Distribution network power supply reliability assessment method and system based on distribution-microgrid interaction mode

Abstract

A distribution network power supply reliability assessment method and system based on the interaction model of distribution and microgrids integrates the dynamic characteristics of multiple power sources, including wind, photovoltaic, energy storage, gas, and diesel generation, and combines the islanding and grid-connected support capabilities of microgrids. Three distribution and microgrid interaction modes are defined: fault island autonomy, non-coordinated grid-connected support, and coordinated control grid-connected support. Corresponding multi-power coordinated control strategies are constructed for each mode. This method comprehensively reflects the power supply reliability level of the distribution system under different interaction modes, effectively improving the accuracy and practicality of the assessment. By introducing sequential Monte Carlo simulation technology to simulate the system's temporal operating state, the method quantitatively evaluates various reliability indicators at the load point and system levels. This method is applicable to future distribution network scenarios involving multiple microgrids, where the coordinated interaction of distribution and microgrids is predominant. It has important theoretical value and engineering application significance for improving distribution network power supply reliability, enhancing energy utilization efficiency, and promoting energy structure transformation.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system reliability assessment, and relates to a distribution network power supply reliability assessment method and system based on a distribution-microgrid interaction mode. Background Art

[0002] With the rapid development of society and the continuous growth of electricity demand, the global energy structure is undergoing a transition from fossil energy to renewable energy. As clean and efficient renewable energy sources, wind and solar energy have seen their installed capacity continue to rise, gradually becoming an important part of the modern power system. However, the randomness and intermittent nature of wind and photovoltaic power generation pose new challenges to the operational stability and reliability of the power system: 1) Grid operation pressure: The power generation of wind and photovoltaic power is affected by multiple factors such as weather and light conditions. The uncertainty of output power can easily lead to problems such as voltage fluctuations and frequency deviations, thereby threatening the operational safety of the power grid; 2) Distribution network power supply reliability risks: As the terminal link of power transmission, the distribution network is the main access point for distributed power sources such as wind and solar power. In the context of a high proportion of renewable energy access, the distribution network needs to cope with the multiple challenges brought about by power fluctuations, uneven energy distribution, and load uncertainty.

[0003] Traditional distribution network reliability assessment methods often ignore the dynamic impact of microgrid operating conditions on the system, failing to reflect the randomness of microgrid output, its ability to operate in isolated islands, and the participation of new loads such as electric vehicles. Establishing a reliability assessment model that considers the multiple interaction modes between microgrids and distribution networks is key to improving assessment accuracy and practicality. Under different interaction modes, the output and regulation strategies of multiple sources in the microgrid, such as wind, solar, storage, and fuel, will directly impact the system's power supply capacity and load reliability. Therefore, it is necessary to integrate modeling and simulation of distribution-microgrid interaction modes into traditional reliability assessment models to obtain power supply reliability assessment results that are more relevant to the new energy era.

[0004] To address these challenges, microgrid technology is emerging as a key solution. By integrating multiple energy sources, such as wind, photovoltaics, energy storage systems, and diesel-fired power generation, microgrids not only complement each other's strengths but also enable autonomous operation in island mode in the event of grid failures, improving power supply stability and flexibility. In particular, microgrids that incorporate multiple power sources, including wind, solar, energy storage, and diesel, have the following key features: 1) Multi-energy synergy: By combining clean energy sources like wind and solar with flexible energy sources like energy storage and diesel, they achieve efficient and reliable power supply. 2) Enhanced grid resilience: During natural disasters or emergencies, they can switch to island mode to ensure continuous power supply to critical loads. 3) Improved energy efficiency: By optimizing the scheduling of wind, solar, energy storage, and diesel resources, they reduce wind and solar curtailment and increase the utilization of renewable energy. Furthermore, with the widespread deployment of multiple microgrids within distribution systems, they not only operate as independent, autonomous units but also increasingly possess the ability to interact at multiple levels with the main distribution network and neighboring microgrids. This "distribution network-microgrid" linkage mechanism is often referred to as the distribution-microgrid interaction model. For example, patent document CN111125877A establishes a multi-state reliability model for distributed power sources and uses a generalized capacity interruption table to model microgrids, thereby evaluating the reliability of microgrids. Another example is patent document CN119787353A, which uses a Monte Carlo simulation framework to develop a modeling process for an autonomous grid-connected microgrid. This model establishes a probabilistic reliability model that describes microgrids applicable to various standard scenarios and evaluates the reliability of distribution networks with multiple MGs connected to the grid. Existing technologies do not fully consider the multi-mode operational responses of microgrids under distribution network fault conditions, nor do they account for the random failures that exist within microgrids. Therefore, they cannot comprehensively and accurately evaluate the reliability of distribution networks under complex energy structures.

[0005] Therefore, there is an urgent need for a distribution network reliability assessment method that is suitable for connecting multiple microgrids and considering the interactive mode of distribution microgrids. This method has important theoretical value in ensuring the stable operation of the power grid, thereby optimizing resource allocation, promoting the efficient use of renewable energy, and promoting the transformation of the energy structure. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention provides a distribution network reliability assessment method and system based on a distribution-microgrid interaction model. This method comprehensively considers the dynamic characteristics and complementary relationships of wind power, photovoltaics, energy storage systems, and diesel-fired power generation. It is adaptable to distribution network systems with varying energy proportions and operating conditions, supports multi-scenario analysis, and enables a more comprehensive and accurate assessment of distribution network reliability under complex energy structures. The implementation of this invention will provide important technical support for improving distribution network reliability assessment in complex microgrid scenarios, while also providing a scientific basis for the efficient utilization and scheduling of new and traditional energy sources.

[0007] The present invention proposes a distribution network power supply reliability assessment method based on a distribution-microgrid interaction model, comprising:

[0008] Step 1: Obtain the grid data of the distribution microgrid, including the topology, load characteristics, component parameters, failure rate, and power parameters of wind, solar, gas, diesel, storage, and charging within the microgrid;

[0009] Step 2: Based on the grid data of the distribution microgrid, a state reliability assessment model of the distribution network non-power components and the microgrid power components is established;

[0010] Step 3: Based on the state reliability assessment model, define the distribution microgrid interaction mode, formulate the corresponding multi-power coordinated control strategy, and obtain the interactive power data between the distribution microgrids;

[0011] Step 4: Establish a failure mode consequence analysis table for the load points, classify the loads according to their power supply status after the failure, and assign reliability index weights to each type of load;

[0012] Step 5: Based on the multi-power coordinated control strategy and the interactive power data between the distribution microgrid, the simulation time of sampling simulation of the timing operation status of the distribution microgrid system is promoted; according to the final simulation results and the reliability index weight, the power supply reliability index of each load point and the system is calculated.

[0013] Furthermore, in step 2, for the non-power supply components of the distribution network, the expressions of component normal operation time and component fault repair time are used as the reliability evaluation model, and the state duration sampling is performed on the normal operation time and fault repair time to obtain the state of the non-power supply components at each time within the evaluation period;

[0014] For microgrid power supply components, considering the failure rate, the wind and solar timing output model is used to represent the status of the power supply components as a reliability assessment model for the power supply components.

[0015] Furthermore, in step 3, the defined distribution and microgrid interaction modes include fault island autonomous mode, non-cooperative grid support mode, and cooperative regulation grid support mode. The coordinated control strategy in each interaction mode is:

[0016] In the fault island autonomous mode, the system enters the off-grid autonomous state. The distributed power sources of wind, solar, fuel, diesel and storage in the island meet the load requirements and achieve real-time power balance.

[0017] In the non-coordinated grid-connected support mode, each microgrid power generation unit, energy storage, and load operates with a local fixed multi-power coordinated control strategy, serving as a backup support power source for the distribution network.

[0018] In the coordinated control grid-connected support mode, distributed power sources give priority to meeting the microgrid's own load and then support the distribution network power gap; electric vehicles are given priority to charging when the total output of the microgrid's distributed power sources is greater than the microgrid's load demand, and discharge support is provided when the total output of the distributed power sources is lower than the microgrid's review demand.

[0019] Furthermore, in step 4, the loads are classified according to their power supply status after the fault, including: loads that are normally powered under the fault state, loads that have power restored through the isolation switch, loads that have power restored through the distributed power supply, and loads that have been without power;

[0020] Different reliability index weights are assigned to different load types.

[0021] Furthermore, in step 5, the specific steps include:

[0022] Step 51, setting and reading the original parameters of the distribution network and initializing them; setting the simulation life according to the initialized original parameters, the simulation life is the operation time of the distribution microgrid system;

[0023] Step 52: Based on the failure rate of the unit, the power data of the interaction between the distribution and microgrids, and the normal working time and failure time of the computer group, a time series of the output of each unit is obtained;

[0024] Step 53: Among all the components, the unit with the shortest fault-free working time is selected as the faulty unit, and the minimum working time of the distribution microgrid system is obtained. The working time of the distribution microgrid system is accumulated to the simulation time to obtain the total output of the distribution network in each corresponding time period;

[0025] Step 54: Based on the time series of each unit output, advance the simulation time , , Is the failure time or repair time; if the component status is faulty, the component repair time is generated Otherwise, the component failure time is generated ;

[0026] Step 55: When the simulation time is greater than the simulation years, the simulation is terminated; and the power supply reliability index of each load point and the system is calculated in combination with the reliability index weight.

[0027] Furthermore, the original parameters include the node name of the entire network, the normal output of the unit, and the failure rate.

[0028] Furthermore, in step 51 , the initial states of the components are all set to be normal.

[0029] Furthermore, the simulation time The calculation formula is , Is the failure time or repair time; if the component status is faulty, the component repair time is generated Otherwise, the component failure time is generated .

[0030] Furthermore, power supply reliability indicators include load point indicators and system-level indicators;

[0031] Among them, load point indicators include the average annual fault frequency of users, the average annual power outage time, and the average power outage duration of a single fault;

[0032] System-level indicators include the system average power outage frequency indicator, the system average power outage duration indicator, the average power supply availability indicator and the system total power shortage indicator, as well as the number of households during power outages; the number of households during power outages refers to the number of users whose power demand cannot be met by the system within a given time period, and the number of households during power outages = Σnumber of users affected by the power outage × duration of the power outage.

[0033] The present invention also proposes a distribution network power supply reliability assessment system based on the distribution-microgrid interaction mode, which includes a data acquisition module, a reliability assessment model design module, a multi-power coordinated control strategy design module, a reliability index weight setting module, and a reliability index calculation module:

[0034] The data acquisition module acquires the grid data of the distribution microgrid, including the topology, load characteristics, component parameters, failure rate, and power parameters of wind, solar, gas, diesel, storage, and charging within the microgrid;

[0035] The reliability assessment model design module establishes a state reliability assessment model for distribution network non-power components and microgrid power components based on the power grid data of the distribution microgrid;

[0036] The multi-power coordinated control strategy design module defines the distribution and microgrid interaction mode based on the state reliability assessment model, formulates the multi-power coordinated control strategy, and obtains the interactive power data between the distribution and microgrids;

[0037] The reliability index weight setting module establishes a failure mode consequence analysis table for load points, classifies loads according to their power supply status after a failure, and assigns reliability index weights to each type of load;

[0038] The reliability index calculation module promotes the simulation time of sampling simulation of the timing operation status of the distribution microgrid system based on the multi-power coordinated control strategy and the interactive power data between the distribution microgrids; according to the final simulation results and the reliability index weight, the power supply reliability index of each load point and the system is calculated.

[0039] The beneficial effects of the present invention are that, compared with the prior art, the distribution network power supply reliability assessment method of the present invention considering the distribution-micro interaction mode has the following significant advantages over the traditional distribution network power supply reliability:

[0040] 1. The present invention comprehensively considers the impact of the distribution-microgrid interaction mode on the system. The three distribution-microgrid interaction modes proposed in the present invention (fault island autonomous mode, non-cooperative grid-connected support mode, and cooperative regulation grid-connected support mode) provide a variety of response strategies for the distribution network, so that under different fault or operating conditions, the distribution network can more flexibly adjust its energy supply and load scheduling. These modes can not only ensure continuous power supply in the event of a fault, but also optimize the coordination between different energy sources, improve energy utilization, and reduce unnecessary resource waste. In addition, the multi-power coordinated control strategy in each mode helps to ensure a smooth supply of energy, reduce the impact of the volatility of renewable energy such as wind power and photovoltaics on the power grid, improve the stability and resilience of the power grid, and ultimately achieve a comprehensive improvement in the power supply reliability of the distribution network.

[0041] 2. The present invention constructs a wind-solar timing output model based on the failure rate of multiple power sources within the microgrid, which can more accurately simulate the energy output fluctuations of the microgrid under fault conditions. The introduction of the wind-solar model enables the reliability assessment to take into account the randomness and sudden failures of components within the microgrid, thereby improving the assessment accuracy of the overall reliability of the distribution network. In traditional distribution network reliability analysis, the impact of microgrid internal failures on distribution network stability is often ignored. However, by incorporating microgrid failure factors, the present invention can more comprehensively assess the operating risks of the distribution network under a complex energy structure, providing a more scientific basis for the design and scheduling of the distribution network.

[0042] 3. Integration of dynamic characteristics and control strategies of multiple types of distributed power sources: This invention incorporates multiple energy types such as wind power, photovoltaics, energy storage systems, gas / diesel power generation, charging piles for electric vehicles, etc. into a unified modeling system, taking into account their output volatility, uncertainty and controllability. It also takes into account certain random failure problems within the microgrid. Therefore, through the design of multi-power coordinated control strategies, dynamic simulation of the system state under multi-energy complementary conditions is achieved.

[0043] 3. This invention enhances the accuracy and practicality of fault response modeling. By establishing a load point fault mode consequence analysis table, the load response under fault conditions is subdivided into four categories (Class A to Class D loads). Combining the fault isolation mechanism with the microgrid's autonomous and collaborative power supply capabilities, this improves the granularity and practicality of reliability assessment under fault conditions.

[0044] 4. This invention introduces a more practical system reliability indicator. In addition to traditional indicators such as SAIFI, SAIDI, CAIDI, and ASAI, this invention adds the "household number during power outage (IHN)" indicator to better reflect the direct impact of fault events on user electricity experience and power supply security, and has greater engineering practicality. This method is applicable to new load access scenarios such as wind, solar, storage, and fuel-fired multi-energy integration, and electric vehicles. It can dynamically adapt to different energy structures, operating strategies, and distribution network topology changes, providing a systematic solution for power supply reliability analysis in multiple scenarios and operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a step diagram of the distribution network power supply reliability assessment method based on the distribution-microgrid interaction mode of the present invention;

[0046] Figure 2 It is a method flow chart of the distribution network power supply reliability assessment method based on the distribution-microgrid interaction mode of the present invention;

[0047] Figure 3 It is a two-state repairable outage model diagram of the non-power supply components of the distribution network in the present invention;

[0048] Figure 4 This is a system diagram of a multi-microgrid distribution network calculation example in the present invention;

[0049] Figure 5 The annual wind speed curve of wind power and the annual photovoltaic irradiance curve of the present invention are shown;

[0050] Figure 6 This is a daily power diagram of the internal combustion engine and diesel generator of microgrid 2 in mode 2 of the present invention;

[0051] Figure 7 This is a daily energy storage diagram of microgrid 1 and microgrid 2 in mode 2 of the present invention;

[0052] Figure 8 This is a daily power diagram of EV in mode 2 for microgrid 1 and microgrid 2 of the present invention;

[0053] Figure 9 This is a daily power diagram of the internal combustion engine and diesel generator of microgrid 2 in mode 3 of the present invention;

[0054] Figure 10 This is a daily energy storage diagram of microgrid 1 and microgrid 2 in mode 3 of the present invention;

[0055] Figure 11 This is a daily graph of EV in microgrid 1 and microgrid 2 of the present invention in mode 3. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0057] Example 1

[0058] The first embodiment of this embodiment provides a distribution network power supply reliability assessment method based on the distribution microgrid interaction mode, such as Figure 1 and Figure 2 The following table shows the steps and flow chart.

[0059] Step 1: Obtain the grid data of the distribution microgrid, including the topology, load characteristics, component parameters, failure rate of the distribution network, and power parameters such as wind, solar, gas, diesel, storage, and charging in the microgrid.

[0060] Specifically, the acquisition of the distribution network topology structure includes: collecting the connection information of the distribution network nodes to form a node-branch relationship matrix or a graph structure model; clarifying the basic forms such as the backbone network, branch lines, feeder configuration, and radial structure; identifying key control points such as microgrid interface nodes, transformer access points, circuit breakers, and switch configurations.

[0061] Furthermore, the information collection of load characteristics includes: obtaining the typical daily / weekly / monthly / yearly load curve of each load point; classifying the load (such as residential, commercial, industrial, agricultural, etc.) and considering its response characteristics; and clarifying the annual average power demand, peak load, load sensitivity and other parameters of each load point.

[0062] Furthermore, the component parameters of the distribution network include transformers, overhead lines, cables, circuit breakers, fuses, disconnectors, etc.; and their electrical parameters are obtained, including rated capacity, voltage level, impedance, conductor type, etc.; the maintenance cycle and environmental adaptability parameters of the distribution network components during their operating life are collected.

[0063] The failure rate of distribution network components is obtained by statistically analyzing historical data or referring to industry experience, such as lines (0.065 times / km•year), transformers (0.015 times / unit•year), etc. The repair time of distribution network components is measured in hours, reflecting the average time required from the occurrence of a fault to the restoration of power supply, and distinguishing between the repair time of planned maintenance and that of sudden faults.

[0064] Furthermore, the parameters of wind, solar, gas, diesel, storage, and charging power sources within the microgrid include: ① PV system parameters: PV module rated capacity, panel efficiency, temperature coefficient, inverter efficiency; light intensity data; factors affecting output, such as location coordinates, altitude, orientation, and tilt angle. ② Wind power system parameters: turbine model, rated power, start / stop wind speeds, hub height; local historical wind speed data and wind speed distribution; wind speed-power output curve. ③ Energy storage system parameters: battery type, rated capacity, maximum charge / discharge power, SOC range, charge / discharge efficiency, and number of cycles. ④ Gas / diesel generator parameters: single-unit power, minimum stable output, ramp rate; start-up time, operating efficiency, natural gas / diesel reserve capacity; output response characteristics, and operation and maintenance requirements. ⑤ Charging station and electric vehicle parameters: number of charging stations, fast / slow charging, rated power, electric vehicle arrival and departure times, power requirements, and vehicle V2G capability.

[0065] The data collection in step 1 will serve as a prerequisite for "state modeling" and "operation control strategy formulation" in step 2, ensuring the accuracy of the system dynamic simulation and the reference value of the simulation results.

[0066] Step 2: Based on the grid data of the distribution microgrid, a state reliability assessment model for the distribution network non-power components and microgrid power components is established.

[0067] The basic states of distribution network components include normal operation and outage. The outage can be divided into planned maintenance outage and fault outage according to the nature of the outage. The fault outage state of the component can be divided into repair outage and non-repairable outage. Generally speaking, in the distribution network power supply reliability assessment, the two-state reliability assessment model is mainly used to represent non-power supply components, such as Figure 3 shown.

[0068] Distribution network non-power components are complex. Traditional reliability assessment methods primarily consider their failure and repair rates to simplify the distribution network reliability assessment process. Traditional distribution networks are complex and include many unique components. Typical examples include distribution transformers, overhead lines (bare and insulated wires), fuses, circuit breakers, disconnectors, cables, and more. The most significant characteristic of these typical components is their repairability. Therefore, in distribution networks, these non-power components are typically represented using a two-state repairable outage model.

[0069] The non-power components described above typically use a two-state repairable outage model. The duration a component maintains in state 1 is called the operating time (TF) and the duration a component maintains in state 0 is called the repair time (TR). A component in the system may transition from state 1 to state 0 due to a component failure or scheduled maintenance. When the component fails or the maintenance is complete, the system returns to state 1 and continues operating. In Monte Carlo simulation, the mathematical expressions for the durations of the two states are:

[0070] ;

[0071] Where, 、 The working time and repair time of each component are respectively, 、 The failure rates and repair rates of each component are as follows: the failure rates of circuit breakers, feeder lines, and transformer components are 0.004, 0.065, and 0.015 (times / (km·year)), respectively; the repair rates are 4, 5, and 200 (h / time), respectively. 、 is a random number uniformly distributed between [0,1]. By sampling the state duration of the normal operation time and the fault repair time, the state of the system components at each moment in the cycle can be obtained.

[0072] Once a microgrid is connected to the grid, its components become larger and more complex. Unlike non-power components like overhead lines and transformers, power components within a microgrid, such as distributed power sources, typically have multiple operating states. Considering the potential for failures within the microgrid, which can impact the distribution network, a time-sequential output model that accounts for failure rates has been constructed for the multiple power sources within the microgrid. This paper uses a wind and solar time-sequential output model to represent the states of power components, such as distributed power sources.

[0073] Specifically, photovoltaic power generation is intermittent, random, and fluctuating, and its time-series power output characteristics are determined by the real-time solar radiation intensity and photovoltaic panel parameters. The model uses a linear proportional relationship to convert the actual light intensity Light intensity relative to standard test conditions Calculate the actual light intensity of a single photovoltaic panel. Output power under The specific mathematical formula of the photovoltaic timing output model is as follows:

[0074] ;

[0075] Where, The rated power of a single photovoltaic panel PV; is the system efficiency parameter, which represents the adjustment coefficient of the efficiency of the photovoltaic panel as the temperature changes; is the failure rate threshold of photovoltaic power supply components; To meet the random failure rate of uniform distribution (0,1), PV is the failure rate threshold of photovoltaic power supply components. Multiply the rated power of a single photovoltaic panel PV by the number of photovoltaic panels , then the maximum grid-connected power of the entire photovoltaic system is obtained ;

[0076] Furthermore, the wind turbine's sequential output is determined by the local wind speed and wind turbine parameters. Wind turbine parameters are mainly composed of the cut-in wind speed, cut-out wind speed, and rated wind speed. After obtaining the real-time wind speed of the wind turbine, the wind turbine's sequential power can be calculated based on the wind turbine parameters. The specific mathematical formula of the wind turbine's sequential output model is as follows:

[0077] ;

[0078] ;

[0079] Where: is the maximum grid-connected power of a single wind turbine at time t, The random failure rate satisfies the uniform distribution of (0,1), is the failure rate threshold of the wind turbine power supply component; is the number of fans; is the maximum grid-connected power of WT at time t; is the rated electrical power of WT; is the cut-in wind speed, which is the minimum wind speed at which the fan starts to work; The cut-out wind speed is the maximum wind speed at which the fan stops working; The rated wind speed is the minimum wind speed at which the wind turbine can achieve maximum power output.

[0080] By considering the internal failure rate of the microgrid and constructing a time-series output model, the reliability assessment of the distribution network is made more realistic. It accurately reflects the impact of failures on the microgrid, thus providing a more reliable assessment basis for the stable operation of the system.

[0081] Step 3: Based on the state reliability assessment model, define the distribution microgrid interaction mode, formulate the corresponding multi-power coordinated control strategy, and obtain the interactive power data between the distribution microgrids.

[0082] Specifically, the distribution and microgrid interaction modes include fault island autonomy mode, non-cooperative grid support mode and cooperative regulation grid support mode.

[0083] ① Fault Island Autonomous Mode: When a serious distribution network fault is detected, the microgrid proactively disconnects from the upper grid and enters an off-grid autonomous state. Its internal power supply independently assumes regional load power supply responsibility through a multi-power coordinated control strategy, forming a self-balancing power island.

[0084] In the fault island autonomous mode, the microgrid ensures that distributed power sources such as wind, solar, fuel, diesel, storage and charging within the island meet the load requirements and achieve real-time power balance. Its fuel, storage and charging operation and control strategy is similar to that in the coordinated regulation and grid support mode, and will not be repeated here.

[0085] ② In the non-coordinated grid-connected support mode, during a distribution network failure, the microgrid maintains grid connection but abandons internal multi-element coordinated control. Each power generation unit, energy storage, and load operates according to a local, fixed, multi-source coordinated control strategy. In this mode, there is no coordinated control with the distribution network, and the microgrid primarily adjusts its output based on local strategies, acting solely as a backup support power source.

[0086] In the non-coordinated grid-connected support mode, the coordinated control strategy for multiple power sources including fuel, diesel, storage, and charging is as follows:

[0087] Energy storage uses a two-charge, two-discharge approach to achieve peak-valley arbitrage;

[0088] ;

[0089] ;

[0090] Where, is the charging and discharging power of the energy storage at time t; and are the maximum charging and discharging powers of energy storage, respectively. and are the charging and discharging powers corresponding to the remaining available capacity of the energy storage; 、 are the start and end time of high electricity price respectively, 、 The starting and ending times of low electricity prices respectively. 、 are the energy storage charge and discharge switching times, 、 They are the maximum switching times corresponding to the energy storage charging and discharging switching times. The total number of time periods within the statistical period.

[0091] Gas-fired generator output guarantees ensure sufficient natural gas reserves to maintain full-load power generation for at least 12 hours, and quickly respond to load fluctuations, reaching at least 95% of full power. Based on engineering experience, the gas turbine failure rate is set at 2%.

[0092] ;

[0093] Where, 、 、 、 、 、 They respectively represent the gas turbine output power, gas turbine rated power, gas consumption, maintenance time, gas turbine electrical efficiency, and gas lower calorific value.

[0094] To guarantee diesel generator output, high-quality diesel storage tanks are installed to ensure sufficient reserves for at least 6-12 hours of full-load power generation. This ensures that the microgrid's diesel generators can maintain efficient operation at least 95% of their full power in the event of a distribution network failure. Based on engineering experience, the diesel generator failure rate is set at 5%.

[0095] ;

[0096] Where, 、 、 、 、 They represent diesel generator power, diesel generator rated power, diesel consumption, maintenance time, and diesel generator efficiency respectively.

[0097] Electric vehicles (EVs) charge unordered at charging stations. After connecting to a charging station, EVs begin charging immediately at a fixed power level until they reach their desired charge level or their time to leave, regardless of external scheduling information such as grid load and pricing.

[0098] ;

[0099] Where, 、 、 、 、 、 、 They are EV output power, maximum charging power, grid-connected time, off-grid time, expected off-grid energy, charging efficiency, and time interval.

[0100] ③ A coordinated control and grid-connected support model establishes a bidirectional coupling of information and control between the microgrid and the distribution network, jointly formulating a multi-power coordinated control strategy that enables coordinated operation, optimized scheduling, joint load response, and fault self-healing. While ensuring local power supply reliability, it also proactively provides support to the distribution network in the event of a fault.

[0101] In a coordinated grid-connected support model, energy storage connected to the microgrid can smooth the output of wind and photovoltaic power generation, reducing the impact of the volatility and intermittency of distributed power sources. The multi-power coordinated control strategy, including fuel, diesel, storage, and charging, is as follows:

[0102] The operation and control strategy of energy storage is:

[0103] ;

[0104] Where, It is the difference between the total output of distributed generation and the original planned output at time t; Plan the system output for time t;

[0105] Gas generators and diesel generators support the power gap of the distribution network after giving priority to meeting the microgrid's own load.

[0106] ;

[0107] in, is the microgrid load demand at time t; is the load distribution coefficient borne by the combustion engine at time t; is the supporting power after fuel-load coordination at time t; The remaining fuel can support the microgrid power.

[0108] Electric vehicles on charging piles are prioritized for charging when the combined output of wind power, photovoltaic power, and energy storage exceeds the load demand, and discharge to support loads when the output of wind, photovoltaic, and energy storage is low. This allows electric vehicles to act as a "flexible load + energy storage," regulating the output of wind and photovoltaic power sources as they fluctuate.

[0109] ;

[0110] Where: It is the difference between the total output of wind power, photovoltaic power and energy storage and the load demand at time t. and are the charging and discharging powers corresponding to the remaining available capacity of the energy storage, respectively. is the maximum discharge power of the electric vehicle.

[0111] The three distribution-microgrid interaction modes and their control strategies help optimize power dispatch between microgrids and distribution networks, improving system flexibility and stability. Coordinated control of multiple power sources in different modes can better cope with grid fluctuations and improve energy efficiency.

[0112] Step 4: Establish a failure mode consequence analysis table for the load points, classify the loads according to their power supply status after the failure, and assign reliability index weights to each type of load.

[0113] In this invention, establishing a failure mode and consequence analysis table for load points is a key step in conducting a time-series power supply reliability assessment. Its purpose is to assign reliability indicator weights to each load type, as shown in Table 1. This analysis table categorizes the power supply status of load points under the influence of fault events, helping to clarify different power restoration paths and methods, thereby improving the accuracy and practical reference value of subsequent simulation analysis. This step systematically analyzes the possible power supply status and recovery paths of various load points under different types of fault events, serving as a basis for determining power supply reliability in subsequent simulations.

[0114] Table 1 Failure mode and consequence analysis of load points

[0115]

[0116] Among them, Class A and Class B loads represent the loads protected by the distribution network through network topology reconstruction capabilities, focusing on analyzing the circuit breaker / disconnector operation logic;

[0117] For Class C loads, the microgrid operation mode (fault island / cooperative support) and its energy output capacity must be comprehensively considered, with a focus on analyzing the response time and capacity constraints of the microgrid's distributed power sources.

[0118] Class D load can be used as an important source of influence for ENS (electricity loss) and IHN (number of households during power outage) in the evaluation indicators.

[0119] Step 5: Based on the multi-power coordinated control strategy and the power data from the distribution microgrid interaction, the simulation time for sampling the time-series operation status of the distribution microgrid system is advanced. Based on the final simulation results and the reliability index weights, the power supply reliability index for each load point and the system is calculated. The main process is as follows.

[0120] Step 51, setting and reading the original parameters of the distribution network and initializing them; setting the simulation life according to the initialized original parameters, the simulation life is the operation time of the distribution microgrid system;

[0121] Step 52: Based on the failure rate of the unit, the power data of the interaction between the distribution and microgrids, and the normal working time and failure time of the computer group, a time series of the output of each unit is obtained;

[0122] Step 53: Among all the components, the unit with the shortest fault-free working time is selected as the faulty unit, and the minimum working time of the distribution microgrid system is obtained. The working time of the distribution microgrid system is accumulated to the simulation time to obtain the total output of the distribution network in each corresponding time period;

[0123] Step 54: Based on the time series of each unit output, advance the simulation time , , Is the failure time or repair time; if the component status is faulty, the component repair time is generated Otherwise, the component failure time is generated ;

[0124] Step 55: When the simulation time is greater than the simulation years, the simulation is terminated; and the power supply reliability index of each load point and the system is calculated in combination with the reliability index weight.

[0125] Specifically, according to the "Guidelines for User Power Supply Reliability Evaluation Indicators GB / T 43794-2024" and the characteristics of the distribution network containing microgrids, the load point reliability index and system-level power supply reliability index of the distribution network connected to multiple microgrids are obtained.

[0126] Specifically, the distribution network power supply reliability index system mainly includes load point indicators and system-level indicators. The load point indicators mainly refer to the power supply reliability of users in a certain load area, while the system-level indicators evaluate the power supply reliability of the entire distribution system. The latter can generally be obtained from the former through statistical calculation. The power supply reliability assessment of the distribution network is mainly achieved by calculating the power supply reliability indicators of the distribution network. According to the "Guidelines for User Power Supply Reliability Evaluation Indicators GB / T 43794-2024" and the characteristics of the distribution network containing multiple microgrids, the reliability indicators considered in this invention are mainly:

[0127] Commonly used load point indicators include the average annual fault frequency of users (times / year), the average annual power outage time (h / year), and the average power outage duration of a single fault (h / time). The calculation method is as follows:

[0128] (1) Annual average failure frequency (times / year), this indicator reflects the frequency characteristics of system power supply interruption.

[0129] This indicator is often used to represent the total number of failures or power outages that occur at a single load point within a statistical unit. A larger value indicates a higher failure frequency at that load point and worse power supply stability.

[0130] (2) Average annual power outage time (h / year), this indicator reflects the system's continuous power supply capability.

[0131] This indicator is often used to represent the total duration of power outages caused by failures or outages at a single load point within a single statistical period. A larger value indicates a longer outage duration at that load point during the statistical period and lower power supply reliability.

[0132] (3) Average power outage duration for a single fault (h / time):

[0133] ;

[0134] This metric represents the average duration of a power outage caused by a fault at a single load point within a single simulation period. Its value reflects the load point's ability to restore power after an outage. A lower metric indicates a stronger system's ability to repair and recover from faults, and greater power supply resilience.

[0135] In order to reflect the overall reliability index of the system due to power outages caused by faults, it is necessary to establish a more appropriate reliability index from the perspective of the system. The present invention selects the system average power outage frequency index, the system average power outage duration index, the average power supply availability index, and the system total power shortage index as the final indicators for the distribution network power supply reliability assessment. The present invention also proposes to use the number of households at the time of power outage as the final indicator for the distribution network power supply reliability assessment. The calculation method is as follows:

[0136] 1) System average power outage frequency index SAIFI, unit: times / household·year.

[0137] ;

[0138] Where R is the set of load points in the system, Load point The number of users.

[0139] 2) System average power outage duration index SAIDI, unit: h / household*year.

[0140] ;

[0141] 3) Average power outage duration (CAIDI): The average power outage duration for each power outage for users during the statistical period, in hours / times.

[0142] ;

[0143] This indicator represents the average duration of each power outage for users during the statistical period, reflecting the system's recovery capability.

[0144] 4) Power Supply Reliability Index (ASAI): The average power supply availability index can be directly derived from the average system outage duration index, where 8760 represents the total number of hours in a year. The ASAI is expressed in percent and is commonly referred to as the "nines" index.

[0145] ;

[0146] 5) System total power shortage indicator ENS

[0147] ;

[0148] Where, Indicates load point The average load of all users, expressed in years or MWh-years. 8760 represents the total number of hours in a year. This metric reflects the system's power supply availability, often referred to as the "nines" indicator. The closer it is to 100%, the higher the system's reliability.

[0149] 6) IHN: The number of households whose electricity demand cannot be met by the system within a given time. IHN = ΣNumber of households affected by the outage × duration of the outage. The mathematical expression is:

[0150] ;

[0151] This indicator reflects the combined impact of the number of users who cannot meet their electricity demand and the duration of power outages within a specific period of time.

[0152] The above are the power supply reliability indicators of the distribution network.

[0153] Example 2

[0154] According to the three interaction modes of the distribution network and the micro-grid, the values ​​of various indicators after the sequential Monte Carlo simulation results are compared and analyzed to verify the impact of different distribution network and micro-grid interaction modes on the power supply reliability results of the distribution network.

[0155] In order to verify the impact of different distribution-micro interaction modes on the power supply reliability results of the distribution network, such as Figure 4 As shown in the figure, a simulation analysis of distribution network reliability assessment is performed using a distribution network containing a solar-storage-charging microgrid and a wind-solar-diesel-storage-charging complementary microgrid (defined as microgrid 1 and microgrid 2, respectively) as examples.

[0156] Specifically, the total feeder load is 7.305MW, the number of industrial users is 26, and there are 55 lines and 56 nodes in the system. Microgrid 1 and Microgrid 2 are located at nodes 9 and 22 respectively. The distributed power sources include wind, solar, fuel, storage, and diesel generation, as well as electric vehicle charging piles. To simplify the calculation, the failure rates of circuit breakers, feeder lines, and transformer components are 0.004, 0.065, and 0.015 (times / (km·year)), respectively; and the repair rates are 4, 5, and 200 (h / times), respectively. Through the proposed wind power and photovoltaic hourly output model, the typical annual scenario data of wind power and photovoltaic are obtained as follows: Figure 5 shown.

[0157] Regarding the failure rates of gas turbines (GTs) and diesel generator sets (MTUs), planned maintenance for gas turbines typically involves a scheduled annual outage of 1-2 weeks, based on operating hours. Unplanned failures are less common but more sudden, potentially causing downtime of more than 2-4 days. Therefore, a 2% downtime rate is set for gas turbines. Planned maintenance for diesel generators typically involves scheduled maintenance based on operating hours (e.g., a minor inspection every 500 hours and a major overhaul every 2000 hours). Unplanned downtime can lead to occasional failures due to factors such as component aging and fuel issues. Therefore, a 5% downtime rate is set for diesel generators.

[0158] In this typical year scenario, according to the proposed operation and control strategy, the time sequence output of the distributed power source in the microgrid under different modes is obtained. Since mode 1 is the microgrid island autonomous mode, it is not necessary to obtain the time sequence output of the microgrid interactive power. Figure 6 The figure shows the power consumption of internal combustion engine and diesel generator of microgrid 2 on a certain day in mode 2. Figure 7 The following is a daily power diagram of energy storage in mode 2 for microgrid 1 and microgrid 2, which uses a two-charge and two-discharge strategy. Figure 8 The figure shows the daily power consumption of EVs in microgrid 1 and microgrid 2 under mode 2, where the EVs adopt a disorderly charging strategy.

[0159] like Figure 9 The following is a power diagram of the internal combustion engine and diesel generator of microgrid 2 in mode 3 on a certain day. Figure 10 The following is a power diagram of energy storage on a certain day for Microgrid 1 and Microgrid 2 under Mode 3, where energy storage is coordinated with wind and solar power. Figure 11 The figure shows the daily power consumption of EVs in microgrid 1 and microgrid 2 under mode 3. EVs adopt an orderly charging and discharging strategy, coordinating with wind, solar, storage, and load.

[0160] In order to verify the impact of different distribution-micro interaction modes on the distribution network power supply reliability, the sequential Monte Carlo simulation method is used to compare and analyze the various indicator values ​​of the distribution network power supply reliability system under different modes.

[0161] Mode 1: Microgrid island autonomous mode;

[0162] Mode 2: Microgrid non-cooperative support mode;

[0163] Mode 3: Microgrid coordinated control support mode;

[0164] The calculation results of power supply reliability indicators under different modes are shown in the following table:

[0165] Table 2 Calculation results of various indicators of distribution network power supply reliability under different modes

[0166]

[0167] The table above shows the reliability of distribution network power supply under different modes; the table lists five indicators: SAIFI, SAIDI, ASAI, IHN, and ENS, and the values ​​are different in each mode.

[0168] Power outage frequency and duration (SAIFI, SAIDI, and IHN): SAIFI decreased from 1.567 to 1.2624, a decrease of approximately 19.4%; SAIDI decreased from 9.93 hours to 7.53 hours, a decrease of approximately 24%; and IHN decreased from 258 minutes to 195 minutes, a decrease of over an hour. This indicates that a higher degree of inter-microgrid coordination significantly reduces the number and duration of outages experienced by users. While the improvement in ASAI from 99.8866% to 99.9140% is small, it translates into significantly fewer hours of power outages per year in the power system. Expected unsupplied energy (ENS) decreased from 51.67 kWh to 32.23 kWh, a reduction of nearly 38%. This demonstrates that greater microgrid coordination not only reduces user-experienced power outages but also improves overall system energy efficiency.

[0169] As the microgrid interaction mode shifts from isolated autonomy to coordinated regulation, the power supply system's fault frequency, outage duration, and user outage duration have significantly decreased, demonstrating greater stability and responsiveness. The average system availability index (ASAI) continues to rise. Although the increase is limited, it is of great significance for improving user satisfaction and power supply quality. The expected unsupplied energy (ENS) has decreased significantly, indicating that coordinated interaction helps reduce energy waste and improve energy utilization efficiency. Overall, the microgrid coordinated regulation support mode (Mode 3) performs best in terms of power supply reliability, providing a feasible direction for the future construction of smart distribution networks.

[0170] Example 3

[0171] The present invention also proposes a distribution network power supply reliability assessment system based on the distribution-microgrid interaction mode, which includes a data acquisition module, a reliability assessment model design module, a multi-power coordinated control strategy design module, a reliability index weight setting module, and a reliability index calculation module:

[0172] The data acquisition module acquires the grid data of the distribution microgrid, including the topology, load characteristics, component parameters, failure rate, and power parameters of wind, solar, gas, diesel, storage, and charging within the microgrid;

[0173] The reliability assessment model design module establishes a state reliability assessment model for distribution network non-power components and microgrid power components based on the power grid data of the distribution microgrid;

[0174] The multi-power coordinated control strategy design module defines the distribution and microgrid interaction mode based on the state reliability assessment model, formulates the multi-power coordinated control strategy, and obtains the interactive power data between the distribution and microgrids;

[0175] The reliability index weight setting module establishes a failure mode consequence analysis table for load points, classifies loads according to their power supply status after a failure, and assigns reliability index weights to each type of load;

[0176] The reliability index calculation module promotes the simulation time of sampling simulation of the timing operation status of the distribution microgrid system based on the multi-power coordinated control strategy and the interactive power data between the distribution microgrids; according to the final simulation results and the reliability index weight, the power supply reliability index of each load point and the system is calculated.

Claims

1. A distribution network power supply reliability assessment method based on the distribution-microgrid interaction model is characterized by: include: Step 1: Obtain the grid data of the distribution microgrid, including the topology, load characteristics, component parameters, failure rate, and power parameters of wind, solar, gas, diesel, storage, and charging within the microgrid; Step 2: Based on the grid data of the distribution microgrid, a state reliability assessment model of the distribution network non-power components and the microgrid power components is established; Step 3: Based on the state reliability assessment model, define the distribution microgrid interaction mode, formulate the corresponding multi-power coordinated control strategy, and obtain the interactive power data between the distribution microgrids; Step 4: Establish a failure mode consequence analysis table for the load points, classify the loads according to their power supply status after the failure, and assign reliability index weights to each type of load; Step 5: Based on the multi-power coordinated control strategy and the interactive power data between the distribution microgrid, the simulation time for sampling simulation of the time sequence operation status of the distribution microgrid system is promoted; based on the final simulation results and the reliability index weight, the power supply reliability index of each load point and the system is calculated; In step 5, the specific steps include: Step 51, setting and reading the original parameters of the distribution network and initializing them; setting the simulation life according to the initialized original parameters, the simulation life is the operation time of the distribution microgrid system; Step 52: Based on the failure rate of the unit, the power data of the interaction between the distribution and microgrids, and the normal working time and failure time of the computer group, a time series of the output of each unit is obtained; Step 53: Among all the components, the unit with the shortest fault-free working time is selected as the faulty unit, and the minimum working time of the distribution microgrid system is obtained. The working time of the distribution microgrid system is accumulated to the simulation time to obtain the total output of the distribution network in each corresponding time period; Step 54: Based on the time series of each unit output, advance the simulation time ; Step 55: When the simulation time is greater than the simulation years, the simulation is terminated; and the power supply reliability index of each load point and the system is calculated in combination with the reliability index weight.

2. The method for evaluating the reliability of power supply of a distribution network based on the distribution-microgrid interaction model according to claim 1 is characterized by: In step 2, for the non-power supply components of the distribution network, the expressions of component normal operation time and component fault repair time are used as the reliability evaluation model. The state duration sampling is performed on the normal operation time and fault repair time to obtain the state of the non-power supply components at each time within the evaluation period. For microgrid power supply components, considering the failure rate, the wind and solar timing output model is used to represent the status of the power supply components as a reliability assessment model for the power supply components.

3. The method for evaluating the reliability of power supply of a distribution network based on the distribution-microgrid interaction model according to claim 1 is characterized by: In step 3, the defined distribution and microgrid interaction modes include fault island autonomous mode, non-cooperative grid support mode, and cooperative regulation grid support mode. The coordinated control strategy in each interaction mode is: In the fault island autonomous mode, the system enters the off-grid autonomous state. The distributed power sources of wind, solar, fuel, diesel and storage in the island meet the load requirements and achieve real-time power balance. In the non-coordinated grid-connected support mode, each microgrid power generation unit, energy storage, and load operates with a local fixed multi-power coordinated control strategy, serving as a backup support power source for the distribution network. In the coordinated control grid-connected support mode, distributed power sources give priority to meeting the microgrid's own load and then support the distribution network power gap; electric vehicles are given priority to charging when the total output of the microgrid's distributed power sources is greater than the microgrid's load demand, and discharge support is provided when the total output of the distributed power sources is lower than the microgrid's review demand.

4. The method for evaluating the reliability of power supply of a distribution network based on the distribution-microgrid interaction model according to claim 1 is characterized by: In step 4, loads are classified according to their power supply status after the fault. The types include: loads that are normally powered under the fault state, loads that have power restored through the isolation switch, loads that have power restored through the distributed power supply, and loads that have been without power. Different reliability index weights are assigned to different load types.

5. The method for evaluating the reliability of power supply of a distribution network based on the distribution-microgrid interaction model according to claim 1 is characterized in that: The original parameters include the node name of the entire network, the normal output of the unit, and the failure rate.

6. The method for evaluating the reliability of power supply of a distribution network based on the distribution-microgrid interaction model according to claim 1 is characterized by: In step 51, the initial states of the components are all normal.

7. The method for evaluating the reliability of power supply of a distribution network based on the distribution-microgrid interaction model according to claim 1 is characterized by: Simulation time The calculation formula is , is the failure time or repair time; If the component status is faulty, the component repair time is generated As Otherwise, the component failure time is generated As .

8. The method for evaluating the reliability of power supply of a distribution network based on the distribution-microgrid interaction model according to claim 1 is characterized by: Power supply reliability indicators include load point indicators and system-level indicators; Among them, load point indicators include the average annual fault frequency of users, the average annual power outage time, and the average power outage duration of a single fault; System-level indicators include the system average power outage frequency indicator, the system average power outage duration indicator, the average power supply availability indicator and the system total power shortage indicator, as well as the number of households during power outages; the number of households during power outages refers to the number of users whose power demand cannot be met by the system within a given time period, and the number of households during power outages = Σnumber of users affected by the power outage × duration of the power outage.

9. A distribution network power supply reliability assessment system based on the distribution-microgrid interaction model includes a data acquisition module, a reliability assessment model design module, a multi-power coordinated control strategy design module, a reliability index weight setting module, and a reliability index calculation module. The system is characterized by: The data acquisition module acquires the grid data of the distribution microgrid, including the topology, load characteristics, component parameters, failure rate, and power parameters of wind, solar, gas, diesel, storage, and charging within the microgrid; The reliability assessment model design module establishes a state reliability assessment model for distribution network non-power components and microgrid power components based on the power grid data of the distribution microgrid; The multi-power coordinated control strategy design module defines the distribution and microgrid interaction mode based on the state reliability assessment model, formulates the multi-power coordinated control strategy, and obtains the interactive power data between the distribution and microgrids; The reliability index weight setting module establishes a failure mode consequence analysis table for load points, classifies loads according to their power supply status after a failure, and assigns reliability index weights to each type of load; The reliability index calculation module, based on the multi-power coordinated control strategy and the interactive power data between the distribution microgrid, promotes the simulation time of sampling simulation of the time sequence operation status of the distribution microgrid system. Based on the final simulation results and the reliability index weight, it calculates the power supply reliability index of each load point and the system. In the reliability index calculation module, the specific steps include: Set and read the original parameters of the distribution network and initialize them; according to the initialized original parameters, set the simulation life, which is the operation time of the distribution microgrid system; Based on the failure rate of the unit, the interactive power data between the distribution and microgrid, and the normal working time and failure time of the computer group, the time series of each unit's output is obtained; Among all the components, the unit with the shortest fault-free working time is selected as the faulty unit, and the minimum working time of the distribution microgrid system is obtained. The working time of the distribution microgrid system is accumulated to the simulation time to obtain the total output of the distribution network in each corresponding time period; Based on the time series of each unit output, advance the simulation time ; When the simulation time is greater than the simulation years, the simulation is terminated; combined with the reliability index weight, the power supply reliability index of each load point and the system is calculated.

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