path optimization model with the goal of maximizing power generation in the power grid;

[0012] The initial path optimization model is transformed according to the startup performance data of each generator set to obtain a path optimization model with the shortest transmission line restoration time as the optimization goal.

[0013] Furthermore, the initial path optimization model is transformed according to the startup performance data of each generator set, and the path optimization model with the shortest transmission line restoration time as the optimization goal is obtained, which includes:

[0014] The initial path optimization model with the goal of maximizing grid power generation is expressed as follows:

[0015] ,

[0016] Among them, E sys Indicates the total power generation of all generator sets, E ggen is the power generation of generator set g, E gcrank is the power consumption of generator set g, Ω G is the node set of all generator sets;

[0017] According to the starting performance data of the generator set, the power generation and power consumption of unit g are as follows:

[0018] ,

[0019] Among them, P g is the rated power of generator set g; is the recovery time of generator set g; is the time required for the startup phase of generator set g; is the time required for the ramp-up phase of generator set g; T is the power grid system recovery time; C g is the starting power of generator set g;

[0020] Then the initial path optimization model is transformed into:

[0021] ;

[0022] The initial path optimization model is transformed into a path optimization model with the shortest transmission line restoration time as the optimization objective as follows:

[0023] .

[0024] Furthermore, the constraint condition includes at least one of the following:

[0025] (1) Transmission line restoration time constraints;

[0026] (2) Restoration time constraints for each node in the black start path;

[0027] (3) Cold and hot start time limit constraints;

[0028] (4) Serial recovery time constraints;

[0029] (5) Transient frequency fluctuation constraints of the power grid system;

[0030] (6) Power constraints of new energy generator sets.

[0031] Furthermore, the cost function of the improved A* algorithm is a weighted operation of the actual cost and the estimated cost, including:

[0032] Determine the weight coefficient according to the distance between the initial node and the target node, the distance between the current node and the target node, the vector between the initial node and the target node, and the vector between the current node and the target node;

[0033] The cost function of the improved A* algorithm is determined according to the weight coefficient.

[0034] Furthermore, the cost function of the improved A* algorithm is calculated as follows:

[0035] ,

[0036] in, Represents the cost function of the improved A* algorithm, Indicates the actual cost, Indicates the estimated cost, represents the weight coefficient, Represents the distance between the initial node and the target node, Indicates the distance between the current node and the target node. Represents the vector of the initial node and the target node, Represents the vector between the current node and the target node, represents the angle between the vectors and .

[0037] Furthermore, interpolating the initial black start path to obtain a smooth black start path includes:

[0038] The path nodes in the initial black start path are used as the point set in the PCHIP interpolation method, and the linear equations in the PCHIP interpolation method are solved to obtain the smooth black start path.

[0039] In a second aspect, the present invention provides a power grid black start path optimization device, comprising:

[0040] The optimization model construction module is used to construct a path optimization model with the goal of maximizing power generation of the power grid and determine the constraints of the path optimization model;

[0041] An optimization model solving module is used to solve the path optimization model using an improved A* algorithm, wherein the cost function of the improved A* algorithm is a weighted operation of the actual cost and the estimated cost, and the initial black start path is determined based on the solution of the path optimization model by the improved A* algorithm;

[0042] The path smoothing processing module is used to perform interpolation calculation on the initial black start path to obtain a smooth black start path.

[0043] In a third aspect, the present invention provides an electronic device comprising a memory storing computer-executable instructions and a processor, wherein when the computer-executable instructions are executed by the processor, the device executes each step of the power grid black start path optimization method provided in the first aspect.

[0044] In a fourth aspect, the present invention provides a readable storage medium storing a computer executable program, which, when executed, can implement the various steps of the power grid black start path optimization method provided in the first aspect.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention provides a black start path optimization method for a power grid. The method takes maximizing the total power generation of the system as the optimization goal, further considers the multi-dimensional constraints of the safe and stable operation of the system, and solves the optimization model using the A* algorithm. Specifically, the A* algorithm is improved. The cost function calculated by simply adding the actual cost and the estimated cost in the conventional A* algorithm is improved to introduce a weight coefficient. The weight coefficient is used to adjust the proportion of the actual cost and the estimated cost, thereby enhancing the efficiency and directionality of the path search. Finally, the PCHIP interpolation method is used to perform curve fitting on the initial black start path output by the A* algorithm to construct a smooth black start path with continuous derivatives and geometric continuity. This solves the problem of excessive turning points between path points and avoids safety hazards such as voltage fluctuations and protection malfunctions caused by path mutations. The optimization method proposed by the present invention is more suitable for engineering scheduling execution and effectively improves the physical feasibility and control friendliness of system recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0048] Figure 1 This is a flowchart of a method for optimizing a black start path for a power grid according to an embodiment of the present invention;

[0049] Figure 2 Schematic diagram of the structure of a power grid black start path optimization device provided by an embodiment of the present invention;

[0050] Figure 3 This is a diagram of the electronic device architecture provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] See Figure 1 One embodiment of the present invention provides a method for optimizing a black start path of a power grid, comprising the following steps:

[0053] Step S110: Construct a path optimization model with the goal of maximizing power generation of the power grid, and determine the constraints of the path optimization model.

[0054] In this step, it is first necessary to clarify that the main goal of path optimization is to maximize the power generation during the grid recovery process by rationally planning the black start path of the grid. To this end, the path optimization model needs to consider multiple constraints while formulating an optimization plan that can maximize the power generation during grid recovery.

[0055] The goal of the black start process is to quickly, safely, and stably rebuild the entire power system after a failure or disaster to ensure the continuity and reliability of power supply. The embodiment of the present invention takes maximizing power generation of the power grid as the optimization goal. Through reasonable scheduling and path planning, it ensures that more units can be put into operation as soon as possible to restore maximum power generation capacity, thereby accelerating the recovery process of the power grid.

[0056] Furthermore, in order to establish a black start path optimization model based on mixed integer linear programming, the path optimization model with the goal of maximizing the power generation of the power grid is transformed. Specifically, the path optimization model initially constructed with the goal of maximizing the power generation of the power grid is defined as the initial path optimization model. The initial path optimization model is transformed according to the starting performance data of each generator set in the power grid system to obtain a path optimization model with the shortest transmission line restoration time as the optimization goal.

[0057] Specifically, the initial path optimization model with the goal of maximizing grid power generation is expressed as follows:

[0058]

[0059] Among them, E sys Indicates the total power generation of all generator sets, E ggen is the power generation of generator set g, E gcrank is the power consumption of generator set g, Ω G is the node set of all generator sets.

[0060] According to the starting performance data of the generator set, the power generation and power consumption of unit g are as follows:

[0061]

[0062] Among them, P g is the rated power of generator set g; is the recovery time of generator set g; is the time required for the startup phase of generator set g; is the time required for the ramp-up phase of generator set g; T is the power grid system recovery time; C g is the starting power of generator set g.

[0063] Then the initial path optimization model is transformed into:

[0064]

[0065] in:

[0066]

[0067] The initial path optimization model is transformed into a path optimization model with the shortest transmission line restoration time as the optimization objective as follows:

[0068]

[0069] In a further embodiment, the path optimization model does not use power generation or time as a single constraint, but considers constraints such as frequency fluctuations, cold and hot starts, and serial recovery logic that must be met in the actual operation of the power grid, making the generated black start path more adaptable.

[0070] Specifically, some embodiments of the present invention consider the following constraints:

[0071] (1) Transmission line restoration time constraints

[0072] To regain starting power after a non-blackstart unit m suffers a fault, a recovery path to the restored area must be established. Therefore, when formulating a recovery plan, the unit's recovery time must be no less than the sum of the start time of the recovery path im and the time required for recharging, as shown in the following formula.

[0073]

[0074] Where, is the recovery time of non-black start unit m; is the initial charging time of the recovery path of unit m; T im is the expected shortest recovery time; b im Ω is a 0-1 variable indicating whether it is selected as a recovery path; bus is the set of all non-unit nodes.

[0075] (2) Restoration time constraints for each node in the black start path.

[0076] To reduce system complexity and more effectively plan and implement restoration strategies during power system restoration, this paper categorizes and manages different types of nodes. Based on their connection to power generation units, these nodes are divided into two main categories: unit nodes and non-unit nodes. Their restoration time constraints are shown in the following equation.

[0077]

[0078] Where, is the recovery time of non-black start unit m; is the recovery time of non-unit node k; M is a 0-1 variable representing the relationship between the non-black start unit m and the non-unit node k; x (x=1,2,…) is a set of sufficiently large numbers in the Big M algorithm. When the 0-1 variable takes certain values, its size can make the corresponding constraint invalid.

[0079] (3) Cold and hot start time limit constraints

[0080] Cold start and hot start are two key stages in a unit's re-entry. A cold start typically refers to the restart of a unit after a complete shutdown, while a hot start is the restart of a unit after a short shutdown. Due to the different startup time limits for these two methods, existing optimization studies have found it difficult to implement both in a model. Therefore, to address this issue, this paper proposes a linearization method that can simultaneously implement both a maximum hot start time constraint and a minimum cold start time constraint, as shown in the following equation:

[0081]

[0082] Where, and A 0-1 variable indicating whether the unit meets the maximum hot start time limit or the minimum cold start time limit.

[0083] (4) Serial recovery time constraints

[0084] The parallel restoration strategy accelerates overall system recovery by simultaneously processing multiple transmission lines. However, due to the capacitive effect of the lines, capacitive reactive power is generated at both ends. If compensation or regulation cannot be performed in a timely manner, voltage fluctuations during the black start process will occur. Therefore, the restoration time for each non-black start unit to establish a connected path is staggered, with the restoration time of each unit starting only after the previous unit has successfully started and is operating stably. The serial restoration constraint thus constructed ensures system voltage stability and reduces the impact on other equipment, as shown in the following equation.

[0085]

[0086] Where, is the initial charging time of the recovery path of the non-black start unit m; is the recovery time of non-black start unit m; is the initial charging time of the recovery path of the non-black start unit g; is the recovery time of non-black start unit g; and 0-1 variables representing the relationship between the recovery time of non-black start units g and m.

[0087] (5) Transient frequency fluctuation constraints of power grid system

[0088] System frequency is an important indicator of the operating status of the power system. Once the frequency fluctuates beyond the safe range, it may cause equipment damage and affect the normal operation of the black start process. Therefore, this embodiment of the present invention introduces transient frequency response constraints to limit the starting power of non-black start units, mitigating the impact of unit startup on the system frequency. For the restored system, the allowable transient frequency fluctuation range is as follows:

[0089]

[0090] Where, Indicates the upper limit of transient frequency fluctuation, set it to 0.5Hz; Indicates the starting power of non-black start unit m; Indicates the set of units that are currently producing power; Represents the transient frequency response coefficient of unit g.

[0091] In order to incorporate the transient frequency response into the black start path optimization model, it needs to be simplified:

[0092]

[0093] Where, A 0-1 variable indicating whether unit m is in the startup phase; and 0-1 variables indicating whether unit g is in the climbing state or the rated output state when unit m is started.

[0094] (6) Power constraints of new energy generator sets.

[0095] To better utilize renewable energy resources and ensure the stability and security of grid restoration, this paper incorporates renewable energy power limit constraints to effectively control and optimize renewable energy generation units during grid blackstart. These constraints primarily address two aspects: First, the power output range of renewable energy generation units, ensuring that renewable energy power stations can adjust their output power according to grid demand during grid restoration; and second, a limit on the rate of change of renewable energy power to prevent rapid fluctuations in renewable energy power from impacting grid stability, as shown in the following equation.

[0096]

[0097] Where, is the predicted power of the new energy unit at time t; is the actual power; is the maximum allowed prediction error; is the maximum allowable power change rate; is the total cumulative error allowed within time T.

[0098] Step S120: Use the improved A* algorithm to solve the path optimization model, wherein the cost function of the improved A* algorithm is a weighted operation of the actual cost and the estimated cost, and determine the initial black start path according to the solution of the path optimization model by the improved A* algorithm.

[0099] The A* algorithm is a heuristic search algorithm widely used in path planning and graph traversal. It preferentially selects nodes with the smallest heuristic value and expands until the shortest path is found. The heuristic value of the A* algorithm reflects the expected cost from the current node to the target node. It consists of the actual cost g(n) of the starting point and the estimated cost h(n) of the target node, as shown in the following formula:

[0100]

[0101] Specifically, the traditional A* algorithm evaluation function is a simple addition of actual cost and heuristic cost. However, in the black start recovery problem of a power grid containing new energy units, the costs of different types of units may have different priorities. In order to further improve the search efficiency of the A* algorithm and adapt to specific problems, the present invention introduces a weight coefficient on the basis of the traditional algorithm. By assigning weight coefficients to different costs, the evaluation function of the A* algorithm is optimized, and the search preference of the algorithm is controlled more flexibly.

[0102] To better meet the needs of path planning during the black start process, the present invention improves the traditional A* algorithm as follows:

[0103]

[0104] in, Represents the cost function of the improved A* algorithm, Indicates the actual cost, Indicates the estimated cost, represents the weight coefficient, Represents the distance between the initial node and the target node, Indicates the distance between the current node and the target node. Represents the vector of the initial node and the target node, Represents the vector between the current node and the target node, represents the angle between the vectors and .

[0105] when When it increases, the algorithm will be more inclined to consider heuristic information when selecting paths. , which is more efficient in the search process but reduces the accuracy of the optimal path; on the contrary, when When it decreases, the algorithm will consider the actual path cost more. It will increase the possibility of obtaining the global optimal path, but it will also increase the number of calculated nodes and search time. Therefore, it is necessary to determine the appropriate weight coefficient to take into account the search efficiency and optimization performance of the algorithm.

[0106] When solving the model specifically, the optimization model is first abstracted into a graph search problem, where nodes represent the components in the power grid and their states, and edges represent the connection relationships between components, while taking into account the constraints mentioned in the aforementioned embodiments.

[0107] Ultimately, by solving the path optimization model, the improved A* algorithm will give the initial black start path. This path is the path that meets the maximum power generation or the shortest recovery time and complies with various constraints, usually including the start-up sequence, start-up time, ramp-up process, recovery time, etc. of each unit.

[0108] Step S130: Perform interpolation calculation on the initial black start path to obtain a smooth black start path.

[0109] In this step, the goal is to make the initial black start path smoother through interpolation calculations, ensuring that the unit startup and power adjustment process is smoother in actual applications and avoiding sudden power fluctuations and instabilities.

[0110] Specifically, the initial path obtained by the A* algorithm during a black start often consists of a series of turning points, which can lead to transient instability during system recovery and trigger protection device activation. Therefore, embodiments of the present invention use a piecewise cubic Hermite interpolating polynomial (PCHIP) to improve the initial black start path output by the A* algorithm, reducing the risk of grid collapse.

[0111] The core principle of PCHIP interpolation is to use the local linearity and curvature information of the data points to ensure the monotonicity and smoothness of the interpolation curve. In each data interval, PCHIP interpolation fits the data points by constructing a cubic polynomial to match the value and first-order derivative of the polynomial at the endpoints of the interval with the data points. n The cubic Hermite interpolation function f on k (x) is defined as follows:

[0112]

[0113] Among them, x k is the ordered node in the interval; y k For ordered nodes x k The corresponding function value; d k is the first derivative of the interpolation function at the endpoints of the interval.

[0114] According to the obtained initial black start path, the path points are set to point set M, and the above polynomial f is determined by solving the linear equation kThe coefficient of (x) is calculated, and Hermite interpolation is applied to the coordinate sequence of the data points. An appropriate number of interpolation points are selected between the starting point and the end point. The interpolated path will be a smoother black start path, ensuring that there will be no excessive power fluctuations during the power grid recovery process, while keeping the start-stop timing and power scheduling of each unit in line with the optimization goal.

[0115] The above embodiments provide a method for optimizing a power grid black start path, which takes maximizing the total power generation of the system as the optimization goal, further considers the multi-dimensional constraints on the safe and stable operation of the system, and uses the A* algorithm to solve the optimization model. Specifically, the A* algorithm is improved by introducing a weight coefficient instead of simply adding the actual cost and the estimated cost to calculate the cost function in the conventional A* algorithm. The weight coefficient is used to adjust the proportion of the actual cost and the estimated cost, thereby enhancing the efficiency and directionality of the path search. Finally, the PCHIP interpolation method is used to perform curve fitting on the initial black start path output by the A* algorithm to construct a smooth black start path with continuous derivatives and geometric continuity. This solves the problem of large transitions between path points and avoids safety hazards such as voltage fluctuations and protection malfunctions caused by path mutations. The optimization method proposed in the present invention is more suitable for engineering scheduling execution and effectively improves the physical feasibility and control friendliness of system recovery.

[0116] The disclosed method can be implemented using various devices, so the present invention also discloses a device corresponding to the method, which will be described in detail with specific embodiments given below.

[0117] like Figure 2 As shown, one embodiment of the present invention provides a power grid black start path optimization device, comprising:

[0118] The optimization model construction module 202 is used to construct a path optimization model with the goal of maximizing power generation of the power grid and determine the constraint conditions of the path optimization model;

[0119] An optimization model solving module 204 is configured to solve the path optimization model using an improved A* algorithm, wherein the cost function of the improved A* algorithm is a weighted calculation of the actual cost and the estimated cost, and to determine the initial black start path based on the solution of the path optimization model using the improved A* algorithm;

[0120] The path smoothing processing module 206 is configured to perform interpolation calculation on the initial black start path to obtain a smoothed black start path.

[0121] The device provided in the embodiment of the present application has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0122] The methods and related devices mentioned in the above embodiments are described with reference to the method flow charts and / or structural diagrams provided in the embodiments of the present application. Specifically, each process and / or block in the method flow charts and / or structural diagrams, as well as the combination of processes and / or blocks in the flow charts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 Schematic diagram of one or more processes and / or structures Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 Schematic diagram of one or more processes and / or structures Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 The flow or flows and / or structures illustrate the steps of the functions specified in one block or multiple blocks.

[0123] The following embodiments illustrate this method using a computer device as an example. It is understood that the computer device may be any device with computing and processing capabilities, including, but not limited to, a server or a personal laptop. In one embodiment, the computer device may be an application server, which may be a server for running the application under test.

[0124] See Figure 3 , which shows a hardware block diagram of an electronic device, which is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0125] like Figure 3As shown, the electronic device includes: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;

[0126] In the embodiment of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 communicate with each other through the communication bus 4;

[0127] The processor 1 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention;

[0128] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory;

[0129] The memory stores a program, and the processor can call the program stored in the memory, wherein the program is used to implement various processing flows of the aforementioned power grid black start path optimization solution.

[0130] An embodiment of the present invention also provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the various processing flows of the power grid black start path optimization solution provided in the above embodiment and / or any possible implementation method in combination with the embodiment.

[0131] The above embodiments have described the invention in particular detail with respect to possible scenarios, and those skilled in the art will recognize that the invention can be practiced through other embodiments. The specific naming of components, capitalization of terms, attributes, data structures, or any other programming or structural aspects are not mandatory or important, and the mechanisms or features of the invention may have different names, forms, or procedures. The system may be implemented through a combination of hardware and software (as described), entirely through hardware elements, or entirely through software elements. The specific division of functions between the various system components described herein is exemplary only and not mandatory; rather, the functions performed by a single system component may be performed by multiple components, or the functions performed by multiple components may be performed by a single component.

[0132] Those skilled in the art will appreciate that the various steps of the method disclosed above can be implemented by a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the embodiments disclosed herein are not limited to any specific combination of hardware and software.

[0133] The programs executable by these computing devices (also referred to as programs, software, software applications, or code) include machine instructions for programmable processors and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0134] Certain aspects of the present invention include the process steps and instructions described herein in the form of algorithms. It should be noted that the process steps and instructions of the present invention can be implemented in software, firmware and / or hardware, and when implemented in software, they can be downloaded, stored on different platforms used by various operating systems, and operated from the platforms.

[0135] Those skilled in the art will understand that the structures shown in the accompanying drawings are merely block diagrams of partial structures related to the scheme of the present application, and do not constitute a limitation on the terminal device to which the scheme of the present application is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "possible design" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples, unless they are mutually inconsistent.

[0137] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing a power grid black start path, characterized in that: include: Constructing a path optimization model with the goal of maximizing power generation of the power grid, and determining the constraints of the path optimization model; Solving the path optimization model using an improved A* algorithm, where the cost function of the improved A* algorithm is a weighted operation of an actual cost and an estimated cost, and determining an initial black start path based on a solution of the path optimization model using the improved A* algorithm; An interpolation calculation is performed on the initial black start path to obtain a smooth black start path.

2. The method according to claim 1, characterized in that The path optimization model constructed with the goal of maximizing power generation of the power grid includes: Establish an initial path optimization model with the goal of maximizing power generation in the power grid; The initial path optimization model is transformed according to the startup performance data of each generator set to obtain a path optimization model with the shortest transmission line restoration time as the optimization goal.

3. The method according to claim 2, characterized in that The initial path optimization model is transformed according to the startup performance data of each generator set to obtain a path optimization model with the shortest transmission line restoration time as the optimization goal, including: The initial path optimization model with the goal of maximizing grid power generation is expressed as follows: ， Among them, E sys Indicates the total power generation of all generator sets, E ggen is the power generation of generator set g, E gcrank is the power consumption of generator set g, Ω G is the node set of all generator sets; According to the starting performance data of the generator set, the power generation and power consumption of unit g are as follows: ， Among them, P g is the rated power of generator set g; is the recovery time of generator set g; is the time required for the startup phase of generator set g; is the time required for the ramp-up phase of generator set g; T is the power grid system recovery time; C g is the starting power of generator set g; Then the initial path optimization model is transformed into: ； The initial path optimization model is transformed into a path optimization model with the shortest transmission line restoration time as the optimization objective as follows: 。 4. The method according to claim 1, wherein The constraint condition includes at least one of the following: (1) Transmission line restoration time constraints; (2) Restoration time constraints for each node in the black start path; (3) Cold and hot start time limit constraints; (4) Serial recovery time constraints; (5) Transient frequency fluctuation constraints of the power grid system; (6) Power constraints of new energy generator sets.

5. The method according to claim 1, wherein The cost function of the improved A* algorithm is a weighted operation of the actual cost and the estimated cost, including: Determine the weight coefficient according to the distance between the initial node and the target node, the distance between the current node and the target node, the vector between the initial node and the target node, and the vector between the current node and the target node; The cost function of the improved A* algorithm is determined according to the weight coefficient.

6. The method according to claim 5, characterized in that The cost function of the improved A* algorithm is calculated as follows: ， in, represents the cost function of the improved A* algorithm, Indicates the actual cost, Indicates the estimated cost, represents the weight coefficient, Represents the distance between the initial node and the target node, Indicates the distance between the current node and the target node. Represents the vector of the initial node and the target node, Represents the vector between the current node and the target node, represents the angle between the vectors and .

7. The method according to claim 1, characterized in that The interpolation calculation of the initial black start path to obtain a smooth black start path includes: The path nodes in the initial black start path are used as the point set of the PCHIP interpolation method, and the linear equation in the PCHIP interpolation method is solved to obtain a smooth black start path.

8. A power grid black start path optimization device, characterized in that: include: An optimization model building module is used to build a path optimization model with the goal of maximizing power generation of the power grid and determine the constraint conditions of the path optimization model; an optimization model solving module, configured to solve the path optimization model using an improved A* algorithm, wherein the cost function of the improved A* algorithm is a weighted operation of an actual cost and an estimated cost, and determine an initial black start path based on the solution of the path optimization model using the improved A* algorithm; The path smoothing processing module is used to perform interpolation calculation on the initial black start path to obtain a smooth black start path.

9. An electronic device, characterized in that: The device comprises a memory storing computer-executable instructions and a processor, and when the computer-executable instructions are executed by the processor, the device executes the power grid black start path optimization method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: A computer executable program is stored, and when the program is executed, the power grid black start path optimization method according to any one of claims 1 to 7 can be implemented.

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