Scenario traversal based active power distribution network fault restoration scheme evaluation method and system

Abstract

A kind of active distribution network fault recovery scheme evaluation method and system based on scene traversal, comprising: fitting the state of charge probability corresponding to the power generation of distributed power supply, user load power and its combination into discrete model to establish source load state sequence;The probability of source load state sequence state of charge is i, the control parameter of load control parameter and operating risk constraint index is calculated using traversal solving algorithm;The expected value of the number of recovery load time and operating risk constraint are calculated, and the expected value of the number of recovery load time and operating risk constraint are used as evaluation index.The present application divides source load storage state space by discrete model and establishes source load state sequence and traversal solving algorithm process to evaluate the recovery effect of pre-set fault recovery scheme, realizes the recovery effect of accurately evaluating fault recovery scheme, and is universally applicable to various distribution networks containing distributed power supply and uncertain load.

Description

Technical Field

[0001] This invention relates to the field of distribution network fault recovery, and specifically to an evaluation method and system for active distribution network fault recovery schemes based on scenario traversal. Background Technology

[0002] In modern active power distribution networks, the widespread integration of distributed generation, distributed energy storage, and new types of loads has made fault recovery more complex. Due to the uncertainties of power sources, loads, and energy storage, traditional recovery methods struggle to provide accurate and reliable recovery solutions that meet the multiple requirements of system safety, economy, and stability when faults occur. Therefore, accurate evaluation of fault recovery plans has become crucial to ensuring the stable operation of active power distribution networks after disasters. Summary of the Invention

[0003] To address the problem that existing technologies struggle to accurately evaluate fault recovery schemes due to the large number of distributed power sources and uncertain loads in current power distribution networks, this invention provides a method and system for evaluating active power distribution network fault recovery schemes based on scenario traversal.

[0004] The technical solution provided by this invention is:

[0005] The source-load state sequence is established by fitting the distributed power generation, user load power consumption and the state of charge probability corresponding to their combination into a discrete model.

[0006] The probability of the charge state sequence being i, the load control parameters, and the control parameters of the operation risk constraint index are calculated using an traversal solution algorithm.

[0007] The expected value of the number of households whose load recovery time is calculated based on the probability of the load state sequence being i and the load control parameters. The operation risk constraint is calculated based on the probability of the load state sequence being i and the control parameters of the operation risk constraint index. The expected value of the number of households whose load recovery time is i and the operation risk constraint are used as evaluation indicators.

[0008] Preferably, the step of using the traversal solution algorithm to calculate the probability of the source-load state sequence being state i, the load control parameters, and the control parameters of the operational risk constraint index includes:

[0009] S1: Let time t = 0, and the source charge state sequence charge state i = 0;

[0010] S2: Store the load shedding state and photovoltaic shedding state of the source load scenario for each time period t in the M matrix;

[0011] S3: Determine if i is less than the total number of source-load power combination states when the number of time periods in the scheme is T. If yes, let i = i + 1; otherwise, jump to step S4; otherwise, jump to step S5.

[0012] S4: Iterate through each t value of this fixed i value, calculate the probability that the source load state sequence is i, the load control parameters, and the control parameters of the operation risk constraint index under each i value t value. After the iteration is completed, jump to S3.

[0013] S5: Calculation complete.

[0014] Preferably, the step of traversing each t value of this fixed i value, calculating the probability of the source load state sequence being i, the load control parameters, and the control parameters of the operation risk constraint index under each i value t value, and after the traversal is completed, jumps to S3, including:

[0015] S4-1: Determine whether t is less than the total number of operating periods T of the fault recovery plan. If yes, let t = t + 1 and jump to step S4-2. Otherwise, calculate the probability of the charge state of the source-load state sequence being i based on the source-load state combination probability, and calculate the control parameters of the operation risk constraint index. Let t = 0 and jump to step S3.

[0016] S4-2: Determine whether the source-load scenario of each time period t matches the load-cutting state and photovoltaic-cutting state in the M matrix. If they match, calculate the source-load state combination probability and load control parameters, and jump to step S4-1. If they do not match, set the source-load state combination probability to zero, and jump to step S4-1.

[0017] Preferably, the probability p of the source-charge state sequence being charge state i is... GL (i) is determined by the following formula:

[0018]

[0019] In the formula, p GL (i) represents the probability that the charge state of the source-charge state sequence is i, where i is the state of the source-charge state sequence, and p GL,t (i) represents the probability of source-load state combination, t represents the time period, and T represents the number of time periods in which the scheme operates.

[0020] Preferably, the expected value e of the number of households requiring load recovery time is determined by the following formula:

[0021]

[0022] In the formula, e is the expected number of households during the load recovery time, and i is the state of the source load state sequence. p represents the total number of source-load power combination states when the number of time periods in the scheme is T. GL(i) represents the probability that the charge state of the source-load state sequence is i, t represents the time period, T is the number of time periods in the scheme operation, b is the load, and n is the total number of loads. Let Δt be the load control parameter for load b during time period t when the state of charge in the source-load state sequence is i, and Δt is the duration of a single time period.

[0023] Preferably, the operational risk constraints include at least one or more of the following: the probability that the load will be cut off due to power imbalance during the fault recovery process, the probability that the photovoltaic system will be cut off due to power imbalance during the fault recovery process, the probability that the energy storage system will be overcharged or over-discharged, the probability that the system will experience power imbalance during the first step, or the probability that the load will be completely cut off during the fault recovery process.

[0024] Based on the same inventive concept, this invention also provides an active distribution network fault recovery scheme evaluation system based on scenario traversal, including: a source-load state sequence establishment module, a traversal calculation module, and an evaluation index calculation module.

[0025] The source-load state sequence module is used to fit the distributed power generation, user load power consumption and their combination-corresponding state of charge probabilities into a discrete model to establish the source-load state sequence.

[0026] Traversal Calculation Module: Used to calculate the probability of the charge state sequence being i, load control parameters, and control parameters of the operation risk constraint index using traversal solution algorithm;

[0027] The evaluation index calculation module is used to calculate the expected value of the number of households with load recovery time based on the probability of the load state sequence being i and the load control parameters, and to calculate the operation risk constraint based on the probability of the load state sequence being i and the control parameters of the operation risk constraint index, and to use the expected value of the number of households with load recovery time and the operation risk constraint as evaluation indexes.

[0028] Preferably, the traversal calculation module is specifically used for:

[0029] S1: Let time t = 0, and the source charge state sequence charge state i = 0;

[0030] S2: Store the load shedding state and photovoltaic shedding state of the source load scenario for each time period t in the M matrix;

[0031] S3: Determine if i is less than the total number of source-load power combination states when the number of time periods in the scheme is T. If yes, let i = i + 1; otherwise, jump to step S4; otherwise, jump to step S5.

[0032] S4: Iterate through each t value of this fixed i value, calculate the probability that the source load state sequence is i, the load control parameters, and the control parameters of the operation risk constraint index under each i value t value. After the iteration is completed, jump to S3.

[0033] S5: Calculation complete.

[0034] Preferably, the traversal calculation module traverses each t value of this fixed i value, calculates the probability that the source load state sequence is i, the load control parameters, and the control parameters of the operation risk constraint index under each i value t value. After the traversal is completed, it jumps to S3, including:

[0035] S4-1: Determine whether t is less than the total number of operating periods T of the fault recovery plan. If yes, let t = t + 1 and jump to step S4-2. Otherwise, calculate the probability of the charge state of the source-load state sequence being i based on the source-load state combination probability, and calculate the control parameters of the operation risk constraint index. Let t = 0 and jump to step S3.

[0036] S4-2: Determine whether the source-load scenario of each time period t matches the load-cutting state and photovoltaic-cutting state in the M matrix. If they match, calculate the source-load state combination probability and load control parameters, and jump to step S4-1. If they do not match, set the source-load state combination probability to zero, and jump to step S4-1.

[0037] Preferably, the probability p of the source charge state sequence being charge state i in the traversal calculation module is... GL (i) is determined by the following formula:

[0038]

[0039] In the formula, p GL (i) represents the probability that the charge state of the source-charge state sequence is i, where i is the state of the source-charge state sequence, and p GL,t (i) represents the probability of source-load state combination, t represents the time period, and T represents the number of time periods in which the scheme operates.

[0040] Preferably, the expected value e of the number of households with recovery time in the calculation and evaluation index module is determined by the following formula:

[0041]

[0042] In the formula, e is the expected number of households during the load recovery time, and i is the state of the source load state sequence. p represents the total number of source-load power combination states when the number of time periods in the scheme is T. GL (i) represents the probability that the charge state of the source-load state sequence is i, t represents the time period, T is the number of time periods in the scheme operation, b is the load, and n is the total number of loads. Let Δt be the load control parameter for load b during time period t when the state of charge in the source-load state sequence is i, and Δt is the duration of a single time period.

[0043] Preferably, the operational risk constraints in the calculation and evaluation index module include at least one or more of the following: the probability that the load will be cut off due to power imbalance during the fault recovery process, the probability that the photovoltaic system will be cut off due to power imbalance during the fault recovery process, the probability that the energy storage system will be overcharged or over-discharged, the probability that the system will experience power imbalance at the first step, or the probability that the load will be completely cut off during the fault recovery process.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] This invention provides a method and system for evaluating active distribution network fault recovery schemes based on scenario traversal. The method includes: fitting a discrete model to the distributed generation power, user load power, and the corresponding state of charge probabilities of their combinations to establish a source-load state sequence; using a traversal solution algorithm to calculate the probability of state of charge i in the source-load state sequence, load control parameters, and control parameters of the operational risk constraint index; calculating the expected value of the number of users requiring load recovery time based on the probability of state of charge i in the source-load state sequence and the load control parameters, and calculating the operational risk constraint based on the probability of state of charge i in the source-load state sequence and the control parameters of the operational risk constraint index; and using the expected value of the number of users requiring load recovery time and the operational risk constraint as evaluation indicators. This invention evaluates the recovery effect of preset fault recovery schemes by dividing the source-load-storage state space using a discrete model and establishing a source-load state sequence and traversal solution algorithm, achieving accurate evaluation of the recovery effect of fault recovery schemes. It has universal applicability to various distribution networks containing distributed generation and uncertain loads. Attached Figure Description

[0046] Figure 1 This is a flowchart of the active power distribution network fault recovery scheme evaluation method based on scene traversal according to the present invention.

[0047] Figure 2 The flowchart of the active distribution network fault recovery scheme evaluation method based on scene traversal of the present invention is a calculation flowchart of the active distribution network fault recovery scheme evaluation method considering the uncertainty of source, load and storage.

[0048] Figure 3 This is a schematic diagram of the active power distribution network fault recovery scheme evaluation system based on scene traversal according to the present invention.

[0049] Figure 4 This is a schematic diagram of the IEEE 33 test system applicable to active distribution networks, representing a specific embodiment of the active distribution network fault recovery scheme evaluation method based on scenario traversal of the present invention. Detailed Implementation

[0050] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0051] Example 1:

[0052] This invention provides a method for evaluating active distribution network fault recovery schemes based on scenario traversal, the flowchart of which is shown below. Figure 1 As shown, it includes:

[0053] A1. Fit the distributed power generation, user load power consumption and their combination of state of charge probabilities into a discrete model to establish the source-load state sequence.

[0054] A2. Calculate the probability of the charge state sequence being i, the load control parameters, and the control parameters of the operation risk constraint index using the traversal solution algorithm.

[0055] A3. Calculate the expected value of the number of households with load recovery time based on the probability of the load state sequence being i and the load control parameters, and calculate the operation risk constraint based on the probability of the load state sequence being i and the control parameters of the operation risk constraint index, and use the expected value of the number of households with load recovery time and the operation risk constraint as evaluation indicators.

[0056] This invention first employs a source-load-storage discrete model to characterize the uncertainties of various elements in the system (such as distributed power sources, loads, and energy storage); then, it establishes the objective function and constraints for fault recovery and evaluates the performance of different recovery schemes; finally, it uses an ergonomic algorithm to calculate the objective function values ​​under different source-load state combinations and the probability of satisfying operational risk constraints, thereby evaluating the overall performance and feasibility of the fault recovery scheme.

[0057] Step A1 specifically includes:

[0058] Characterizing source-load uncertainty using discrete models:

[0059] This invention first discretizes the continuous probability distribution of the power generation of distributed power sources and the power consumption of user loads into a finite number of state values ​​and their corresponding state probabilities, and uses the form of a discrete probability state space to characterize the uncertainty of all source-load units, as shown in Table 1:

[0060]

[0061] Table 1 Discrete Model of Source Load Power in Time Period t

[0062] In the table: N GL This represents the total number of source-load power combination states.

[0063] The total number of source-load power combination states when the number of time periods in the scheme is T, and

[0064]

[0065] m and n are the total number of distributed generation sources and loads in the distribution network, respectively; x G,a and x L,b p represents the total number of power states of distributed power source a (a = 1, ..., m) and load b (b = 1, ..., n), respectively; GL,t (i) represents the probability that the source-load power combination is in state i during time period t, and is calculated as follows:

[0066]

[0067] In the formula, i a The source-load power combination state is i a The state number corresponding to the a-th distributed power source, i b The source-load power combination state is i b The state number corresponding to the b-th load, p G,a,t (i a Let t be the state of distributed source a at time t, i. a The probability of time p L,b,t (i b Let t represent the state i of load b at time t. b The probability of that time.

[0068] In this invention, the total number of power states of distributed power sources and loads, as well as the power value of each state, are pre-set constants. Based on the preset total number of states, the state characterization value is determined by proportional division. The energy storage state of charge is affected by the uncertainty of distributed power sources and loads, and also passively exhibits uncertainty. In this invention, a discrete model, i.e., a discrete state space, is used to characterize the uncertainty of the energy storage state in the distribution network, as shown in Table 2.

[0069]

[0070] Table 2 Discrete Model of Energy Storage State of Charge at Time t

[0071] In the table, N E Let N be the total number of energy storage combination states, and N be the total number of combination states. E =x E,1 ×…×x E,l ; l represents the total amount of energy storage in the distribution network, x E,k The total number of states of charge for energy storage k (k = 1, ..., l); SOC1(k) represents the state of energy storage node k; p E,t (i) represents the probability that the energy storage combination is in state i during time period t, and is calculated as follows:

[0072]

[0073] In the formula: i k p is the state number of energy storage k when the energy storage combination state is i; E,t (i k Let t be the state of energy storage k at time t. k The probability of that time.

[0074] In this invention, the total number of energy storage states of charge and the state of charge characterization values ​​of each state are preset constants. Based on the preset total number of states, the state values ​​are determined by proportional division.

[0075] In the fault recovery scheme, the source load states under multiple time periods can be further combined. Therefore, a source load state sequence model is set to represent all time periods. In the model, the length of the sequence is the number of time periods T in which the scheme operates, and the number of source load power combination states under each time period is the total number of time periods T in which the scheme operates. Therefore, the number of time periods in which the scheme operates is the total number of source-load power combination states at time T. For N GL ×N GL ×…×N GL , (N GL (where P is the total number of source-load power combination states) and the probability of the sequence occurring is P. GL (i) is The multi-state model of the source-load state sequence is shown in Table 3.

[0076]

[0077] Table 3. Multi-state model of source-load state sequence

[0078] This invention establishes a source-load-storage state transition matrix M to characterize the source-load-storage state and network topology changes, as shown in the following equation:

[0079]

[0080] M i,j =[qieL,qieG,Soc]

[0081] In the formula: matrix element M i,j Included in the source-load power combination i (i = 1, 2, ..., N) GL ) and the energy storage state of charge combination j (j=1,2,…,N) E Under these conditions, the operating status of the load group control switch QL (qieL), the operating status of the distributed power source control switch QG (qieG), and the status of the energy storage node (Soc) are recorded.

[0082] Step A2 specifically includes:

[0083] The calculation flowchart of the active distribution network fault recovery scheme evaluation method based on scenario ergonomics, considering the uncertainty of source, load, and storage, is as follows: Figure 2 .

[0084] Step 1): Obtain basic parameters such as the initial topology of the distribution network, source-load-storage location, and line parameters. Based on the distributed photovoltaic and controllable load group disconnection strategy, obtain all possible fault recovery schemes. Given the number of time periods T for the fault recovery scheme, the fault recovery scheme is only for the first time period, i.e., the switching action at t=1. In each subsequent time period, the source-load disconnection situation must be dynamically adjusted according to the power flow calculation results of the previous time period until the power difference of the balance node is 0.

[0085] Step 2): Select a fault recovery scheme and update the network topology. The uncertainties of distributed power sources, loads, and energy storage are all characterized using a discrete model. Define the total number of source-load scenarios N in each time period. GL The product of the number of states of each distributed power source and the number of states of each load is used to construct a source-load state sequence that includes the states of all source and loads in all time periods. The total number is denoted as .

[0086] Step 3): Calculate the network power flow under all source-load scenarios and obtain the power deficit P of the balancing node. loss Determine the load shedding state qieL, photovoltaic shedding state qieG, and energy storage node state Soc at the next moment, and store them in matrix M;

[0087] Step 4): Determine whether the total number of source-load power combination states when the number of time periods of operation of all schemes is T has been traversed. If all sequences have been traversed, jump to step 7; otherwise, i = i + 1 and jump to step 5.

[0088] Step 5): Determine whether all time periods T under the source load state sequence i have been traversed. If so, calculate the source load state sequence probability p. GL (i) Control parameters of indicators f1-f5 (See step A3) Let t = 0 and jump to step 4; otherwise, t = t + 1 and jump to step 6.

[0089] Step 6): Update the source load scene sequence to the scene probability p under time period t at time i. GL,t (i) Determine whether the source-load scenario for each time period matches the load and photovoltaic disconnection status in the M matrix (the verification logic is that if a power source or load is disconnected, its power value can only be 0, otherwise it cannot be 0). If they do not match, then p GL (i) Set to 0. If a match is found, calculate the load control parameters. Proceed to step 5).

[0090] Step A3 specifically includes:

[0091] Step 7): Calculate the expected value e of the load recovery time and number of households as the evaluation index of the fault recovery plan, and the operation risk constraints P[f1]-P[f5].

[0092] Establish assessment objectives and operational risk constraints for fault recovery plans:

[0093] The selection of a new power distribution system fault recovery scheme depends on a quantitative assessment of the recovery objectives and operational risks. This invention establishes the following evaluation indicators for the fault recovery scheme:

[0094] The present invention aims to maximize the number of households when power is restored after the implementation of the scheme. The number of households when power is restored is defined as the product of the number of loads restored and the duration of power restoration. The expected value e of the number of households during the load restoration time is calculated according to formula (5).

[0095]

[0096] In the formula: p GL (i) represents the probability of source load state sequence i, which is equal to the product of the probabilities of source load state combinations at all times; T represents the total number of operation periods of the fault recovery scheme. Let t be the control parameters for load b under sequence i during time period t. If load b resumes power supply, then otherwise Δt represents the duration of a single time period.

[0097] This invention considers five operational risks during the implementation of fault recovery schemes, and therefore establishes five operational risk constraints as evaluation indicators:

[0098] ①. The probability that power imbalance during fault recovery will lead to load shedding:

[0099]

[0100] In the formula, f1 is the control parameter for the index f1 under the source load state sequence i.

[0101] ②. The probability of photovoltaic power being shut down due to power imbalance during fault recovery:

[0102]

[0103] In the formula, f2 is the control parameter for the index f2 under the source load state sequence i.

[0104] ③. The probability of overcharging or over-discharging in the energy storage system:

[0105]

[0106] In the formula, f3 is the control parameter for the index f3 under the source load state sequence i.

[0107] ④. The probability of power imbalance occurring in the system during the first step:

[0108]

[0109] In the formula, f4 is the control parameter for the source load state sequence i.

[0110] ⑤. There is a probability that the load may be completely disconnected during the fault recovery process:

[0111]

[0112] In the formula, f5 is the control parameter for the index f5 under the source load state sequence i.

[0113] Example 2:

[0114] Based on the same inventive concept, this invention also provides an active power distribution network fault recovery scheme evaluation system based on scenario traversal, such as... Figure 3 As shown, it includes: a module for establishing the source load state sequence, a traversal calculation module, and a module for calculating evaluation indicators;

[0115] The source-load state sequence module is used to fit the distributed power generation, user load power consumption and their combination-corresponding state of charge probabilities into a discrete model to establish the source-load state sequence.

[0116] Traversal Calculation Module: Used to calculate the probability of the charge state sequence being i, load control parameters, and control parameters of the operation risk constraint index using traversal solution algorithm;

[0117] The evaluation index calculation module is used to calculate the expected value of the number of households with load recovery time based on the probability of the load state sequence being i and the load control parameters, and to calculate the operation risk constraint based on the probability of the load state sequence being i and the control parameters of the operation risk constraint index, and to use the expected value of the number of households with load recovery time and the operation risk constraint as evaluation indexes.

[0118] Preferably, the traversal calculation module is specifically used for:

[0119] S1: Let time t = 0, and the source charge state sequence charge state i = 0;

[0120] S2: Store the load shedding state and photovoltaic shedding state of the source load scenario for each time period t in the M matrix;

[0121] S3: Determine if i is less than the total number of source-load power combination states. If yes, let i = i + 1; otherwise, jump to step S4; otherwise, jump to step S5.

[0122] S4: Iterate through each t value of this fixed i value, calculate the probability that the source load state sequence is i, the load control parameters, and the control parameters of the operation risk constraint index under each i value t value. After the iteration is completed, jump to S3.

[0123] S5: Calculation complete.

[0124] Preferably, the traversal calculation module traverses each t value of this fixed i value, calculates the probability that the source load state sequence is i, the load control parameters, and the control parameters of the operation risk constraint index under each i value t value. After the traversal is completed, it jumps to S3, including:

[0125] S4-1: Determine whether t is less than the total number of operating periods T of the fault recovery plan. If yes, let t = t + 1 and jump to step S4-2. Otherwise, calculate the probability of the charge state of the source-load state sequence being i based on the source-load state combination probability, and calculate the control parameters of the operation risk constraint index. Let t = 0 and jump to step S3.

[0126] S4-2: Determine whether the source-load scenario of each time period t matches the load-cutting state and photovoltaic-cutting state in the M matrix. If they match, calculate the source-load state combination probability and load control parameters, and jump to step S4-1. If they do not match, set the source-load state combination probability to zero, and jump to step S4-1.

[0127] Preferably, the probability p of the source charge state sequence being charge state i in the traversal calculation module is... GL (i) is determined by the following formula:

[0128]

[0129] In the formula, p GL (i) represents the probability that the charge state of the source-charge state sequence is i, where i is the state of the source-charge state sequence, and p GL,t (i) represents the probability of source-load state combination, t represents the time period, and T represents the number of time periods in which the scheme operates.

[0130] Preferably, the expected value e of the number of households with recovery time in the calculation and evaluation index module is determined by the following formula:

[0131]

[0132] In the formula, e is the expected number of households during the load recovery time, and i is the state of the source load state sequence. p represents the total number of source-load power combination states when the number of time periods in the scheme is T. GL(i) represents the probability that the charge state of the source-load state sequence is i, t represents the time period, T is the number of time periods in the scheme operation, b is the load, and n is the total number of loads. Let Δt be the load control parameter for load b during time period t when the state of charge in the source-load state sequence is i, and Δt is the duration of a single time period.

[0133] Preferably, the operational risk constraints in the calculation and evaluation index module include at least one or more of the following: the probability that the load will be cut off due to power imbalance during the fault recovery process, the probability that the photovoltaic system will be cut off due to power imbalance during the fault recovery process, the probability that the energy storage system will be overcharged or over-discharged, the probability that the system will experience power imbalance at the first step, or the probability that the load will be completely cut off during the fault recovery process.

[0134] Example 3:

[0135] IEEE 33 test system suitable for active power distribution networks, such as Figure 4 As shown, the system is connected to one diesel engine FG, two distributed photovoltaic systems PV1-PV2, and three energy storage systems ES1-ES3. When a fault occurs at the system bus, some disconnect switches QS will activate to form an initial fault recovery plan. How to accurately evaluate the effectiveness of the fault recovery plan is the core problem solved by this invention.

[0136] Assume that the discrete model of load power and source power is the same at all times, as shown in Table 4. Each power source and load has 3 states. The power value of state 1 is half of the peak power of the power source or load, and the state probability is 0.4. The power value of state 2 is the peak power of the power source or load, and the state probability is 0.6. State 3 is the cut-off state.

[0137] Table 4 Discrete Model of Source and Load Power

[0138]

[0139] When a system busbar fault occurs, circuit breaker QF disconnects. Based on the on / off states of the three disconnecting switches QS1-QS3 and network topology constraints, multiple possible fault recovery schemes are generated, as shown in Table 5. Using the method proposed in this invention, based on the expected value of the number of households to be restored within the load recovery time and the calculation of operational risk constraints, the recovery effectiveness of each fault recovery scheme is quantitatively evaluated using the maximum number of restored loads and the operational risk tolerance as evaluation indicators.

[0140]

[0141] Table 5 Fault Recovery Plan

[0142] Taking Scheme 6 as an example, the optimized objective function value and the probabilities of various operational risks are obtained by evaluating the scheme using the method proposed in this invention, as shown in Table 6:

[0143] Optimize target E / (time·user) Indicator f1 Indicator f2 Indicator f3 Indicator f4 Indicator f5 9.0208 24.48％ 100％ 53.18％ 0％ 0％

[0144] Table 6. Evaluation Results of the Scheme

[0145] In summary, this invention provides a method and system for evaluating active distribution network fault recovery schemes based on scenario traversal. The method includes: fitting a discrete model to the distributed generation power, user load power, and the corresponding state of charge probabilities of their combinations to establish a source-load state sequence; using a traversal solution algorithm to calculate the probability of state of charge i in the source-load state sequence, load control parameters, and control parameters of the operational risk constraint index; calculating the expected value of the number of households requiring load recovery time based on the probability of state of charge i in the source-load state sequence and the load control parameters, and calculating the operational risk constraint based on the probability of state of charge i in the source-load state sequence and the control parameters of the operational risk constraint index; and using the expected value of the number of households requiring load recovery time and the operational risk constraint as evaluation indicators. This invention evaluates the recovery effect of preset fault recovery schemes by dividing the source-load-storage state space using a discrete model and establishing a source-load state sequence and traversal solution algorithm, achieving accurate evaluation of the recovery effect of fault recovery schemes. It has universal applicability to various distribution networks containing distributed generation and uncertain loads.

[0146] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0150] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for evaluating active distribution network fault recovery schemes based on scenario traversal, characterized in that, include: The source-load state sequence is established by fitting the distributed power generation, user load power consumption and the state of charge probability corresponding to their combination into a discrete model. The probability of the charge state sequence being i, the load control parameters, and the control parameters of the operation risk constraint index are calculated using an traversal solution algorithm. The expected value of the number of households whose load recovery time is calculated based on the probability of the load state sequence being i and the load control parameters. The operation risk constraint is calculated based on the probability of the load state sequence being i and the control parameters of the operation risk constraint index. The expected value of the number of households whose load recovery time is i and the operation risk constraint are used as evaluation indicators.

2. The method as described in claim 1, characterized in that, The control parameters for calculating the probability of the source-load state sequence being state i, the load control parameters, and the operational risk constraint index using the traversal solution algorithm include: S1: Let time t = 0, and the source charge state sequence charge state i = 0; S2: Store the load shedding state and photovoltaic shedding state of the source load scenario for each time period t in the M matrix; S3: Determine if i is less than the total number of source-load power combination states when the number of time periods in the scheme is T. If yes, let i = i + 1 and jump to step S4; otherwise, jump to step S5. S4: Iterate through each t value of this fixed i value, calculate the probability that the source load state sequence is i, the load control parameters, and the control parameters of the operation risk constraint index under each i value t value. After the iteration is completed, jump to S3. S5: Calculation complete.

3. The method as described in claim 2, characterized in that... The process iterates through each t value of this fixed i value, calculates the probability of the source load state sequence being i, the load control parameters, and the control parameters of the operational risk constraint index for each i value t value. After the iteration is complete, it jumps to S3, which includes: S4-1: Determine whether t is less than the total number of operating periods T of the fault recovery plan. If yes, let t = t + 1 and jump to step S4-2. Otherwise, calculate the probability of the charge state of the source-load state sequence being i based on the source-load state combination probability, and calculate the control parameters of the operation risk constraint index. Let t = 0 and jump to step S3. S4-2: Determine whether the source-load scenario of each time period t matches the load-cutting state and photovoltaic-cutting state in the M matrix. If they match, calculate the source-load state combination probability and load control parameters, and jump to step S4-1. If they do not match, set the source-load state combination probability to zero, and jump to step S4-1.

4. The method as described in claim 1, characterized in that, The probability p that the charge state sequence is i is... GL (i) is determined by the following formula: In the formula, p GL (i) represents the probability that the charge state of the source-charge state sequence is i, where i is the state of the source-charge state sequence, and p GL,t (i) represents the probability of source-load state combination, t represents the time period, and T represents the number of time periods in which the scheme operates.

5. The method as described in claim 1, characterized in that, The expected value e of the number of households during the load recovery time is determined by the following formula: In the formula, e is the expected number of households during the load recovery time, and i is the state of the source load state sequence. p represents the total number of source-load power combination states when the number of time periods in the scheme is T. GL (i) represents the probability that the charge state of the source-load state sequence is i, t represents the time period, T is the number of time periods in the scheme operation, b is the load, and n is the total number of loads. Let Δt be the load control parameter for load b during time period t when the state of charge in the source-load state sequence is i, and Δt is the duration of a single time period.

6. The method as described in claim 1, characterized in that, The operational risk constraints include at least one or more of the following: the probability that the load will be cut off due to power imbalance during the fault recovery process, the probability that the photovoltaic system will be cut off due to power imbalance during the fault recovery process, the probability that the energy storage system will be overcharged or over-discharged, the probability that the system will experience power imbalance during the first step, or the probability that the load will be completely cut off during the fault recovery process.

7. A fault recovery scheme evaluation system for active distribution networks based on scenario traversal, characterized in that, include: Establish a source load state sequence module, a traversal calculation module, and a calculation evaluation index module; The source-load state sequence module is used to fit the distributed power generation, user load power consumption and their combination-corresponding state of charge probabilities into a discrete model to establish the source-load state sequence. Traversal Calculation Module: Used to calculate the probability of the charge state sequence being i, load control parameters, and control parameters of the operation risk constraint index using traversal solution algorithm; The evaluation index calculation module is used to calculate the expected value of the number of households with load recovery time based on the probability of the load state sequence being i and the load control parameters, and to calculate the operation risk constraint based on the probability of the load state sequence being i and the control parameters of the operation risk constraint index, and to use the expected value of the number of households with load recovery time and the operation risk constraint as evaluation indexes.

8. The system as described in claim 7, characterized in that, The traversal calculation module is specifically used for: S1: Let time t = 0, and the source charge state sequence charge state i = 0; S2: Store the load shedding state and photovoltaic shedding state of the source load scenario for each time period t in the M matrix; S3: Determine if i is less than the total number of source-load power combination states when the number of time periods in the scheme is T. If yes, let i = i + 1 and jump to step S4; otherwise, jump to step S5. S4: Iterate through each t value of this fixed i value, calculate the probability that the source load state sequence is i, the load control parameters, and the control parameters of the operation risk constraint index under each i value t value. After the iteration is completed, jump to S3. S5: Calculation complete.

9. The system as described in claim 8, characterized in that... The traversal calculation module traverses each t value of this fixed i value, calculates the probability that the source load state sequence is i, the load control parameters, and the control parameters of the operation risk constraint index under each i value t value. After the traversal is completed, it jumps to S3, which includes: S4-1: Determine whether t is less than the total number of operating periods T of the fault recovery plan. If yes, let t = t + 1 and jump to step S4-2. Otherwise, calculate the probability of the charge state of the source-load state sequence being i based on the source-load state combination probability, and calculate the control parameters of the operation risk constraint index. Let t = 0 and jump to step S3. S4-2: Determine whether the source-load scenario of each time period t matches the load-cutting state and photovoltaic-cutting state in the M matrix. If they match, calculate the source-load state combination probability and load control parameters, and jump to step S4-1. If they do not match, set the source-load state combination probability to zero, and jump to step S4-1.

10. The system as described in claim 7, characterized in that, The probability p that the source-charge state sequence in the traversal calculation module has a charge state of i is... GL (i) is determined by the following formula: In the formula, p GL (i) represents the probability that the charge state of the source-charge state sequence is i, where i is the state of the source-charge state sequence, and p GL,t (i) represents the probability of source-load state combination, t represents the time period, and T represents the number of time periods in which the scheme operates.

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