Reliability evaluation method and system considering microgrid to support power distribution network

Abstract

The invention provides a reliability evaluation method and system considering that a micro-grid supports a power distribution network, and the method comprises the steps: employing a Monte Carlo simulation framework, building a probability reliability model through the development of a modeling process of a self-control grid-connected micro-grid, describing the micro-grid suitable for various standard scenes, and evaluating the reliability of a multi-MG grid-connected power distribution network. Scheduling and resource management are optimized by using a mixed integer linear programming technology; a micro-grid component is simplified into an equivalent distributed power generation unit, and the equivalent distributed power generation unit is integrated into an overall analysis framework, so that the calculation complexity is effectively reduced, the reliability evaluation efficiency of the power distribution system is remarkably improved, and the function of performing reliability evaluation on the power distribution network is realized. The framework and the model provided by the invention are adaptive to economic and technical research, and the reliability of the multi-MG power distribution system is enhanced; the method is an accurate and simple reliability evaluation method, reduces the operation burden, and improves the reliability evaluation efficiency of the power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of reliability assessment of power distribution systems, and in particular relates to a reliability assessment method and system for a power distribution network that takes into account a microgrid supporting the power distribution network. Background Art

[0002] The increasingly developed new power system has become active and intelligent. In order to ensure the stability of power supply in the distribution network to improve the quality of power supply, and optimize its internal resource allocation and emergency response capabilities, it is necessary to evaluate the reliability of the distribution network. Existing technologies are mostly based on Monte Carlo simulation technology to evaluate the reliability of distribution networks including microgrids, using the concept of virtual power plants to characterize microgrids, describing distributed power units in microgrids based on probability models, and using analytical formulas for the reliability of active distribution systems including microgrids to evaluate distribution networks. Another analysis method is to consider instantaneous interruptions to evaluate the reliability of distribution systems equipped with distributed power resources and microgrids, and at the same time incorporate energy storage systems into the simulation-based reliability assessment framework, considering the probability of successful operation of microgrids and backup distributed power sources during the recovery process, so as to model the impact of distributed power sources and microgrids on system reliability indicators.

[0003] At present, most of the methods for evaluating the reliability of distribution networks at home and abroad adopt the analysis method of the reliability of distribution systems containing backup distributed power sources and microgrids, especially focusing on instantaneous interruptions. The modeling is simplified by the probability of successful operation of microgrids and backup distributed power sources during the recovery period, which reduces the complexity of calculations, but the applicability to backup distributed power systems with different ownership is limited. The formula for the reliability of active distribution systems containing microgrids is adopted, and the probability model is used to describe conventional and renewable distributed power sources, but this method consumes more computing resources. The model of the distribution network containing microgrids is evaluated based on Monte Carlo simulation technology for analyzing the reliability of active distribution systems. The concept of virtual power plant is introduced to model microgrids, but it is not suitable for analysis technology.

[0004] Traditional distribution network reliability assessment is to analyze the impact of distributed power installation on distribution system reliability by introducing distributed power unit reliability model. However, it is too simplistic to only consider the installation of distributed power, which may lead to errors in quantifying network reliability. Although simulation technology can be used to develop microgrid reliability models suitable for distribution system reliability assessment, the processing time of reliability assessment will increase significantly as the network scale increases. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a reliability assessment method and system for a distribution network considering a microgrid supporting the distribution network, so as to perform reliability assessment on the distribution network.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a reliability assessment method considering a microgrid supporting a distribution network, comprising the following steps: S1: Obtain the technical information and topological structure of the microgrid, the load power consumption of distributed power sources and nodes, and the interruption time and recovery time of the power system; use random sampling to identify the system status in the upstream of the distribution network, and sort the distribution network faults within the year in chronological order; S2: Perform optimal dispatch on the microgrid on the day of the fault, with the goal of minimizing the operating cost of the microgrid, and obtain the hourly state of charge curve of the energy storage system; S3: Optimize the microgrid resource management on the day of the failure, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded every hour during the recovery period; S4: Equivalently treat the microgrid in the distribution network as a series of distributed power units connected at the common coupling point, and establish a multi-state probability model of the distributed power units equivalent to the microgrid; S5: Perform reliability assessment on the distribution network with sudden faults according to the model established in step S4, record reliability indicators, cyclically analyze faults in all years, and return to step S1 after completing the analysis of all sudden events; if the microgrid in the power system is equivalent to the node of the distributed power source and converges to the standard value, or if there is a preset sample volume in the historical simulation data so that the parameters of the microgrid converge, execute step S6; S6: Apply the microgrid model to the test system based on the microgrid reliability model and evaluate the reliability of the distribution system, and terminate the simulation when the result reaches the expected accuracy.

[0007] According to the above scheme, in step S1, the interruption time and restoration time of the power system are obtained by using historical data or component method.

[0008] According to the above scheme, in step S2, the operating cost of the microgrid includes power exchange cost, power cost provided by the backup power supply, impedance reduction cost and load reduction cost; Calculate the power exchange cost based on the price of energy purchased from the distribution system at each moment in a certain period and the active power input to the microgrid from the common coupling point; Calculate the cost of electricity provided by the backup power source based on the cost of backup power source production and the active power generated by the backup power source; Calculate impedance reduction costs based on solar power generation reduction costs and active power generated by photovoltaics, and wind power generation reduction costs and active power generated by wind turbines; The load reduction cost is calculated based on the lost load value and the reduced active power at the load point.

[0009] Furthermore, in step S2, According to the Distflow power flow model, the linear power flow constraints inside the microgrid are obtained, including associating the backup generator, wind turbine unit, photovoltaic unit, energy storage system, node demand, common coupling point and static VAR compensator to the corresponding microgrid node set, and calculating the active and reactive power injected into the node to obtain the relationship between the two and the capacity range of the active power injected into the node; Set load shedding constraints to ensure safe operation of static VAR compensators; Setting constraints on purchasing electricity from the distribution grid to supply power to the microgrid from the point of common coupling upstream in the power system; Setting active and reactive power constraints for backup power sources and fuel limits for their generation; Set limits on the reactive power of static VAR compensators and the power output of wind turbines and photovoltaic units; Set constraints on the energy storage system's charging and discharging and hourly state of charge.

[0010] According to the above scheme, in step S3, the operating cost of the microgrid is calculated based on the income from exporting energy to the distribution system, the electricity cost provided by the backup power source SG, the impedance reduction cost and the load reduction cost, and minimizing the operating cost of the microgrid is used as the objective function of the resource management optimization problem.

[0011] Furthermore, in step S3, Set the power flow constraints corresponding to the microgrid bus; Set the charge level of the energy storage system at the start of islanding operation; Set the power limit for operators to bid for blocks.

[0012] According to the above scheme, in step S4, the specific steps are: Obtain the power allocation of the microgrid to each power purchasing block and the power selling price during the recovery period; Each microgrid is modeled as a group of equivalent distributed power units, namely, fictitious distributed power units; Construct a multi-state probabilistic model of distributed power units; The simulation output is generated by determining the probability of occurrence of each data point, providing information on power distribution and pricing.

[0013] Furthermore, in step S4, the specific steps of simplifying the multi-state probability model are: The allocation method is used to simplify the model states and construct a multi-state model with specified derated states; The states generated by the simulation are assigned to specific states to represent the multi-state probabilistic model of the fictitious distributed power unit; the set of states is retained, including the selected derated states and up and down states.

[0014] According to the above scheme, in step S5, the specific steps are: Modeling of distribution networks under sudden fault conditions; Determine whether the components of the distribution network are effective; Each modeled accident is analyzed separately, and the impact of the fault on the reliability indicators of the network load point is recorded based on the power outage time of the load point caused by the sudden fault and the annual failure rate; the power outage time of the load point caused by the sudden fault is calculated based on the failure rate, maintenance time, the number of potential scenarios in which the distribution system provides backup energy, the probability of scenario occurrence, the time of island formation, and the state variables of the load point in the scenario.

[0015] A reliability assessment system considering microgrid-supported distribution network, The data acquisition submodule is used to obtain the technical information and topological structure of the microgrid, the load power consumption of the distributed power sources and nodes, and the interruption time and recovery time of the power system; random sampling is used to identify the system status in the upstream of the distribution network, and the distribution network faults within the year are sorted in chronological order; The optimal scheduling submodule is used to perform optimal scheduling on the microgrid on the day of the fault, with the goal of minimizing the operating cost of the microgrid and obtaining the hourly state of charge curve of the energy storage system; The optimization management submodule is used to optimize the microgrid resource management on the day of the failure, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded every hour during the recovery period; The model equivalent submodule is used to equate the microgrid in the distribution network to a series of distributed power units connected at the common coupling point, and to establish a multi-state probability model of the distributed power units equivalent to the microgrid; The reliability assessment submodule is used to evaluate the reliability of the distribution network with sudden faults according to the multi-state probability model, record the reliability index, cyclically analyze the faults of all years, and execute the function of the data acquisition submodule after all the sudden events are analyzed; if the microgrid in the power system is equivalent to the node of the distributed power source and converges to the standard value, or if there is a preset sample size in the historical simulation data so that the parameters of the microgrid converge, the function of the test submodule is executed; The test submodule is used to apply the microgrid model to the test system according to the microgrid reliability model and evaluate the reliability of the distribution system, and terminate the simulation when the result reaches the expected accuracy.

[0016] The beneficial effects of the present invention are: 1. The reliability assessment method and system of the microgrid supporting the distribution network of the present invention is based on the Monte Carlo simulation framework. Through the innovative development of the modeling process of the self-controlled grid-connected microgrid, a probabilistic reliability model is established to describe the microgrid applicable to a variety of standard scenarios in a concise and accurate manner, and the microgrid components are simplified into equivalent distributed power generation units, which effectively reduces the complexity of calculation and realizes the function of reliability assessment of the distribution network.

[0017] 2. The present invention adopts a method based on a simulation framework to evaluate the reliability of a distribution network with multiple MGs connected to the grid. The reliability model uses mixed integer linear programming technology to optimize scheduling and resource management. The effectiveness of the model is confirmed through a 33-node system with dual MGs.

[0018] 3. The present invention significantly improves the efficiency of distribution system reliability assessment by simplifying MG into DG units and integrating them into the overall analysis framework.

[0019] 4. The framework and model proposed in this invention are suitable for economic and technical research, and enhance the reliability of the multi-MG distribution system; it is an accurate and simple reliability assessment method, which reduces the computational burden and improves the efficiency of distribution network reliability assessment.

[0020] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 is a flow chart of an embodiment of the present invention.

[0023] Figure 2 It is a flow chart of reliability assessment of a distribution network according to an embodiment of the present invention.

[0024] Figure 3 4 is a block diagram of an improved IEEE33 system including two microgrids according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] Example 1 See also Figure 1 ,The specific steps of a reliability assessment method considering ,microgrid supporting distribution network are as follows: S1: Obtain the technical information and topological structure of the microgrid, the load power consumption of distributed power sources and nodes, and the interruption time and recovery time of the power system; use random sampling to identify the system status in the upstream of the distribution network, and sort the distribution network faults within the year in chronological order; S2: Perform optimal dispatch on the microgrid on the day of the fault, with the goal of minimizing the operating cost of the microgrid, and obtain the hourly state of charge curve of the energy storage system; S3: Optimize the microgrid resource management on the day of the failure, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded every hour during the recovery period; S4: Equivalently treat the microgrid in the distribution network as a series of distributed power units connected at the common coupling point, and establish a multi-state probability model of the distributed power units equivalent to the microgrid; S5: Perform reliability assessment on the distribution network with sudden faults according to the model established in step S4, record reliability indicators, cyclically analyze faults in all years, and return to step S1 after completing the analysis of all sudden events; if the microgrid in the power system is equivalent to the node of the distributed power source and converges to the standard value, or if there is a preset sample volume in the historical simulation data so that the parameters of the microgrid converge, execute step S6; S6: Apply the microgrid model to the test system based on the microgrid reliability model and evaluate the reliability of the distribution system, and terminate the simulation when the result reaches the expected accuracy.

[0027] Furthermore, in step S1, historical data or a component method is used to obtain the interruption time and restoration time of the power system.

[0028] In step S2, the operating cost of the microgrid includes power exchange cost, power cost provided by the backup power source, impedance reduction cost and load reduction cost; Calculate the power exchange cost based on the price of energy purchased from the distribution system at each moment in a certain period and the active power input to the microgrid from the common coupling point; Calculate the cost of electricity provided by the backup power source based on the cost of backup power source production and the active power generated by the backup power source; Calculate impedance reduction costs based on solar power generation reduction costs and active power generated by photovoltaics, and wind power generation reduction costs and active power generated by wind turbines; The load reduction cost is calculated based on the lost load value and the reduced active power at the load point.

[0029] Further, in step S2, According to the Distflow power flow model, the linear power flow constraints inside the microgrid are obtained, including associating the backup generator, wind turbine unit, photovoltaic unit, energy storage system, node demand, common coupling point and static VAR compensator to the corresponding microgrid node set, and calculating the active and reactive power injected into the node to obtain the relationship between the two and the capacity range of the active power injected into the node; Set load shedding constraints to ensure safe operation of static VAR compensators; Setting constraints on purchasing electricity from the distribution grid to supply power to the microgrid from the point of common coupling upstream in the power system; Setting active and reactive power constraints for backup power sources and fuel limits for their generation; Set limits on the reactive power of static VAR compensators and the power output of wind turbines and photovoltaic units; Set constraints on the energy storage system's charging and discharging and hourly state of charge.

[0030] In step S3, the operating cost of the microgrid is calculated based on the revenue from exporting energy to the distribution system, the cost of electricity provided by the backup power source SG, the impedance reduction cost, and the load reduction cost, and minimizing the operating cost of the microgrid is used as the objective function of the resource management optimization problem.

[0031] Further, in step S3, Set the power flow constraints corresponding to the microgrid bus; Set the charge level of the energy storage system at the start of islanding operation; Set the power limit for operators to bid for blocks.

[0032] In step S4, the specific steps are: Obtain the power allocation of the microgrid to each power purchasing block and the power selling price during the recovery period; Each microgrid is modeled as a group of equivalent distributed power units, namely, fictitious distributed power units; Construct a multi-state probabilistic model of distributed power units; The simulation output is generated by determining the probability of occurrence of each data point, providing information on power distribution and pricing.

[0033] Furthermore, in step S4, the specific steps of simplifying the multi-state probability model are: The allocation method is used to simplify the model states and construct a multi-state model with specified derated states; The states generated by the simulation are assigned to specific states to represent the multi-state probabilistic model of the fictitious distributed power unit; the set of states is retained, including the selected derated states and up and down states.

[0034] In step S5, the specific steps are: Modeling of distribution networks under sudden fault conditions; Determine whether the components of the distribution network are effective; Each modeled accident is analyzed separately, and the impact of the fault on the reliability indicators of the network load point is recorded based on the power outage time of the load point caused by the sudden fault and the annual failure rate; the power outage time of the load point caused by the sudden fault is calculated based on the failure rate, maintenance time, the number of potential scenarios in which the distribution system provides backup energy, the probability of scenario occurrence, the time of island formation, and the state variables of the load point in the scenario.

[0035] This embodiment is based on the Monte Carlo simulation framework. By innovatively developing the modeling process of the self-controlled grid-connected microgrid, a probabilistic reliability model is established to describe the microgrid applicable to a variety of standard scenarios in a concise and accurate manner, and the microgrid components are simplified into equivalent distributed power generation units, which effectively reduces the complexity of calculation and realizes the function of reliability assessment of the distribution network.

[0036] Example 2 The steps of this embodiment are the same as those of embodiment 1, except that each step is applied to the improved IEEE-33 node system of a specific example. Specifically, the following steps are included: S1: Based on the purpose of microgrid MG reliability modeling, historical data or component method is used to input data to the microgrid model; Depend on Figure 1 It can be seen that data needs to be input when modeling the microgrid MG. These data come from the technical information and topological structure of the microgrid MG, the power consumption of distributed power sources DG and node loads, and the time it takes to disconnect the power supply when a fault occurs upstream in the power system and the time it takes for the power system to restore stability. The interruption time and recovery time of the power system can be estimated from historical data. If there is no historical data, these two parameters can be obtained from the failure rate of various internal power devices.

[0037] After the data are collected, random sampling is used to identify the system status in the upstream of the distribution network, and the distribution network faults within the year are sorted and analyzed in time. After that, each fault situation is analyzed separately in the following steps.

[0038] S2: Execute the optimal dispatch of the microgrid on the day of the fault, optimize the resource operation cost of the microgrid by simulating the state of the microgrid at the beginning of the island, and obtain the hourly state of charge SOC of the energy storage system ESSs; To minimize the operating cost of the microgrid The objective function for the goal is: ; in: For electricity exchange costs; The cost of electricity provided to the backup power SG; Cost reduction for impedance; Cost reduction for load.

[0039] The various costs in the above formula are expressed as: , , , ; in: for t the price at which energy is purchased from the distribution system at any given moment; It is the active power input from the common coupling point PCC to the microgrid MG; The cost of producing the backup power SG; Active power generated by the backup power source SG; and cutting costs for solar and wind power generation; and are the active power generated by photovoltaic PV and wind turbine WT respectively; , , and They are the load sets of backup power source SG, wind turbine WT, photovoltaic PV and microgrid MG; For load point d The loss load value; For the credit for the reduction.

[0040] The premise of optimizing the operating cost of the microgrid MG is to first obtain the optimal power flow model in the microgrid MG, and then obtain the linear power flow constraints inside the microgrid MG according to the Distflow power flow model:

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] in: , , , , , and They are the mapping sets that associate the backup generators, wind turbine WT units, photovoltaic PV units, energy storage systems ESSs, node demands, common coupling points PCC and static VAR compensators SVC to their corresponding microgrid MG node sets; and are the charging and discharging power of the energy storage system ESSs respectively; and is the active and reactive power injected into the node; and For slave nodes n Flow to Node j Active power and reactive power; It is the reactive power output by the static VAR compensator SVC; To connect nodes n and nodes j The resistance of the branch; To connect nodes n and nodes j The reactance of the branch; To connect nodes n and nodes j The maximum capacity of the branch; It is a 0-1 variable, indicating a node n and j The connection status; and are the upper and lower limits of the node voltage; To connect to n In order to ensure the safe operation of the static VAR compensator SVC in the microgrid MG, it is necessary to constrain the load reduction, then:

[0052]

[0053]

[0054] in: is a 0-1 variable, indicating the load reduction status in the microgrid MG; For load point d The maximum number of interruptions allowed; and Load nodes d Active power and reactive power; Reactive power for load shedding; For resource management is the time frame.

[0055] Since the power generation capacity of the microgrid MG is limited, it is necessary to supply power to the microgrid MG from the common coupling point PCC upstream of the power system. The constraints for purchasing power from the distribution network are as follows:

[0056]

[0057] in: and are the upper bounds of the active power and reactive power purchased from the distribution system, respectively.

[0058] The power constraints issued by the backup power source SG of the distributed power generation DG and the resulting fuel restrictions are as follows:

[0059]

[0060]

[0061] in: It is a 0-1 variable. When it is 1, it means that the microgrid MG is in an island state, and when it is 0, it is the opposite. and The upper and lower limits of the reactive power of the backup power source SG; For backup power SG g The upper limit of the energy produced by the unit relative to the available fuel; and are the active power and reactive power of distributed energy respectively; It is the power generated by the backup power source SG of the distributed power source DG.

[0062] The reactive power output by the static VAR compensator SVC and the power output by the wind and solar generators are limited as follows:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] in: and It is the upper and lower limits of the reactive output of the static VAR compensator SVC; and The power generation capacity of wind turbine WT units and photovoltaic PV units; and is the availability factor of wind turbine WT unit and photovoltaic PV unit; is the power factor of wind turbine WT; and are the active power and reactive power of the wind turbine respectively; and are the active power and reactive power of photovoltaic respectively; and They are the active power generated by photovoltaic PV and wind turbine WT respectively.

[0070] The hourly state of charge SOC curve is derived based on the characteristic that the charging and discharging of the energy storage system ESSs in the distribution network affects the hourly state of charge SOC. At the same time, the charging and discharging of the energy storage system ESSs also has certain restrictions. In addition, it is necessary to ensure that the hourly state of charge SOC of the energy storage system ESSs is maintained within the convergence range. The energy storage system ESSs charging and discharging and hourly state of charge SOC constraints are as follows:

[0071]

[0072]

[0073]

[0074]

[0075] in: It is expressed as the electrical energy stored in the energy storage system ESSs; and are charge and discharge efficiency respectively; Expressed as a time interval; It is a 0-1 variable, which represents the charging and discharging state of the energy storage system ESSs; and are the maximum values ​​of charging and discharging power of energy storage systems ESSs, respectively; It is expressed as the maximum value of electric energy stored in the energy storage system ESSs.

[0076] Similar to step S1, the hourly state of charge SOC of the energy storage system ESSs is obtained within 24 hours on the day when the fault occurs. The initial hourly state of charge SOC can be determined at the beginning of the microgrid MG island operation.

[0077] S3: Solve the problem of optimizing microgrid resource management on the day of the failure, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded every hour during the recovery period; The optimization of the microgrid MG dispatching cost is similar to step S2, which divides the dispatching cost into several parts and then optimizes each part. The objective function of the resource management optimization problem is:

[0078]

[0079] in: To minimize the operating cost of the microgrid; revenue for exporting energy to the distribution system; For distribution system operators (DSOs) to bid for blocks k Electricity purchased at a price of bidding blocks for procurement; for prices associated with bidding blocks for the sale of energy to the grid during distribution system emergencies; , and All of this has been explained in the previous step, so I won’t go into details here.

[0080] In case of emergencies, the operator prepares emergency power purchase bids for the microgrid MG and constructs power purchase blocks based on load priority classification. When the power purchase blocks are announced, the microgrid MG operator solves the optimal energy management problem, pays the bids according to the price, and prioritizes the high-price power purchase bids corresponding to important loads.

[0081] For the corresponding power flow constraints on the microgrid MG bus, there are:

[0082]

[0083]

[0084] in: It is the total power output from the microgrid MG to the distribution network during an emergency.

[0085] The constraints of the energy storage system ESSs are similar to those in the previous step, and their constraints are modified to also apply to the statement of this problem, as shown below:

[0086] in: is the expected charge state of ESSs at the start of island operation; is the energy stored in the initial state of ESSs. This formula restores the charge level of the energy storage system at the beginning of island operation.

[0087] The operator's power limits for bidding blocks are as follows:

[0088] in: The maximum power required to purchase the bidding block.

[0089] S4: Equivalent each microgrid in the distribution network to a group of distributed power generation DG units, and establish a multi-state probability model of distributed power generation units equivalent to the microgrid; In the case of an island formed after a fault occurs in the upstream part of the power system, the microgrid MG can be used as a backup energy source to supply power to important loads, so the above-mentioned microgrid MG equivalent distributed power generation DG reflects this function. When modeling the microgrid MG model, the power allocation of the microgrid MG to each power purchase block and the power selling price during the recovery period are obtained, and each microgrid MG is modeled as a group of equivalent distributed power generation DG units. These distributed power generation DG units are called fictitious distributed power generation DG units. Then, based on the above steps, a multi-state probability model of the distributed power generation DG unit is constructed to provide a basis for the reliability analysis of the distribution network containing multiple microgrid MGs. By determining the probability of occurrence of each data point, the simulation output provides a lot of information about power allocation and price. The same capacity state is merged, but there are still many derated states in the model, which increases the computational complexity. Therefore, it is necessary to simplify the multi-state probability model with a reasonable number of derated states to reduce the computational burden.

[0090] The allocation method is used to simplify the model states and construct a multi-state model with specified derated states. The states generated by the simulation are allocated between specific states to represent the multi-state probabilistic model of the fictitious distributed power unit, retaining the state set, including the selected derated states and upper and lower states, and the mathematical form is as follows:

[0091] in: denoted as the number of reserved states used to represent the fictitious DG probabilistic model; It means that after derivation and adjustment, the probability of a fictitious DG remaining in state; is the derating adjustment probability of the fictitious distributed generation unit operating at full load; is the probability of unavailability of a fictitious distributed power unit after derating adjustment; The power generation capacity for simulating power generation operation; is the operating state generated by the simulation f probability.

[0092] The above method models the response of the microgrid MG to each power purchase bid as an equivalent fictitious distributed generation DG unit with a probabilistic multi-state availability model, that is, the microgrid MG is represented as a series of equivalent distributed generation DG units connected in the distribution system at the common coupling point PCC.

[0093] S5: Conduct reliability assessment on the distribution network based on the obtained microgrid model, record reliability indicators, and cyclically analyze faults in all years; The process of evaluating the reliability of distribution network based on microgrid model is as follows: Figure 2 As shown. First, it is necessary to model the distribution network in the case of sudden faults. Since there may be damage to the components that trigger the protection device in the distribution network when the fault occurs, it is also necessary to determine whether the components of the distribution network are effective. Then analyze each modeled accident separately and record the impact of the fault on the reliability index of the network load point. The calculation of the impact of the fault on the reliability of the load point is as follows:

[0094]

[0095] in: and The load points caused by sudden failure are Power outage time and annual failure rate; is the failure rate; For maintenance time; The number of potential scenarios for providing backup energy to the distribution system; For the scene Probability of occurrence; The time for the island to form; Indicates the situation Medium load point 0-1 variable of state, when load It is 0 when connected and 1 when disconnected.

[0096] After all emergencies are analyzed, the process returns to step S1. If the nodes of the microgrid in the power system that are equivalent to distributed power sources converge to the standard values, or if there are enough samples in a large amount of historical simulation data to make the parameters of the microgrid converge, the process proceeds to the next step.

[0097] S6: Based on the proposed framework for microgrid reliability model and distribution system reliability assessment, the microgrid model is applied to the test system; a convergence criterion for the final result is determined to achieve the reliability modeling algorithm of the developed microgrid MG, the coefficient of variation is checked at a specified number of iterations, and the simulation is terminated when the result reaches the expected accuracy.

[0098] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0099] Example 3 The principle of this embodiment is the same as that of Embodiment 1, except that it includes the following steps: The proposed framework has been applied to the improved IEEE 33-bus system, which contains two MGs, such as Figure 3 For ease of presentation, Figure 3 The details of the model are simplified, where MG1 is a grid topology network including 3 PVs, 3 backup DGs connected to the bus 101, and ESSs adjacent to the 3 PVs respectively; MG2 is a radial topology network including a backup DG connected to the bus 201, 2 WTs, 6 PVs, and ESSs adjacent thereto.

[0100] After building the reliability model of MGs connected to the improved IEEE33 node, in order to achieve the convergence standard of the final result of the developed MG reliability modeling algorithm, it is necessary to check the coefficient of variation according to the specified number of iterations, and terminate the simulation when the result reaches the expected accuracy. Table 1 shows the reliability indicators of the two cases of whether there is emergency energy trading to restore the power supply of the load point, where the reliability indicators include: system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), expected unsupplied energy (EENS), average service availability index (ASAI) and average service unavailability index (ASUI). It can be seen from the table that the comparison of system reliability in the two cases highlights the value of emergency energy trading.

[0101] Table 1 Reliability indicators in two cases

[0102] Considering the sufficient capacity of MGs and the high power generation capacity of backup generators, Table 2 compares the evaluation results of backup generators in three different scenarios, where method 1 is the MG model in the above steps, and method 2 corresponds to the intensive analysis method. As can be seen from the table, the above methods can accurately reflect the impact of power generation capacity changes on system reliability. In contrast, although the intensive analysis method shows higher reliability, it is not suitable for low capacity margin scenarios, and it ignores some influencing factors, resulting in large errors.

[0103] Table 2 Comparison of test system reliability indicators evaluated by different analysis methods

[0104] Notes: 1Number of outages per household per year 2 Hours per household per year 3 MWh per year Example 4 This embodiment is used to implement the principle construction system of the above method embodiment, including a data acquisition submodule, an optimal scheduling submodule, an optimization management submodule, a model equivalent submodule, a reliability evaluation submodule, and a testing submodule.

[0105] The data acquisition submodule is used to obtain the technical information and topological structure of the microgrid, the load power consumption of the distributed power sources and nodes, and the interruption time and recovery time of the power system; random sampling is used to identify the system status in the upstream of the distribution network, and the distribution network faults within the year are sorted in chronological order; The optimal scheduling submodule is used to perform optimal scheduling on the microgrid on the day of the fault, with the goal of minimizing the operating cost of the microgrid and obtaining the hourly state of charge curve of the energy storage system; The optimization management submodule is used to optimize the microgrid resource management on the day of the failure, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded every hour during the recovery period; The model equivalent submodule is used to equate the microgrid in the distribution network to a series of distributed power units connected at the common coupling point, and to establish a multi-state probability model of the distributed power units equivalent to the microgrid; The reliability assessment submodule is used to evaluate the reliability of the distribution network with sudden faults according to the multi-state probability model, record the reliability index, cyclically analyze the faults of all years, and execute the function of the data acquisition submodule after all the sudden events are analyzed; if the microgrid in the power system is equivalent to the node of the distributed power source and converges to the standard value, or if there is a preset sample size in the historical simulation data so that the parameters of the microgrid converge, the function of the test submodule is executed; The test submodule is used to apply the microgrid model to the test system according to the microgrid reliability model and evaluate the reliability of the distribution system, and terminate the simulation when the result reaches the expected accuracy.

[0106] Each sub-module is mainly used to implement each step of the method embodiment, which will not be described in detail here.

[0107] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0108] This embodiment also includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the communication bus; a computer program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a reliability assessment method considering a microgrid supporting a distribution network.

[0109] This embodiment also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement a reliability assessment method that takes into account a microgrid supporting a distribution network.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0111] Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0112] The present application is described with reference to the flowchart of the method and computer program product according to Embodiment 1 of the present application. It should be understood that each process in the flowchart, as well as the combination of processes in the flowchart, can be implemented by computer program instructions.

[0113] These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A process or multiple processes or boxes Figure 1 A reliability assessment system for a distribution network supported by a microgrid taking into account the functions specified in one or more blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes or boxes Figure 1 The steps of a reliability assessment method considering a microgrid supporting a distribution network are specified in one or more boxes.

[0116] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. A reliability assessment method considering a microgrid supporting a distribution network, characterized in that: The following steps are involved: S1: Obtain the technical information and topological structure of the microgrid, the load power consumption of distributed power sources and nodes, and the interruption time and recovery time of the power system; use random sampling to identify the system status in the upstream of the distribution network, and sort the distribution network faults within the year in chronological order; S2: Perform optimal dispatch on the microgrid on the day of the fault, with the goal of minimizing the operating cost of the microgrid, and obtain the hourly state of charge curve of the energy storage system; S3: Optimize the microgrid resource management on the day of the failure, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded every hour during the recovery period; S4: Equivalently treat the microgrid in the distribution network as a series of distributed power units connected at the common coupling point, and establish a multi-state probability model of the distributed power units equivalent to the microgrid; S5: Perform reliability assessment on the distribution network with sudden faults according to the model established in step S4, record reliability indicators, cyclically analyze faults in all years, and return to step S1 after completing the analysis of all sudden events; if the microgrid in the power system is equivalent to the node of the distributed power source and converges to the standard value, or if there is a preset sample volume in the historical simulation data so that the parameters of the microgrid converge, execute step S6; S6: Apply the microgrid model to the test system based on the microgrid reliability model and evaluate the reliability of the distribution system, and terminate the simulation when the result reaches the expected accuracy.

2. According to claim 1, a reliability assessment method considering a microgrid supporting a distribution network is characterized in that: In the step S1, the interruption time and restoration time of the power system are obtained by using historical data or component method.

3. The reliability assessment method considering a microgrid supporting a distribution network according to claim 1, characterized in that: In step S2, the operating cost of the microgrid includes power exchange cost, power cost provided by the backup power supply, impedance reduction cost and load reduction cost; Calculate the power exchange cost based on the price of energy purchased from the distribution system at each moment in a certain period and the active power input to the microgrid from the common coupling point; Calculate the cost of electricity provided by the backup power source based on the cost of backup power source production and the active power generated by the backup power source; Calculate impedance reduction costs based on solar power generation reduction costs and active power generated by photovoltaics, and wind power generation reduction costs and active power generated by wind turbines; The load reduction cost is calculated based on the lost load value and the reduced active power at the load point.

4. The reliability assessment method considering a microgrid supporting a distribution network according to claim 3 is characterized in that: In the step S2, According to the Distflow power flow model, the linear power flow constraints inside the microgrid are obtained, including associating the backup generator, wind turbine unit, photovoltaic unit, energy storage system, node demand, common coupling point and static VAR compensator to the corresponding microgrid node set, and calculating the active and reactive power injected into the node to obtain the relationship between the two and the capacity range of the active power injected into the node; Set load shedding constraints to ensure safe operation of static VAR compensators; Setting constraints on purchasing electricity from the distribution grid to supply power to the microgrid from the point of common coupling upstream in the power system; Setting active and reactive power constraints for backup power sources and fuel limits for their generation; Set limits on the reactive power of static VAR compensators and the power output of wind turbines and photovoltaic units; Set constraints on the energy storage system's charging and discharging and hourly state of charge.

5. The reliability assessment method considering a microgrid supporting a distribution network according to claim 1, characterized in that: In step S3, the operating cost of the microgrid is calculated based on the income from exporting energy to the distribution system, the electricity cost provided by the backup power source SG, the impedance reduction cost and the load reduction cost, and minimizing the operating cost of the microgrid is used as the objective function of the resource management optimization problem.

6. A reliability assessment method considering a microgrid supporting a distribution network according to claim 5, characterized in that: In the step S3, Set the power flow constraints corresponding to the microgrid bus; Set the charge level of the energy storage system at the start of islanding operation; Set the power limit for operators to bid for blocks.

7. The reliability assessment method considering a microgrid supporting a distribution network according to claim 1, characterized in that: In the step S4, the specific steps are: Obtain the power allocation of the microgrid to each power purchasing block and the power selling price during the recovery period; Each microgrid is modeled as a group of equivalent distributed power units, namely, fictitious distributed power units; Construct a multi-state probabilistic model of distributed power units; The simulation output is generated by determining the probability of occurrence of each data point, providing information on power distribution and pricing.

8. A reliability assessment method considering a microgrid supporting a distribution network according to claim 7, characterized in that: In step S4, the specific steps of simplifying the multi-state probability model are: The allocation method is used to simplify the model states and construct a multi-state model with specified derated states; The states generated by the simulation are assigned to specific states to represent the multi-state probabilistic model of the fictitious distributed power unit; the set of states is retained, including the selected derated states and up and down states.

9. The reliability assessment method considering a microgrid supporting a distribution network according to claim 1, characterized in that: In the step S5, the specific steps are: Modeling of distribution networks under sudden fault conditions; Determine whether the components of the distribution network are effective; Analyze each modeled accident separately, and record the impact of the fault on the reliability index of the network load point according to the outage time and annual failure rate of the load point caused by the sudden fault; The outage time of the load point caused by the sudden fault is calculated based on the failure rate, maintenance time, the number of potential scenarios in which the distribution system provides backup energy, the probability of the scenario occurrence, the time of island formation, and the state variables of the load point in the scenario.

10. A reliability assessment system considering a microgrid supporting a distribution network, characterized in that: The data acquisition submodule is used to obtain the technical information and topological structure of the microgrid, the load power consumption of the distributed power sources and nodes, and the interruption time and recovery time of the power system; random sampling is used to identify the system status in the upstream of the distribution network, and the distribution network faults within the year are sorted in chronological order; The optimal scheduling submodule is used to perform optimal scheduling on the microgrid on the day of the fault, with the goal of minimizing the operating cost of the microgrid and obtaining the hourly state of charge curve of the energy storage system; The optimization management submodule is used to optimize the microgrid resource management on the day of the failure, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded every hour during the recovery period; The model equivalent submodule is used to equate the microgrid in the distribution network to a series of distributed power units connected at the common coupling point, and to establish a multi-state probability model of the distributed power units equivalent to the microgrid; The reliability assessment submodule is used to conduct reliability assessment on the distribution network with sudden faults based on the multi-state probability model, record reliability indicators, cyclically analyze faults in all years, and execute the functions of the data acquisition submodule after the analysis of all sudden events is completed; If the nodes of the microgrid equivalent to the distributed power source in the power system converge to the standard value, or if there is a preset sample volume in the historical simulation data so that the parameters of the microgrid converge, the function of the test submodule is executed; The test submodule is used to apply the microgrid model to the test system according to the microgrid reliability model and evaluate the reliability of the distribution system, and terminate the simulation when the result reaches the expected accuracy.

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