A power distribution network vulnerability evaluation and collaborative scheduling method, device and medium

Abstract

The present application relates to the technical field of power grid dispatching, and specifically provides a power distribution network vulnerability assessment and collaborative dispatching method, device and medium, the method comprising: based on the historical data and prediction error distribution of new energy output in the power distribution network, a Monte Carlo simulation is used to sample and generate multiple scenes with occurrence probability; according to the multiple scenes, a two-stage stochastic programming model is constructed with the comprehensive minimization of power distribution network operation cost and conditional risk value as the target; wherein the two-stage stochastic programming model includes a day-ahead pre-decision stage, a real-time balancing stage and a risk measurement item; the two-stage stochastic programming model is solved to obtain the vulnerability assessment result and collaborative dispatching scheme of the power distribution network within the target time. The present application method significantly improves the economy and safety of the power distribution network operation under the high proportion of new energy access scene through deep integration of uncertainty modeling and risk quantification technology.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to a method, device and medium for vulnerability assessment and coordinated dispatching of distribution networks. Background Technology

[0002] The penetration rate of intermittent renewable energy sources such as wind and solar power in the power grid continues to rise. The randomness, volatility, and difficulty in accurately predicting their output have shifted the power grid's operating state from a traditional deterministic-driven model to one dominated by strong uncertainty. Traditional power grid security assessment methods based on deterministic and static scenarios are no longer effective in addressing this change. Therefore, there is an urgent need to develop new assessment methods that can quantify temporal risks and proactively utilize diverse and flexible resources for collaborative defense.

[0003] However, existing power grid safety assessment technologies still have the following significant limitations: First, assessment and decision-making are disconnected. Current methods mostly remain at the post-hoc risk assessment level, only answering "what is the risk and how great is it," outputting indicators and probabilities. This is a "diagnostic" approach, lacking the "prescription" function to transform assessment results into specific dispatching schemes, and thus offering weak guidance for actual operation. Second, the dynamic synergistic value of resources is ignored. Traditional models fail to embed flexible resources such as energy storage and adjustable loads as active decision-making variables into the assessment process, resulting in a serious underestimation or neglect of their dynamic adjustment capabilities and their spatiotemporal complementarity with renewable energy fluctuations. Finally, the risk dimension is singular. Existing assessments mainly focus on physical safety risks, such as overload and voltage exceedances, while lacking effective forward-looking quantitative tools for economic risks caused by renewable energy fluctuations, such as the high real-time electricity purchase costs or penalty fees incurred in balancing power in extreme scenarios. This makes it difficult to achieve deep integration with optimized dispatching. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, equipment and medium for assessing the vulnerability of power distribution networks and coordinating their dispatch, which can dynamically and proactively assess the comprehensive vulnerability of the power grid under the fluctuation of new energy sources, while taking into account both operational economy and risk prevention and control.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, embodiments of the present invention provide a method for vulnerability assessment and coordinated dispatching of a power distribution network, comprising:

[0007] Based on historical data and prediction error distribution of renewable energy output in the distribution network, Monte Carlo simulation is used to sample and generate multiple scenarios with occurrence probabilities; wherein, the scenario is the set of renewable energy output for each time period within the target time.

[0008] Based on the various scenarios described, a two-stage stochastic programming model is constructed with the goal of minimizing the combined operating costs and conditional risk value of the distribution network. The two-stage stochastic programming model includes a day-ahead pre-decision stage, a real-time balancing stage, and a risk measurement term.

[0009] The two-stage stochastic programming model is solved to obtain the vulnerability assessment results of the distribution network and the coordinated dispatch scheme within the target time.

[0010] In some embodiments of the present invention, after generating multiple scenarios with occurrence probabilities by sampling based on historical data and prediction error distribution of new energy output in the distribution network using Monte Carlo simulation, the method further includes: reducing the number of scenarios by using a fast forward selection algorithm and reallocating the occurrence probabilities of the retained scenarios.

[0011] In some embodiments of the present invention, based on multiple scenarios, a two-stage stochastic programming model is constructed with the objective of comprehensively minimizing the operating cost and conditional risk value of the distribution network, including:

[0012] The objective function is to minimize the combined operating cost and conditional risk value of the distribution network; wherein the operating cost of the distribution network includes the deterministic cost in the day-ahead pre-decision stage and the expected cost for the scenario in the real-time balancing stage.

[0013] Constraints are set for the day-ahead pre-decision stage and the real-time balancing stage. These constraints include power balance constraints, equipment operation constraints, line power flow constraints, and voltage safety constraints.

[0014] In some embodiments of the present invention, the objective function is as follows:

[0015] ;

[0016] in, Represent the objective function; This represents the certainty cost of the pre-decision stage. This represents the expected cost during the real-time balancing phase. Indicates the risk aversion coefficient; Indicates at confidence level Conditional Value at Risk (VaR).

[0017] In some embodiments of the present invention, the formula for calculating the deterministic cost of the day-ahead pre-decision stage is as follows:

[0018] ;

[0019] in, This indicates the power generation cost of a thermal power unit; Represents a node During the period of the thermal power unit The plan is to contribute; Indicates the cost of purchasing electricity; Represents a node During the period The planned power purchase capacity.

[0020] In some embodiments of the present invention, the formula for calculating the expected cost of the real-time balancing phase is as follows:

[0021] ;

[0022] in, Representing a scene The probability of occurrence; This indicates the operating cost of energy storage; Represents a node Energy storage scenarios Time period The charging power; Represents a node Energy storage scenarios Time period The discharge power; This indicates the compensation cost for invoking interruptible loads; Represents a node In the scene Time period The amount of load reduction; This indicates the unit penalty price for crossing the voltage line; Represents a node In the scene Time period The voltage is limited.

[0023] In some embodiments of the present invention, at confidence levels The formula for calculating the conditional value at risk is as follows:

[0024] , , ;

[0025] in, Indicates a threshold variable; As an auxiliary variable, it represents the scene Below, the loss exceeds the threshold. Part of; Indicates in the scene The following losses;

[0026] The calculation formula is as follows:

[0027] .

[0028] In some embodiments of the present invention, the power balance constraint is as follows:

[0029] ;

[0030] in, Represents a node New energy in various scenarios Time period Actual output; Represents a node During the period The load forecast value; Represents nodes The set of directly connected nodes; Indicates the connection node and nodes The line susceptance; Represents a node In the scene Time period The voltage phase angle; Represents a node In the scene Time period The voltage phase angle;

[0031] The operating constraints of the equipment are as follows:

[0032] ;

[0033] ;

[0034] , ;

[0035] ;

[0036] ;

[0037] ;

[0038] in, Represents a node Energy storage scenarios Time period The state of charge; Indicates the charging efficiency of energy storage; Indicates the discharge efficiency of energy storage; Indicates the minimum state of charge; Indicates the maximum state of charge; Indicates the upper limit of the charging power of energy storage; Indicates the upper limit of the discharge power of the energy storage; Represents a node The lower limit of the output of the thermal power unit; Represents a node The upper limit of the output of the thermal power unit; Indicates the slope rate limit; Indicates the length of the time period; Represents a node The maximum amount of interruptible load reduction;

[0039] The power flow constraints of the line are as follows:

[0040] ;

[0041] in, Indicates the connection node and nodes The maximum active power of the line;

[0042] The voltage safety constraints are as follows:

[0043] ;

[0044] in, Represents a node In the scene Time period The voltage amplitude; This represents the minimum value of the voltage amplitude; This indicates the maximum voltage amplitude.

[0045] Secondly, the present invention also provides an electronic device, including: a processor, and a memory storing a program, the program including instructions, which, when executed by the processor, cause the processor to perform the above-described distribution network vulnerability assessment and coordinated scheduling method.

[0046] Thirdly, the present invention also provides a non-transitory machine-readable medium storing computer instructions for causing the computer to execute the above-described distribution network vulnerability assessment and coordinated scheduling method.

[0047] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0048] The distribution network vulnerability assessment and collaborative scheduling method provided in this invention constructs a two-stage stochastic programming model with the goal of minimizing the combined operating cost and conditional value of risk (CVaR). This model optimizes day-ahead pre-decision and real-time balancing scheduling, balancing the foresight and flexibility of decision-making. The model incorporates a CVaR risk metric to quantify and control potential losses in extreme scenarios, enhancing the system's resilience to uncertainty. Furthermore, it comprehensively considers constraints such as power balance, equipment operation, line flow, and voltage safety to ensure the feasibility and safety of scheduling schemes under various scenarios. Solving the model yields distribution network vulnerability assessment results and collaborative scheduling schemes for multiple resource types, providing scientific decision support for dispatchers and effectively improving the economy, safety, and resilience of distribution network operation. Attached Figure Description

[0049] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating a method for assessing the vulnerability of a power distribution network and coordinating its scheduling, provided in an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0052] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0053] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for vulnerability assessment and coordinated scheduling of power distribution networks. Figure 1 This is a flowchart illustrating the vulnerability assessment and coordinated dispatch method for distribution networks. This flowchart only shows the logical sequence of the method in this embodiment; however, in other possible embodiments of the invention, different methods may be used, provided there are no conflicts. Figure 1 Complete the steps shown or described in the order indicated.

[0054] See Figure 1 The method of this invention specifically includes the following steps:

[0055] Step S101: Based on historical data and prediction error distribution of new energy output in the distribution network, Monte Carlo simulation is used to sample and generate multiple scenarios with occurrence probabilities.

[0056] Since the output of new energy sources such as wind power and photovoltaics is affected by factors such as weather, it is random and volatile. In order to simulate the uncertainty of the output of new energy sources in the future prediction period, it is necessary to generate a large number of possible time series to characterize their probability distribution.

[0057] By statistically analyzing historical data on renewable energy output, such as the actual output data of wind and solar power plants over the past year or several years, and combining this with predicted values ​​obtained from physical models, the probability distribution characteristics of the prediction error are first calculated and fitted. Based on this, using the renewable energy output point prediction curve within the target time period as a benchmark, Monte Carlo simulation technology is employed to perform large-scale random sampling of the prediction errors for each time period. The error sequence obtained from each sampling is then superimposed onto the point prediction curve, thereby generating a complete output time series within the target time period. This series represents a possible renewable energy output scenario. By repeatedly performing the random sampling process, a scenario set containing a massive number of scenarios can be obtained, which comprehensively depicts the various fluctuation patterns and uncertainty ranges that renewable energy output may exhibit within the target time period.

[0058] In this embodiment of the invention, the target time is the next day, and there are 96 time periods in total, each lasting 15 minutes.

[0059] The predicted values ​​obtained from the physical model refer to the day-ahead predicted values ​​of renewable energy output. These are obtained as follows: First, numerical weather prediction (NWP) data for the areas where wind farms and photovoltaic power plants are located are obtained from meteorological departments, including predicted values ​​for key meteorological elements such as wind speed, wind direction, irradiance, and temperature. Then, using historical operating data from wind farms and photovoltaic power plants, a high-precision power prediction model is established to convert the aforementioned meteorological forecast values ​​into power prediction values. Specifically, for wind power, this is typically based on the standard power curve of wind turbine units or statistical learning methods, such as neural networks, support vector machines, and random forests, to fit the wind speed-power mapping relationship. For photovoltaic power, the conversion is based on an irradiance-temperature-power physical model or machine learning algorithms. Finally, the predicted power sequence for each renewable energy plant within the future scheduling cycle (96 15-minute time slots) is obtained.

[0060] Each scene Corresponding to an occurrence probability In some embodiments, it can be assumed that all scenarios have equal probability, i.e. , These probabilities reflect the likelihood of each scenario occurring, and can be assigned different weights based on the total number of scenarios or the sampling distribution.

[0061] While many scenarios can accurately describe uncertainty, directly using them in subsequent optimization models can lead to huge model sizes, excessively long computation times, or even failure to solve the problem. Therefore, it is necessary to reduce the number of scenarios while maintaining the original probability distribution characteristics.

[0062] In this embodiment of the invention, the number of scenes is reduced by using a fast forward selection algorithm, and the occurrence probability is redistributed for the retained scenes.

[0063] First, the Kantorovich distance is used as a metric to measure the difference between two scenes, and the selected scene set is initialized to empty. Then, an iterative process begins: in each round, all currently unselected scenes are traversed, the Kantorovich distance from each candidate scene to each scene in the current selected scene set is calculated, and the minimum value is taken as the "distance" from the candidate scene to the selected set. Then, the scene that minimizes the sum of the product of this distance value and its corresponding original probability is selected, removed from the original set, and added to the selected set. This process is repeated until the number of selected scenes reaches the preset target size K. After obtaining the final K typical scenes, their probabilities are redistributed, and each retained scene... New probability It equals its own original probability, plus the sum of the original probabilities of all unretained scenarios that were judged to be "closest to this scenario" during the reduction process, thereby ensuring that the total probability of all scenarios is 1, and making the probability distribution after reduction approximate the uncertainty characteristics of the original distribution as closely as possible.

[0064] Step S102: Based on multiple scenarios, construct a two-stage stochastic programming model with the goal of minimizing the combined operating cost and conditional risk value of the distribution network.

[0065] In this embodiment of the invention, constructing a two-stage stochastic programming model specifically includes:

[0066] Step S1021: The objective function is to minimize the combined operating cost and conditional risk value of the distribution network; wherein, the operating cost of the distribution network includes the deterministic cost in the day-ahead pre-decision stage and the expected cost for the scenario in the real-time balancing stage.

[0067] The objective function of the two-stage stochastic programming model is as follows:

[0068] ;

[0069] in, Represent the objective function; This represents the certainty cost of the pre-decision stage. This represents the expected cost during the real-time balancing phase. Indicates the risk aversion coefficient; Indicates at confidence level Conditional Value at Risk (VaR).

[0070] The formula for calculating the deterministic costs in the current pre-decision stage is as follows:

[0071] ;

[0072] in, This indicates the power generation cost of a thermal power unit; Represents a node During the period of the thermal power unit The plan is to contribute; Indicates the cost of purchasing electricity; Represents a node During the period The planned power purchase capacity.

[0073] The formula for calculating the expected cost during the real-time balancing phase is as follows:

[0074] ;

[0075] in, Representing a scene The probability of occurrence; This indicates the operating cost of energy storage; Represents a node Energy storage scenarios Time period The charging power; Represents a node Energy storage scenarios Time period The discharge power; This indicates the compensation cost for invoking interruptible loads; Represents a node In the scene Time period The amount of load reduction; This indicates the unit penalty price for crossing the voltage line; Represents a node In the scene Time period The voltage is limited.

[0076] At confidence level The formula for calculating the conditional value at risk is as follows:

[0077] , , ;

[0078] in, Indicates a threshold variable; As an auxiliary variable, it represents the scene Below, the loss exceeds the threshold. Part of; Indicates in the scene The following losses;

[0079] The calculation formula is as follows:

[0080] .

[0081] Step S1022: Set constraints for the day-ahead pre-decision stage and the real-time balancing stage. The constraints include power balance constraints, equipment operation constraints, line power flow constraints, and voltage safety constraints.

[0082] Power balance constraints are applied to each node, each time period, and each scenario. Specifically, the power balance constraints are as follows:

[0083] ;

[0084] in, Represents a node New energy in various scenarios Time period Actual output; Represents a node During the period The load forecast value; Represents nodes The set of directly connected nodes; Indicates the connection node and nodes The line susceptance; Represents a node In the scene Time period The voltage phase angle; Represents a node In the scene Time period The voltage phase angle.

[0085] The equipment operation constraints are respectively for the energy storage dynamic constraints of the energy storage node, the thermal power unit operation constraints of the thermal power unit node, and the demand response constraints of the interruptible load node.

[0086] The dynamic constraints for energy storage are as follows:

[0087] ;

[0088] ;

[0089] , .

[0090] The operating constraints of thermal power units are as follows:

[0091] ;

[0092] .

[0093] The demand response constraints are as follows:

[0094] ;

[0095] in, Represents a node Energy storage scenarios Time period The state of charge; Indicates the charging efficiency of energy storage; Indicates the discharge efficiency of energy storage; Indicates the minimum state of charge; Indicates the maximum state of charge; Indicates the upper limit of the charging power of energy storage; Indicates the upper limit of the discharge power of the energy storage; Represents a node The lower limit of the output of the thermal power unit; Represents a node The upper limit of the output of the thermal power unit; Indicates the slope rate limit; Indicates the length of the time period; Represents a node The maximum amount of interruptible load reduction.

[0096] The power flow constraints for the line are as follows:

[0097] ;

[0098] in, Indicates the connection node and nodes The maximum active power of the line.

[0099] Voltage safety constraints are as follows:

[0100] ;

[0101] in, Represents a node In the scene Time period The voltage amplitude; This represents the minimum value of the voltage amplitude; This indicates the maximum voltage amplitude.

[0102] Voltage overrun at nodes and voltage amplitude They are directly related. The voltage exceedance is calculated from the voltage amplitude, and it measures the degree to which the voltage amplitude deviates from the allowable range. The relationship between the two can be expressed as:

[0103] .

[0104] This invention employs the DisFlow model, which can represent voltage as a linear function of node-injected power:

[0105] ;

[0106] in, This represents the relevant elements of the node impedance matrix.

[0107] Step S103: Solve the two-stage stochastic programming model to obtain the vulnerability assessment results of the distribution network and the coordinated dispatch scheme within the target time.

[0108] The constructed two-stage stochastic programming model constitutes a large-scale mixed-integer linear programming (MILP) problem. In this embodiment of the invention, a mature commercial optimization solver is used for efficient solution, specifically MILP solvers such as CPLEX or Gurobi. These solvers are based on branch and bound, cutting plane, and heuristic algorithms, and can converge to the global optimum or an approximate optimum that meets the preset gap requirements within a reasonable time.

[0109] The vulnerability assessment results and coordinated dispatch scheme of the distribution network within the target time period specifically include:

[0110] (1) Day-ahead deterministic dispatch plan, corresponding to the optimal solution of decision variables in the day-ahead pre-decision stage, including: start-up and shutdown status of thermal power units; planned output of units. Planned power purchase capacity Energy storage plan SOC curve.

[0111] (2) Real-time collaborative strategy set: corresponding to the optimal solution of decision variables in the real-time balance stage, including: real-time charging and discharging adjustment amount of energy storage. and Interruptible load reduction .

[0112] (3) Vulnerability assessment indicators: Value; voltage exceeding the limit Distribution network operating costs .

[0113] An embodiment of the present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the power distribution network vulnerability assessment and coordinated scheduling method of the embodiment of the present invention.

[0114] The present invention also provides a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the power distribution network vulnerability assessment and coordinated scheduling method of the present invention.

[0115] Embodiments of this invention also provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the power distribution network vulnerability assessment and coordinated dispatch method of embodiments of this invention.

[0116] refer to Figure 2 The present invention will now describe a structural block diagram of an electronic device that can serve as an embodiment of the present invention, serving as an example of a hardware device applicable to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0117] like Figure 2 As shown, the electronic device includes a computing unit 101, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of the electronic device. The computing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0118] Multiple components in the electronic device are connected to I / O interface 105, including: input unit 106, output unit 107, storage unit 108, and communication unit 109. Input unit 106 can be any type of device capable of inputting information into the electronic device. Input unit 106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 107 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 108 may include, but is not limited to, disks and optical discs. Communication unit 109 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0119] The computing unit 101 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 101 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 102 and / or communication unit 109. In some embodiments, the computing unit 101 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0120] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0121] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0122] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0123] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0124] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0125] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0126] The above embodiments merely illustrate several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for vulnerability assessment and coordinated dispatching of distribution networks, characterized in that, include: Based on historical data and prediction error distribution of renewable energy output in the distribution network, Monte Carlo simulation is used to sample and generate multiple scenarios with occurrence probabilities; wherein, the scenario is the set of renewable energy output for each time period within the target time. Based on the various scenarios described, a two-stage stochastic programming model is constructed with the goal of minimizing the combined operating costs and conditional risk value of the distribution network. The two-stage stochastic programming model includes a day-ahead pre-decision stage, a real-time balancing stage, and a risk measurement term. The two-stage stochastic programming model is solved to obtain the vulnerability assessment results of the distribution network and the coordinated dispatch scheme within the target time.

2. The method for distribution network vulnerability assessment and coordinated dispatch according to claim 1, characterized in that, Based on historical data and prediction error distribution of renewable energy output in the distribution network, Monte Carlo simulation is used to sample and generate multiple scenarios with occurrence probabilities. The method also includes: reducing the number of scenarios using a fast forward selection algorithm and reallocating the occurrence probabilities of the retained scenarios.

3. The method for distribution network vulnerability assessment and coordinated dispatch according to claim 1, characterized in that, Based on the aforementioned scenarios, a two-stage stochastic programming model is constructed with the objective of minimizing the combined operating costs and conditional risk values ​​of the distribution network, including: The objective function is to minimize the combined operating cost and conditional risk value of the distribution network; wherein the operating cost of the distribution network includes the deterministic cost in the day-ahead pre-decision stage and the expected cost for the scenario in the real-time balancing stage. Constraints are set for the day-ahead pre-decision stage and the real-time balancing stage. These constraints include power balance constraints, equipment operation constraints, line power flow constraints, and voltage safety constraints.

4. The method for vulnerability assessment and coordinated dispatch of distribution networks according to claim 3, characterized in that, The objective function is as follows: ； in, Represent the objective function; This represents the certainty cost of the pre-decision stage. This represents the expected cost during the real-time balancing phase. Indicates the risk aversion coefficient; Indicates at confidence level Conditional Value at Risk (VaR).

5. The method for distribution network vulnerability assessment and coordinated dispatch according to claim 4, characterized in that, The formula for calculating the deterministic cost in the pre-decision stage is as follows: ； in, This indicates the power generation cost of a thermal power unit; Represents a node During the period of the thermal power unit The plan is to contribute; Indicates the cost of purchasing electricity; Represents a node During the period The planned power purchase capacity.

6. The method for vulnerability assessment and coordinated dispatch of distribution networks according to claim 5, characterized in that, The formula for calculating the expected cost of the real-time balancing phase is as follows: ； in, Representing a scene The probability of occurrence; This indicates the operating cost of energy storage; Represents a node Energy storage scenarios Time period The charging power; Represents a node Energy storage scenarios Time period The discharge power; This indicates the compensation cost for invoking interruptible loads; Represents a node In the scene Time period The amount of load reduction; This indicates the unit penalty price for crossing the voltage line; Represents a node In the scene Time period The voltage is limited.

7. The method for distribution network vulnerability assessment and coordinated dispatch according to claim 6, characterized in that, At confidence level The formula for calculating the conditional value at risk is as follows: ， ， ； in, Indicates a threshold variable; As an auxiliary variable, it represents the scene Below, the loss exceeds the threshold. Part of; Indicates in the scene The following losses; The calculation formula is as follows: 。 8. The method for vulnerability assessment and coordinated dispatch of distribution networks according to claim 6, characterized in that, The power balance constraints are as follows: ； in, Represents a node New energy in various scenarios Time period Actual output; Represents a node During the period The load forecast value; Represents nodes The set of directly connected nodes; Indicates the connection node and nodes The line susceptance; Represents a node In the scene Time period The voltage phase angle; Represents a node In the scene Time period The voltage phase angle; The equipment operating constraints are as follows: ； ； ， ； ； ； ； in, Represents a node Energy storage scenarios Time period The state of charge; Indicates the charging efficiency of energy storage; Indicates the discharge efficiency of energy storage; Indicates the minimum state of charge; Indicates the maximum state of charge; Indicates the upper limit of the charging power of energy storage; Indicates the upper limit of the discharge power of the energy storage; Represents a node The lower limit of the output of the thermal power unit; Represents a node The upper limit of the output of the thermal power unit; Indicates the slope rate limit; Indicates the length of the time period; Represents a node The maximum amount of interruptible load reduction; The power flow constraints of the line are as follows: ； in, Indicates the connection node and nodes The maximum active power of the line; The voltage safety constraints are as follows: ； in, Represents a node In the scene Time period The voltage amplitude; This represents the minimum value of the voltage amplitude; This indicates the maximum voltage amplitude.

9. An electronic device, comprising: A processor and a memory storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the distribution network vulnerability assessment and coordinated dispatch method according to any one of claims 1 to 8.

10. A non-transitory machine-readable medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the distribution network vulnerability assessment and coordinated dispatch method according to any one of claims 1 to 8.

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