A method and device for evaluating the reliability of a distribution network considering the uncertainty of power sources and loads

Abstract

Embodiments of the present disclosure provide a method for evaluating the reliability of a distribution network considering the uncertainty of power sources and loads. The method includes: generating typical scenarios based on the operation data and environmental data of the active distribution network; obtaining a load demand response strategy under the typical scenarios, and performing fault simulation on the active distribution network using a pre-constructed reliability evaluation model of the distribution network to generate the reliability calculation results of the distribution network under the load demand response strategy and the load demand response plan when a fault occurs; generating a load demand response set under different fault conditions according to the reliability calculation results and the load demand response plan. The method includes. In this way, the operation risk of the distribution network can be accurately evaluated to guide the operation, maintenance and construction of the distribution network.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power system simulation calculations, and particularly to a method and device for evaluating the reliability of a distribution network considering the uncertainties of power sources and loads. Background Art

[0002] With the large-scale access of distributed power sources represented by wind turbines and photovoltaic power to the power grid, the penetration rate of renewable energy power generation has been continuously increasing, reducing the use of fossil fuels such as coal and oil, effectively reducing carbon emissions, and promoting the continuous development of the energy structure of the power industry towards a high proportion of renewable energy. At the same time, the large-scale access of distributed power sources has changed the structure of the traditional distribution network, causing problems such as complex power flow distribution in the distribution network, increased uncertainty in operation and dispatching, and increased difficulty in system operation and maintenance.

[0003] The traditional distribution network reliability evaluation model is based on historical operation data, calculates the failure probability of distribution network equipment, obtains data such as the number of load point failures and power outage time through program simulation, calculates system reliability indicators, and obtains the distribution network reliability evaluation result. Summary of the Invention

[0004] The present disclosure provides a method and device for evaluating the reliability of a distribution network considering the uncertainties of power sources and loads.

[0005] According to a first aspect of the present disclosure, there is provided a method for evaluating the reliability of a distribution network considering the uncertainties of power sources and loads. The method includes:

[0006] Generating a typical scenario according to the operation data and environmental data of the active distribution network;

[0007] Under the typical scenario, obtaining a load demand response strategy, and performing a fault simulation on the active distribution network using a pre-constructed distribution network reliability evaluation model to generate a reliability calculation result of the distribution network under the load demand response strategy and a load demand response plan when a fault occurs;

[0008] Generating a load demand response set under different fault conditions according to the reliability calculation result and the load demand response plan.

[0009] Further, the generating a typical scenario according to the operation data and environmental data of the active distribution network includes:

[0010] Predicting the load power according to the distribution network structure, load type, and load historical data; predicting the wind speed and light intensity according to the environmental data, and further predicting the output of wind turbines and photovoltaic power;

[0011] According to the probability distributions of the prediction errors of wind turbines, photovoltaics, and loads, the roulette wheel algorithm is used to sample the prediction errors; the predicted values of wind turbine output, photovoltaic output, and load are superimposed with the prediction errors to generate scenarios of wind turbine output, photovoltaic output, and load, and the scenario with the highest probability is selected as the typical scenario.

[0012] Further, the scenario with the highest probability is selected as the typical scenario, and the calculation formula for the probability of each scenario is as follows:

[0013]

[0014] Where, represents the prediction error of wind and light output or load; ρ s represents the probability generated by scenario s; α i,t represents the probability of wind and light output or electrical load at time t in interval i; n represents the total number of discretized intervals; N S represents the total number of generated target scenarios.

[0015] Further, the distribution network reliability evaluation model is constructed through the following steps:

[0016] Determine the average normal operation time and average fault repair time of each component according to the operation status of each component of the distribution network equipment;

[0017] Determine the operation status of each component according to the relationship between the output power under the island operation mode and the total load in the island;

[0018] Calculate and determine the load point reliability index and system reliability index according to the average normal operation time of the component, the average fault repair time of the component, and the operation status of each component, and then construct a distribution network reliability evaluation model.

[0019] Further, the load demand response strategy includes:

[0020] According to the characteristics of the load itself and whether it participates in the demand response, the load is divided into non-adjustable load, shiftable load, and interruptible load; among them, the non-adjustable load does not participate in the demand response;

[0021] When a component fails in the distribution network, resulting in a power outage of the load and the formation of an island, the shiftable load and the interruptible load participate in the demand response.

[0022] Further, the active distribution network fault simulation using the pre-constructed distribution network reliability evaluation model includes:

[0023] Simulate the faults of the distribution network by the sequential Monte Carlo method to determine whether the load points of the distribution network faults are in the island; if in the island, count the normal operation time and fault time of the load points in the island; if not in the island, count the normal operation time and fault time of the load points.

[0024] Further, the step of counting the normal operation time and fault time of the load points in the island when in the island includes:

[0025] If all interruptible loads in the island still cannot achieve supply-demand matching when participating in the response, load shedding is carried out according to the importance of the loads until supply-demand balance is achieved; count the normal operation time and fault time of the load points in the island, and record the load participation demand response plan for this time.

[0026] According to the second aspect of the present disclosure, a reliability evaluation device for an active distribution network considering source-load uncertainty is provided. The device includes:

[0027] A typical scenario generation module, configured to generate typical scenarios according to the operation data and environmental data of the active distribution network;

[0028] A load demand response strategy generation module, configured to determine the load demand response strategy according to the typical scenarios, perform active distribution network fault simulation using a pre-constructed distribution network reliability evaluation model, and generate the reliability calculation results of the distribution network under the load demand response strategy and the load demand response plan when a fault occurs;

[0029] A load demand response set generation module, configured to generate a load demand response set under different fault conditions according to the reliability calculation results and the load demand response plan.

[0030] According to the third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor, and a computer program is stored on the memory, and when the processor executes the program, the method as described above is implemented.

[0031] According to the fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0032] The present disclosure provides a reliability evaluation method for an active distribution network considering source-load uncertainty, and a reliability optimization model for an active distribution network considering demand response, which are used to accurately evaluate the operation risk of the distribution network and guide the operation, maintenance and construction of the distribution network.

[0033] It should be understood that the content described in the Summary of the Invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In conjunction with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, where:

[0035] Figure 1 FIG. shows a flowchart of a method for evaluating the reliability of a distribution network considering source-load uncertainty according to an embodiment of the present disclosure;

[0036] Figure 2 FIG. shows a flowchart of a process for constructing a reliability model of a distribution network considering source-load uncertainty according to another embodiment of the present disclosure;

[0037] Figure 3 FIG. shows a flowchart of a method for evaluating the reliability of a distribution network considering source-load uncertainty according to another embodiment of the present disclosure;

[0038] Figure 4 FIG. shows a block diagram of an apparatus for evaluating the reliability of a distribution network considering source-load uncertainty according to an embodiment of the present disclosure;

[0039] Figure 5 FIG. shows a block diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments of the present disclosure fall within the scope of the present disclosure.

[0041] In addition, the term "and / or" in this document is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after.

[0042] The above - mentioned method for evaluating the reliability of the distribution network does not consider the impact of the access of distributed power sources and the uncertainty of their output on the reliability of the distribution network, and there is a large gap between the evaluation results and the actual operation scenario. Therefore, when evaluating the reliability of the distribution network, it is necessary to consider not only the equipment failure probability and the access of renewable energy, but also the actual operation scenario, comprehensively consider the load uncertainty and the uncertainty of renewable energy output, and evaluate the reliability of the distribution network to ensure the accuracy of the evaluation results.

[0043] At the same time, demand response also plays an increasingly important role in today's active distribution network. When evaluating the reliability of the distribution network, it is also necessary to consider the role of load demand response, study the load demand response strategy and the impact of load demand response on the reliability of the distribution network.

[0044] This disclosure considers the source - load uncertainty and proposes a method for evaluating the operational reliability of an active distribution network. On this basis, a load classification method is proposed, which divides the load into non - adjustable load, shiftable load, and interruptible load, and a load demand response strategy during island formation is proposed. Applying this load demand response strategy to the reliability evaluation process of the active distribution network based on sequential Monte Carlo simulation, the load demand response sets in different situations of the active distribution network are obtained, which are used to cope with the distribution network fault risk and guide the operation and maintenance of the active distribution network.

[0045] Figure 1 The flowchart of the method for evaluating the reliability of the distribution network considering source - load uncertainty according to an embodiment of the present disclosure is shown. The method includes:

[0046] S1: Generate typical scenarios according to the operation data and environmental data of the active distribution network.

[0047] Specifically, S1 in this disclosure includes the following sub - steps:

[0048] S10: Obtain the historical operation parameters of the distribution network.

[0049] Obtain the historical parameter data of the distribution network required during the deployment of the active distribution network. The required historical parameter data includes distribution network equipment data, distribution network line operation data, load type and load power, and load characteristic data, and distributed power source operation environment data. Further, the distribution network line operation data includes node power information, line capacity, line loss, micro - grid node information, equipment operation status information, equipment failure times and repair history information. The distributed power source operation environment data includes environmental wind speed and light intensity.

[0050] S11: Predict the output of wind turbines and photovoltaic power.

[0051] According to the distribution information of wind speed and light intensity, preliminarily predict the wind turbine power generation and photovoltaic power generation.

[0052] Among them, the wind speed basically follows the Weibull distribution, and its probability density function is as follows:

[0053]

[0054] In the formula, v t is the wind speed at time t, k t and λ t are the shape parameter and scale parameter at time t, respectively.

[0055] The relationship between the output of the wind turbine and the wind speed is described by a simplified piecewise linear function, expressed as follows:

[0056]

[0057] In the formula, v in , v out and v R are the cut-in wind speed, cut-out wind speed and rated wind speed, respectively, is the rated power, is the effective output power of the wind turbine at time t.

[0058] The light intensity generally follows the Beta distribution, and its probability density function can be expressed as follows:

[0059]

[0060] In the formula, x t is the light intensity at time t, and x t ∈[0,1]; v t and ξ t are the shape parameters at time t, respectively; Γ(·) is the Gamma function, and the calculation formulas of the above parameters are as follows:

[0061]

[0062]

[0063]

[0064] Among them, and are the effective output power and rated power of the photovoltaic unit at time t, respectively; μ t and σ s,t are the expected value and variance of the light intensity at time t, respectively.

[0065] S12: Predict the load curve according to the load type, load power and load characteristic data.

[0066] The multi - load of the distribution network is predicted by combining the total quantity prediction and the characteristic prediction.

[0067] Step 12 includes the following sub - steps:

[0068] S121: Conduct load characteristic analysis. According to the installed capacity of conventional users such as industrial, commercial, residential, and administrative users, the "natural growth + large users" method is used to predict the total load. Analyze the typical user curves, then normalize them to obtain the total load curves of various new typical users, and then superimpose the current conventional load curves.

[0069] S122: Predict new - type loads such as electric vehicle loads, port on - shore power loads, 5G base station loads, etc., to obtain new - type load prediction curves.

[0070] S123: Superimpose the curves to obtain the prediction results for verification.

[0071] S13: The uncertainties of the source and load are characterized by the superposition of the wind turbine output, photovoltaic output, and load prediction values and prediction errors.

[0072] S13 includes the following sub - steps:

[0073] S131: On the premise of the prediction value, superimpose the probability value distributions of the wind, light output, and electric load prediction errors to describe their respective values. The power of the wind, light output, and load in each scenario is expressed as the following formula:

[0074] P i (t) = P i f (t)+ΔP i (t)

[0075] In the formula, i refers to the wind, light, and electric load values generated by the i - th simulation; P i (t) is the wind, light, and electric load value at time t; P i f (t) is the predicted value of the wind, light, and electric load at time t; ΔP i (t) is the prediction error of the wind, light, and electric load at time t.

[0076] The error of the photovoltaic output follows a beta distribution, and then the situation of the photovoltaic output is obtained. Its probability density function is:

[0077]

[0078] In the formula, α represents the parameter of the beta distribution; β is the parameter of the beta distribution; E max represents the maximum light intensity.

[0079] The normal distribution model is adopted to describe the wind power prediction error distribution and the load prediction error distribution. The error probability density functions of wind power output and load are as follows:

[0080]

[0081] In the formula, μ is the expectation, σ is the standard deviation.

[0082] S132: Discretize the probability density functions of the error random variables of the fan, photovoltaic, and electrical load into several intervals. The interval width is the magnitude of the prediction error. The probability of each interval at time t is expressed as α i,t ; i represents the i-th scenario. Specifically, the method of generating typical scenarios is adopted here to represent the uncertainties of wind, light, and load. Each i corresponds to a typical scenario, and each scenario contains the values of the probability error descriptions of wind, light, and load usage.

[0083] S133: Generate a random number between 0 and 1, and compare this random number with the cumulative probability of the discretized probability density function intervals of the error random variables from small to large. Select the first interval whose probability is greater than or equal to the random number to generate all scenario plans.

[0084] The probability generated by each scenario is expressed by the following formula:

[0085]

[0086] In the formula, is the prediction error of wind and light output or load; ρ s is the probability generated by scenario s; α i,t is the probability of wind and light output or electrical load at time t in the i-th interval; n is the total number of discretized intervals; N S is the total number of generated target scenarios.

[0087] S134: Combine the wind, light output, and load scenarios with the highest probability to generate a typical scenario, which characterizes the uncertainties of the power source and load in the distribution network.

[0088] S2: Under the described typical scenario, obtain the load demand response strategy, and use the pre-constructed distribution network reliability assessment model to simulate the faults of the active distribution network, and generate the reliability calculation results of the distribution network under the load demand response strategy and the load demand response plan when a fault occurs.

[0089] Specifically, the fault simulation is to simulate the distribution network. Build a simulation model of the distribution network on platforms such as PSCAD, EMTDC, or IEEE RBTS, simulate the influence of different fault conditions on the voltage at the fault point and the amplitude and phase characteristics of the current, and obtain the waveforms of the node voltage and current at each node. For fault analysis, change the fault occurrence location, fault occurrence time, and fault type for simulation, observe the simulation curves of the current and voltage at each node, and conduct a simple analysis of the results.

[0090] See Figure 2 , the pre-constructed distribution network reliability assessment model is constructed through the following steps:

[0091] S201: Calculate the normal operating time TTF of each component in the distribution network and the component fault repair time.

[0092] (1) Calculate the values of MTTF and MTTR of the distribution network components. MTTF represents the average normal operating time of the component, and MTTR represents the average fault repair time of the component.

[0093]

[0094]

[0095] In the formula, λ represents the failure rate of the component, and μ represents the fault repair rate of the component.

[0096] (2) It is considered that the distribution network equipment components do not have memory, that is, there is no correlation between any two component failures. Assume that the component failure rate λ and repair rate μ follow an exponential distribution, as shown in the following formula.

[0097]

[0098] In the formula, f(t) is the probability of component failure at time t, and g(t) is the probability of component fault repair at time t.

[0099] (3) The probability distribution functions of the component normal operating time and fault repair time are as shown in the following formula.

[0100]

[0101] (4) Take the derivative of the above formula to obtain:

[0102]

[0103] Wherein, F'(t) represents the probability that the normal working time of the component is t, and G'(t) represents the probability that the fault repair time of the component is t. Both F'(t) and G'(t) are numbers between [0 - 1]. By randomly sampling to generate numbers between [0 - 1], and further converting them into random numbers that follow an exponential distribution.

[0104] (5) The time to failure (TTF) of the component and the time to repair (TTR) of the component can be expressed by the following formula.

[0105]

[0106] Where R t1 and R t2 represent numbers between [0 - 1] generated by a computer.

[0107] S202: Specify the reliability evaluation index of the distribution network.

[0108] During the reliability evaluation process of the distribution system of the present disclosure, the reliability of the distribution system is mainly reflected by reliability indexes. According to different evaluation objects, the reliability indexes of the distribution network are divided into load point indexes and system indexes. The load point indexes are used to evaluate the reliability degree of a single load point in the system, and the system indexes are mainly used to evaluate the reliability degree of the entire distribution network system.

[0109] Specify the reliability evaluation index of the load point.

[0110] (1) The average failure probability λ of the load point.

[0111] The average failure rate of the load point is the expected value of the number of power outages of the load point within the statistical time, and the unit is times / year.

[0112] (2) The average annual power outage time U of the load point.

[0113] The average annual power outage time of the load point refers to the expected value of the continuous time of the load point due to faults within the statistical time, and the unit is hours / year.

[0114] Specify the system reliability index.

[0115] (1) System average interruption frequency index (SAIFI).

[0116]

[0117] In the formula, λ i represents the average failure rate of load point i, and N i represents the number of users of load point i. The unit of SAIFI is times / (household·year).

[0118] (2) System average interruption duration index (SAIDI).

[0119]

[0120] Wherein, U i represents the annual average power outage time of load point i, and the unit of SAIDI is hour / (household·year).

[0121] (3) System total power shortage index (ENS).

[0122] ENS = ∑L i U i

[0123] Wherein, L i represents the average load of load point i, and the unit is kW·h / year.

[0124] (4) System average power supply reliability rate (ASAI).

[0125]

[0126] S203: Classify the loads according to the obtained load types and load characteristics.

[0127] According to the characteristics of the load itself and whether it participates in demand response, the loads are divided into non-adjustable loads, shiftable loads and interruptible loads. Among them, non-adjustable loads do not participate in demand response. When a component fails in the distribution network, resulting in a power outage of the load and forming an island, the shiftable loads and interruptible loads participate in demand response to reduce the power outage probability of the load point.

[0128] (1) Divide non-adjustable loads.

[0129] Non-adjustable loads refer to important loads that cannot be adjusted, which will have a great impact on the normal life or production of power users and generally do not participate in power response activities.

[0130] (2) Divide shiftable loads.

[0131] Shiftable loads refer to loads that can convert the current load quantity into the load quantity used by electrical appliances at other time periods without affecting the normal life or social production operation of power users at present. Generally, they are load types that can be flexibly adjusted. The use of such loads has a certain time tolerance, and off-peak use will not have a negative impact. Its model is shown as follows:[[]]

[0132]

[0133] Wherein, ΔE tr is the load quantity that can be transferred at time t; are the load values before and after load transfer respectively; + indicates that there is a load transfer in at time t; - indicates that there is a load transfer out at time t.

[0134] (3) Classify interruptible loads.

[0135] Interruptible load refers to the electric load that users can interrupt according to the load reduction signal of the distribution network in response to demand. Generally, it is of non - critical load type. The interruptible load model is shown as follows:

[0136]

[0137] In the formula, ΔE tr is the load that can be interrupted at time t; α is a binary variable. When α = 0, it means the load cannot be interrupted at this time. When α = 1, it means the load can be interrupted; are the load values before and after interrupting the load respectively.

[0138] The demand response principle when considering the distribution network fails and forms an island in this disclosure is as follows.

[0139] When the distribution network fails and forms an island, and the wind turbine and photovoltaic power generation in the island cannot meet the load demand when the fault occurs, the distribution network issues a demand response signal, and the loads in the island participate in the response. The demand response principle is as follows:

[0140] According to the difference between the output of distributed power sources and the load demand in the island, first, the shiftable loads in the loads participate in the response. When the shiftable loads participate in the response, it is necessary to determine when to shift the shiftable loads. The shifting principle is to shift the load to the time period with lower electricity price and smaller net difference between the distributed power generation power and the load power as much as possible, so as to achieve the purpose of obtaining more economic benefits while meeting the load demand. When the shiftable loads fully participate in the response and the distributed power generation power still cannot meet the load demand, the interruptible loads participate in the demand response. To minimize the amount of load interrupted by the interruptible loads, if partial participation of the interruptible loads can make the overall load in the island match the renewable energy output, the interruptible loads participate partially; otherwise, the interruptible loads participate fully.

[0141] When participating in the demand response, it should be noted that the shiftable loads and interruptible loads in the non - critical loads in the island participate in the response first, and the shiftable loads and interruptible loads in the critical loads do not participate in the demand response unless necessary.

[0142] In this disclosure, the reliability evaluation index of the distribution network obtained through fault simulation calculation can adopt the sequential Monte Carlo method to simulate the distribution network fault and evaluate the system reliability.

[0143] Specifically, according to the load demand response strategy, use a computing device to conduct active distribution network fault simulation, and obtain the reliability evaluation index of the distribution network under the load demand response strategy and the load demand response plan when a fault occurs, including the following sub - steps:

[0144] S20: Select the scenario with the highest probability in S133 as the typical scenario. Simulate and analyze the distribution network by using the load demand response strategy under the typical scenario.

[0145] S21: Set the number of simulations, and set the initial number of simulations to 1;

[0146] S22: Determine the simulation time, and set the initial time to 0;

[0147] S23: Assume that the components in the system are in normal working condition, generate a random number uniformly distributed between [0-1] for the non-power components in the system, and convert it into the normal working time TTF of the components;

[0148] S24: By comparing the trouble-free working times TTF of all non-power components randomly generated in S23, find the minimum TTFmin, and advance the simulation time step to TTFmin;

[0149] S25: For the component found in S24, generate a random number uniformly distributed between [0-1], and convert it into the repair time TTR of the component;

[0150] When using the Monte Carlo method to simulate faults, the impacts on the reliability of the distribution network after faults occur in different lines, equipment, and nodes in the distribution network are simulated. According to the simulated faults, control operations such as islanding division and load demand response are carried out to reduce the losses caused by distribution network faults.

[0151] S26: According to the selected test system, find other load points affected by the faulty component, and determine whether these load points are within the island. If so, execute S29; otherwise, jump to S210;

[0152] S27: If the load point can continue to be powered through islanding operation, determine whether the total output power of the wind turbines and photovoltaic systems within the island is greater than the total load level within the island. If so, the load point is not affected; if not, first enable the shiftable loads at each load point within the island to participate in demand response, and use the atomic orbital search algorithm to solve the amount of shiftable load participating in the response and the time when the load is shifted; if the output power of the renewable energy within the island matches the load after the shiftable loads participate in demand response, the load point is not affected; if the output power of the renewable energy still cannot meet the load demand after all the shiftable loads participate in the response, enable the interruptible loads of non-critical load points to participate in demand response. If the distributed power generation can meet the load demand after participating in the response, the load point is not affected; otherwise, the interruptible loads of critical load points participate in demand response; if all the interruptible loads within the island still cannot achieve supply-demand matching after participating in the response, load shedding is performed according to the importance of the loads until supply-demand balance is achieved; count the normal operation time TTF and the fault time TTR of the load points within the island, and record the demand response plan for this load participation.

[0153] It should be noted that due to the direct connection between distributed renewable power sources and loads, a new operation mode - islanding operation - has emerged in the distribution network. The islanding operation mode described in this disclosure is a planned islanding operation mode with uninterrupted power supply. In this islanding operation mode, when a component in the distribution system fails, the distributed power sources are not disconnected, but form an island by instantaneously tripping the tie switch. The distributed power sources within the island can continue to supply the required electricity to some loads. At this time, the relationship between the output power during the islanding operation and the total load within the island is shown by the following formula:

[0154]

[0155] In the formula, n represents the total number of renewable power sources within the island, P i represents the output power of the i-th distributed renewable power source within the island, m represents the total number of loads within the island, and L j represents the magnitude of the j-th load within the island. When the power generation of the distributed power sources within the island is insufficient to meet the load demand within the island, load shedding is performed according to the load type and the load importance level until the power generation of the distributed power sources can meet the load demand within the island.

[0156] Step 28: If the load point cannot continue to be powered through islanding operation, count its normal operation time TTF and the fault time TTR;

[0157] Step 29: Determine whether the total simulation time is greater than the preset simulation time. If so, execute S219; otherwise, jump to S23;

[0158] Among them, the simulation time is generally set to 8760 hours * the number of years to be simulated. However, the setting of the simulation time is not limited to this method and can also be set according to the service life of different components.

[0159] Step 210: Calculate the reliability index and record all the demand response schemes generated by this simulation.

[0160] Step 211: Determine whether the total number of simulations is greater than the preset number of simulations. If so, execute S3; otherwise, jump to S22.

[0161] S3: Generate a load demand response set under different fault conditions according to the reliability calculation result and the load demand response scheme.

[0162] See Figure 3 , and S3 includes the following sub-steps:

[0163] S31: Output the reliability calculation result of each simulation and the scheme of the distribution network load participating in the demand response during each simulation process, and generate a load demand response set under different fault conditions.

[0164] S32: Store the reliability evaluation result and the load demand response set when different faults occur in the storage device.

[0165] Monte Carlo simulation yields various fault conditions of the distribution network, and at the same time obtains island division and load demand response schemes under different conditions, resulting in a load demand response set under different fault conditions. During the actual operation of the distribution network, when a certain device, node, or line fails, find the corresponding control scheme in the previously obtained response scheme set, and then perform control measures such as island division and load response, which are applied to the actual control of the distribution network.

[0166] This disclosure takes into account the uncertainty of the source and load, proposes a method for evaluating the operation reliability of an active distribution network, and on this basis, proposes a load classification method to divide the load into non-adjustable load, shiftable load, and interruptible load, and proposes a load demand response strategy when an island is formed. Applying this load demand response strategy to the reliability assessment process of the active distribution network based on sequential Monte Carlo simulation, a load demand response set under different conditions of the active distribution network is obtained, which is used to cope with the fault risk of the distribution network and guide the operation and maintenance of the active distribution network.

[0167] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0168] The above is the introduction to the method embodiments. The following further illustrates the solution of the present disclosure through device embodiments.

[0169] Figure 4 The block diagram of a distribution network reliability evaluation device 400 considering source-load uncertainty according to an embodiment of the present disclosure is shown. The device 400 includes:

[0170] A typical scenario generation module 410, configured to generate a typical scenario according to the operation data and environmental data of the active distribution network.

[0171] A load demand response strategy generation module 420, configured to determine a load demand response strategy according to the typical scenario, perform a fault simulation of the active distribution network using a pre-constructed distribution network reliability evaluation model, and generate the reliability calculation result of the distribution network and the load demand response plan when a fault occurs under the load demand response strategy.

[0172] A load demand response set generation module 430, configured to generate a load demand response set under different fault conditions according to the reliability calculation result and the load demand response plan.

[0173] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the described modules can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0174] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0175] Figure 5FIG. 0 shows a schematic block diagram of an electronic device 500 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0176] The device 500 includes a computing unit 501 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0177] A plurality of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0178] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 executes the various methods and processes described above, such as the distribution network reliability assessment method considering source-load uncertainty. For example, in some embodiments, the distribution network reliability assessment method considering source-load uncertainty can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the distribution network reliability assessment method considering source-load uncertainty described above can be executed. Alternatively, in other embodiments, the computing unit 501 can be configured to execute the distribution network reliability assessment method considering source-load uncertainty in any other suitable manner (e.g., by means of firmware).

[0179] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0180] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0181] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0182] For providing interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0183] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0184] A computer system may include a client and a server. The client and the server are generally far away from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0185] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this is not limited herein.

[0186] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A method for evaluating the reliability of a distribution network considering the uncertainties of power sources and loads, characterized in that, The method includes: Predicting the load power according to the distribution network structure, load type, and load historical data; predicting the wind speed and light intensity based on environmental data, and then predicting the output of wind turbines and photovoltaic power generation. Sampling the prediction errors using the roulette wheel algorithm according to the probability distribution of the prediction errors of wind turbines, photovoltaic power generation, and load; adding the prediction errors to the predicted values of wind turbine output, photovoltaic output, and load to generate scenarios of wind turbine output, photovoltaic output, and load, and selecting the scenario with the highest probability as the typical scenario. Under the typical scenario, obtaining the load demand response strategy, and using the pre-constructed distribution network reliability evaluation model to simulate the faults of the active distribution network, generating the reliability calculation results of the distribution network under the load demand response strategy and the load demand response plan when a fault occurs; where The distribution network reliability evaluation model is constructed through the following steps: Determining the average normal operation time and average fault repair time of each component according to the operation state of each component of the distribution network equipment. Determining the operation state of each component according to the relationship between the output power in the island operation mode and the total load in the island. Calculating and determining the reliability indexes of load points and system reliability indexes according to the average normal operation time of the components, the average fault repair time of the components, and the operation state of each component, and then constructing a distribution network reliability evaluation model. The simulation of the active distribution network fault using the pre-constructed distribution network reliability evaluation model includes: Simulating the distribution network fault through the sequential Monte Carlo method, and judging whether the load point of the distribution network fault is in the island; if it is in the island, counting the normal operation time and fault time of the load points in the island; if it is not in the island, counting the normal operation time and fault time of the load point. Generating a load demand response set under different fault conditions according to the reliability calculation results and the load demand response plan.

2. The method according to claim 1, characterized in that, Selecting the scenario with the highest probability as the typical scenario, where the calculation formula for the probability of each scenario is as follows: Among them, represents the prediction error of wind, light output or load; ρ s represents the probability generated by scenario s; α i,t represents the probability of wind, light output or electrical load at time t in interval i; n represents the total number of discretized intervals; N S represents the total number of generated target scenarios.

3. The method according to claim 1, characterized in that, The load demand response strategy includes: Classifying the load into non-adjustable load, shiftable load, and interruptible load according to the characteristics of the load itself and whether it participates in demand response; among them, the non-adjustable load does not participate in demand response. When a component in the distribution network fails, causing a power outage of the load and forming an island, the shiftable load and the interruptible load participate in demand response.

4. The method according to claim 3, characterized in that, The step of counting the normal operation time and fault time of the load points in the island if it is in the island includes: If all the interruptible loads in the island still cannot achieve supply-demand matching when they all participate in the response, load shedding is carried out according to the importance of the load until supply-demand balance is achieved; counting the normal operation time and fault time of the load points in the island, and recording the load demand response plan for this time.

5. A device for evaluating the reliability of a distribution network considering the uncertainties of power sources and loads, characterized in that, The device includes: A typical scenario generation module, configured to predict the load power according to the distribution network structure, load type, and load historical data; predict the wind speed and light intensity based on environmental data, and then predict the output of wind turbines and photovoltaic power generation. According to the probability distributions of the prediction errors of wind turbines, photovoltaic power, and load, the roulette wheel algorithm is used to sample the prediction errors; the predicted values of wind turbine output, photovoltaic power output, and load are superimposed with the prediction errors to generate scenarios of wind turbine output, photovoltaic power output, and load, and the scenario with the highest probability is selected as the typical scenario; The load demand response strategy generation module is used to determine the load demand response strategy according to the typical scenario, and use the pre-constructed distribution network reliability evaluation model to simulate the faults of the active distribution network, and generate the reliability calculation results of the distribution network under the load demand response strategy and the load demand response plan when a fault occurs; where, The distribution network reliability evaluation model is constructed through the following steps: Determine the average normal operation time and average fault repair time of each component according to the operation status of each component of the distribution network equipment; Determine the operation status of each component according to the relationship between the output power in the island operation mode and the total load in the island; Calculate and determine the load point reliability index and system reliability index according to the average normal operation time of the component, the average fault repair time of the component, and the operation status of each component, and then construct a distribution network reliability evaluation model; The use of the pre-constructed distribution network reliability evaluation model to simulate the faults of the active distribution network includes: Simulate the faults of the distribution network through the sequential Monte Carlo method, and judge whether the load point of the distribution network fault is in the island; if it is in the island, count the normal operation time and fault time of the load points in the island; if it is not in the island, count the normal operation time and fault time of the load point; The load demand response set generation module is used to generate a load demand response set under different fault conditions according to the reliability calculation results and the load demand response plan.

6. An electronic device, characterized in that, Including: At least one processor; And A memory communicatively connected to the at least one processor; where, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

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