Source load-based island operation capability assessment method and apparatus, and medium

Abstract

The invention discloses a source load-based island operation capability evaluation method and device and a medium. The method comprises the steps of generating an anticipated fault set according to operation data of a power distribution network; the method comprises the following steps: modeling a distributed power supply and a load of a disaster-affected power distribution network to obtain initial operation data of the distributed power supply and each load node; carrying out island division on the affected power distribution network according to the anticipated fault set and the initial operation data to obtain an island division result; and evaluating the operation capability of the island division result according to a preset index set to obtain an evaluation result. According to the source load-based island operation capability assessment method and apparatus, and the medium provided by the invention, the anticipated fault set and the initial operation data are comprehensively considered, the distributed power supply and the load are reasonably configured, the reliability of island division can be ensured, and the island operation capability can be effectively assessed according to the preset index set. The method can solve the problem that it is difficult to carry out effective island division and island operation capability evaluation on a disaster-affected power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems and automation thereof, and in particular to a source-load-based island operation capability assessment method, device and medium. Background Art

[0002] In recent years, facing the dual challenges of the increasing depletion of global fossil energy and the intensification of environmental pollution, countries have begun to devote themselves to the development and utilization of new energy. In this context, distributed power sources, as an important form of new energy application, have begun to be connected to the distribution network on a large scale, and distributed power generation technology has also become the focus of attention; the gradual maturity of distributed power generation technology and the continuous improvement of the penetration rate of distributed power sources in the power grid have made the island operation strategy a new method for the distribution network to quickly restore power supply after a fault; if the island division is unreasonable, the continuous power supply of important loads cannot be guaranteed, which will cause users to suffer greater losses due to secondary power outages. At present, the island division is mainly carried out in static and dynamic ways, and the operation capacity of the island is obtained by evaluating the basic balance of power generation and load in the island.

[0003] However, since the output of distributed power sources is affected by many factors such as weather and environment, and is intermittent and uncertain, it brings challenges to the island division; and most of the existing studies evaluate the overall reliability of the distribution network, while the power supply method and network structure of the islanded microgrid are different from those of the distribution network. Therefore, the evaluation index system of the distribution network is not suitable for the evaluation of the island operation capacity; in addition, some studies regard the output power and load power of distributed power sources in the island as fixed values ​​for reliability evaluation, which is not in line with the actual situation. Since the output power and load size of distributed power sources fluctuate over time, the operation capacity of the island is also time-varying. Summary of the invention

[0004] The present invention provides a source-load based island operation capability assessment method, device and medium to solve the problem that it is difficult to effectively divide the disaster-stricken distribution network into isolated islands and assess the island operation capability.

[0005] Obtaining the operation data of the distribution network;

[0006] Establishing the cumulative failure probability of the poles and line sections according to the operation data, and calculating the comprehensive failure probability of the line, and generating the expected failure set of the affected distribution network through sampling;

[0007] By establishing a corresponding power output model and an active power model of the load for the distributed power source of the affected distribution network, initial operation data of the distributed power source and each load node are obtained;

[0008] Performing island division on the affected distribution network according to the expected fault set and the initial operation data to obtain an island division result;

[0009] The operational capability of the island division result is evaluated according to a preset indicator set to obtain an evaluation result.

[0010] The present invention generates a set of anticipated faults based on operating data, which enables the distribution network to consider various situations that may be encountered in advance before the actual fault occurs, which helps to improve the system's coping ability and resilience and reduce confusion and losses when a fault occurs. By accurately modeling the distributed power sources and loads of the affected distribution network to obtain the initial operating data of the distributed power sources and each load node, a reliable data basis can be provided for subsequent island division and evaluation. By dividing the affected distribution network into islands based on the anticipated fault set and initial operating data, the scope and configuration of the islands can be flexibly adjusted according to different fault conditions and operating conditions. This method helps to maximize the power supply of important loads while reducing the power outage time and impact range. Finally, the operating capacity of the island division result is evaluated according to the preset indicator set, and a comprehensive and objective evaluation result can be obtained.

[0011] Compared with the prior art, the present invention can achieve efficient resource utilization and ensure the reliability of island division by comprehensively considering the expected fault set and initial operation data, and reasonably configuring distributed power sources and loads. It can also effectively evaluate the island operation capacity according to the preset indicator set, thereby solving the problem of difficulty in effectively performing island division and island operation capacity evaluation on the affected distribution network.

[0012] As a preferred solution, the cumulative failure probability of the poles and line sections is established based on the operation data, and the comprehensive failure probability of the line is calculated, and the expected failure set of the affected distribution network is generated by sampling, specifically:

[0013] According to the operation data, the wind speed of the affected distribution network is obtained by performing typhoon wind field modeling;

[0014] Based on the wind speed, the comprehensive failure probability of the line is calculated in combination with the failure probability of the poles and the line sections;

[0015] According to the comprehensive fault probability, a preset method is used to sample the system components of the affected distribution network to obtain a number of faulty and outage components;

[0016] The plurality of faulty and out-of-service components are recorded as the expected fault set of the affected distribution network.

[0017] This preferred solution can accurately simulate the actual wind speed impact of a typhoon on the affected area through typhoon wind field modeling, ensuring the accuracy of wind speed data. By sampling system components using a preset method, a set of several faulty and outage components can be quickly generated, namely the expected fault set. This method not only improves the efficiency of fault simulation, but also provides practical data support for subsequent island division.

[0018] As a preferred solution, by establishing a corresponding power output model and an active power model of the load for the distributed power source of the affected distribution network, the initial operation data of the distributed power source and each load node are obtained, specifically:

[0019] According to the light intensity at different times and the output power of the wind turbine generator, the output timing model of the photovoltaic power generation system and the output timing model of the wind turbine generator set are established respectively;

[0020] According to the output power of the constant power source and the charge state of the energy storage system at different times, a constant power source model and an energy storage system timing model are established respectively;

[0021] Establishing a load power model according to the load active power of the distribution network;

[0022] The initial operation data of the distributed power source and each load node are obtained according to the photovoltaic power generation system output timing model, the wind turbine generator set output timing model, the constant power supply model, the energy storage system timing model and the load power model.

[0023] This preferred solution can accurately reflect the output characteristics and state changes of different energy types, energy storage systems and loads by fine-tuning modeling. The timing model can capture and reflect the state of the system at different time points, so it has strong real-time performance and helps to achieve more accurate operation control and optimized scheduling. Finally, by comprehensively considering the impact of multiple distributed power sources and energy storage systems as well as loads, the required information can be obtained, which helps to comprehensively evaluate the overall performance and potential of the affected distribution network.

[0024] As a preferred solution, the affected distribution network is islanded according to the expected fault set and the initial operation data to obtain an islanding result, which is specifically:

[0025] Based on the expected fault set and the initial operation data, an objective function is established with the goal of maximizing the equivalent restoration amount of nodes in the affected distribution network;

[0026] Establishing an auxiliary indicator set according to the power value of the node;

[0027] Establishing a constraint set according to the power balance state, node voltage and branch current of the affected distribution network;

[0028] Based on the objective function and according to the auxiliary indicator set, the affected distribution network is islanded to obtain the initial islanding result;

[0029] The initial island division result is verified according to the constraint set to obtain the island division result.

[0030] This preferred solution maximizes the equivalent recovery amount of the node by establishing an objective function, so that the division work has a clear quantitative goal. By using the power value of the node to establish an auxiliary indicator set, the importance of each node in system recovery can be more comprehensively evaluated. These auxiliary indicators not only consider the direct contribution of the node, but also may include its impact and dependency on other nodes, thereby providing a more scientific basis for island division. A constraint set is established based on the power balance state, node voltage and branch current of the affected distribution network. These constraints take into account the physical characteristics and operating requirements of the power system, and can avoid voltage over-limit, current overload and other problems that may occur during the recovery process.

[0031] As a preferred solution, the initial island division result is verified according to the constraint set to obtain the island division result, which is specifically:

[0032] If there is an island intersection in the initial island division result, performing a fusion transformation on the distributed power sources in the initial island division result to obtain a first island division result;

[0033] If the first island partition result satisfies the constraint set, recording the first island partition result as the island partition result;

[0034] If the first island division result does not satisfy the constraint set, the load node with the lowest equivalent recovery amount in the first island division result is deleted, and the first island division result after deletion is recorded as the island division result.

[0035] This preferred solution can eliminate the intersection of isolated islands by integrating and transforming distributed power sources, ensuring that each isolated island is physically and electrically separated, thereby avoiding problems such as unreasonable power flow, unstable voltage, or malfunction of protection equipment. When deleting the load node with the lowest equivalent recovery amount, priority is given to the part with lower recovery amount, which helps to prioritize the power supply of important loads and loads with greater recovery potential under limited resources, thereby minimizing the impact and losses of disasters on the economy and society.

[0036] As a preferred solution, the preset indicator set is specifically:

[0037] According to the output availability and output interruption rate of the intermittent DG-ES system, the reliability index of the intermittent DG-ES is established;

[0038] Establish the reliability index of island operation status based on DG supply and demand margin and island equivalent power supply quantity;

[0039] The preset indicator set is composed of the intermittent DG-ES reliability indicator and the island operation state reliability indicator.

[0040] This preferred solution not only takes into account the reliability of the intermittent DG-ES system itself, such as output availability and output interruption rate, but also takes into account the reliability under the island operation state, such as DG supply and demand margin and island equivalent power supply quantity. This comprehensive evaluation method can more accurately reflect the overall reliability of island division in actual operation.

[0041] The present invention also provides a source-load based island operation capability evaluation device, comprising a data module, a fault module, an operation module, a division module and an evaluation module;

[0042] Wherein, the data module is used to obtain the operation data of the distribution network;

[0043] The fault module is used to establish the cumulative fault probability of the poles and line sections according to the operation data, calculate the comprehensive fault probability of the line, and generate the expected fault set of the affected distribution network through sampling;

[0044] The operation module obtains initial operation data of the distributed power source and each load node by establishing a corresponding power output model and an active power model of the load for the distributed power source of the affected distribution network;

[0045] The partitioning module is used to perform island partitioning on the affected distribution network according to the expected fault set and the initial operation data to obtain an island partitioning result;

[0046] The evaluation module is used to evaluate the operating capability of the island division result according to a preset indicator set to obtain an evaluation result.

[0047] As a preferred solution, the partitioning module includes a function unit, an indicator unit, a constraint unit and a partitioning unit;

[0048] The function unit is used to establish an objective function based on the expected fault set and the initial operation data with the goal of maximizing the equivalent recovery amount of the nodes in the affected distribution network;

[0049] The indicator unit is used to establish an auxiliary indicator set according to the power value of the node;

[0050] The constraint unit is used to establish a constraint set according to the power balance state, node voltage and branch current of the affected distribution network;

[0051] The partitioning unit is configured to perform island partitioning on the affected distribution network based on the objective function and the auxiliary indicator set to obtain the initial island partitioning result;

[0052] The verification unit is used to verify the initial island division result according to the constraint set to obtain the island division result.

[0053] As a preferred solution, the verification unit is specifically:

[0054] If there is an island intersection in the initial island division result, performing a fusion transformation on the distributed power sources in the initial island division result to obtain a first island division result;

[0055] If the first island partitioning result satisfies the constraint set, recording the first island partitioning result as the island partitioning result;

[0056] If the first island division result does not satisfy the constraint set, the load node with the lowest equivalent recovery amount in the first island division result is deleted, and the first island division result after deletion is recorded as the island division result.

[0057] As a preferred solution, the preset indicator set is specifically:

[0058] According to the output availability and output interruption rate of the intermittent DG-ES system, the reliability index of the intermittent DG-ES is established;

[0059] Establish the reliability index of island operation status based on DG supply and demand margin and island equivalent power supply quantity;

[0060] The preset indicator set is composed of the intermittent DG-ES reliability indicator and the island operation state reliability indicator.

[0061] The present application also provides a storage medium, on which a computer program is stored. The computer program is called and executed by a computer to implement the above-mentioned method for evaluating the island operation capability based on source and load. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flow chart of a method for evaluating the island operation capability based on source load provided in an embodiment of the present application;

[0063] Figure 2 is a system flow chart provided by an embodiment of the present application;

[0064] Figure 3 It is a structural schematic diagram of a source-load-based island operation capability assessment device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] In the description of this application, it should be understood that the term "first" is used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "several" means two or more.

[0067] In the description of this application, it should be noted that the DG-ES system refers to a system that combines distributed generation and an energy storage device (Energy Storage); DG is the abbreviation of Distributed Generation, which refers to distributed generation.

[0068] The method for evaluating the operation capability of an island based on source load provided in an embodiment of the present application is mainly used in situations where effective island division is required and the operation capability of an island is evaluated by considering the uncertainty of source load.

[0069] Embodiment 1:

[0070] See also Figure 1 The embodiment of the present application provides a method for evaluating the island operation capability based on source load, including S1 to S5, and the specific implementation steps are as follows:

[0071] S1. Obtain the operation data of the distribution network.

[0072] Step S1 of the embodiment of the present application is specifically as follows:

[0073] The operation data of the distribution network is obtained through on-site measurement and monitoring.

[0074] S2. Establish the cumulative failure probability of the poles and line sections according to the operation data, calculate the comprehensive failure probability of the line, and generate the expected failure set of the affected distribution network through sampling.

[0075] Step S2 of the embodiment of the present application includes S2.1 to S2.3; wherein S2.1 is a process of simulating and predicting the wind speed of the affected distribution network by using a typhoon wind field model, S2.2 is a process of calculating the comprehensive failure probability of the line according to the wind speed, and S2.3 is a process of sampling system components by using the Monte Carlo simulation method to obtain faulty and outage components, specifically:

[0076] S2.1. Model the typhoon wind field based on the operating data to obtain a horizontal moving wind field model and an annular circulation wind field model. The horizontal moving wind field model reflects the overall operation of the typhoon, and the annular circulation wind field model reflects the rotating flow field inside the typhoon.

[0077] Obtain the wind speed of the affected distribution network based on the horizontal moving wind field model and the circular circulation wind field model;

[0078] Among them, the expression for the wind speed at each point in the typhoon wind field is:

[0079]

[0080] Among them, for the parameter r i (t), V rmax (t) and have:

[0081]

[0082] Among them, V i (t) is the typhoon wind speed borne by the i-th component in the distribution network at time t; V0 is the moving speed of the typhoon center; R max is the maximum wind speed radius of the typhoon at time t; r i (t) is the distance between the typhoon center and the i-th element in the distribution network at time t; V rmax (t) is the maximum circulation wind speed of the typhoon at time t;

[0083] x i and i are the horizontal and vertical coordinates of the ith element in the distribution network; x0(t) and y0(t) are the horizontal and vertical coordinates of the typhoon center at time t; V max (t) is the wind speed near the center of the typhoon at time t; r7 is the radius of the level 7 wind circle; V 7min It is the minimum value of instantaneous wind speed in the 7-level wind circle, which is generally 13.9m / s; R max (t) The last equation above is obtained.

[0084] Embodiment S2.1 of the present application can more accurately simulate the actual wind speed impact of a typhoon on the affected area through typhoon wind field modeling, characterize the magnitude of the typhoon wind speed to which the system components are exposed, and ensure the accuracy of the wind speed data.

[0085] S2.2. Based on wind speed, the fragility curve is used to describe the resistance of the pole under typhoon disasters, and the fragility curve expression p of the pole under typhoon disasters is established. m,k (t);

[0086] According to the duration of the typhoon, the fragile curve expression p m,k (t) Perform cumulative calculation to obtain the cumulative failure probability expression P of the kth pole in the mth line during the typhoon-affected time period T m,k ;

[0087] Based on the wind speed, the line is considered to be composed of multiple series line segments. If a line segment fails, the entire line is considered to be in a fault state. The line segment failure probability expression p is constructed accordingly. m,i (t);

[0088] According to the duration of the typhoon, the line section failure probability expression p m,i (t) is cumulatively calculated to obtain the cumulative failure probability expression P of the ith line segment in the mth line during the typhoon-affected time period T. m,i ;

[0089] The cumulative failure probability expression of the pole P m,k And the line cumulative failure probability expression P m,i Construct a time-varying fault model for distribution network;

[0090] Since the poles and lines in the distribution network are regarded as a series system, when any line section or pole in a line fails, the entire line will fail and stop running; and each component exists in isolation and has no connection with other components. Therefore, the comprehensive failure probability P of the line is calculated according to the time-varying fault model of the distribution network. m ;

[0091] The fragility curve expression is:

[0092]

[0093] The cumulative failure probability of the pole is expressed as:

[0094]

[0095] The line section failure probability expression is:

[0096]

[0097] The expression of line cumulative failure probability is:

[0098]

[0099] The comprehensive failure probability is:

[0100]

[0101] Among them, p m,k (t) is the failure probability of the kth pole in the mth line at time t; V m,k (t) is the wind speed borne by the k-th pole of the m-th line at time t; v g is the design wind speed of the pole; v gmax is the maximum wind speed that the pole can withstand; a g is the empirical coefficient, which is obtained through experiments;

[0102] P m,k is the cumulative failure probability of the kth pole in the mth line; T is the duration of the typhoon impact; Δt is the length of each time period t;

[0103] p m,i (t) is the failure probability of the ith line segment of the mth line at time t; a l and b l are empirical coefficients, obtained through experiments; V m,i (t) is the wind speed of the ith line section of the mth line at time t; v l is the design wind speed of the line segment; l is the length of each line segment;

[0104] P m is the cumulative failure probability of the mth line within the T time period affected by the typhoon; M is the number of poles on the mth line; N is the number of line sections on the mth line.

[0105] It should be noted that the components that are easily damaged by typhoon disasters in the distribution network are mainly poles and lines, while the probability of other components being damaged is relatively small. Therefore, this embodiment 1 only considers the impact of typhoon disasters on poles and lines; considering that the typhoon wind speed that the components are borne will change with the passage of time and their own spatial positions, a time-varying fault model of the distribution network is established.

[0106] Embodiment S2.2 of the present application can accurately describe the resistance and failure probability of electric poles under different wind speed conditions by constructing a fragility curve expression for electric poles under typhoon disasters. Taking the duration of the typhoon into consideration, the fragility curve expression and the failure probability expression are cumulatively calculated to obtain the cumulative failure probability expression for electric poles and lines. This method not only considers the direct impact of wind speed on failure probability, but also considers the impact of time factors on the cumulative effect of failures, making the failure probability prediction results more comprehensive and accurate.

[0107] S2.3, based on the dynamic perspective of time series analysis and random sampling technology of Monte Carlo simulation, for each component of the affected distribution network, according to the comprehensive failure probability P m A random number is generated based on the corresponding failure probability in the equation, and the random number is used to determine whether the corresponding component is faulty, thereby obtaining the component failure result. For example, if the failure probability of a component is P, a random number in the interval [0,1) is generated. If the number is less than P, the component is considered to be faulty.

[0108] According to the results of system analysis, simulate load management strategies (such as overload protection, load separation strategy, etc.), record the total amount of load that needs to be removed, and screen out fault scenarios that have a significant impact on the system based on the amount of load removal or other key performance indicators (such as voltage stability, frequency offset, etc.), and record the screened fault scenarios and their corresponding load removal amount and other information;

[0109] The expected fault set of the affected distribution network is generated based on the component failure results and the recorded information.

[0110] Embodiment S2.3 of the present application adopts the Monte Carlo simulation method to sample system components, which can quickly generate a set of several faulty and outage components, namely, the expected fault set. This method not only improves the efficiency of fault simulation, but also provides practical data support for subsequent island division. This method can simulate complex systems with time dependence, and provide a comprehensive overview of system behavior through a large number of simulation experiments and statistical analysis, so as to effectively analyze problems in complex systems.

[0111] In addition, by generating a random number for each component and judging whether the component is faulty based on the random number, the randomness and probability consistency of the fault simulation are ensured. Since the generation of random numbers is based on the specific fault probability of each component, the process is not affected by human subjective judgment, ensuring the objectivity and accuracy of the fault simulation results. In addition, by generating random numbers for all components at one time and making judgments, a snapshot of the fault status of the entire distribution network can be efficiently generated, which greatly improves the efficiency of fault simulation compared to manually judging or simulating the fault status of each component one by one.

[0112] S3. Obtaining initial operation data of the distributed power source and each load node by establishing a corresponding power output model and an active power model of the load for the distributed power source of the affected distribution network.

[0113] Step S3 of the embodiment of the present application is specifically as follows:

[0114] According to the light intensity at different times and the output power of the wind turbine generator, the output timing model of the photovoltaic power generation system and the output timing model of the wind turbine generator set are established respectively;

[0115] According to the output power of the constant power source and the charge state of the energy storage system at different times, a constant power source model and an energy storage system timing model are established respectively;

[0116] According to the load active power of the distribution network, a load power model is established;

[0117] According to the photovoltaic power generation system output timing model, the wind turbine generator output timing model, the constant power supply model, the energy storage system timing model and the load power model, the initial operation data of the distributed power source and each load node are obtained.

[0118] The following is a detailed description of each model:

[0119] 1) Photovoltaic power generation system output timing model:

[0120] Since the main influencing factor of the active power output by the photovoltaic power generation system is the light intensity, and the light intensity obeys the Beta distribution within a certain period of time, the probability density function f(r) is used to obtain the light intensity in each time period during the island evaluation time, thereby obtaining the output power of the photovoltaic power generation system;

[0121] Establish output constraint conditions of the photovoltaic power generation system according to the maximum output of the photovoltaic power generation system;

[0122] According to the output power of the photovoltaic power generation system and the output constraint conditions of the photovoltaic power generation system, a photovoltaic power generation system output timing model is established;

[0123] Among them, the output power of the photovoltaic power generation system is:

[0124]

[0125] The probability density function is:

[0126]

[0127] For parameters α and β:

[0128]

[0129] The output constraints of the photovoltaic power generation system are:

[0130]

[0131] Among them, P PV (t) is the output power of the photovoltaic power generation system at time t; P stc is the output rated power of the photovoltaic cell under standard conditions; r(t) is the actual light intensity at time t; r stc is the standard light intensity; k P is the power temperature coefficient; T(t) is the temperature at time t; Tstc is the standard temperature;

[0132] f(r) is the probability density function of the light intensity r, which is used to obtain the light intensity in each time period during the island evaluation time; r max is the maximum value of light intensity; α and β are the two shape parameters of Beta distribution; μ r and σ r are the mean and variance of light intensity within the statistical time range respectively;

[0133] P PV (t) is the output value of the photovoltaic power generation system at time t; It is the maximum output of the photovoltaic power generation system, generally taken as the rated value.

[0134] 2) Wind turbine output timing model:

[0135] Since the output power of wind turbines is mainly affected by wind speed, an expression between output power and typhoon wind speed is established;

[0136] Establish output constraint conditions of wind generator sets according to the maximum output of wind generator sets;

[0137] The output timing model of wind turbine generator set is established according to the expression between output power and typhoon wind speed and the output constraint conditions of wind turbine generator set;

[0138] Among them, the expression between output power and typhoon wind speed is:

[0139]

[0140] The output constraints of wind turbines are:

[0141]

[0142] Among them, P W (t) is the output power of the wind turbine at time t; P WN is the rated power of the wind turbine; v N 、v in and v out are the rated, cut-in and cut-out wind speeds of the wind turbine respectively; V(t) is the typhoon wind speed at time t;

[0143] P W is the output value of the wind turbine generator set; It is the maximum output of the wind turbine generator set, generally taken as the rated value.

[0144] 3) Constant power supply model:

[0145] The output power of some distributed power sources in the distribution network is constant, such as micro gas turbines, diesel generators, etc., so the output power of the constant power source can be calculated from this;

[0146] Establish output constraint conditions of the constant power source according to the minimum and maximum output values ​​of the constant power source;

[0147] A constant power source model is established according to the output power of the constant power source and the output constraint condition of the constant power source;

[0148] Among them, the output power of the constant power supply is:

[0149]

[0150] The output constraints of the constant power source are:

[0151]

[0152] Among them, P C is the output power of the constant power supply; U and U e are the output voltage and terminal voltage of the unit respectively; m is the power control parameter; δ is U and U e By adjusting m and δ, the output power of this part of distributed power can be kept constant; X is the reactance between the machine end and the node to which it is connected to the island;

[0153] P C is the output value of the constant power source; P C and They are the minimum and maximum values ​​of the constant power supply output respectively.

[0154] 4) Energy storage system timing model:

[0155] Since the output of intermittent distributed power sources is volatile, in order to smooth out their output fluctuations, distribution networks usually have to be equipped with energy storage systems to play a role in complementary power supply; especially under the influence of typhoon disasters, energy storage devices can jointly support the load with intermittent distributed power sources, thereby ensuring the load's electricity demand.

[0156] According to the state of charge of the energy storage system at different times, establish the state of charge expression of the energy storage system; wherein, since the state of charge of the energy storage system is different during the charging process and the discharging process, the state of charge expression of the energy storage system will also have different expressions;

[0157] Establish the constraint conditions of the energy storage system's charge state according to the minimum and maximum charge limit values ​​of the energy storage system;

[0158] Establishing a time series model of the energy storage system according to the state of charge expression of the energy storage system and the constraints of the state of charge of the energy storage system;

[0159] Among them, the charge state expression of the energy storage system is:

[0160]

[0161] The constraints of the charge state of the energy storage system are:

[0162]

[0163] The expressions of the maximum charging power and maximum discharging power of the energy storage system are:

[0164]

[0165] Where SOC(t) is the state of charge of the energy storage system at time t; λ is the self-sustaining discharge rate of the energy storage system at time t; P c is the charging power of the energy storage system; η c is the charging efficiency of the energy storage system; P d is the discharge power of the energy storage system; η d is the discharge efficiency of the energy storage system; E c is the rated capacity of the energy storage system;

[0166] SOC(t) is the charge value of the energy storage system at time t; SOC and are the minimum charge limit value and the maximum charge limit value of the energy storage system respectively;

[0167] P c,max and P d,max are the maximum charging power and maximum discharging power of the energy storage system at time t respectively; and They are the maximum continuous charging power and maximum continuous discharging power of the energy storage system respectively.

[0168] 5) Load power model:

[0169] Since the load in the distribution network changes in real time and has a certain degree of volatility, this will affect the stability of the distribution network's island operation. Therefore, the volatility of the load needs to be considered when evaluating the distribution network's island operation capability.

[0170] The load variation characteristics are described by using a normal distribution load model, which is usually formed by a typical load curve or statistically obtained raw load data. Therefore, the load power can be expressed by the active power probability density function formula.

[0171] The load power is:

[0172]

[0173] in, is the variance of load active power; is the mean value of the load active power.

[0174] Embodiment S3 of the present application can accurately reflect the output characteristics and state changes of different energy types, energy storage systems and loads at different times by fine-tuning modeling. The timing model can capture and reflect the state of the system at different time points, so it has strong real-time performance and helps to achieve more accurate operation control and optimized scheduling. Finally, by comprehensively considering the impact of multiple distributed power sources, energy storage systems and loads, the required information can be obtained, which helps to comprehensively evaluate the overall performance and potential of the affected distribution network.

[0175] S4. According to the expected fault set and the initial operation data, the affected distribution network is divided into islands to obtain the island division result.

[0176] Step S4 of the embodiment of the present application includes S4.1 to S4.3; wherein S4.1 is a process of establishing an objective function and an auxiliary indicator set, S4.2 is a process of performing an initial partition to obtain an initial island partition result, and S4.3 is a process of performing a verification to obtain an island partition result, specifically:

[0177] S4.1. Based on the expected fault set and initial operation data, an objective function F is established with the goal of maximizing the equivalent restoration amount of nodes in the affected distribution network;

[0178] Establish the contribution index G according to the power value of the node i and consumption index X i , by the contribution index G i and consumption index X i Constitute an auxiliary indicator set;

[0179] Among them, the objective function is:

[0180]

[0181] The contribution indicators are:

[0182]

[0183] The consumption indicators are:

[0184]

[0185] Where T is the island evaluation running time; I is the number of nodes in the unrecovered area; x i is the state of node i, x i=1 means the node is connected to an island, x i =0 means that the node is not connected to the island; ω i is the load weight of node i. According to the importance, the load is divided into three levels: primary load, secondary load and tertiary load. The load level is determined according to the conventional determination method; P i,t is the power value of node i at time t; ω i P i,t is the equivalent recovery amount of node i;

[0186] G i is the contribution index of node i; N is the upper load point of the branch where node i is located and the set of its own load points; ω i is the load weight of node i. According to the importance, the load is divided into three levels: primary load, secondary load and tertiary load. The load level is determined according to the conventional determination method; P i is the power value of node i;

[0187] X i is the consumption index of node i; N is the upper load point of the branch where node i is located and the set of its own load points; P i is the power value of node i.

[0188] Embodiment S4.1 of the present application maximizes the equivalent recovery amount of the node by establishing an objective function, so that the division work has a clear quantitative goal. By using the power value of the node to establish an auxiliary indicator set, the importance of each node in system recovery can be more comprehensively evaluated. These auxiliary indicators not only consider the direct contribution of the node, but also may include its impact and dependency on other nodes, thereby providing a more scientific basis for island division;

[0189] Moreover, by defining the objective function and auxiliary indicators, the complex tree structure is avoided, the search time is reduced, and the affected distribution network can be quickly islanded to facilitate subsequent evaluation.

[0190] S4.2. Based on the expected fault set and initial operation data, and with the condition that the output capacity of the distributed generation is greater than the capacity of the load node, a depth-first search algorithm is used to determine the power supply boundary of the island, thereby excluding load nodes that cannot be connected to the island;

[0191] First, the parent-child relationship between nodes is established according to the output power flow of distributed power sources. For the load node of a branch, the power flows from the upper node of the branch to the lower node. Then, based on the island boundary, the initial island optimization matrix A is established according to the objective function F and the auxiliary indicator set, and the distributed power source with the largest capacity is used as the root node.

[0192] If the contribution index of node k is the only maximum value, then the load node is taken as the object, and the initial island optimization matrix A is initially modified according to the contribution and consumption of each load node in the affected distribution network; and if the maximum value of the contribution index is not unique, the consumption index of the load node corresponding to the maximum value of the contribution index is calculated, and the load node with the maximum consumption index is taken as the object, and the matrix A is modified in the same way;

[0193] Determine whether the difference between the capacity of the distributed power source and the capacity of the selected load node k is greater than 0. If it is greater than 0, merge the root node to the load node k into a new root node, and then modify the initial island optimization matrix A again according to the difference between the capacity of the distributed power source and the capacity of the load node. If it is less than 0, do not modify it again.

[0194] Repeat the initial modification and re-modification steps until the difference between the distributed generation capacity and the selected load node capacity is less than 0, thereby obtaining the final island optimization matrix;

[0195] Among the remaining distributed power sources, the distributed power source with the largest capacity is selected to perform initial island division on the affected distribution network, and the above process of constructing the initial island optimization matrix A and modifying it is repeated until all distributed power sources have completed the initial island division, and an initial island division result consisting of several final island optimization matrices is obtained;

[0196] Among them, the initial island optimization matrix is:

[0197]

[0198] The initial modification of the initial island optimization matrix A can be expressed as:

[0199] O i =1,1≤i≤k

[0200] The initial island optimization matrix A is modified again and can be expressed as:

[0201]

[0202] Among them, A is the initial island optimization matrix; F is the island division target, and its initial value is 0; G i is the contribution index of node i; m is the number of load nodes in the island candidate tree, and the node number 1→m indicates the direction of power flow; S is the difference between the capacity of the distributed generation and the capacity of the node connected to the island load; X i is the consumption index of node i; O0 is the root node; O i is the selection identifier of load node i. When the value is 1, it means it is selected, and when the value is 0, it means it is not selected. The initial value is 0. k is the total number of load nodes.

[0203] Embodiment S4.2 of the present application performs the steps of determining the island boundary, establishing the island candidate tree, selecting the load node according to the auxiliary index, modifying the matrix, and selecting other distributed power sources. By constructing the initial island optimization matrix and modifying it based on the load node contribution, consumption, and the difference between the distributed power source and the load capacity, a systematic decision-making process is formed. Selecting the distributed power source with the largest capacity for initial island division can quickly provide power support for a large number of loads, thereby improving recovery efficiency.

[0204] S4.3. Establish a constraint set according to the power balance state, node voltage and branch current of the affected distribution network; wherein the constraint set includes power balance constraint, node voltage constraint, branch current constraint and line transmission limit constraint;

[0205] If there is an island intersection in the initial island division result, the distributed power sources in the initial island division result are fused and transformed to obtain the first island division result S new ; If it does not exist, check whether the island satisfies the constraint set;

[0206] If the first island partition result S new If the constraint set is satisfied, the first island partition result S new Recorded as the island division result;

[0207] If the first island partition result S new If the constraint set is not satisfied, the first island partition result S is deleted. new The load node with the lowest equivalent recovery amount in the bottom layer is deleted until the constraint set is satisfied. The first island partition result S after the deletion process is new Recorded as the island division result;

[0208] Among them, the first island division result is:

[0209]

[0210] The power balance constraint is:

[0211] P C +P ES (t)+P DG (t)≥P L (t)

[0212] The node voltage constraints are:

[0213]

[0214] The branch current constraint is:

[0215]

[0216] The line transmission quota constraints are:

[0217]

[0218] Among them, S new is the difference between the capacity of the distributed generation after fusion transformation and the power of the node connected to the island load; Z DG交汇 is the collection of distributed power sources in the intersection island; S x is the capacity of the xth distributed generation in the intersection island; Z P交汇 is the set of load nodes in the intersection island; P y is the yth load power in the intersection island;

[0219] P C is the active output of the constant power source in the island; P ES (t) is the charge and discharge amount of energy storage in the island at time t; P DG (t) is the active output of DG in the island at time t; P L (t) is the total load recovery amount at time t;

[0220] V i (t) is the actual voltage amplitude of node i at time t; V i and are the minimum voltage amplitude and the maximum voltage amplitude of node i respectively; Ω N is the set of nodes in the island;

[0221] I k (t) is the current flowing through branch k at time t; is the maximum current that can flow through branch k;

[0222] P k (t) and Q k (t) is the active power and reactive power transmitted by branch k at time t; P k and are the minimum and maximum values ​​of the active power transmitted by branch k respectively; Q k and are the minimum and maximum values ​​of the reactive power transmitted by branch k respectively.

[0223] Embodiment S4.3 of the present application establishes a constraint set based on the power balance state, node voltage and branch current of the affected distribution network. These constraint conditions take into account the physical characteristics and operation requirements of the power system and can avoid problems such as voltage exceeding the limit and current overload that may occur during the recovery process.

[0224] In addition, by integrating and transforming distributed power sources, the intersection of isolated islands can be eliminated, ensuring that each isolated island is physically and electrically separated, thereby avoiding problems such as unreasonable power flow, unstable voltage, or malfunction of protection equipment. When deleting the load node with the lowest equivalent recovery amount, priority is given to the part with lower recovery amount, which helps to prioritize the power supply of important loads and loads with greater recovery potential under limited resources, thereby minimizing the impact and losses of disasters on the economy and society.

[0225] S5. Evaluate the operational capability of the island division result according to the preset indicator set to obtain an evaluation result.

[0226] Step S5 of the embodiment of the present application is specifically as follows:

[0227] According to the output availability and output interruption rate of the intermittent DG-ES system, the reliability index of the intermittent DG-ES is established;

[0228] The reliability index of the island operation state is established according to the DG supply and demand margin and the island equivalent power supply quantity. The reliability index of the island operation state includes the DG supply and demand margin, the adaptability of DG and load, the island equivalent power supply quantity and the degree of loss of important loads.

[0229] The preset indicator set is composed of the intermittent DG-ES reliability indicator and the island operation state reliability indicator;

[0230] The operational capability of the island division result is evaluated according to a preset indicator set to obtain an evaluation result;

[0231] The output availability is:

[0232]

[0233] The output interruption rate is:

[0234]

[0235] The DG supply and demand margin is:

[0236]

[0237] The adaptability of DG to load is:

[0238]

[0239] The equivalent power supply of the isolated island is:

[0240]

[0241] The degree of loss of important loads is:

[0242]

[0243] Among them, K AIHDG is the output availability of the intermittent DG-ES system; P DG (t) is the output power of the intermittent DG at time t; SOC(t) is the remaining power of the energy storage device at time t; P DG-ES,N is the rated output power of the intermittent DG-ES system; T is the island operation evaluation time; the value limit of the output availability index is set according to the actual situation;

[0244] K AIHP is the output interruption rate of the intermittent DG-ES system; N(t) is the number of output interruptions occurring in time period t; P DG (t) is the output power of the intermittent DG at time t; SOC(t) is the remaining power of the energy storage device at time t; SOC is the minimum power value of the energy storage device; T is the island operation evaluation time; the value limit of the output interruption rate index is set according to the actual situation;

[0245] K PSC % is the DG supply and demand margin indicator; is the maximum load power in the island; P G,min is the minimum output of distributed power in the island; when K PSC %<1, it indicates that the power supply in the island is sufficient and has strong endurance, which can adapt to the fluctuation and uncertainty of the load; when K PSC When % ≥ 1, it indicates that the load in the island is too heavy and the output of the distributed generation cannot meet the requirement of continuous and reliable power supply for the load. It is necessary to remove part of the load or re-divide the island.

[0246] K A (t)% is the DG and load adaptability index at time t; P L (t) is the total load in the island at time t; P G (t) is the output of the distributed generation in the island at time t; when the change law of DG and load is closer, it is more adaptable to the volatility of load, but in actual operation, a certain safety margin needs to be retained to meet the requirements of priority and continuous reliable power supply of important loads; therefore, the DG and load adaptability index should be set according to the actual situation, and the interval value is the best;

[0247] K EAE is the island equivalent power supply indicator; T is the island operation evaluation time; P G (t) is the total active output sequence of all distributed power sources in the island; P L(t) is the total active power sequence of all loads in the island; the positive and negative values ​​of the island equivalent power supply indicator reflect the self-sufficiency level of power generation and consumption in the island, which can be used to determine whether the island is allowed to operate in an isolated island for a long time, and the value limit of the indicator is set according to the actual situation;

[0248] K CLL (t) is the degree of loss of important loads at time t; m and n are the number of important loads lost in the island and the total number of important loads, respectively; ω j and ω i are the weights of the jth and ith important loads respectively; P Lj (t) and P Li (t) are the powers of the j-th and ith important loads respectively; the value limit of the important load loss degree indicator is set according to the actual situation.

[0249] It should be noted that, since the output power of photovoltaic power generation and wind power generation is affected by environmental factors and exhibits intermittent and fluctuating characteristics, energy storage devices are connected to the distribution network to smooth out their output fluctuations and play a role in complementary power supply; therefore, the intermittent distributed power source and energy storage device are taken as a whole to study their power supply reliability. In this embodiment 1, the output availability rate and output interruption rate are used to reflect the ability of the energy storage device to smooth out the intermittent DG output fluctuations and interruptions;

[0250] In addition, the reliability of the island operation status is evaluated by using the DG supply and demand margin, DG and load adaptability, island equivalent power supply quantity and important load loss degree. The DG supply and demand margin index reflects the power supply capacity of distributed power sources during island operation, the DG and load adaptability index reflects the source-load adaptability within the island, the island equivalent power supply quantity index reflects the level of self-sufficiency in island power generation and consumption, and the important load loss degree index reflects the continuous and reliable power supply of distributed power sources to important loads within the island.

[0251] Embodiment S5 of the present application not only considers the reliability of the intermittent DG-ES system itself, such as the output availability and output interruption rate, but also takes into account the reliability under the island operation state, such as the DG supply and demand margin and the island equivalent power supply quantity. This comprehensive evaluation method can more accurately reflect the overall reliability of the island division in actual operation.

[0252] To apply the embodiments of this application, please refer to Figure 2 , Figure 2 : is a system flow chart provided in an embodiment of the present application, which shows the general process of island division and evaluation in the first embodiment of the present application, specifically:

[0253] 1) Obtain typhoon-related data and determine the affected areas of the distribution network based on the data, and then predict data such as wind speed in the affected areas.

[0254] 2) The distribution network fault model is used to calculate the component failure probability, and the Monte Carlo simulation method is used to sample and generate a set of component failure states. The total number of load shedding is then counted and an expected fault set is generated.

[0255] 3) Obtain the local historical data of light intensity during typhoon weather, fit the Beta probability distribution parameters according to the data, and then simulate the light intensity, so as to calculate the output power of the photovoltaic power generation system in each time period.

[0256] 4) Calculate the output power of the wind turbine in each time period based on the wind speed data calculated by the typhoon model.

[0257] 5) Obtain the output power of the constant power distributed power supply and the initial charge state of the energy storage device.

[0258] 6) According to the historical data of each load node, the probability distribution characteristics are statistically analyzed, and then the load size of each node in each time period is simulated.

[0259] 7) According to the fault line information, the initial operation data of the distributed power source and each load node, the affected distribution network is divided into islands; in the following time period, the operation range of the island is adjusted according to the operation status of the distributed power source and each load node in each time period to ensure that the island meets the verification conditions.

[0260] 8) Calculate the evaluation index value for each time period.

[0261] 9) Summarize and calculate the values ​​of each evaluation index, and evaluate the operating capacity of the island based on the index results.

[0262] Overall, this embodiment has the following beneficial effects:

[0263] The present invention generates a set of anticipated faults based on operating data, which enables the distribution network to consider various situations that may be encountered in advance before the actual fault occurs, which helps to improve the system's response capability and resilience and reduce confusion and losses when a fault occurs. By accurately modeling the distributed power sources and loads of the affected distribution network to obtain the initial operating data of the distributed power sources and each load node, a reliable data basis can be provided for subsequent island division and evaluation. By dividing the affected distribution network into islands based on the anticipated fault set and initial operating data, the scope and configuration of the islands can be flexibly adjusted according to different fault conditions and operating conditions. This method helps to maximize the power supply of important loads while reducing the power outage time and impact range. Finally, the operating capacity of the island division result is evaluated according to the preset indicator set, and a comprehensive and objective evaluation result can be obtained;

[0264] Therefore, by using the source-load-based island operation capability assessment method proposed in this embodiment, the affected distribution network can be quickly divided into islands through distributed power sources under extreme disasters, and then the six indicators of the island operation capability assessment index system are used to evaluate the island division plan to ensure that the island division plan is reasonable and can provide the necessary technical support for the stable and safe operation of the island.

[0265] Embodiment 2:

[0266] See also Figure 3 , an embodiment of the present application provides an island operation capability evaluation device based on source and load, comprising a data module 10, a fault module 20, an operation module 30, a division module 40 and an evaluation module 50;

[0267] Wherein, the data module 10 is used to obtain the operation data of the distribution network;

[0268] The fault module 20 establishes the cumulative fault probability of the poles and line sections according to the operation data, calculates the comprehensive fault probability of the line, and generates the expected fault set of the affected distribution network through sampling;

[0269] The operation module 30 obtains initial operation data of the distributed power source and each load node by establishing a corresponding power output model and an active power model of the load for the distributed power source of the affected distribution network;

[0270] A partitioning module 40 is used to perform island partitioning on the affected distribution network according to the expected fault set and the initial operation data to obtain an island partitioning result;

[0271] The evaluation module 50 is used to evaluate the operating capability of the island division result according to a preset indicator set to obtain an evaluation result.

[0272] In one embodiment, the data module 10 is specifically:

[0273] The operation data of the distribution network is obtained through on-site measurement and monitoring.

[0274] In one embodiment, the fault module 20 is specifically:

[0275] Typhoon wind field modeling is carried out according to the operating data to obtain the horizontal moving wind field model and the circular circulation wind field model. The horizontal moving wind field model reflects the overall operation of the typhoon, and the circular circulation wind field model reflects the rotating flow field inside the typhoon. The wind speed of the affected distribution network is obtained based on the horizontal moving wind field model and the circular circulation wind field model.

[0276] Among them, the expression for the wind speed at each point in the typhoon wind field is:

[0277]

[0278] Among them, for the parameter r i (t), V rmax (t) and have:

[0279]

[0280] Among them, V i (t) is the typhoon wind speed borne by the i-th component in the distribution network at time t; V0 is the moving speed of the typhoon center; R max is the maximum wind speed radius of the typhoon at time t; r i (t) is the distance between the typhoon center and the i-th element in the distribution network at time t; V rmax (t) is the maximum circulation wind speed of the typhoon at time t;

[0281] x i and i are the horizontal and vertical coordinates of the ith element in the distribution network; x0(t) and y0(t) are the horizontal and vertical coordinates of the typhoon center at time t; V max (t) is the wind speed near the center of the typhoon at time t; r7 is the radius of the level 7 wind circle; V 7min It is the minimum value of instantaneous wind speed in the 7-level wind circle, which is generally 13.9m / s; R max (t) The last equation above is obtained.

[0282] The fault module 20 of the embodiment of the present application can more accurately simulate the actual wind speed impact of the typhoon on the disaster-stricken area through typhoon wind field modeling, characterize the size of the typhoon wind speed suffered by the system components, and ensure the accuracy of the wind speed data.

[0283] Using the typhoon wind speed, the fragility curve is used to describe the resistance of the pole under typhoon disasters, and the fragility curve expression of the pole under typhoon disasters is established. m,k (t); According to the duration of the typhoon, the fragile curve expression p m,k (t) Perform cumulative calculation to obtain the cumulative failure probability expression P of the kth pole in the mth line during the typhoon-affected time period T m,k ;

[0284] Based on the typhoon wind speed, the line is considered to be composed of multiple series line segments. If a line segment fails, the entire line is considered to be in a fault state. The line segment failure probability expression p is constructed. m,i (t); According to the duration of the typhoon, the line section failure probability expression p m,i(t) is cumulatively calculated to obtain the cumulative failure probability expression P of the ith line segment in the mth line during the typhoon-affected time period T. m,i ;

[0285] The cumulative failure probability expression of the pole is P m,k And the line cumulative failure probability expression P m,i The time-varying fault model of the distribution network is constructed. Since the poles and lines in the distribution network are regarded as a series system, when any line segment or pole in a line fails, the entire line will fail and exit operation. Moreover, each component exists in isolation and has no association with other components. Therefore, the comprehensive failure probability P of the line is calculated according to the time-varying fault model of the distribution network. m ;

[0286] The fragility curve expression is:

[0287]

[0288] The cumulative failure probability of the pole is expressed as:

[0289]

[0290] The line section failure probability expression is:

[0291]

[0292] The expression of line cumulative failure probability is:

[0293]

[0294] The comprehensive failure probability is:

[0295]

[0296] Among them, p m,k (t) is the failure probability of the kth pole in the mth line at time t; V m,k (t) is the wind speed borne by the k-th pole of the m-th line at time t; v g is the design wind speed of the pole; v gmax is the maximum wind speed that the pole can withstand; a g is the empirical coefficient, which is obtained through experiments;

[0297] P m,k is the cumulative failure probability of the kth pole in the mth line; T is the duration of the typhoon impact; Δt is the length of each time period t;

[0298] p m,i (t) is the failure probability of the ith line segment of the mth line at time t; al and b l are empirical coefficients, obtained through experiments; V m,i (t) is the wind speed of the ith line section of the mth line at time t; v l is the design wind speed of the line segment; l is the length of each line segment;

[0299] P m is the cumulative failure probability of the mth line within the T time period affected by the typhoon; M is the number of poles on the mth line; N is the number of line sections on the mth line.

[0300] It should be noted that the components that are easily damaged by typhoon disasters in the distribution network are mainly poles and lines, while the probability of other components being damaged is relatively small. Therefore, this embodiment 2 only considers the impact of typhoon disasters on poles and lines; considering that the typhoon wind speed that the components are borne will change with the passage of time and their own spatial positions, a time-varying fault model of the distribution network is established.

[0301] The fault module 20 of the embodiment of the present application can accurately describe the resistance and failure probability of the poles under different wind speed conditions by constructing a fragility curve expression of the poles under typhoon disasters. Taking the duration of the typhoon into consideration, the fragility curve expression and the failure probability expression are cumulatively calculated to obtain the cumulative failure probability expression of the poles and lines. This method not only considers the direct impact of wind speed on the failure probability, but also considers the impact of time factors on the cumulative effect of failures, making the failure probability prediction results more comprehensive and accurate.

[0302] Based on the dynamic perspective of time series analysis and the random sampling technology of Monte Carlo simulation, for each component of the affected distribution network, according to the comprehensive failure probability P m A random number is generated based on the corresponding failure probability in the equation, and the random number is used to determine whether the corresponding component is faulty, thereby obtaining the component failure result. For example, if the failure probability of a component is P, a random number in the interval [0,1) is generated. If the number is less than P, the component is considered to be faulty.

[0303] According to the results of system analysis, load management strategies (such as overload protection, decoupling strategy, etc.) are simulated, the total amount of load that needs to be removed is recorded, and according to the number of load removals or other key performance indicators (such as voltage stability, frequency offset, etc.), fault scenarios with significant impact on the system are screened out, and the screened out fault scenarios and their corresponding load removal amounts and other information are recorded; based on the component failure results and the recorded information, the expected fault set of the affected distribution network is generated.

[0304] The fault module 20 of the embodiment of the present application adopts the Monte Carlo simulation method to sample system components, and can quickly generate a set of several faulty and outage components, namely, the expected fault set. This method not only improves the efficiency of fault simulation, but also provides practical data support for subsequent island division; this method can simulate complex systems with time dependence, and provide a comprehensive overview of system behavior through a large number of simulation experiments and statistical analysis, so as to effectively analyze problems in complex systems;

[0305] In addition, by generating a random number for each component and judging whether the component is faulty based on the random number, the randomness and probability consistency of the fault simulation are ensured. Since the generation of random numbers is based on the specific fault probability of each component, the process is not affected by human subjective judgment, ensuring the objectivity and accuracy of the fault simulation results. In addition, by generating random numbers for all components at one time and making judgments, a snapshot of the fault status of the entire distribution network can be efficiently generated, which greatly improves the efficiency of fault simulation compared to manually judging or simulating the fault status of each component one by one.

[0306] In one embodiment, each model of the operation module 30 is specifically described as follows:

[0307] 1) Photovoltaic power generation system output timing model:

[0308] Since the main influencing factor of the active power output by the photovoltaic power generation system is the light intensity, and the light intensity obeys the Beta distribution within a certain period of time, the probability density function f(r) is used to obtain the light intensity in each time period during the island evaluation time, thereby obtaining the output power of the photovoltaic power generation system;

[0309] Establish output constraint conditions of the photovoltaic power generation system according to the maximum output of the photovoltaic power generation system;

[0310] According to the output power of the photovoltaic power generation system and the output constraint conditions of the photovoltaic power generation system, a photovoltaic power generation system output timing model is established;

[0311] Among them, the output power of the photovoltaic power generation system is:

[0312]

[0313] The probability density function is:

[0314]

[0315] For parameters α and β:

[0316]

[0317] The output constraints of the photovoltaic power generation system are:

[0318]

[0319] Among them, P PV (t) is the output power of the photovoltaic power generation system at time t; P stc is the output rated power of the photovoltaic cell under standard conditions; r(t) is the actual light intensity at time t; r stc is the standard light intensity; k P is the power temperature coefficient; T(t) is the temperature at time t; T stc is the standard temperature;

[0320] f(r) is the probability density function of the light intensity r, which is used to obtain the light intensity in each time period during the island evaluation time; r max is the maximum value of light intensity; α and β are the two shape parameters of Beta distribution; μ r and σ r are the mean and variance of light intensity within the statistical time range respectively;

[0321] P PV (t) is the output value of the photovoltaic power generation system at time t; It is the maximum output of the photovoltaic power generation system, generally taken as the rated value.

[0322] 2) Wind turbine output timing model:

[0323] Since the output power of wind turbines is mainly affected by wind speed, an expression between output power and typhoon wind speed is established;

[0324] Establish output constraint conditions of wind generator sets according to the maximum output of wind generator sets;

[0325] The output timing model of wind turbine generator set is established according to the expression between output power and typhoon wind speed and the output constraint conditions of wind turbine generator set;

[0326] Among them, the expression between output power and typhoon wind speed is:

[0327]

[0328] The output constraints of wind turbines are:

[0329]

[0330] Among them, P W (t) is the output power of the wind turbine at time t; P WN is the rated power of the wind turbine; v N 、v in and v outare the rated, cut-in and cut-out wind speeds of the wind turbine respectively; V(t) is the typhoon wind speed at time t;

[0331] P W is the output value of the wind turbine generator set; It is the maximum output of the wind turbine generator set, generally taken as the rated value.

[0332] 3) Constant power supply model:

[0333] The output power of some distributed power sources in the distribution network is constant, such as micro gas turbines, diesel generators, etc., so the output power of the constant power source can be calculated from this;

[0334] Establish output constraint conditions of the constant power source according to the minimum and maximum output values ​​of the constant power source;

[0335] A constant power source model is established according to the output power of the constant power source and the output constraint condition of the constant power source;

[0336] Among them, the output power of the constant power supply is:

[0337]

[0338] The output constraints of the constant power source are:

[0339]

[0340] Among them, P C is the output power of the constant power supply; U and U e are the output voltage and terminal voltage of the unit respectively; m is the power control parameter; δ is U and U e By adjusting m and δ, the output power of this part of distributed power can be kept constant; X is the reactance between the machine end and the node to which it is connected to the island;

[0341] P C is the output value of the constant power source; P C and They are the minimum and maximum values ​​of the constant power supply output respectively.

[0342] 4) Energy storage system timing model:

[0343] Since the output of intermittent distributed power sources is volatile, in order to smooth out their output fluctuations, distribution networks usually have to be equipped with energy storage systems to play a role in complementary power supply; especially under the influence of typhoon disasters, energy storage devices can jointly support the load with intermittent distributed power sources, thereby ensuring the load's electricity demand.

[0344] According to the state of charge of the energy storage system at different times, establish the state of charge expression of the energy storage system; wherein, since the state of charge of the energy storage system is different during the charging process and the discharging process, the state of charge expression of the energy storage system will also have different expressions;

[0345] Establish the constraint conditions of the energy storage system's charge state according to the minimum and maximum charge limit values ​​of the energy storage system;

[0346] Establishing a time series model of the energy storage system according to the state of charge expression of the energy storage system and the constraints of the state of charge of the energy storage system;

[0347] Among them, the charge state expression of the energy storage system is:

[0348]

[0349] The constraints of the charge state of the energy storage system are:

[0350]

[0351] The expressions of the maximum charging power and maximum discharging power of the energy storage system are:

[0352]

[0353] Where SOC(t) is the state of charge of the energy storage system at time t; λ is the self-sustaining discharge rate of the energy storage system at time t; P c is the charging power of the energy storage system; η c is the charging efficiency of the energy storage system; P d is the discharge power of the energy storage system; η d is the discharge efficiency of the energy storage system; E c is the rated capacity of the energy storage system;

[0354] SOC(t) is the charge value of the energy storage system at time t; SOC and are the minimum charge limit value and the maximum charge limit value of the energy storage system respectively;

[0355] P c,max and P d,max are the maximum charging power and maximum discharging power of the energy storage system at time t respectively; and They are the maximum continuous charging power and maximum continuous discharging power of the energy storage system respectively.

[0356] 5) Load power model:

[0357] Since the load in the distribution network changes in real time and has a certain degree of volatility, this will affect the stability of the distribution network's island operation. Therefore, the volatility of the load needs to be considered when evaluating the distribution network's island operation capability.

[0358] The load variation characteristics are described by using a normal distribution load model, which is usually formed by a typical load curve or statistically obtained raw load data. Therefore, the load power can be expressed by the active power probability density function formula.

[0359] The load power is:

[0360]

[0361] in, is the variance of load active power; is the mean value of the load active power.

[0362] The operation module 30 of the embodiment of the present application can accurately reflect the output characteristics and state changes of different energy types, energy storage systems and loads at different times by fine-tuning modeling. The timing model can capture and reflect the state of the system at different time points, so it has strong real-time performance, which helps to achieve more accurate operation control and optimized scheduling. Finally, by comprehensively considering the impact of multiple distributed power sources and energy storage systems and loads, the required information can be obtained, which helps to comprehensively evaluate the overall performance and potential of the affected distribution network.

[0363] In one embodiment, the partitioning module 40 includes a function unit, an indicator unit, a constraint unit, a partitioning unit, and a verification unit; wherein the function unit and the indicator unit are processes of establishing an objective function and an auxiliary indicator set, the partitioning unit is a process of performing an initial partition to obtain an initial island partitioning result, and the constraint unit and the verification unit are processes of performing a verification to obtain an island partitioning result, specifically:

[0364] The function unit is used to establish the objective function F based on the expected fault set and the initial operation data, with the goal of maximizing the equivalent restoration amount of the nodes in the affected distribution network;

[0365] Index unit, used to establish contribution index G according to the power value of the node i and consumption index X i , by the contribution index G i and consumption index X i Constitute an auxiliary indicator set;

[0366] Among them, the objective function is:

[0367]

[0368] The contribution indicators are:

[0369]

[0370] The consumption indicators are:

[0371]

[0372] Where T is the island evaluation running time; I is the number of nodes in the unrecovered area; x i is the state of node i, x i =1 means the node is connected to an island, x i =0 means that the node is not connected to the island; ω i is the load weight of node i. According to the importance, the load is divided into three levels: primary load, secondary load and tertiary load. The load level is determined according to the conventional determination method; P i,t is the power value of node i at time t; ω i P i,t is the equivalent recovery amount of node i;

[0373] G i is the contribution index of node i; N is the upper load point of the branch where node i is located and the set of its own load points; ω i is the load weight of node i. According to the importance, the load is divided into three levels: primary load, secondary load and tertiary load. The load level is determined according to the conventional determination method; P i is the power value of node i;

[0374] X i is the consumption index of node i; N is the upper load point of the branch where node i is located and the set of its own load points; P i is the power value of node i.

[0375] The function unit and the index unit of the embodiment of the present application maximize the equivalent recovery amount of the node by establishing an objective function, so that the division work has a clear quantitative goal. By using the power value of the node to establish an auxiliary index set, the importance of each node in system recovery can be more comprehensively evaluated. These auxiliary indicators not only consider the direct contribution of the node, but also may include its impact and dependency on other nodes, thereby providing a more scientific basis for island division;

[0376] Moreover, by defining the objective function and auxiliary indicators, the complex tree structure is avoided, the search time is reduced, and the affected distribution network can be quickly islanded to facilitate subsequent evaluation.

[0377] A partitioning unit is used to perform island partitioning on the affected distribution network based on the objective function and the auxiliary indicator set to obtain the initial island partitioning result, which is specifically:

[0378] Based on the expected fault set and initial operation data, and with the condition that the output capacity of the distributed generation is greater than the capacity of the load node, a depth-first search algorithm is used to determine the power supply boundary of the island, thereby excluding the load nodes that cannot be connected to the island;

[0379] The parent-child relationship between nodes is established according to the output power flow of distributed power sources. For the load node of a branch, the power flows from the upper node of the branch to the lower node. Then, based on the island boundary, the initial island optimization matrix A is established according to the objective function F and the auxiliary indicator set, and the distributed power source with the largest capacity is used as the root node.

[0380] If the contribution index of node k is the only maximum value, then the load node is taken as the object, and the initial island optimization matrix A is initially modified according to the contribution and consumption of each load node in the affected distribution network; and if the maximum value of the contribution index is not unique, the consumption index of the load node corresponding to the maximum value of the contribution index is calculated, and the load node with the maximum consumption index is taken as the object, and the matrix A is modified in the same way;

[0381] Determine whether the difference between the capacity of the distributed power source and the capacity of the selected load node k is greater than 0. If it is greater than 0, merge the root node to the load node k into a new root node, and then modify the initial island optimization matrix A again according to the difference between the capacity of the distributed power source and the capacity of the load node. If it is less than 0, do not modify it again.

[0382] Repeat the initial modification and re-modification steps until the difference between the distributed generation capacity and the selected load node capacity is less than 0, thereby obtaining the final island optimization matrix;

[0383] Among the remaining distributed power sources, the distributed power source with the largest capacity is selected to perform initial island division on the affected distribution network, and the above process of constructing the initial island optimization matrix A and modifying it is repeated until all distributed power sources have completed the initial island division, and an initial island division result consisting of several final island optimization matrices is obtained;

[0384] Among them, the initial island optimization matrix is:

[0385]

[0386] The initial modification of the initial island optimization matrix A can be expressed as:

[0387] O i =1,1≤i≤k

[0388] The initial island optimization matrix A is modified again and can be expressed as:

[0389]

[0390] Among them, A is the initial island optimization matrix; F is the island division target, and its initial value is 0; G i is the contribution index of node i; m is the number of load nodes in the island candidate tree, and the node number 1→m indicates the direction of power flow; S is the difference between the capacity of the distributed generation and the capacity of the node connected to the island load; X i is the consumption index of node i; O0 is the root node; O i is the selection identifier of load node i. When the value is 1, it means it is selected, and when the value is 0, it means it is not selected. The initial value is 0. k is the total number of load nodes.

[0391] The division unit of the embodiment of the present application performs the steps of determining the island boundary, establishing the island candidate tree, selecting the load node according to the auxiliary index, modifying the matrix, and selecting other distributed power sources. By constructing the initial island optimization matrix and modifying it based on the load node contribution, consumption, and the difference between the distributed power source and the load capacity, a systematic decision-making process is formed. Selecting the largest capacity distributed power source for initial island division can quickly provide power support for a large number of loads, thereby improving recovery efficiency.

[0392] A constraint unit is used to establish a constraint set according to the power balance state, node voltage and branch current of the affected distribution network; wherein the constraint set includes power balance constraint, node voltage constraint, branch current constraint and line transmission limit constraint;

[0393] The verification unit is used to perform fusion transformation on the distributed power sources in the initial island division result if there is an island intersection in the initial island division result to obtain the first island division result S new ; If it does not exist, check whether the island satisfies the constraint set;

[0394] The verification unit is also used to check if the first island division result S new If the constraint set is satisfied, the first island partition result S new Recorded as the island partition result; if the first island partition result S new If the constraint set is not satisfied, the first island partition result S is deleted. new The load node with the lowest equivalent recovery amount in the bottom layer is deleted until the constraint set is satisfied. The first island partition result S after the deletion process is new Recorded as the island division result;

[0395] Among them, the first island division result is:

[0396]

[0397] The power balance constraint is:

[0398] PC +P ES (t)+P DG (t)≥P L (t)

[0399] The node voltage constraints are:

[0400]

[0401] The branch current constraint is:

[0402]

[0403] The line transmission quota constraints are:

[0404]

[0405] Among them, S new is the difference between the capacity of the distributed generation after fusion transformation and the power of the node connected to the island load; Z DG交汇 is the collection of distributed power sources in the intersection island; S x is the capacity of the xth distributed generation in the intersection island; Z P交汇 is the set of load nodes in the intersection island; P y is the yth load power in the intersection island;

[0406] P C is the active output of the constant power source in the island; P ES (t) is the charge and discharge amount of energy storage in the island at time t; P DG (t) is the active output of DG in the island at time t; P L (t) is the total load recovery amount at time t;

[0407] V i (t) is the actual voltage amplitude of node i at time t; V i and are the minimum voltage amplitude and the maximum voltage amplitude of node i respectively; Ω N is the set of nodes in the island;

[0408] I k (t) is the current flowing through branch k at time t; is the maximum current that can flow through branch k;

[0409] P k (t) and Q k (t) is the active power and reactive power transmitted by branch k at time t; P k and are the minimum and maximum values ​​of the active power transmitted by branch k respectively; Qk and are the minimum and maximum values ​​of the reactive power transmitted by branch k respectively.

[0410] The constraint unit and the verification unit of the embodiment of the present application establish a constraint set based on the power balance state, node voltage and branch current of the affected distribution network. These constraint conditions take into account the physical characteristics and operation requirements of the power system, and can avoid voltage over-limit, current overload and other problems that may occur during the recovery process;

[0411] In addition, by integrating and transforming distributed power sources, the intersection of isolated islands can be eliminated, ensuring that each isolated island is physically and electrically separated, thereby avoiding problems such as unreasonable power flow, unstable voltage, or malfunction of protection equipment. When deleting the load node with the lowest equivalent recovery amount, priority is given to the part with lower recovery amount, which helps to prioritize the power supply of important loads and loads with greater recovery potential under limited resources, thereby minimizing the impact and losses of disasters on the economy and society.

[0412] In one embodiment, the evaluation module 50 is specifically:

[0413] According to the output availability and output interruption rate of the intermittent DG-ES system, the reliability index of the intermittent DG-ES is established;

[0414] The reliability index of the island operation state is established according to the DG supply and demand margin and the island equivalent power supply quantity. The reliability index of the island operation state includes the DG supply and demand margin, the adaptability of DG and load, the island equivalent power supply quantity and the degree of loss of important loads.

[0415] The preset indicator set is composed of the intermittent DG-ES reliability indicator and the island operation state reliability indicator;

[0416] The operational capability of the island division result is evaluated according to a preset indicator set to obtain an evaluation result;

[0417] The output availability is:

[0418]

[0419] The output interruption rate is:

[0420]

[0421] The DG supply and demand margin is:

[0422]

[0423] The adaptability of DG to load is:

[0424]

[0425] The equivalent power supply of the isolated island is:

[0426]

[0427] The degree of loss of important loads is:

[0428]

[0429] Among them, K AIHDG is the output availability of the intermittent DG-ES system; P DG (t) is the output power of the intermittent DG at time t; SOC(t) is the remaining power of the energy storage device at time t; P DG-ES,N is the rated output power of the intermittent DG-ES system; T is the island operation evaluation time; the value limit of the output availability index is set according to the actual situation;

[0430] K AIHP is the output interruption rate of the intermittent DG-ES system; N(t) is the number of output interruptions occurring in time period t; P DG (t) is the output power of the intermittent DG at time t; SOC(t) is the remaining power of the energy storage device at time t; SOC is the minimum power value of the energy storage device; T is the island operation evaluation time; the value limit of the output interruption rate index is set according to the actual situation;

[0431] K PSC % is the DG supply and demand margin index; P L,max is the maximum load power in the island; P G,min is the minimum output of distributed power in the island; when K PSC %<1, it indicates that the power supply in the island is sufficient and has strong endurance, which can adapt to the fluctuation and uncertainty of the load; when K PSC When % ≥ 1, it indicates that the load in the island is too heavy and the output of the distributed generation cannot meet the requirement of continuous and reliable power supply for the load. It is necessary to remove part of the load or re-divide the island.

[0432] K A (t)% is the DG and load adaptability index at time t; P L (t) is the total load in the island at time t; P G (t) is the output of the distributed generation in the island at time t; when the change law of DG and load is closer, it is more adaptable to the volatility of load, but in actual operation, a certain safety margin needs to be retained to meet the requirements of priority and continuous reliable power supply of important loads; therefore, the DG and load adaptability index should be set according to the actual situation, and the interval value is the best;

[0433] K EAE is the island equivalent power supply indicator; T is the island operation evaluation time; P G (t) is the total active output sequence of all distributed power sources in the island; P L (t) is the total active power sequence of all loads in the island; the positive and negative values ​​of the island equivalent power supply indicator reflect the self-sufficiency level of power generation and consumption in the island, which can be used to determine whether the island is allowed to operate in an isolated island for a long time, and the value limit of the indicator is set according to the actual situation;

[0434] K CLL (t) is the degree of loss of important loads at time t; m and n are the number of important loads lost in the island and the total number of important loads, respectively; ω j and ω i are the weights of the jth and ith important loads respectively; P Lj (t) and P Li (t) are the powers of the j-th and ith important loads respectively; the value limit of the important load loss degree indicator is set according to the actual situation.

[0435] It should be noted that, since the output power of photovoltaic power generation and wind power generation is affected by environmental factors and exhibits intermittent and fluctuating characteristics, energy storage devices are connected to the distribution network to smooth out their output fluctuations and play a role in complementary power supply; therefore, the intermittent distributed power source and energy storage device are taken as a whole to study their power supply reliability. In this embodiment 2, the output availability rate and output interruption rate are used to reflect the ability of the energy storage device to smooth out the intermittent DG output fluctuations and interruptions;

[0436] In addition, the reliability of the island operation status is evaluated by using the DG supply and demand margin, DG and load adaptability, island equivalent power supply quantity and important load loss degree. The DG supply and demand margin index reflects the power supply capacity of distributed power sources during island operation, the DG and load adaptability index reflects the source-load adaptability within the island, the island equivalent power supply quantity index reflects the level of self-sufficiency in island power generation and consumption, and the important load loss degree index reflects the continuous and reliable power supply of distributed power sources to important loads within the island.

[0437] The evaluation module 50 of the embodiment of the present application not only takes into account the reliability of the intermittent DG-ES system itself, such as the output availability and the output interruption rate, but also takes into account the reliability under the island operation state, such as the DG supply and demand margin and the island equivalent power supply power. This comprehensive evaluation method can more accurately reflect the overall reliability of the island division in actual operation.

[0438] To apply the embodiments of this application, please refer to Figure 2 , Figure 2: is a system flow chart provided in an embodiment of the present application, which shows the general process of island division and evaluation in the second embodiment of the present application, specifically:

[0439] 1) Obtain typhoon-related data and determine the affected areas of the distribution network based on the data, and then predict data such as wind speed in the affected areas.

[0440] 2) The distribution network fault model is used to calculate the component failure probability, and the Monte Carlo simulation method is used to sample and generate a set of component failure states. The total number of load shedding is then counted and an expected fault set is generated.

[0441] 3) Obtain the local historical data of light intensity during typhoon weather, fit the Beta probability distribution parameters according to the data, and then simulate the light intensity, so as to calculate the output power of the photovoltaic power generation system in each time period.

[0442] 4) Calculate the output power of the wind turbine in each time period based on the wind speed data calculated by the typhoon model.

[0443] 5) Obtain the output power of the constant power distributed power supply and the initial charge state of the energy storage device.

[0444] 6) According to the historical data of each load node, the probability distribution characteristics are statistically analyzed, and then the load size of each node in each time period is simulated.

[0445] 7) According to the fault line information, the initial operation data of the distributed power source and each load node, the affected distribution network is divided into islands; in the following time period, the operation range of the island is adjusted according to the operation status of the distributed power source and each load node in each time period to ensure that the island meets the verification conditions.

[0446] 8) Calculate the evaluation index value for each time period.

[0447] 9) Summarize and calculate the values ​​of each evaluation index, and evaluate the operating capacity of the island based on the index results.

[0448] Overall, this embodiment has the following beneficial effects:

[0449] The present invention generates a set of anticipated faults based on operating data, which enables the distribution network to consider various situations that may be encountered in advance before the actual fault occurs, which helps to improve the system's response capability and resilience and reduce confusion and losses when a fault occurs. By accurately modeling the distributed power sources and loads of the affected distribution network to obtain the initial operating data of the distributed power sources and each load node, a reliable data basis can be provided for subsequent island division and evaluation. By dividing the affected distribution network into islands based on the anticipated fault set and initial operating data, the scope and configuration of the islands can be flexibly adjusted according to different fault conditions and operating conditions. This method helps to maximize the power supply of important loads while reducing the power outage time and impact range. Finally, the operating capacity of the island division result is evaluated according to the preset indicator set, and a comprehensive and objective evaluation result can be obtained;

[0450] Therefore, by using the source-load-based island operation capability assessment method proposed in this embodiment, the affected distribution network can be quickly divided into islands through distributed power sources under extreme disasters, and then the six indicators of the island operation capability assessment index system are used to evaluate the island division plan to ensure that the island division plan is reasonable and can provide the necessary technical support for the stable and safe operation of the island.

[0451] Embodiment three:

[0452] An embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the source-load-based island operation capability evaluation method;

[0453] Wherein, the source-load-based island operation capability assessment method, if implemented in the form of a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0454] The above are preferred embodiments of the present invention. It should be noted that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating island operation capability based on source load, characterized in that: include: Obtaining the operation data of the distribution network; generating a set of expected faults of the affected distribution network according to the operation data; By modeling the distributed power sources and loads of the affected distribution network, initial operation data of the distributed power sources and each load node are obtained; Performing island division on the affected distribution network according to the expected fault set and the initial operation data to obtain an island division result; The operational capability of the island division result is evaluated according to a preset indicator set to obtain an evaluation result.

2. The method for evaluating the island operation capability based on source load as claimed in claim 1, characterized in that: The expected fault set of the affected distribution network is generated according to the operation data, specifically: According to the operation data, the wind speed of the affected distribution network is obtained by performing typhoon wind field modeling; Based on the wind speed, the comprehensive failure probability of the line is calculated in combination with the failure probability of the poles and the line sections; According to the comprehensive fault probability, a preset method is used to sample the system components of the affected distribution network to obtain a number of faulty and outage components; The plurality of faulty and out-of-service components are recorded as the expected fault set of the affected distribution network.

3. The method for evaluating the island operation capability based on source load as claimed in claim 2, characterized in that: Based on the wind speed, the comprehensive failure probability of the line is calculated in combination with the failure probability of the poles and the line sections, specifically: Based on the wind speed, the resistance of the pole under typhoon disaster is described by the fragility curve, and the fragility curve expression of the pole under typhoon disaster is established; According to the duration of the typhoon, the fragile curve expression is cumulatively calculated to obtain the cumulative failure probability expression of the pole; Based on the wind speed, constructing a line segment failure probability expression by considering the line as consisting of a plurality of serially connected line segments; According to the duration of the typhoon, the line segment fault probability expression is cumulatively calculated to obtain the line cumulative fault probability expression; The comprehensive failure probability of the line is calculated according to the cumulative failure probability expression of the pole and the cumulative failure probability expression of the line.

4. The method for evaluating the island operation capability based on source load as claimed in claim 2, characterized in that: According to the comprehensive fault probability, a preset method is used to sample the system components of the affected distribution network to obtain a number of fault outage components, specifically: For each component of the affected distribution network, a random number is generated according to the corresponding fault probability in the comprehensive fault probability, and the random number is used to determine whether the corresponding component is faulty, thereby obtaining a component fault result; Based on the component failure result, the plurality of faulty outage components are obtained by counting the total number of preset loads removed.

5. The method for evaluating the island operation capability based on source load as claimed in claim 1, characterized in that: By modeling the distributed power sources and loads of the affected distribution network, the initial operation data of the distributed power sources and each load node are obtained, specifically: According to the light intensity at different times and the output power of the wind turbine generator, the output timing model of the photovoltaic power generation system and the output timing model of the wind turbine generator set are established respectively; According to the output power of the constant power source and the charge state of the energy storage system at different times, a constant power source model and an energy storage system timing model are established respectively; Establishing a load power model according to the load active power of the distribution network; The initial operation data of the distributed power source and each load node are obtained according to the photovoltaic power generation system output timing model, the wind turbine generator set output timing model, the constant power supply model, the energy storage system timing model and the load power model.

6. The method for evaluating the island operation capability based on source load as claimed in claim 1, characterized in that: The affected distribution network is islanded according to the expected fault set and the initial operation data to obtain an islanding result, which is specifically: Based on the expected fault set and the initial operation data, an objective function is established with the goal of maximizing the equivalent restoration amount of nodes in the affected distribution network; Establishing an auxiliary indicator set according to the power value of the node; Establishing a constraint set according to the power balance state, node voltage and branch current of the affected distribution network; Based on the objective function, according to the auxiliary indicator set and the constraint set, the affected distribution network is islanded to obtain the islanding result.

7. The method for evaluating the island operation capability based on source load as claimed in claim 6, characterized in that: Based on the objective function, according to the auxiliary indicator set and the constraint set, the affected distribution network is islanded to obtain the islanding result, which is specifically: Establishing an initial island optimization matrix according to the objective function and the auxiliary indicator set; According to the contribution and consumption of each load node in the affected distribution network, and the difference between the capacity of the distributed power source and the capacity of the load node, the initial island optimization matrix is ​​modified to obtain a final island optimization matrix; Based on the final island optimization matrix, select the distributed power source with the largest capacity to perform initial island division on the affected distribution network to obtain an initial island division result; The initial island division result is verified according to the constraint set to obtain the island division result.

8. The method for evaluating the island operation capability based on source load as claimed in claim 7, characterized in that: The initial island division result is verified according to the constraint set to obtain the island division result, which is specifically: If there is an island intersection in the initial island division result, performing a fusion transformation on the distributed power sources in the initial island division result to obtain a first island division result; If the first island partitioning result satisfies the constraint set, recording the first island partitioning result as the island partitioning result; If the first island division result does not satisfy the constraint set, the load node with the lowest equivalent recovery amount in the first island division result is deleted, and the first island division result after deletion is recorded as the island division result.

9. A source-load-based island operation capability assessment device, characterized in that: It includes data module, fault module, operation module, division module and evaluation module; Wherein, the data module is used to obtain the operation data of the distribution network; The fault module is used to generate a set of expected faults of the affected distribution network according to the operation data; The operation module is used to obtain initial operation data of the distributed power source and each load node by modeling the distributed power source and load of the affected distribution network; The partitioning module is used to perform island partitioning on the affected distribution network according to the expected fault set and the initial operation data to obtain an island partitioning result; The evaluation module is used to evaluate the operating capability of the island division result according to a preset indicator set to obtain an evaluation result.

10. A storage medium, characterized in that: The storage medium stores a computer program, which is called and executed by a computer to implement any one of the source-load-based island operation capability assessment methods of claims 1 to 8.

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