A power distribution network reliability evaluation method and device

Abstract

The present application relates to the technical field of power distribution network quantitative analysis, and specifically provides a power distribution network reliability evaluation method and device, comprising: substituting electrical parameters in a power distribution network simulation system into a pre-constructed day-ahead optimal scheduling model and solving, to obtain the residual capacity of energy storage units in the power distribution network simulation system; substituting the residual capacity of energy storage units in the power distribution network simulation system into a pre-constructed island division model and solving, to obtain load nodes falling into island power supply range; performing island planning for the power distribution network simulation system based on the load nodes falling into island power supply range; recording the number of power outages and cumulative power outage time of equipment in the power distribution network simulation system, and analyzing the reliability of the power distribution network based on the number of power outages and cumulative power outage time of equipment in the power distribution network simulation system. The technical scheme provided by the present application has reference significance for profit evaluation of wind-solar-storage investors and planning of future power grids.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network quantitative analysis, and particularly relates to a power distribution network reliability evaluation method and device. BACKGROUND

[0002] In recent years, due to the gradual depletion of traditional non-renewable energy such as coal, oil and natural gas, and the serious damage caused by its large-scale use to the environment, the development and utilization of new energy such as wind power and photovoltaic has become the trend of energy development in recent years and in the future.

[0003] The large-scale access of new energy and energy storage to the power distribution network will also affect the reliability evaluation of the power distribution network. After the access of new energy and energy storage, the operation mode of the power distribution network will change. Generally speaking, the power distribution network is mainly in a radial form, and under the network architecture of the radial form, the large power grid and the power distribution network are connected through the upstream to supply power. If a fault occurs in the power distribution network at a certain position, the power supply at the downstream position of the fault point will be affected, and all the loads downstream may be affected. However, with the addition of new energy and energy storage, this mode will change. In normal operation, the loads in the power distribution network will not only be supplied by the large power grid, but also by the large power grid, new energy and energy storage. When a fault occurs, the new energy and energy storage system can form an island to continuously supply power to the affected loads.

[0004] With more and more uncertain factors added to the power distribution network, the safe and reliable operation of the power distribution network is challenged, so it is particularly important to evaluate the reliability of the power distribution network with new energy and energy storage. SUMMARY

[0005] In order to overcome the above defects, the present application provides a power distribution network reliability evaluation method and device.

[0006] In a first aspect, a power distribution network reliability evaluation method is provided, which comprises:

[0007] Substituting the electrical parameters in the power distribution network simulation system into the pre-constructed day-ahead optimal scheduling model and solving, the residual capacity of the energy storage unit in the power distribution network simulation system is obtained;

[0008] Substituting the residual capacity of the energy storage unit in the power distribution network simulation system into the pre-constructed island division model and solving, the load node belonging to the island power supply range is obtained;

[0009] Based on the load node belonging to the island power supply range, island planning is performed on the power distribution network simulation system;

[0010] Record the number of power outages and the cumulative outage time of the equipment in the power distribution network simulation system, and analyze the reliability of the power distribution network based on the number of power outages and the cumulative outage time of the equipment in the power distribution network simulation system.

[0011] Preferably, before solving the pre-constructed daily optimization scheduling model to obtain the remaining capacity of the energy storage unit in the power distribution network simulation system, the method comprises the following steps of:

[0012] Sampling the fault state of the equipment in each period based on the average fault-free working time of the equipment and the average fault duration time of the equipment after failure;

[0013] Simulating the power distribution network based on the fault state of the equipment in each period to obtain the power distribution network simulation system.

[0014] Further, the method for sampling the fault state of the equipment in each period based on the average fault-free working time of the equipment and the average fault duration time of the equipment after failure comprises the following steps of:

[0015] Using a Latin hypercube sampling algorithm to sample the fault state of the equipment in each period;

[0016] Wherein, the calculation formula of the number of layers of the Latin hypercube sampling algorithm is as follows:

[0017]

[0018] In the above formula, N is the number of layers of the Latin hypercube sampling algorithm, T is the simulation time, t average is the average state cycle time of the equipment from fault-free state to fault state, λ ep is the failure rate of the equipment, μ ep is the repair rate of the equipment, and [·] is the rounding symbol.

[0019] Wherein, the calculation formula of the average state cycle time of the equipment from fault-free state to fault state is as follows:

[0020]

[0021] In the above formula, MTTF is the average fault-free working time of the equipment, and MTTR is the average fault duration time of the equipment after failure.

[0022] Further, the sampling function corresponding to the sampling of the fault-free working time of the equipment in the Latin hypercube sampling algorithm is:

[0023]

[0024] The sampling function corresponding to the sampling of the fault duration time of the equipment after failure in the Latin hypercube sampling algorithm is:

[0025]

[0026] In the above formula, TTF is the fault-free operation time of the sampling acquisition device, TTR is the fault duration after the sampling acquisition device fails, Z ∈ [1, N], and U is a random number between 0 and 1.

[0027] Preferably, the objective function in the pre-constructed day-ahead optimal scheduling model is:

[0028]

[0029] In the above formula, P dpv (t) is the output of the photovoltaic at time t in the power distribution network simulation system, P dwt (t) is the output of the wind turbine at time t in the power distribution network simulation system, P ESS (t) is the output of the energy storage at time t in the power distribution network simulation system, and P plan (t) is the planned output at time t in the power distribution network simulation system.

[0030] Further, the constraint condition in the pre-constructed day-ahead optimal scheduling model is:

[0031]

[0032] In the above formula, P pv (t) is the upper limit of the output of the photovoltaic, P wt (t) is the upper limit of the output of the wind turbine, P ESSin is the lower limit of the output of the energy storage, P ESSout is the upper limit of the output of the energy storage, SOC min is the lower limit of the capacity of the energy storage unit in the power distribution network simulation system, S SOC is the remaining capacity of the energy storage unit in the power distribution network simulation system, SOC max is the upper limit of the capacity of the energy storage unit in the power distribution network simulation system.

[0033] Preferably, the objective function in the pre-constructed island division model is:

[0034]

[0035] In the above formula, P i,t is the power of the load node i at time t in the power distribution network simulation system, w i is the importance degree coefficient of the load node i in the power distribution network simulation system, ζ i is the island coefficient of the load node i in the power distribution network simulation system, wherein ζ i = 1 or 0, when ζ i = 1, the load node i in the power distribution network simulation system is included in the island power supply range, and when ζi When =0, the load node i in the power distribution network simulation system is not included in the island power supply range, T is the simulation time, and n is the total number of load nodes in the power distribution network simulation system.

[0036] Further, the constraint condition in the pre-constructed island division model is:

[0037]

[0038] P ESS = min[P PCS ,P i ζ i -P t DG ]

[0039] P ESS ·T≤S SOC

[0040] In the above formula, P t DG is the output of wind power and photovoltaic at the t period, P ESS is the output of energy storage, P PCS is the maximum power of the energy storage converter, S SOC is the remaining capacity of the energy storage unit in the power distribution network simulation system.

[0041] Preferably, the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system are recorded, and the reliability of the power distribution network is analyzed based on the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system, including:

[0042] The average failure rate λ i of the load point i in the power distribution network is analyzed according to the following formula:

[0043]

[0044] The annual average outage time of the load point i in the power distribution network is analyzed according to the following formula:

[0045]

[0046] The average outage duration r i of the load point i in the power distribution network is analyzed according to the following formula:

[0047]

[0048] In the above formula, k i is the number of power outages of the load point i in the power distribution network, T is the simulation time, and U i is the cumulative power outage time of the load point i in the power distribution network.

[0049] Furthermore, the recording of the number of power outages and the cumulative power outage time of the equipment in the distribution network simulation system, and analyzing the reliability of the distribution network based on the number of power outages and the cumulative power outage time of the equipment in the distribution network simulation system, includes:

[0050] The average power outage frequency SAIFAI of the distribution network can be obtained by the following analysis:

[0051]

[0052] The average outage duration SAIDI of the distribution network can be obtained by the following analysis:

[0053]

[0054] The average power outage duration CAIDI of users in the distribution network can be obtained by the following analysis:

[0055]

[0056] The system reliability index ASAI of the distribution network is obtained by the following analysis:

[0057]

[0058] The total power shortage index ENS of the distribution network can be obtained by the following analysis:

[0059] ENS=∑P i U i

[0060] In the above formula, N i is the total number of users at load point i in the distribution network, P i is the average load at load point i in the distribution network.

[0061] In a second aspect, a distribution network reliability assessment device is provided, the distribution network reliability assessment device comprising:

[0062] The first analysis module is used to substitute the electrical parameters in the distribution network simulation system into the pre-built day-ahead optimization scheduling model and solve it to obtain the remaining capacity of the energy storage unit in the distribution network simulation system;

[0063] A second analysis module is used to substitute the remaining capacity of the energy storage unit in the distribution network simulation system into a pre-built island partition model and solve it to obtain the load nodes that fall within the island power supply range;

[0064] A planning module, configured to perform island planning on the distribution network simulation system based on the load nodes included in the island power supply range;

[0065] The third analysis module is configured to record the number of power outages and the cumulative outage time of the equipment in the power distribution network simulation system, and analyze the reliability of the power distribution network based on the number of power outages and the cumulative outage time of the equipment in the power distribution network simulation system.

[0066] Preferably, before solving the pre-constructed daily optimization scheduling model to obtain the residual capacity of the energy storage unit in the power distribution network simulation system, the method comprises the following steps of:

[0067] The failure state of the equipment in each period is sampled based on the average failure-free operation time of the equipment and the average failure duration of the equipment after failure;

[0068] The power distribution network is simulated based on the failure state of the equipment in each period to obtain the power distribution network simulation system.

[0069] Further, the failure state of the equipment in each period is sampled based on the average failure-free operation time of the equipment and the average failure duration of the equipment after failure, and the method comprises the following steps of:

[0070] The failure state of the equipment in each period is sampled by using a Latin hypercube sampling algorithm;

[0071] The calculation formula of the number of layers of the Latin hypercube sampling algorithm is as follows:

[0072]

[0073] In the above formula, N is the number of layers of the Latin hypercube sampling algorithm, T is the simulation time, t average is the average state cycle time of the equipment from the failure-free state to the failure state, λ ep is the failure rate of the equipment, and μ ep is the repair rate of the equipment, and [·] is the rounding symbol.

[0074] The calculation formula of the average state cycle time of the equipment from the failure-free state to the failure state is as follows:

[0075]

[0076] In the above formula, MTTF is the average failure-free operation time of the equipment, and MTTR is the average failure duration of the equipment after failure.

[0077] Further, the sampling function corresponding to the sampling of the failure-free operation time of the equipment in the Latin hypercube sampling algorithm is as follows:

[0078]

[0079] The sampling function corresponding to the sampling of the failure duration of the equipment after failure in the Latin hypercube sampling algorithm is as follows:

[0080]

[0081] In the above formula, TTF is the fault-free operation time of the sampling acquisition device, TTR is the fault duration after the sampling acquisition device fails, Z ∈ [1, N], and U is a random number between 0 and 1.

[0082] Preferably, the objective function in the pre-constructed day-ahead optimal scheduling model is:

[0083]

[0084] In the above formula, P dpv (t) is the output of the photovoltaic at time t in the power distribution network simulation system, P dwt (t) is the output of the wind turbine at time t in the power distribution network simulation system, P ESS (t) is the output of the energy storage at time t in the power distribution network simulation system, P plan (t) is the planned output at time t in the power distribution network simulation system.

[0085] Further, the constraint condition in the pre-constructed day-ahead optimal scheduling model is:

[0086]

[0087] In the above formula, P pv (t) is the upper limit of the output of the photovoltaic, P wt (t) is the upper limit of the output of the wind turbine, P ESSin is the lower limit of the output of the energy storage, P ESSout is the upper limit of the output of the energy storage, SOC min is the lower limit of the capacity of the energy storage unit in the power distribution network simulation system, S SOC is the remaining capacity of the energy storage unit in the power distribution network simulation system, SOC max is the upper limit of the capacity of the energy storage unit in the power distribution network simulation system.

[0088] Preferably, the objective function in the pre-constructed island division model is:

[0089]

[0090] In the above formula, P i,t is the power of the load node i at time t in the power distribution network simulation system, w i is the importance degree coefficient of the load node i in the power distribution network simulation system, ζ i is the island coefficient of the load node i in the power distribution network simulation system, wherein ζ i = 1 or 0, when ζ i = 1, the load node i in the power distribution network simulation system is included in the island power supply range, and when ζi when P ESS =0, the load node i in the power distribution network simulation system is not included in the island power supply range, T is the simulation time, and n is the total number of load nodes in the power distribution network simulation system.

[0091] Further, the constraint condition in the pre-constructed island division model is:

[0092]

[0093] P PCS = min[P i , P i ] t DG ]

[0094] P ESS · T ≤ S SOC

[0095] In the above formula, P t DG is the output of wind power and photovoltaic at the t period, P ESS is the output of energy storage, P PCS is the maximum power of the energy storage converter, S SOC is the remaining capacity of the energy storage unit in the power distribution network simulation system.

[0096] Preferably, the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system are recorded, and the reliability of the power distribution network is analyzed based on the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system, including:

[0097] The average failure rate λ i of the load point i in the power distribution network is analyzed by the following formula:

[0098]

[0099] The annual average outage time of the load point i in the power distribution network is analyzed by the following formula:

[0100]

[0101] The average outage duration r i of the load point i in the power distribution network is analyzed by the following formula:

[0102]

[0103] In the above formula, k i is the number of power outages of the load point i in the power distribution network, T is the simulation time, and U i is the cumulative power outage time of the load point i in the power distribution network.

[0104] Further, the power distribution network simulation system records the number of power outages and accumulative power outage time of the devices in the power distribution network, and analyzes the reliability of the power distribution network based on the number of power outages and accumulative power outage time of the devices in the power distribution network, comprising:

[0105] The system average interruption frequency index (SAIFI) of the power distribution network is analyzed according to the following formula:

[0106]

[0107] The system average interruption duration index (SAIDI) of the power distribution network is analyzed according to the following formula:

[0108]

[0109] The customer average interruption duration index (CAIDI) of the power distribution network is analyzed according to the following formula:

[0110]

[0111] The system reliability index (ASAI) of the power distribution network is analyzed according to the following formula:

[0112]

[0113] The system total energy not supplied (ENS) of the power distribution network is analyzed according to the following formula:

[0114] ENS = ∑ P i U i

[0115] In the above formula, N i is the total number of users of the load point i in the power distribution network, and P i is the average load of the load point i in the power distribution network.

[0116] In a third aspect, a computer device is provided, comprising: one or more processors;

[0117] The processor is configured to store one or more programs;

[0118] When the one or more programs are executed by the one or more processors, the power distribution network reliability evaluation method is implemented.

[0119] In a fourth aspect, a computer readable storage medium is provided, which has a computer program stored thereon, and the computer program is executed to implement the power distribution network reliability evaluation method.

[0120] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0121] The present invention provides a distribution network reliability assessment method and device, comprising: substituting electrical parameters in a distribution network simulation system into a pre-built day-ahead optimization scheduling model and solving it to obtain the remaining capacity of the energy storage unit in the distribution network simulation system; substituting the remaining capacity of the energy storage unit in the distribution network simulation system into a pre-built island partitioning model and solving it to obtain the load nodes that fall within the island power supply range; performing island planning on the distribution network simulation system based on the load nodes that fall within the island power supply range; recording the number of power outages and the cumulative power outage time of the equipment in the distribution network simulation system, and analyzing the reliability of the distribution network based on the number of power outages and the cumulative power outage time of the equipment in the distribution network simulation system. The technical solution provided by the present invention takes into account the sequential operating status of wind and solar power and the uncertainty of the SOC state of energy storage, and can quantify the impact of wind, solar and storage access on reliability, more accurately assess the power supply reliability of the distribution network containing wind, solar and storage, thereby quantifying the benefits of wind, solar and storage access in improving the reliability of the distribution network and providing a reference for power grid companies to rationally plan wind, solar and storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] Figure 1 1 is a flow chart showing the main steps of the distribution network reliability assessment method according to an embodiment of the present invention;

[0123] Figure 2 This is a main structural block diagram of a distribution network reliability assessment device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0124] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0125] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0126] Example 1

[0127] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a method for evaluating the reliability of a distribution network according to an embodiment of the present invention. Figure 1 As shown, the distribution network reliability assessment method in the embodiment of the present invention mainly includes the following steps:

[0128] Step S101: Substituting electrical parameters in the distribution network simulation system into a pre-built day-ahead optimization scheduling model and solving it to obtain the remaining capacity of the energy storage unit in the distribution network simulation system;

[0129] Step S102: substituting the remaining capacity of the energy storage unit in the power distribution network simulation system into the pre-constructed island division model and solving, to obtain a load node falling into the island power supply range;

[0130] Step S103: island planning of the power distribution network simulation system based on the load node falling into the island power supply range;

[0131] Step S104: recording the outage frequency and cumulative outage time of the equipment in the power distribution network simulation system, and analyzing the reliability of the power distribution network based on the outage frequency and cumulative outage time of the equipment in the power distribution network simulation system.

[0132] In the embodiment, before solving the pre-constructed day-ahead optimization scheduling model to obtain the remaining capacity of the energy storage unit in the power distribution network simulation system, the following steps are included:

[0133] Based on the average fault-free working time of the equipment and the average fault duration after the equipment fails, the fault state of the equipment in each period is sampled and obtained;

[0134] Based on the fault state of the equipment in each period, the power distribution network is simulated to obtain the power distribution network simulation system.

[0135] In one embodiment, based on the average fault-free working time of the equipment and the average fault duration after the equipment fails, the fault state of the equipment in each period is sampled and obtained, including:

[0136] The Latin hypercube sampling algorithm is used to sample and obtain the fault state of the equipment in each period;

[0137] Wherein, the calculation formula of the number of layers of the Latin hypercube sampling algorithm is as follows:

[0138]

[0139] In the above formula, N is the number of layers of the Latin hypercube sampling algorithm, T is the simulation time, t average is the state cycle average time of the equipment from the fault-free state to the fault state, λ ep is the failure rate of the equipment, μ ep is the repair rate of the equipment, and [·] is the rounding symbol;

[0140] Wherein, the calculation formula of the state cycle average time of the equipment from the fault-free state to the fault state is as follows:

[0141]

[0142] In the above formula, MTTF is the average fault-free working time of the equipment, and MTTR is the average fault duration after the equipment fails.

[0143] In one embodiment, the sampling function corresponding to sampling the failure duration of the device after failure in the Latin hypercube sampling algorithm is:

[0144]

[0145] The sampling function corresponding to sampling the failure duration of the device after failure in the Latin hypercube sampling algorithm is:

[0146]

[0147] In the above formula, TTF is the failure-free operation time of the device, TTR is the failure duration of the device after failure, Z ∈ [1, N], and U is a random number between 0 and 1.

[0148] In this embodiment, the objective function in the pre-constructed day-ahead optimal scheduling model is:

[0149]

[0150] In the above formula, P dpv (t) is the output of the photovoltaic at time t in the distribution network simulation system, P dwt (t) is the output of the wind turbine at time t in the distribution network simulation system, P ESS (t) is the output of the energy storage at time t in the distribution network simulation system, and P plan (t) is the planned output at time t in the distribution network simulation system.

[0151] In one embodiment, the constraint condition in the pre-constructed day-ahead optimal scheduling model is:

[0152]

[0153] In the above formula, P pv (t) is the upper limit of the output of the photovoltaic, P wt (t) is the upper limit of the output of the wind turbine, P ESSin is the lower limit of the output of the energy storage, P ESSout is the upper limit of the output of the energy storage, SOC min is the lower limit of the capacity of the energy storage unit in the distribution network simulation system, S SOC is the remaining capacity of the energy storage unit in the distribution network simulation system, SOC max is the upper limit of the capacity of the energy storage unit in the distribution network simulation system.

[0154] In this embodiment, the objective function in the pre-constructed island division model is:

[0155]

[0156] In the above formula, P i,t is the power of load node i at time t in the distribution network simulation system, w i is the importance coefficient of load node i in the distribution network simulation system, ζ i is the islanding coefficient of load node i in the distribution network simulation system, where ζ i =1or0, when ζ i = 1, the load node i in the distribution network simulation system is classified into the island power supply range. i =0, the load node i in the distribution network simulation system is not included in the island power supply range, T is the simulation time, and n is the total number of load nodes in the distribution network simulation system.

[0157] In one embodiment, the constraints in the pre-built island partitioning model are:

[0158]

[0159] P ESS =min[P PCS ,P i ζ i -P t DG ]

[0160] P ESS T≤S SOC

[0161] In the above formula, P t DG is the output of wind power and photovoltaic power in period t, P ESS is the output of energy storage, P PCS is the maximum power of the energy storage converter, S SOC is the remaining capacity of the energy storage unit in the distribution network simulation system.

[0162] In this embodiment, recording the number of power outages and the cumulative power outage time of devices in the distribution network simulation system, and analyzing the reliability of the distribution network based on the number of power outages and the cumulative power outage time of devices in the distribution network simulation system, includes:

[0163] The average failure rate λ of load point i in the distribution network is obtained by the following analysis: i :

[0164]

[0165] The annual average power outage time of load point i in the distribution network can be obtained by analysis as follows:

[0166]

[0167] The average outage duration r of the load point i in the power distribution network is analyzed by the following formula i :

[0168]

[0169] In the above formula, k i is the outage number of the load point i in the power distribution network, T is the simulation time, U i is the cumulative outage time of the load point i in the power distribution network.

[0170] In one embodiment, the outage number and cumulative outage time of the equipment in the power distribution network simulation system are recorded, and the reliability of the power distribution network is analyzed based on the outage number and cumulative outage time of the equipment in the power distribution network simulation system, comprising:

[0171] The system average interruption frequency (SAIFAI) of the power distribution network is analyzed by the following formula:

[0172]

[0173] The system average interruption duration index (SAIDI) of the power distribution network is analyzed by the following formula:

[0174]

[0175] The customer average interruption duration index (CAIDI) of the power distribution network is analyzed by the following formula:

[0176]

[0177] The system reliability index (ASAI) of the power distribution network is analyzed by the following formula:

[0178]

[0179] The system total energy not supplied (ENS) of the power distribution network is analyzed by the following formula:

[0180] ENS = ∑P i U i

[0181] In the above formula, N i is the total number of customers of the load point i in the power distribution network, and P i is the average load of the load point i in the power distribution network.

[0182] Embodiment 2

[0183] Based on the same inventive concept, the present application also provides a power distribution network reliability evaluation device, as shown in the accompanying drawings, which comprises: Figure 2

[0184] ​The first analysis module is configured to substitute the electrical parameters in the power distribution network simulation system into a pre-constructed day-ahead optimal scheduling model and solve the model to obtain the residual capacity of the energy storage unit in the power distribution network simulation system.

[0185] The second analysis module is configured to substitute the residual capacity of the energy storage unit in the power distribution network simulation system into a pre-constructed island division model and solve the model to obtain the load node falling into the island power supply range.

[0186] The planning module is configured to perform island planning for the power distribution network simulation system based on the load node falling into the island power supply range.

[0187] The third analysis module is configured to record the outage frequency and cumulative outage time of the equipment in the power distribution network simulation system, and analyze the reliability of the power distribution network based on the outage frequency and cumulative outage time of the equipment in the power distribution network simulation system.

[0188] Preferably, before solving the pre-constructed day-ahead optimal scheduling model to obtain the residual capacity of the energy storage unit in the power distribution network simulation system, the method comprises:

[0189] Sampling the fault state of the equipment in each period based on the average fault-free working time of the equipment and the average fault duration after the equipment fails;

[0190] Simulating the power distribution network based on the fault state of the equipment in each period to obtain the power distribution network simulation system.

[0191] Further, the method of sampling the fault state of the equipment in each period based on the average fault-free working time of the equipment and the average fault duration after the equipment fails comprises:

[0192] Using a Latin hypercube sampling algorithm to sample the fault state of the equipment in each period;

[0193] The calculation formula of the number of layers of the Latin hypercube sampling algorithm is as follows:

[0194]

[0195] In the above formula, N is the number of layers of the Latin hypercube sampling algorithm, T is the simulation time, t average is the state cycle average time of the equipment changing from a fault-free state to a fault state, λ ep is the failure rate of the equipment, and μ ep is the repair rate of the equipment, and [·] is the rounding symbol.

[0196] The calculation formula of the state cycle average time of the equipment changing from a fault-free state to a fault state is as follows:

[0197]

[0198] In the above formula, MTTF is the average failure-free operating time of the device, and MTTR is the average failure duration after the device fails.

[0199] Further, the sampling function corresponding to sampling the failure-free operating time of the device in the Latin hypercube sampling algorithm is:

[0200]

[0201] The sampling function corresponding to sampling the failure duration of the device after the device fails in the Latin hypercube sampling algorithm is:

[0202]

[0203] In the above formula, TTF is the failure-free operating time of the device obtained by sampling, TTR is the failure duration of the device obtained by sampling after the device fails, Z ∈ [1, N], and U is a random number between 0 and 1.

[0204] Preferably, the objective function in the pre-constructed day-ahead optimal scheduling model is:

[0205]

[0206] In the above formula, P dpv (t) is the output of the photovoltaic at time t in the distribution network simulation system, P dwt (t) is the output of the wind turbine at time t in the distribution network simulation system, P ESS (t) is the output of the energy storage at time t in the distribution network simulation system, P plan (t) is the planned output at time t in the distribution network simulation system.

[0207] Further, the constraint condition in the pre-constructed day-ahead optimal scheduling model is:

[0208]

[0209] In the above formula, P pv (t) is the upper limit of the output of the photovoltaic, P wt (t) is the upper limit of the output of the wind turbine, P ESSin is the lower limit of the output of the energy storage, P ESSout is the upper limit of the output of the energy storage, SOC min is the lower limit of the capacity of the energy storage unit in the distribution network simulation system, S SOC is the remaining capacity of the energy storage unit in the distribution network simulation system, SOC max is the upper limit of the capacity of the energy storage unit in the distribution network simulation system.

[0210] Preferably, the objective function in the pre-constructed island division model is:

[0211]

[0212] In the above formula, P i,t is the power of the load node i in the power distribution network simulation system at time t, w i is the importance coefficient of the load node i in the power distribution network simulation system, ζ i is the island coefficient of the load node i in the power distribution network simulation system, wherein ζ i = 1 or 0, when ζ i = 1, the load node i in the power distribution network simulation system is included in the island power supply range, when ζ i = 0, the load node i in the power distribution network simulation system is not included in the island power supply range, T is the simulation time, and n is the total number of load nodes in the power distribution network simulation system.

[0213] Further, the constraint condition in the pre-constructed island division model is:

[0214]

[0215] P ESS = min[P PCS ,P i ζ i -P t DG ]

[0216] P ESS · T ≤ S SOC

[0217] In the above formula, P t DG is the output of the wind power and photovoltaic at time t, P ESS is the output of the energy storage, P PCS is the maximum power of the energy storage converter, S SOC is the remaining capacity of the energy storage unit in the power distribution network simulation system.

[0218] Preferably, the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system are recorded, and the reliability of the power distribution network is analyzed based on the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system, including:

[0219] The average failure rate λ i of the load node i in the power distribution network is analyzed according to the following formula:

[0220]

[0221] The annual average outage time of the load node i in the power distribution network is analyzed according to the following formula:

[0222]

[0223] The average power outage duration r of load point i in the distribution network can be obtained by analysis as follows: i :

[0224]

[0225] In the above formula, k i is the number of power outages at load point i in the distribution network, T is the simulation time, U i is the cumulative power outage time of load point i in the distribution network.

[0226] Furthermore, the recording of the number of power outages and the cumulative power outage time of the equipment in the distribution network simulation system, and analyzing the reliability of the distribution network based on the number of power outages and the cumulative power outage time of the equipment in the distribution network simulation system, includes:

[0227] The average power outage frequency SAIFAI of the distribution network can be obtained by the following analysis:

[0228]

[0229] The average outage duration SAIDI of the distribution network can be obtained by the following analysis:

[0230]

[0231] The average power outage duration CAIDI of users in the distribution network can be obtained by the following analysis:

[0232]

[0233] The system reliability index ASAI of the distribution network is obtained by the following analysis:

[0234]

[0235] The total power shortage index ENS of the distribution network can be obtained by the following analysis:

[0236] ENS=∑P i U i

[0237] In the above formula, N i is the total number of users at load point i in the distribution network, P i is the average load at load point i in the distribution network.

[0238] Example 3

[0239] Based on the same inventive concept, the present application further provides a computer device, which comprises a processor and a memory, the memory is used to store a computer program, the computer program comprises program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of the power distribution network reliability evaluation method in the above embodiment.

[0240] Embodiment 4

[0241] Based on the same inventive concept, the present application further provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in the computer device, and is used to store programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the computer device, and of course can also include the expansion storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores the operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the steps of the power distribution network reliability evaluation method in the above embodiment.

[0242] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0243] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. 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, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0244] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0245] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0246] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing and illustrating, not limiting the technical solutions of the present application. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and any modifications or equivalent replacements without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for reliability evaluation of an electric power distribution network, characterized by, The method comprises: substituting electrical parameters in the power distribution network simulation system into a pre-constructed day-ahead optimal scheduling model and solving the model to obtain the residual capacity of the energy storage unit in the power distribution network simulation system; substituting the residual capacity of the energy storage unit in the power distribution network simulation system into a pre-constructed island division model and solving the model to obtain the load node falling into the island power supply range; performing island planning on the power distribution network simulation system based on the load node falling into the island power supply range; recording the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system, and analyzing the reliability of the power distribution network based on the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system; Before the step of solving the pre-constructed day-ahead optimal scheduling model to obtain the residual capacity of the energy storage unit in the power distribution network simulation system, the method comprises: sampling the fault state of the equipment in each period based on the average fault-free operating time of the equipment and the average fault duration after the equipment fails; performing simulation on the power distribution network based on the fault state of the equipment in each period to obtain the power distribution network simulation system; The step of sampling the fault state of the equipment in each period based on the average fault-free operating time of the equipment and the average fault duration after the equipment fails comprises: sampling the fault state of the equipment in each period by using a Latin hypercube sampling algorithm; wherein the calculation formula of the number of layers of the Latin hypercube sampling algorithm is as follows: In the above formula, N is the number of Latin hypercube sampling algorithm, T is the simulation time, t average is the average cycle time of the state of the device from the failure-free state to the failure state, λ ep is the failure rate of the device, μ ep is the repair rate of the device, and [·] is the rounding symbol. wherein the calculation formula of the state cycle average time of the equipment changing from a fault-free state to a fault state is as follows: In the above formula, MTTF is the average fault-free operating time of the equipment, and MTTR is the average fault duration after the equipment fails; The objective function in the pre-constructed day-ahead optimal scheduling model is: In the above formula, P dpv (t) is the output of the photovoltaic at time t in the distribution network simulation system, P dwt (t) is the output of the wind turbine at time t in the distribution network simulation system, P ESS (t) is the output of the energy storage at time t in the distribution network simulation system, P plan (t) is the planned output at time t in the distribution network simulation system; The objective function in the pre-constructed island division model is: In the above formula, P i,t is the power of the load node i in the power distribution network simulation system at time t, w i is the importance degree coefficient of the load node i in the power distribution network simulation system, ζ i is the island coefficient of the load node i in the power distribution network simulation system, wherein ζ i = 1 or 0, when ζ i = 1, the load node i in the power distribution network simulation system is included in the island power supply range, when ζ i = 0, the load node i in the power distribution network simulation system is not included in the island power supply range, T is the simulation time, and n is the total number of load nodes in the power distribution network simulation system. The constraint condition in the pre-constructed island division model is: P ESS = min[P PCS ,P i ζ i -P t DG ] P ESS • T < S SOC In the above formula, P t DG P is the output of wind power and photovoltaic at time t ESS P is the output of energy storage PCS S is the maximum power of energy storage converter SOC S is the remaining capacity of energy storage unit in the distribution network simulation system.

2. The method of claim 1, wherein, The sampling function corresponding to the sampling of the fault-free operating time of the equipment in the Latin hypercube sampling algorithm is: The sampling function corresponding to the sampling of the fault duration after the equipment fails in the Latin hypercube sampling algorithm is: In the above formula, TTF is the fault-free operating time of the equipment, TTR is the fault duration after the equipment fails, Z ∈ [1, N], and U is a random number between 0 and 1.

3. The method of claim 1, wherein, The constraint condition in the pre-constructed day-ahead optimal scheduling model is: In the above equation, P pv (t) is the upper limit of the output of the photovoltaic, P wt (t) is the upper limit of the output of the wind turbine, P ESSin is the lower limit of the output of the energy storage, P ESSout is the upper limit of the output of the energy storage, SOC min is the lower limit of the capacity of the energy storage unit in the power distribution network simulation system, S SOC is the remaining capacity of the energy storage unit in the power distribution network simulation system, SOC max is the upper limit of the capacity of the energy storage unit in the power distribution network simulation system.

4. The method of claim 1, wherein, The step of recording the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system, and analyzing the reliability of the power distribution network based on the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system comprises: The average failure rate λ of the load point i in the distribution network is analyzed as follows i : The annual average outage time of the load point i in the power distribution network is analyzed by the following formula The average outage duration r of the load point i in the distribution network is analyzed as follows i : In the above formula, k i is the number of power outages of the load point i in the distribution network, T is the simulation time, U i is the cumulative outage time of the load point i in the distribution network.

5. The method of claim 4, wherein, The step of recording the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system, and analyzing the reliability of the power distribution network based on the number of power outages and the cumulative power outage time of the equipment in the power distribution network simulation system comprises: The system average interruption frequency index (SAIFAI) of the power distribution network is analyzed according to the following formula: The system average interruption duration index (SAIDI) of the power distribution network is analyzed according to the following formula: The customer average interruption duration index (CAIDI) of the power distribution network is analyzed according to the following formula: The system reliability index (ASAI) of the power distribution network is analyzed according to the following formula: The system total power shortage index ENS of the power distribution network is obtained by analysis according to the following formula: ENS =∑P i U i In the above formula, N i is the total number of users of the load point i in the distribution network, P i is the average load of the load point i in the distribution network.

6. An apparatus for reliability evaluation of a power distribution network, characterized by The device comprises: A first analysis module configured to substitute electrical parameters in the power distribution network simulation system into a pre-constructed day-ahead optimal scheduling model and solve the model to obtain the residual capacity of the energy storage unit in the power distribution network simulation system; A second analysis module configured to substitute the residual capacity of the energy storage unit in the power distribution network simulation system into a pre-constructed island division model and solve the model to obtain load nodes falling into the island power supply range; A planning module configured to perform island planning on the power distribution network simulation system based on the load nodes falling into the island power supply range; A third analysis module configured to record the number of power outages and the cumulative power outage time of equipment in the power distribution network simulation system and analyze the reliability of the power distribution network based on the number of power outages and the cumulative power outage time of equipment in the power distribution network simulation system; Before the solving of the pre-constructed day-ahead optimal scheduling model to obtain the residual capacity of the energy storage unit in the power distribution network simulation system, the method comprises: Sampling the fault state of the equipment in each period based on the average fault-free working time of the equipment and the average fault duration after the equipment fails; Simulating the power distribution network based on the fault state of the equipment in each period to obtain the power distribution network simulation system; The sampling of the fault state of the equipment in each period based on the average fault-free working time of the equipment and the average fault duration after the equipment fails comprises: Sampling the fault state of the equipment in each period by using a Latin hypercube sampling algorithm; The calculation formula of the number of layers of the Latin hypercube sampling algorithm is as follows: In the above formula, N is the number of Latin hypercube sampling algorithm, T is the simulation time, t average is the average cycle time of the state of the device from the failure-free state to the failure state, λ ep is the failure rate of the device, μ ep is the repair rate of the device, [·] is the rounding symbol; The calculation formula of the state cycle average time of the equipment changing from a fault-free state to a fault state is as follows: In the above formula, MTTF is the average fault-free working time of the equipment, and MTTR is the average fault duration after the equipment fails; The objective function in the pre-constructed day-ahead optimal scheduling model is as follows: In the above formula, P dpv (t) is the output of the photovoltaic at time t in the distribution network simulation system, P dwt (t) is the output of the wind turbine at time t in the distribution network simulation system, P ESS (t) is the output of the energy storage at time t in the distribution network simulation system, P plan (t) is the planned output at time t in the distribution network simulation system; The objective function in the pre-constructed island division model is as follows: In the above formula, P i,t is the power of the load node i in the power distribution network simulation system at time t, w i is the importance coefficient of the load node i in the power distribution network simulation system, ζ i is the island coefficient of the load node i in the power distribution network simulation system, wherein ζ i = 1 or 0, when ζ i = 1, the load node i in the power distribution network simulation system is included in the island power supply range, when ζ i = 0, the load node i in the power distribution network simulation system is not included in the island power supply range, T is the simulation time, and n is the total number of load nodes in the power distribution network simulation system. The constraint condition in the pre-constructed island division model is as follows: P ESS = min[P PCS ,P i ζ i -P t DG ] P ESS • T < S SOC In the above equation, P t DG P is the power output of the wind and photovoltaic power at time t ESS P is the power output of the energy storage PCS S is the maximum power of the energy storage converter SOC S is the remaining capacity of the energy storage unit in the distribution network simulation system.

7. The apparatus of claim 6, wherein, The sampling function corresponding to the sampling of the fault-free working time of the equipment in the Latin hypercube sampling algorithm is as follows: The sampling function corresponding to the sampling of the fault duration after the equipment fails in the Latin hypercube sampling algorithm is as follows: In the above formula, TTF is the fault-free working time of the equipment, TTR is the fault duration after the equipment fails, Z ∈ [1, N], and U is a random number between 0 and 1.

8. The apparatus of claim 7, wherein, The constraint condition in the pre-constructed day-ahead optimal scheduling model is as follows: In the above equations, P pv (t) is the upper limit of the photovoltaic power output, P wt (t) is the upper limit of the wind turbine power output, P ESSin is the lower limit of the energy storage power output, P ESSout is the upper limit of the energy storage power output, SOC min is the lower limit of the energy storage capacity in the power distribution network simulation system, S SOC is the remaining capacity of the energy storage unit in the power distribution network simulation system, SOC max is the upper limit of the energy storage capacity in the power distribution network simulation system.

9. The apparatus of claim 6, wherein, The recording of the number of power outages and the cumulative power outage time of equipment in the power distribution network simulation system and the analysis of the reliability of the power distribution network based on the number of power outages and the cumulative power outage time of equipment in the power distribution network simulation system comprise: The average failure rate λ of the load point i in the distribution network is analyzed as follows i : The annual average outage time of the load point i in the power distribution network is analyzed by the following formula The average outage duration r of the load point i in the distribution network is analyzed as follows i : In the above formula, k i is the number of power outages of the load point i in the distribution network, T is the simulation time, U i is the cumulative outage time of the load point i in the distribution network.

10. The apparatus of claim 9, wherein, The recording of the number of power outages and the cumulative power outage time of equipment in the power distribution network simulation system and the analysis of the reliability of the power distribution network based on the number of power outages and the cumulative power outage time of equipment in the power distribution network simulation system comprise: The system average outage frequency SAIFAI of the power distribution network is obtained by analysis according to the following formula: The system average outage duration SAIDI of the power distribution network is obtained by analysis according to the following formula: The customer average interruption duration index CAIDI of the power distribution network is obtained by analysis as follows: The system adequacy index ASAI of the power distribution network is obtained by analysis as follows: The system energy not supplied index ENS of the power distribution network is obtained by analysis as follows: ENS = ∑P i U i In the above formula, N i is the total number of users of the load point i in the distribution network, P i is the average load of the load point i in the distribution network.

11. A computer device, comprising: The system energy not supplied index ENS of the power distribution network is obtained by analysis as follows: one or more processors; the processor is configured to store one or more programs; when the one or more programs are executed by the one or more processors, the power distribution network reliability evaluation method according to any one of claims 1 to 5 is implemented.

12. A computer-readable storage medium, characterized in that, a computer program is stored thereon, and the computer program is executed to implement the power distribution network reliability evaluation method according to any one of claims 1 to 5.

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