Power distribution network energy supply method based on multi-target constraint and fault assessment

Abstract

The invention discloses a power distribution network energy supply method based on multi-target constraint and fault assessment, and the method comprises the steps: carrying out the multi-target constraint of different energy systems at a power supply end of a power distribution network, carrying out the reliability assessment of the power supply of each load point and a terminal user at a power utilization end of the power distribution network, and achieving the common adjustment of the upstream and downstream of the power distribution network. The power distribution of the power consumption end of the power distribution network is controlled according to the power fluctuation of the power supply end of the power distribution network, the reliability of the power consumption end of the power distribution network is ensured on the premise of meeting the multi-target constraint of the power supply end of the power distribution network, and feedback regulation of the multi-target constraint of the power supply end of the power distribution network is realized according to the reliability. The output accuracy of the power supply end and the distribution accuracy of the power utilization end are improved, and the reliability of regional, feeder and terminal user power supply is promoted, so that the overall operation efficiency of a power distribution network and the user power supply service quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a distribution network energy supply method based on multi-objective constraints and fault assessment. Background Art

[0002] Building an efficient and flexible integrated energy system will become the focus of the construction of a new energy system. However, there are often differences between different energy systems, and the energy supply of various energy sources in the system is prone to lack of coordination, low reliability, and low energy utilization. It is urgent to study and plan the optimization planning methods and key technologies of renewable energy-fossil energy-multiple types of energy storage, and build a multi-energy coupled low-carbon energy supply system for different regions, different energy resource endowments, and different energy needs.

[0003] As a key sector in the national economy, the power system plays an important role in improving people's quality of life. People have higher and higher demands for the reliability and quality of electricity. Accurate and efficient distribution network reliability assessment methods have become an important support for power grid companies to formulate grid planning, resource allocation and fault recovery measures, which is of great significance to improving the safe power supply capacity of the power system and reducing the losses caused by power outages. In recent years, a large number of distributed power sources have been connected to the power grid. Distributed power sources can supply power to regional loads that lose the main power supply connection path after a distribution network failure, improve the recovery capacity of the distribution network, and consider the impact of distributed power sources on fault handling. The traditional reliability assessment model and method will also change.

[0004] Data show that the current reliability management of medium-voltage distribution network power supply relies on operation events and performs post-statistical calculation of reliability indicators. This method has limitations. First, post-statistics cannot accurately trace the cause of the failure, which limits the ability of post-analysis and the formulation of preventive measures. Second, the lack of comprehensive data collection and analysis, focusing only on the feeders that have failed and ignoring other potential risk points, may lead to overall reliability loopholes in the system and make it impossible to evaluate the future state.

[0005] Chinese patent document CN107671414A discloses a "distribution network reliability assessment method and device". The method includes: constructing a component failure model, determining the equipment that needs regular maintenance, analyzing the impact of regular maintenance on the equipment failure rate and calculating the trouble-free operation time of the distribution network components; calculating the repair time of the distribution network components that have failed; simulating the operating state and fault state of the distribution network components, and simulating the state changes and reliability of the distribution network components under fault maintenance and regular maintenance strategies; performing fault detection and judgment, and determining the equipment that needs regular maintenance; recording the impact of the maintenance strategy on the reliability status of the load point, and calculating the mean value of the system reliability index based on the maintenance situation. The above technical solution relies on post-statistical calculation of reliability indicators, which has limitations. Summary of the invention

[0006] The present invention mainly solves the technical problem that the original technical solution relies on post-statistical calculation of reliability indicators and has limitations. It provides a distribution network energy supply method based on multi-objective constraints and fault assessment. By performing multi-objective constraints on different energy systems at the power supply end of the distribution network, and performing reliability assessment on the power supply of each load point and end-user at the power consumption end of the distribution network, the joint regulation of the upstream and downstream of the distribution network is realized, and the power supply distribution at the power consumption end of the distribution network is controlled according to the power fluctuation at the power supply end of the distribution network. The reliability of the power consumption end of the distribution network is ensured on the premise of satisfying the multi-objective constraints of the power supply end of the distribution network. At the same time, feedback adjustment of the multi-objective constraints of the power supply end of the distribution network is realized according to the reliability of the power consumption end of the distribution network, so as to improve the accuracy of the output of the power supply end and the distribution of the power consumption end, promote the reliability of power supply to the region, feeder and end user, thereby improving the overall operation efficiency of the distribution network and the quality of user power supply service.

[0007] The above technical problem of the present invention is mainly solved by the following technical solution: The present invention comprises the following steps: S1 establishes an output model of a multi-energy coupling energy supply system at the power supply end of a distribution network; S2 determines the constraints of the multi-energy coupling energy supply system and builds an optimization model; S3 establishes a segmented network fault state model at the power consumption end of the distribution network; S4 calculates the reliability index of the power consumption end of the distribution network according to the impact of the segmented network failure on the load point; S5 controls the power supply distribution at the power consumption end of the distribution network according to the power fluctuation at the power supply end of the distribution network.

[0008] By performing multi-objective constraints on different energy systems at the power supply end of the distribution network and conducting reliability assessments on the power supply to each load point and end-user at the power consumption end of the distribution network, joint regulation is achieved at the upstream and downstream of the distribution network. The power supply distribution at the power consumption end of the distribution network is controlled according to the power fluctuations at the power supply end of the distribution network. The reliability of the power consumption end of the distribution network is ensured while meeting the multi-objective constraints at the power supply end of the distribution network. At the same time, feedback adjustment of the multi-objective constraints at the power supply end of the distribution network is achieved according to the reliability of the power consumption end of the distribution network, thereby improving the accuracy of the output at the power supply end and the distribution at the power consumption end.

[0009] Preferably, the output model of the multi-energy coupling energy supply system in step S1 includes a variety of power generation output models, and also includes a variety of energy storage models and heat storage models.

[0010] The multi-energy coupling energy supply system based on multi-objective constraints includes wind power generation equipment, photovoltaic power generation equipment, solar thermal power generation equipment, coal-fired power generation equipment, nuclear power, hydropower and other power sources, electric energy storage, molten salt heat storage, phase change heat storage, green hydrogen production and energy storage, green methanol synthesis and energy storage equipment and other types of energy storage equipment, including: Establish output models corresponding to various devices of the multi-energy coupling energy supply system based on multi-objective constraints, including wind power output model, photovoltaic power generation output model, solar thermal power generation output model, coal-fired power plant output model, nuclear power output model, hydropower output model, electric energy storage model, molten salt heat storage model, phase change heat storage model, green hydrogen production and energy storage model, green methanol synthesis and energy storage model; Based on the safety, economy, energy efficiency, carbon emissions and other evaluation models corresponding to each device, a corresponding parameter database is constructed, and the safety, economy, carbon emissions and other constraints corresponding to the multi-energy coupling low-carbon energy supply system are determined; An optimization model corresponding to the multi-energy coupled low-carbon energy supply system based on multi-objective constraints is established, and the optimization model is solved with preset multi-objective optimization such as safety, economy, carbon emissions, etc. to obtain the corresponding configuration optimization solution.

[0011] Preferably, the constraints for different coupled energy supply equipment in step S2 specifically include: power constraints of different equipment, cost constraints of wind power generation equipment and photovoltaic power generation equipment, charging and discharging state constraints and SOC constraints of electric energy storage equipment, load climbing constraints of coal-fired power units, carbon emission constraints per kilowatt-hour of multi-energy complementary power generation systems, and operating cost constraints per kilowatt-hour of multi-energy complementary power generation systems.

[0012] Preferably, the step S2 establishes an optimization model corresponding to the multi-energy coupling energy supply system, and solves the optimization model with a preset optimization target to obtain a corresponding configuration optimization scheme, wherein the preset optimization target includes minimizing the levelized energy supply cost of the multi-energy complementary power generation system, improving the utilization rate of renewable energy and reducing the carbon emission coefficient.

[0013] Preferably, the step S3 specifically includes creating a three-state model of the network, including the operating state, fault outage state and pre-scheduled outage state of the facility, and simulating it through a steady-state "operation-fault outage-pre-scheduled outage" state transition diagram, wherein the state transition includes the facility pre-scheduled outage rate and the facility pre-scheduled outage repair rate.

[0014] The three-state network model created according to the principle of network simplification is as follows: the three-state model mainly considers the operating state, fault outage state and pre-scheduled outage state of the facility, which can be simulated through the steady-state "operation-fault outage-pre-scheduled outage" state transition diagram, and assumes that fault outage and pre-scheduled outage are not mutually exclusive.

[0015] According to the principle of network simplification, an innovative segmented network model is created through the facility models of key facilities such as substation busbars, overhead and cable lines, distribution transformers and circuit breakers and their topological relationships.

[0016] Preferably, the influence of the segmented network fault on the load point is calculated, specifically including taking into account the power supply restoration project after the fault, determining the fault power outage rate of each load point, and the average total number of fault power outages per year at the load point, recorded as λ LP-F (times / year), Load point fault outage rate = ∑ N Facility failure outage rate Where N is the set of facilities that cause power outage at the load point after a fault.

[0017] As a preferred embodiment, the calculation of the impact of the segmented network fault on the load point also includes taking into account the power supply restoration project after the fault, determining the fault power outage time of each load point, and the average annual fault power outage hours of the load point, recorded as u LP-F (h / year), Where N is the set of facilities that cause power outage at the load point after a fault.

[0018] Preferably, the step S4 of calculating the reliability index specifically includes: S4.1 Enumerate individual facility failures and take into account the impact of facility failures on power outages at each load point; S4.2 List the failure outage rate and failure outage time of each load point after all facilities fail individually; S4.3 Calculate other reliability indicators of the load point based on the load point fault outage rate and fault outage time; S4.4 Calculate the system reliability index based on the reliability index of each load point.

[0019] The failure outage rate and failure outage time of each load point after all segmented network failures are listed, and a failure mode consequence analysis table is automatically generated. The reliability index of each load point is calculated in turn, and the reliability index of the system is calculated on this basis.

[0020] Preferably, the step S4.2 calculates the fault outage rate λ of the load point LP-F And fault outage time u LP-F Specifically: Among them, the set of all facilities that can cause power outages at the load point LP due to faults is N, and the fault outage rate and fault repair time of the i-th facility in the facility set are λ i 、r i .

[0021] Preferably, the step S5 specifically includes that the multi-energy coupling energy supply system at the power supply end of the distribution network supplies energy according to a preset optimization target, counts the energy supply fluctuations at the power supply end of the distribution network, controls the power supply distribution at the power consumption end of the distribution network according to the reliability index of the power consumption end of the distribution network when the energy supply is insufficient, and preferentially disconnects the segmented network that has a small impact on the reliability index of the power consumption end of the distribution network. A common regulation is achieved in the upstream and downstream of the distribution network, and the power supply distribution at the power consumption end of the distribution network is controlled according to the power fluctuation at the power supply end of the distribution network. The reliability of the power consumption end of the distribution network is ensured under the premise of satisfying the multi-objective constraints of the power supply end of the distribution network. At the same time, the feedback regulation of the multi-objective constraints of the power supply end of the distribution network is realized according to the reliability of the power consumption end of the distribution network, so as to improve the accuracy of the output of the power supply end and the distribution of the power consumption end.

[0022] The beneficial effects of the present invention are as follows: by performing multi-objective constraints on different energy systems at the power supply end of the distribution network and performing reliability assessment on the power supply to each load point and end-user at the power consumption end of the distribution network, joint regulation at the upstream and downstream of the distribution network is achieved, and the power supply distribution at the power consumption end of the distribution network is controlled according to the power fluctuation at the power supply end of the distribution network. The reliability of the power consumption end of the distribution network is ensured on the premise of meeting the multi-objective constraints at the power supply end of the distribution network, and feedback adjustment of the multi-objective constraints at the power supply end of the distribution network is achieved according to the reliability of the power consumption end of the distribution network, thereby improving the accuracy of the output of the power supply end and the distribution of the power consumption end, promoting the reliability of power supply to regions, feeders and end users, thereby improving the overall operation efficiency of the distribution network and the quality of power supply services for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of the present invention.

[0024] Figure 2 It is a diagram of a multi-energy coupling energy supply system at the power supply end of a distribution network of the present invention.

[0025] Figure 3 It is a three-state model diagram of the present invention.

[0026] Figure 4 It is a calculation example network diagram of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the technical solution of the present invention is further described in detail below through embodiments and in combination with the accompanying drawings. It should be understood that the specific implementation method described here is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0029] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0030] Embodiment: A distribution network energy supply method based on multi-objective constraints and fault assessment in this embodiment is as follows: Figure 1 As shown, the following steps are included: S1 establishes the output model of the multi-energy coupling energy supply system at the power supply end of the distribution network, including multiple power generation output models, as well as multiple energy storage models and heat storage models.

[0031] The multi-energy coupling energy supply system based on multi-objective constraints includes wind power generation equipment, photovoltaic power generation equipment, solar thermal power generation equipment, coal-fired power generation equipment, nuclear power, hydropower and other power sources, electric energy storage, molten salt heat storage, phase change heat storage, green hydrogen production and energy storage, green methanol synthesis and energy storage equipment and other types of energy storage equipment, including: Establish output models corresponding to various devices of the multi-energy coupling energy supply system based on multi-objective constraints, including wind power output model, photovoltaic power generation output model, solar thermal power generation output model, coal-fired power plant output model, nuclear power output model, hydropower output model, electric energy storage model, molten salt heat storage model, phase change heat storage model, green hydrogen production and energy storage model, green methanol synthesis and energy storage model; Based on the safety, economy, energy efficiency, carbon emissions and other evaluation models corresponding to each device, a corresponding parameter database is constructed, and the safety, economy, carbon emissions and other constraints corresponding to the multi-energy coupling low-carbon energy supply system are determined; An optimization model corresponding to the multi-energy coupled low-carbon energy supply system based on multi-objective constraints is established, and the optimization model is solved with preset multi-objective optimization such as safety, economy, carbon emissions, etc. to obtain the corresponding configuration optimization solution.

[0032] S2 determines the constraints of the multi-energy coupling energy supply system, including the constraints of different coupling energy supply equipment, including: power constraints of different equipment, cost constraints of wind power generation equipment and photovoltaic power generation equipment, charging and discharging state constraints and SOC constraints of electric energy storage equipment, load climbing constraints of coal-fired power units, carbon emission constraints per kilowatt-hour of the multi-energy complementary power generation system, and operating cost constraints per kilowatt-hour of the multi-energy complementary power generation system.

[0033] An optimization model corresponding to the multi-energy coupling energy supply system is established, and the optimization model is solved with preset optimization objectives to obtain a corresponding configuration optimization scheme. The preset optimization objectives include minimizing the levelized energy supply cost of the multi-energy complementary power generation system, improving the utilization rate of renewable energy and reducing the carbon emission coefficient.

[0034] Wind power output models include: Among them, P w and Pe w Respectively represent the real-time power generation and rated power of the wind turbine; V r , V cut-in , V cut-out They respectively represent the rated wind speed, cut-in wind speed and cut-out wind speed of the wind turbine.

[0035] Preferably, the photovoltaic output model includes: Among them, P pv and Pe pv Respectively represent the real-time power generation and rated power generation of the photovoltaic unit; η pv Indicates the performance coefficient of the photovoltaic unit; G and G ref They represent the hourly average solar radiation intensity and the solar radiation intensity under standard conditions respectively; K T Indicates the power temperature coefficient of the photovoltaic unit; T and T ref They represent the operating temperature (°C) of the photovoltaic panel and the reference temperature under standard operating conditions respectively.

[0036] The coal-fired power plant output model includes: in, and Respectively represent the minimum output and maximum output of the i-th type thermal power unit; P th,i (t) represents the output power of the i-th thermal power unit at time t; R th,i Represents the ramp rate limit of the i-th category thermal power unit.

[0037] The electric energy storage model includes: W es =ρ es ·Pe es in, and Respectively represent the charging power and discharging power of the electric energy storage unit; Pe es and W es Respectively represent the rated power and rated energy storage capacity of the electric energy storage unit; ρ es It represents the power capacity conversion factor of the electric energy storage unit, and is used to characterize the rated energy storage capacity corresponding to each electric energy storage unit configured with rated charge and discharge power.

[0038] The water electrolysis hydrogen production model includes: Among them, P e,i Indicates the power of the water electrolysis hydrogen production system; P e,input,i is the input power; P e,nom is the nominal power of the system.

[0039] Hydrogen energy storage models include: Among them, SOC hs , σ hs They represent the gas storage state and gas dissipation coefficient of the hydrogen storage tank equipment respectively; Δt represents the unit time step; and They represent the filling efficiency and deflation efficiency of the hydrogen storage tank respectively; and They respectively represent the lower limit and upper limit of the gas storage state of the hydrogen storage tank.

[0040] The process model of synthesizing methanol from hydrogen and carbon dioxide includes: m H2 =α H2 ·m MeOH m CO2 =α CO2 ·m MeOH m CO2,e =(1-β)·m CO2 P MeOH =E MeOH ·m MeOH Among them, m H2 , m CO2 , m MEOH Respectively represent the mass flow rate of hydrogen, carbon dioxide and methanol per unit time; α H2 , α CO2Respectively represent the mass of hydrogen and carbon dioxide required to produce a unit mass of methanol; m CO2,e represents the carbon dioxide emissions per unit time of the methanol conversion equipment; β represents the ratio of carbon dioxide converted to methanol during the catalytic reaction; E MEOH P represents the external power input required to produce unit mass of methanol; MEOH Indicates the real-time power of the methanol conversion equipment.

[0041] S3 establishes a segmented network fault state model for the power consumption end of the distribution network, specifically including creating a three-state network model, including the operating state, fault shutdown state and pre-scheduled shutdown state of the facility, and simulating it through a steady-state "operation-fault shutdown-pre-scheduled shutdown" state transition diagram, wherein the state transition includes the pre-scheduled shutdown rate of the facility and the repair rate of the pre-scheduled shutdown of the facility.

[0042] The three-state network model created according to the principle of network simplification is as follows: the three-state model mainly considers the operating state, fault outage state and pre-scheduled outage state of the facility, which can be simulated through the steady-state "operation-fault outage-pre-scheduled outage" state transition diagram, and assumes that fault outage and pre-scheduled outage are not mutually exclusive.

[0043] According to the principle of network simplification, an innovative segmented network model is created through the facility models of key facilities such as substation busbars, overhead and cable lines, distribution transformers and circuit breakers and their topological relationships.

[0044] S4 calculates the reliability index of the power consumption end of the distribution network according to the impact of the segmented network failure on the load point.

[0045] Calculate the impact of the segmented network failure on the load point, including the power supply restoration project after the failure, determine the failure power outage rate of each load point, and the average total number of failure power outages per year at the load point, denoted as λ LP-F (times / year), load point failure outage rate = ∑ N Facility failure outage rate Where N is the set of facilities that cause power outage at the load point after a fault.

[0046] Calculate the impact of the segmented network fault on the load point, including the power supply restoration project after the fault, determine the fault outage time of each load point, and the average annual fault outage hours of the load point, recorded as u LP-F (h / year), Where N is the set of facilities that cause power outage at the load point after a fault.

[0047] The calculation reliability indicators include: S4.1 Enumerate individual facility failures and take into account the impact of facility failures on power outages at each load point; S4.2 List the failure outage rate and failure outage time of each load point after all facilities fail individually, and calculate the failure outage rate λ of the load point LP-F And fault outage time u LP-F Specifically: Among them, the set of all facilities that can cause power outages at the load point LP due to faults is N, and the fault outage rate and fault repair time of the i-th facility in the facility set are λ i 、r i .

[0048] S4.3 Calculate other reliability indicators of the load point based on the load point fault outage rate and fault outage time; S4.4 Calculate the system reliability index based on the reliability index of each load point.

[0049] The failure outage rate and failure outage time of each load point after all segmented network failures are listed, and a failure mode consequence analysis table is automatically generated. The reliability index of each load point is calculated in turn, and the reliability index of the system is calculated on this basis.

[0050] The effectiveness of medium-voltage distribution network power supply reliability management is improved through future state assessment, and long-term rolling prediction of computing innovation future state reliability assessment methods is carried out, including: Data collection and preprocessing: collect historical operating data, equipment parameters, meteorological data, load forecast information, etc., and perform preprocessing operations such as data cleaning, missing value processing, standardization or normalization.

[0051] Grid modeling and scenario setting: Establish a power network model that includes distributed energy, energy storage facilities, microgrids and other elements, and set different future state scenarios (such as high renewable energy penetration, extreme climate conditions, etc.).

[0052] Calculate the failure rate λ of various types of power equipment; Component reliability calculation; Calculate the failure rate (λ) of various types of power equipment; λ = 1 / MTBF (λ (lambda) represents the failure rate, MTBF (Mean Time Between Failures) refers to the mean time between failures).

[0053] Simulate the system's operating conditions under various random events and calculate key reliability indicators, including: System average power outage frequency index SAIFI = SAIFI = total power outages / total number of power supply users System availability A = e^(-λt) (where λ is the system failure rate and t is the time period of the investigation) Energy unavailability EENS = ∑ (expected power shortage amount at each load point × probability of occurrence of the power shortage).

[0054] Use real-time monitoring data to update model parameters and dynamically adjust evaluation results based on new data to conduct long-term rolling forecasts. Dynamic evaluation and feedback adjustment.

[0055] The effectiveness of reliability management of power supply in medium-voltage distribution network is improved through future-state assessment. Through the models of substations, overhead and cable lines, distribution transformers, circuit breakers and other facilities and their topological relationships, a distribution network segmentation model is constructed. The failure mode and consequence analysis algorithm is used to analyze the impact of single facility failures on power outages. The frequency of facility failures and the duration of repair are comprehensively examined, the reliability indicators of each load point and the overall system are established, and a failure mode and consequence analysis table is formed. Through this assessment method, the accuracy of the assessment is improved, the reliability of power supply to regions, feeders and end users is promoted, thereby improving the overall operating efficiency of the medium-voltage distribution network and the quality of power supply services to users.

[0056] by Figure 4 Taking the distribution network shown in the figure as an example, the FMEA method is used to establish a failure mode consequence analysis table to achieve reliability assessment. In the example network, the backup power capacity is 1500kW, the overhead line model is LGJ-240, the cable line model is YJV-300, the distribution transformer model is S9, and the load points are ranked from important to less important as e, d, c, d, a. The remaining parameters are shown in Tables 1 and 2. In the reliability calculation, the following simplifications are made, and the influence of the upper power grid and the influence of the failure of the disconnectors on both sides adjacent to the circuit breaker (load switch) are not considered; Figure 4 The distribution transformers connected to each load point are represented by Ta, Tb, Tc, Td, and Te, and the fuses are represented by Fa, Fb, Fc, Fd, Fe, and F5, respectively.

[0057] Table 1 List of load point fault outage rate and fault outage time Table 2 Example network data table Enumerate the facility outages, analyze the impact of facility outages on power outages at each load point according to the three modes of trunk facility failure, branch line facility failure, and pre-arranged outage, and determine the outage frequency λLP, average duration of each outage rLP (h / time), and outage time uLP (h / a) at each load point. Establish a failure consequence mode analysis table as shown in Table 3.

[0058] Table 3 Failure consequence mode analysis table According to the analysis results in Table 3, other reliability indicators of load points and system reliability indicators can be calculated. The calculation results of some indicators are shown in Table 4 and Table 5.

[0059] Table 4 Load point reliability index System indicators Index value System average power outage duration SAIDI (h / household / year) 1.88382 Average power supply availability rate ASAI 99.978％ System average power outage frequency SAIFI (times / household / year) 0.795 System power shortage ENS (kWh) 1765.81 System average power shortage AENS (kWh / household / year) 353.162 Table 5 System reliability indicators S5 controls the power supply distribution at the power consumption end of the distribution network according to the power fluctuation at the power supply end of the distribution network. Specifically, the multi-energy coupling power supply system at the power supply end of the distribution network supplies energy according to the preset optimization target, counts the power supply fluctuation at the power supply end of the distribution network, and controls the power supply distribution at the power consumption end of the distribution network according to the reliability index of the power consumption end of the distribution network when the energy supply is insufficient, and preferentially disconnects the segmented network with small impact on the reliability index of the power consumption end of the distribution network. Control the power supply distribution at the power consumption end of the distribution network according to the power fluctuation at the power supply end of the distribution network. Under the premise of meeting the multi-objective constraints of the power supply end of the distribution network, first determine the power supply amount at the power supply end of the distribution network, and compare whether it meets the power required by the power consumption end of the distribution network. If not, perform a long-term rolling forecast based on the innovative future state reliability assessment method to obtain the latest reliability assessment results, and select the disconnected distribution network segmented network according to the power gap and the degree of influence of the reliability index. If the power gap is too large to meet the reliability index, feedback adjustment is performed to adjust the multi-objective constraints of the power supply end of the distribution network to ensure the reliability of the power consumption end of the distribution network.

[0060] By performing multi-objective constraints on different energy systems at the power supply end of the distribution network and conducting reliability assessments on the power supply to each load point and end-user at the power consumption end of the distribution network, joint regulation is achieved at the upstream and downstream of the distribution network. The power supply distribution at the power consumption end of the distribution network is controlled according to the power fluctuations at the power supply end of the distribution network. The reliability of the power consumption end of the distribution network is ensured while meeting the multi-objective constraints at the power supply end of the distribution network. At the same time, feedback adjustment of the multi-objective constraints at the power supply end of the distribution network is achieved according to the reliability of the power consumption end of the distribution network, thereby improving the accuracy of the output at the power supply end and the distribution at the power consumption end.

[0061] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0062] The specific embodiments described herein are merely examples of the spirit of the present invention. The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that technicians in the technical field to which the present invention belongs can make various modifications or supplements to the described specific embodiments or replace them in a similar manner, but will not deviate from the spirit of the present invention or exceed the scope defined in the attached claims. For ordinary technicians in this field, multiple deformations and improvements can also be made without departing from the concept of the present invention. Therefore, the scope of protection of the patent of the present invention shall be based on the attached claims.

Claims

1. A distribution network energy supply method based on multi-objective constraints and fault assessment, characterized in that: The following steps are involved: S1 establishes the output model of the multi-energy coupling energy supply system at the power supply end of the distribution network; S2 determines the constraints of the multi-energy coupling energy supply system and builds an optimization model; S3 establishes a segmented network fault state model at the power consumption end of the distribution network; S4 calculates the reliability index of the power consumption end of the distribution network according to the impact of the segmented network failure on the load point; S5 controls the power supply distribution at the power consumption end of the distribution network according to the power fluctuation at the power supply end of the distribution network.

2. A distribution network energy supply method based on multi-objective constraints and fault assessment according to claim 1, characterized in that: The output model of the multi-energy coupling energy supply system in step S1 includes a variety of power generation output models, and also includes a variety of energy storage models and heat storage models.

3. A distribution network energy supply method based on multi-objective constraints and fault assessment according to claim 1, characterized in that: The constraints for different coupled energy supply equipment in step S2 specifically include: power constraints of different equipment, cost constraints of wind power generation equipment and photovoltaic power generation equipment, charging and discharging state constraints and SOC constraints of electric energy storage equipment, load climbing constraints of coal-fired power units, carbon emission constraints per kilowatt-hour of multi-energy complementary power generation systems, and operating cost constraints per kilowatt-hour of multi-energy complementary power generation systems.

4. A distribution network energy supply method based on multi-objective constraints and fault assessment according to claim 1 or 3, characterized in that: The step S2 establishes an optimization model corresponding to the multi-energy coupling energy supply system, and solves the optimization model with a preset optimization target to obtain a corresponding configuration optimization scheme. The preset optimization target includes minimizing the levelized energy supply cost of the multi-energy complementary power generation system, improving the utilization rate of renewable energy and reducing the carbon emission coefficient.

5. A distribution network energy supply method based on multi-objective constraints and fault assessment according to claim 1, characterized in that: The step S3 specifically includes creating a three-state network model, including the operating state, fault outage state and pre-scheduled outage state of the facility, and simulating it through a steady-state "operation-fault outage-pre-scheduled outage" state transition diagram, wherein the state transition includes the facility pre-scheduled outage rate and the repair rate of the facility pre-scheduled outage.

6. A distribution network energy supply method based on multi-objective constraints and fault assessment according to claim 1, characterized in that: The calculation of the impact of the segmented network failure on the load point specifically includes taking into account the power supply restoration project after the failure, determining the failure power outage rate of each load point, and the average total number of failure power outages per year at the load point, recorded as λ LP-F (times / year), Load point fault outage rate = ∑ N Facility failure outage rate Where N is the set of facilities that cause power outage at the load point after a fault.

7. A distribution network energy supply method based on multi-objective constraints and fault assessment according to claim 1, characterized in that: The calculation of the impact of the segmented network failure on the load point also includes taking into account the power supply restoration project after the failure, determining the fault power outage time of each load point, and the average annual fault power outage hours of the load point, recorded as u LP-F (h / year), Where N is the set of facilities that cause power outage at the load point after a fault.

8. A distribution network energy supply method based on multi-objective constraints and fault assessment according to claim 1, 6 or 7, characterized in that: The step S4 of calculating the reliability index specifically includes: S4.1 Enumerate individual facility failures and take into account the impact of facility failures on power outages at each load point; S4.2 List the failure outage rate and failure outage time of each load point after all facilities fail individually; S4.3 Calculate other reliability indicators of the load point based on the fault outage rate and fault outage time of the load point; S4.4 Calculate the system reliability indicator based on the reliability indicator of each load point.

9. A distribution network energy supply method based on multi-objective constraints and fault assessment according to claim 8, characterized in that: The step S4.2 calculates the fault outage rate λ of the load point LP-F And fault outage time u LP-F Specifically: Among them, the set of all facilities that can cause power outages at the load point LP due to faults is N, and the fault outage rate and fault repair time of the i-th facility in the facility set are λ i 、r i .

10. A distribution network energy supply method based on multi-objective constraints and fault assessment according to claim 1, characterized in that: The step S5 specifically includes: the multi-energy coupling energy supply system at the power supply end of the distribution network supplies energy according to a preset optimization target, counting the energy supply fluctuations at the power supply end of the distribution network, and controlling the power supply distribution at the power consumption end of the distribution network according to the reliability index of the power consumption end of the distribution network when the energy supply is insufficient, and preferentially disconnecting the segmented network that has a small impact on the reliability index of the power consumption end of the distribution network.

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