A reliability assessment method and system considering microgrid-supported distribution network

Abstract

The present invention provides a reliability assessment method and system considering the microgrid supporting the distribution network. Based on the Monte Carlo simulation framework, by developing the modeling process of the self-controlled grid-connected microgrid, a probabilistic reliability model is established to describe the microgrid applicable to various standard scenarios, and the reliability of the distribution network with multiple MGs connected to the grid is assessed. The mixed integer linear programming technology is used to optimize the scheduling and resource management; the microgrid components are simplified into equivalent distributed generation units and integrated into the overall analysis framework, which effectively reduces the complexity of the calculation, significantly improves the efficiency of the reliability assessment of the distribution system, and realizes the function of reliability assessment of the distribution network. The framework and model proposed by the present invention are adapted to economic and technical research, and enhance the reliability of the distribution system with multiple MGs; it is an accurate and simple reliability assessment method, which reduces the computational burden and improves the efficiency of the reliability assessment of the distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution system reliability assessment, and particularly relates to a reliability assessment method and system for a distribution network supported by a microgrid. Background Art

[0002] The increasingly developing new power system has become active and intelligent. In order to ensure the stability of power supply in the distribution network to improve the power supply quality, and at the same time optimize the internal resource allocation and emergency response capabilities, it is necessary to evaluate the reliability of the distribution network. Most of the existing technologies evaluate the reliability of the distribution network containing a microgrid based on Monte Carlo simulation technology, use the concept of a virtual power plant to represent the microgrid, describe the distributed power generation units in the microgrid based on a probability model, and use the analysis formula for the reliability of the active distribution system containing the microgrid to evaluate the distribution network. Another analysis method is to consider instantaneous interruptions to evaluate the reliability of the distribution system equipped with distributed power resources and a microgrid, and at the same time incorporate the energy storage system into the simulation-based reliability assessment framework, considering the probability of successful operation of the microgrid and standby distributed power generation during the restoration process to model the impact of distributed power generation and the microgrid on the system reliability index.

[0003] Currently, the methods for evaluating the reliability of distribution networks at home and abroad mostly adopt the analysis method for the reliability of the distribution system containing standby distributed power generation and a microgrid, especially focusing on instantaneous interruptions. By simplifying the modeling through the probability of successful operation of the microgrid and standby distributed power generation during the restoration period, the complexity of calculation is reduced, but the applicability to the standby distributed power generation system with different ownerships is limited. Adopting the formula for the reliability of the active distribution system containing a microgrid and using the probability model to describe conventional and renewable distributed power generation, but this method requires consuming more computing resources. The Monte Carlo-based simulation technology for evaluating the model of the distribution network containing a microgrid is used to analyze the reliability of the active distribution system, and the concept of a virtual power plant is introduced to model the microgrid, but it is not applicable to the analysis technology.

[0004] Traditional distribution network reliability assessment analyzes the impact of distributed power generation unit installation on distribution system reliability by introducing a distributed power generation unit reliability model, but only considering the installation of distributed power generation is too simplistic and may lead to errors in quantifying network reliability. Although simulation technology can be developed to be applicable to the microgrid reliability model for distribution system reliability assessment, as the network scale increases, the processing time for reliability assessment will increase significantly. 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 supported by a microgrid, which is used for evaluating the reliability of the distribution network.

[0006] The technical solution adopted by the present invention to solve the above technical problems is as follows: A reliability evaluation method for a microgrid considering the support of a distribution network, comprising the following steps:

[0007] 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 state in the upstream of the distribution network, and sort the distribution network faults within a year in chronological order;

[0008] S2: Execute optimal scheduling for the microgrid on the day of the fault, aiming to minimize the operating cost of the microgrid, and obtain the hourly state of charge curve of the energy storage system;

[0009] S3: Optimize the microgrid resource management on the day of the fault, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded per hour during the recovery period;

[0010] S4: Equivalent the microgrid in the distribution network to a series of distributed power source units connected at the point of common coupling, and establish a multi-state probability model of the distributed power source units equivalent to the microgrid;

[0011] S5: Perform reliability evaluation on the distribution network with sudden faults according to the model established in step S4, record the reliability indexes, cycle through the faults in all years, and return to step S1 after analyzing all emergencies; if the nodes of the microgrid equivalent to distributed power sources in the power system converge to the standard value, or when there is a preset sample size in the historical simulation data such that the parameters of the microgrid converge, execute step S6;

[0012] S6: 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.

[0013] According to the above solution, in step S1, the interruption time and recovery time of the power system are obtained by using historical data or the component method.

[0014] According to the above solution, in step S2, the operating cost of the microgrid includes power exchange cost, power cost provided by standby power, impedance reduction cost, and load reduction cost;

[0015] Calculate the power exchange cost according to the price of purchasing energy from the distribution system at each moment within a certain period and the active power input to the microgrid at the point of common coupling;

[0016] Calculate the power cost provided by the standby power according to the production cost of the standby power and the active power generated by the standby power;

[0017] Calculate the impedance reduction cost based on the cost reduction of solar power generation and the active power generated by photovoltaic, as well as the cost reduction of wind power generation and the active power generated by wind turbines.

[0018] Calculate the load reduction cost based on the loss load value at the load point and the reduced active power.

[0019] Furthermore, in the step S2,

[0020] Obtain the linear power flow constraints inside the microgrid according to the Distflow power flow model, including associating the standby generator, wind turbine units, photovoltaic units, energy storage system, node demand, point of common coupling, and static var compensator to the corresponding microgrid node sets respectively, and obtaining the relationship between the active power and reactive power injected into the node and the capacity range of the active power injected into the node through calculation.

[0021] Set the load reduction constraint to ensure the safe operation of the static var compensator.

[0022] Set the constraint for purchasing electricity from the distribution network to supply power to the microgrid from the point of common coupling upstream of the power system.

[0023] Set the active and reactive power constraints of the standby power supply and the fuel limit generated by it.

[0024] Set the limits on the reactive power of the static var compensator and the power generated by the wind turbine units and photovoltaic units.

[0025] Set the constraints on the charging and discharging of the energy storage system and the state of charge per hour.

[0026] According to the above scheme, in the step S3, calculate the operating cost of the microgrid based on the income from exporting energy to the distribution system, the power cost provided by the standby power supply SG, the impedance reduction cost, and the load reduction cost, and take the minimization of the operating cost of the microgrid as the objective function of the resource management optimization problem.

[0027] Furthermore, in the step S3,

[0028] Set the power flow constraints corresponding to the microgrid bus.

[0029] Set the charging level of the energy storage system at the start of island operation.

[0030] Set the power limit of the operator for the bid block.

[0031] According to the above scheme, in the step S4, the specific steps are as follows:

[0032] Obtain the power distribution of the microgrid for each power purchase block and the selling electricity price during the restoration period.

[0033] Model each microgrid as a set of equivalent distributed power units, namely virtual distributed power units;

[0034] Construct a multi-state probability model for the distributed power units;

[0035] By determining the occurrence probability of each data point to simulate the output and provide information on power distribution and price.

[0036] Furthermore, in the step S4, the specific steps for simplifying the multi-state probability model are as follows:

[0037] Adopt an assignment method to streamline the model states and construct a multi-state model for the specified derated states;

[0038] The simulated generated states are assigned between specific states to represent the multi-state probability model of the virtual distributed power units; retain the state set, including the selected derated states and the upper and lower states.

[0039] According to the above scheme, in the step S5, the specific steps are as follows:

[0040] Model the distribution network under the condition of sudden faults;

[0041] Judge whether the components of the distribution network are effective;

[0042] Analyze each modeled accident separately, and record the impact of the fault on the reliability index of the network load point according to the power outage time and annual failure rate of the load point caused by the sudden fault; the power outage time of the load point caused by the sudden fault is calculated according to the failure rate, repair time, number of potential scenarios for the distribution system to provide backup energy, probability of the scenario occurrence, time for island formation, and state variables of the load point in the scenario.

[0043] A reliability evaluation system for considering the support of microgrids to the distribution network,

[0044] A data acquisition sub-module, 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; randomly sample to identify the system states in the upstream of the distribution network, and sort the annual distribution network faults in chronological order;

[0045] An optimal scheduling sub-module, used to perform optimal scheduling on the microgrid on the day of the fault, aiming to minimize the operating cost of the microgrid, and obtain the hourly state of charge curve of the energy storage system;

[0046] An optimization management sub-module, used to optimize the resource management of the microgrid on the day of the fault, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded per hour during the recovery period;

[0047] A model equivalent sub-module, which is used to equivalent the microgrid in the distribution network into a series of distributed power supply units connected at the common coupling point, and establish a multi-state probability model of the distributed power supply units equivalent to the microgrid;

[0048] A reliability assessment sub-module, which is used to perform reliability assessment on the distribution network with sudden faults according to the multi-state probability model, record the reliability indexes, analyze the faults of all years cyclically, and execute the functions of the data acquisition sub-module after analyzing all emergencies; If the nodes of the microgrid equivalent to distributed power sources in the power system converge to the standard value, or when there is a preset sample size in the historical simulation data such that the parameters of the microgrid converge, execute the functions of the test sub-module;

[0049] A test sub-module, which 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.

[0050] The beneficial effects of the present invention are as follows:

[0051] 1. A reliability assessment method and system for a distribution network considering the support of a microgrid according to the present invention, based on the Monte Carlo simulation framework, by innovatively developing the modeling process of a self-controlled grid-connected microgrid, establishing a probabilistic reliability model to describe the microgrid applicable to various specification scenarios in a concise and accurate manner, simplifying the microgrid components into equivalent distributed generation units, effectively reducing the computational complexity, and realizing the function of performing reliability assessment on the distribution network.

[0052] 2. The present invention adopts a method based on a simulation framework to evaluate the reliability of a distribution network with multiple MG grid-connected. The reliability model uses mixed integer linear programming technology to optimize scheduling and resource management. Through the verification of a 33-node system with dual MG, the effectiveness of the model is confirmed.

[0053] 3. By simplifying the MG into DG units and integrating them into the overall analysis framework, the present invention significantly improves the efficiency of reliability assessment of the distribution system.

[0054] 4. The framework and model proposed by the present invention are adapted to economic and technical research, strengthening the reliability of the distribution system with multiple MG; it is an accurate and simple reliability assessment method, reducing the computational burden and improving the efficiency of reliability assessment of the distribution network.

[0055] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Description of the Drawings

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0057] Figure 1 is the flowchart of the embodiment of the present invention.

[0058] Figure 2 is the flowchart for reliability assessment of the distribution network in the embodiment of the present invention.

[0059] Figure 3 is the block diagram of the improved IEEE33 system including two microgrids in the embodiment of the present invention. Detailed implementation manners

[0060] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be 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.

[0061] Embodiment 1

[0062] See Figure 1 , the specific steps of a reliability assessment method considering the support of the microgrid to the distribution network are as follows:

[0063] 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; randomly sample to identify the system state in the upstream of the distribution network, and sort the distribution network faults within the year in chronological order;

[0064] S2: Perform optimal scheduling on the microgrid on the day of the fault, aiming to minimize the operating cost of the microgrid, and obtain the hourly state of charge curve of the energy storage system;

[0065] S3: Optimize the microgrid resource management on the day of the fault, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded per hour during the recovery period;

[0066] S4: Equivalent the microgrid in the distribution network to a series of distributed power source units connected at the point of common coupling, and establish a multi-state probability model of the distributed power source units equivalent to the microgrid;

[0067] S5: Evaluate the reliability of the distribution network with sudden faults according to the model established in step S4, record the reliability indicators, analyze the faults in all years in a loop, and return to step S1 after analyzing all emergencies; if the nodes of the microgrid in the power system equivalent to distributed power sources converge to the standard value, or when there is a preset sample size in the historical simulation data such that the parameters of the microgrid converge, execute step S6;

[0068] S6: 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.

[0069] Furthermore, in step S1, the interruption time and restoration time of the power system are obtained by using historical data or the component method.

[0070] In step S2, the operating cost of the microgrid includes the power exchange cost, the power cost provided by the backup power supply, the impedance reduction cost, and the load shedding cost;

[0071] Calculate the power exchange cost according to the price of purchasing energy from the distribution system at each moment within a certain period and the active power input to the microgrid at the point of common coupling;

[0072] Calculate the power cost provided by the backup power supply according to the production cost of the backup power supply and the active power generated by the backup power supply;

[0073] Calculate the impedance reduction cost according to the solar power generation reduction cost and the active power generated by the photovoltaic, as well as the wind power generation reduction cost and the active power generated by the wind turbine;

[0074] Calculate the load shedding cost according to the loss load value of the load point and the reduced active power.

[0075] Furthermore, in step S2,

[0076] Obtain the linear power flow constraints inside the microgrid according to the Distflow power flow model, including respectively associating the backup generator, the wind turbine unit, the photovoltaic unit, the energy storage system, the node demand, the point of common coupling, and the static var compensator to the corresponding microgrid node sets, and obtaining the relationship between the two and the capacity range of the active power injected into the node by calculating the active and reactive powers injected into the node;

[0077] Set the load shedding constraint to ensure the safe operation of the static var compensator;

[0078] Set the constraint for purchasing electricity from the distribution network to supply power to the microgrid from the point of common coupling upstream of the power system;

[0079] Set the active and reactive power constraints of the backup power supply and the fuel limit generated by it;

[0080] Set the reactive power limits of the static var compensator and the power output limits of the wind turbine units and photovoltaic units;

[0081] Set the constraints on the charge and discharge of the energy storage system and the state of charge per hour.

[0082] In step S3, calculate the operating cost of the microgrid based on the revenue from exporting energy to the distribution system, the power cost provided by the standby power supply SG, the impedance reduction cost, and the load reduction cost, and use the minimization of the operating cost of the microgrid as the objective function of the resource management optimization problem.

[0083] Furthermore, in step S3,

[0084] Set the power flow constraints corresponding to the microgrid bus;

[0085] Set the charging level of the energy storage system at the start of island operation;

[0086] Set the power limit of the operator for the bid block.

[0087] In step S4, the specific steps are as follows:

[0088] Obtain the power distribution of the microgrid for each power purchase block and the selling price during the restoration period;

[0089] Model each microgrid as a set of equivalent distributed power units, namely virtual distributed power units;

[0090] Construct a multi-state probability model of the distributed power units;

[0091] Provide information on power distribution and price by simulating the output by determining the occurrence probability of each data point.

[0092] Furthermore, in step S4, the specific steps to simplify the multi-state probability model are as follows:

[0093] Adopt an allocation method to streamline the model states and construct a multi-state model with specified derated states;

[0094] The simulated generated states are assigned between specific states to represent the multi-state probability model of the virtual distributed power units; retain the state set, including the selected derated states and the upper and lower states.

[0095] In step S5, the specific steps are as follows:

[0096] Model the distribution network in the case of a sudden fault;

[0097] Judge whether the components of the distribution network are effective;

[0098] Analyze each modeled accident individually, and record the impact of the fault on the reliability index of the network load point according to 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 according to the failure rate, repair time, the number of potential scenarios for the distribution system to provide backup energy, the probability of scenario occurrence, the time for island formation, and the state variables of the load point in the scenario.

[0099] 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 various standard scenarios in a concise and accurate manner. The microgrid components are simplified into equivalent distributed generation units, effectively reducing the computational complexity and realizing the function of reliability assessment for the distribution network.

[0100] Embodiment 2

[0101] 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, it includes the following steps:

[0102] S1: According to the purpose of microgrid MG reliability modeling, input data for the microgrid model using historical data or the component method;

[0103] It can be seen from Figure 1 that when modeling the microgrid MG, data needs to be input. These data come from the technical information and topological structure of the microgrid MG, the power consumption of distributed generation DG and node loads, as well as the time when power supply is disconnected when a fault occurs upstream of the power system and the time when the power system resumes stability. Among them, both the interruption time and the 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 rates of various internal power devices.

[0104] After collecting the data, randomly sample to identify the system state upstream of the distribution network, sort by time and analyze the distribution network faults within a year, and then analyze each fault situation separately in the following steps.

[0105] S2: Execute the optimal dispatch of the microgrid for the fault day. By simulating the state of the microgrid at the beginning of islanding, optimize the resource operation cost of the microgrid and obtain the state of charge SOC of the energy storage system ESSs per hour;

[0106] With the goal of minimizing the operating cost of the microgrid The objective function is:

[0107] ;

[0108] Where:

[0109] is the power exchange cost;

[0110] is the power cost provided by the standby power supply SG;

[0111] is the impedance reduction cost;

[0112] is the load reduction cost.

[0113] The costs in the above formula are respectively expressed as:

[0114] ,

[0115] ,

[0116] ,

[0117] ;

[0118] Among them:

[0119] is t the price of purchasing energy from the distribution system at time;

[0120] is the active power input from the point of common coupling PCC to the microgrid MG;

[0121] is the production cost of the standby power supply SG;

[0122] is the active power generated by the standby power supply SG;

[0123] and are the solar and wind power generation reduction costs;

[0124] and are respectively the active powers generated by the photovoltaic PV and the wind turbine WT;

[0125] , , and are respectively the load sets of the standby power supply SG, the wind turbine WT, the photovoltaic PV and the microgrid MG;

[0126] is the load point d 's loss load value;

[0127] is the reduced active power.

[0128] The prerequisite for optimizing the operating cost of the microgrid MG is to first obtain the optimal power flow model in the microgrid MG. According to the Distflow power flow model, the linear power flow constraints within the microgrid MG are obtained:

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] Where:

[0141] 、 、 、 、 、 and are the mapping sets that associate the backup generator, wind turbine WT units, photovoltaic PV units, energy storage systems ESSs, node demands, point of common coupling PCC, and static var compensator SVC to their respective microgrid MG node sets;

[0142] and are the charging and discharging powers of the energy storage systems ESSs, respectively;

[0143] and are the active and reactive powers injected into the node;

[0144] and are from node n flow to node j the active power and reactive power;

[0145] is the reactive power output by the static var compensator SVC;

[0146] is the connection node n and node j resistance of the branch;

[0147] is the connection node n and node j reactance of the branch;

[0148] is the connection node n and node j maximum capacity of the branch;

[0149] is a 0-1 variable indicating the connection status between node n and j ;

[0150] and are the upper and lower limits of the node voltage;

[0151] is the set of nodes connected to n . To ensure the safe operation of the static var compensator SVC in the microgrid MG, load shedding needs to be constrained, so there is:

[0152]

[0153]

[0154]

[0155] Among them:

[0156] is a 0-1 variable indicating the load shedding status in the microgrid MG;

[0157] is the load point d maximum allowable interruption times;

[0158] and are the active power and reactive power of the load node d respectively;

[0159] is the reactive power of load shedding;

[0160] is the time range of resource management.

[0161] Since the power generation capacity of the microgrid MG is limited, it is necessary to supply power to the microgrid MG from the point of common coupling PCC upstream of the power system. The constraints for purchasing electricity from the distribution network are as follows:

[0162]

[0163]

[0164] Where:

[0165] and are the upper bounds of the active power and reactive power purchased from the distribution system, respectively.

[0166] The power constraints on the standby power supply SG of the distributed generation DG and its fuel limitations are as follows:

[0167]

[0168]

[0169]

[0170] Where:

[0171] is a 0-1 variable, which is 1 when the microgrid MG is in the island state and 0 otherwise;

[0172] and are the upper and lower limits of the reactive power of the standby power supply SG;

[0173] is the upper limit of the energy generated by the g unit in the standby power supply SG relative to the available fuel;

[0174] and are the active power and reactive power of the distributed energy, respectively;

[0175] is the power output by the standby power supply SG of the distributed generation DG.

[0176] The reactive power output by the static var compensator SVC and the power output limitations of the wind-solar generating units are as follows:

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184] Wherein:

[0185] and are the upper and lower limits of the reactive power output of the static var compensator SVC;

[0186] and are the power generation capabilities of the wind turbine WT unit and the photovoltaic PV unit;

[0187] and are the availability factors of the wind turbine WT unit and the photovoltaic PV unit;

[0188] is the power factor of the wind turbine WT;

[0189] and are the active power and reactive power of the wind turbine respectively;

[0190] and are the active power and reactive power of the photovoltaic respectively;

[0191] and are the active powers generated by the photovoltaic PV and the wind turbine WT respectively.

[0192] According to the characteristic that the charge and discharge of the energy storage system ESSs in the distribution network affects the state of charge SOC per hour, the curve of the state of charge SOC per hour is obtained. At the same time, there are certain limitations on the charge and discharge of the energy storage system ESSs. In addition, it is necessary to ensure that the state of charge SOC of the energy storage system ESSs per hour is maintained within the convergence range. The charge and discharge of the energy storage system ESSs and the constraints of the state of charge SOC per hour are as follows:

[0193]

[0194]

[0195]

[0196]

[0197]

[0198] Wherein:

[0199] is expressed as the electric energy stored in the energy storage systems ESSs;

[0200] and are the charge-discharge efficiencies respectively;

[0201] is expressed as the time interval;

[0202] is a 0-1 variable, expressed as the charge-discharge state of the energy storage systems ESSs;

[0203] and are the maximum values of the charge-discharge powers of the energy storage systems ESSs respectively;

[0204] is expressed as the maximum value of the electric energy stored in the energy storage systems ESSs.

[0205] Similar to step S1, obtaining the state of charge SOC of the energy storage systems ESSs per hour needs to be obtained within 24 hours on the day of the fault occurrence, and the initial state of charge SOC per hour can be determined at the start of the island operation of the microgrid MG.

[0206] S3: Solve the optimization problem of the microgrid resource management on the day of the fault, reduce the power supply to the ordinary loads while increasing the power supply to the important loads, and record the energy and price traded per hour during the recovery period;

[0207] The optimization of the microgrid MG dispatch cost is similar to step S2. The dispatch cost is divided into several parts and then each part is optimized. The objective function of the resource management optimization problem is:

[0208]

[0209]

[0210] Wherein:

[0211] is the minimized microgrid operation cost;

[0212] is the income from exporting energy to the distribution system;

[0213] is the electricity purchased by the distribution system operator (DSO) at the price of the bidding block k ;

[0214] is for the procurement bid block;

[0215] is the price related to the bid block for selling energy to the power grid during emergencies in the distribution system;

[0216] and and were all explained in the previous step and will not be elaborated here.

[0217] 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 gives priority to processing high-price power purchase bids for corresponding important loads.

[0218] For the corresponding power flow constraints on the microgrid MG bus, there are:

[0219]

[0220]

[0221]

[0222] Where:

[0223] is the total power output from the microgrid MG to the distribution network during emergencies.

[0224] The constraints of the energy storage systems ESSs are similar to those in the previous step, and the modified constraint conditions also apply to the description of this problem, as follows:

[0225]

[0226] Where:

[0227] is the predetermined charge state of the ESSs at the start of island operation;

[0228] is the electrical energy stored in the initial state of the ESSs. This formula restores the charging level of the energy storage system obtained at the start of island operation.

[0229] The operator's power limit for the bid block is as follows:

[0230]

[0231] Where:

[0232] is the maximum demand power for purchasing the bid block.

[0233] S4: Equivalent each microgrid in the distribution network to a group of distributed generation (DG) units, and establish a multi-state probability model of the distributed generation units equivalent to the microgrid.

[0234] In the case of forming an island after a fault occurs in the upstream part of the power system, the microgrid (MG) can supply power to important loads as a backup energy source. Therefore, the above-mentioned equivalent distributed generation (DG) of the microgrid (MG) reflects this function. When modeling the microgrid (MG) model, the power distribution of the microgrid (MG) to each power purchase block and the selling price during the restoration period are obtained. Each microgrid (MG) is modeled as a group of equivalent distributed generation (DG) units, and these distributed generation (DG) units are called fictitious distributed generation (DG) units. Then, based on the above steps, a multi-state probability model of the distributed generation (DG) units is constructed to provide a basis for the reliability analysis of the distribution network containing multiple microgrids (MG). By determining the occurrence probability of each data point, the simulation output provides a large amount of information about power distribution and price. The same capacity states are merged, but there are still many derated states in the model, which increases the computational complexity. Therefore, it is necessary to adopt a reasonable number of derated states to simplify the multi-state probability model to reduce the computational burden.

[0235] Adopt an allocation method to streamline the model states and construct a multi-state model with specified derated states. The states generated by the simulation are allocated among specific states to represent the multi-state probability model of the fictitious distributed generation unit, and the state set is retained, including the selected derated states and the upper and lower states. The mathematical form is as follows:

[0236]

[0237] Where: represents the number of retained states used to represent the probability model of the fictitious distributed generation; represents that after derivation and adjustment, it is in the probability of the fictitious distributed generation in the retained state; is the derating adjustment probability of the fictitious distributed generation unit operating at full load; is the unavailability probability of the fictitious distributed generation unit after derating adjustment; is the generating capacity of the simulated power generation operation; is the operating state generated by the simulation f probability.

[0238] 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 to the distribution system at the point of common coupling (PCC).

[0239] S5: Evaluate the reliability of the distribution network based on the obtained microgrid model, record the reliability indices, and cyclically analyze the faults for all years;

[0240] The process for evaluating the reliability of the distribution network based on the microgrid model is as Figure 2 shown. First, it is necessary to model the distribution network under sudden faults. Since there may be cases where components triggering protection devices in the distribution network are damaged when a 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 indices of the network load points. The calculation of the impact of the fault on the reliability of the load point is as follows:

[0241]

[0242]

[0243] Where:

[0244] and are the power outage time and annual failure rate of load point caused by sudden faults, respectively;

[0245] is the failure rate;

[0246] is the repair time;

[0247] is the number of potential scenarios for the distribution system to provide backup energy;

[0248] is the scenario occurrence probability;

[0249] is the islanding formation time;

[0250] represents the 0 - 1 variable of the state of load point in scenario , which is 0 when the load is connected and 1 when it is disconnected.

[0251] After analyzing all the emergencies, return to step S1. If the nodes of the microgrid equivalent to distributed power sources in the power system converge to the standard value, or when there is a sufficient sample size in a large amount of historical simulation data such that the parameters of the microgrid converge, proceed to the next step.

[0252] S6: Propose a framework based on the microgrid reliability model and the evaluation of the distribution system reliability, and apply the microgrid model to the test system; determine a convergence criterion for the final result of the developed reliability modeling algorithm for the microgrid MG, check the coefficient of variation under the specified number of iterations, and terminate the simulation when the result reaches the expected accuracy.

[0253] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply 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 to the implementation process of the embodiments of the present application.

[0254] Embodiment 3

[0255] The principle of this embodiment is the same as that of Embodiment 1, except that it includes the following steps:

[0256] The improved IEEE 33-node system has applied the proposed framework, and the system contains two MGs, as Figure 3 shown. For the convenience of display, Figure 3 the details of the model are simplified. Among them, MG1 is a grid topology network including 3 PVs, 3 standby DGs connected to bus 101, and ESSs adjacent to the 3 PVs respectively; MG2 is a radial topology network of a standby DG connected to bus 201, 2 WTs, 6 PVs, and ESSs close to it.

[0257] After constructing the reliability models of the MGs connected to the improved IEEE 33-node, in order to determine a convergence criterion for the final result of the developed reliability modeling algorithm for the MG, it is necessary to check the coefficient of variation under the specified number of iterations, and terminate the simulation when the result reaches the expected accuracy. Table 1 shows the reliability indexes in two cases of whether there is an emergency energy transaction to restore the power supply of the load point. The reliability indexes include: System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Expected Energy Not Supplied (EENS), Average Service Availability Index (ASAI), and Average Service Unavailability Index (ASUI). It can be seen from the table that the comparison of the system reliability in the two cases highlights the value of the emergency energy transaction.

[0258] Table 1 Reliability indexes in two cases

[0259]

[0260] Considering the abundant capacity of MGs and the high power generation capacity of backup generators, Table 2 compares the evaluation results of three different scenarios of backup generators, where Method 1 is the MG model in the above steps, and Method 2 corresponds to the intensive analysis method. It can be seen from the table that 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 applicable to low-capacity margin scenarios, and it ignores some influencing factors, resulting in large errors.

[0261] Table 2 Comparison of reliability indexes of the test system evaluated by different analysis methods

[0262]

[0263] Note:

[0264] 1 Number of interruptions per household per year

[0265] 2 Hours per household per year

[0266] 3 MWh per year

[0267] Example 4

[0268] This example is used to implement the principle construction of the above method example system, including a data acquisition sub-module, an optimal scheduling sub-module, an optimization management sub-module, a model equivalence sub-module, a reliability evaluation sub-module, and a test sub-module.

[0269] The data acquisition sub-module is used to 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; randomly sample and identify the system state upstream of the distribution network, and sort the distribution network faults within the year in chronological order;

[0270] The optimal scheduling sub-module is used to perform optimal scheduling on the microgrid on the day of the fault, aiming to minimize the operating cost of the microgrid, and obtain the hourly state of charge curve of the energy storage system;

[0271] The optimization management sub-module is used to optimize the microgrid resource management on the day of the fault, reduce the power supply to ordinary loads while increasing the power supply to important loads, and record the energy and price traded per hour during the recovery period;

[0272] The model equivalence sub-module is used to equivalent the microgrid in the distribution network to a series of distributed power supply units connected at the common coupling point, and establish a multi-state probability model of the distributed power supply units equivalent to the microgrid;

[0273] A reliability assessment sub-module, which is used to perform a reliability assessment on a distribution network with sudden faults according to a multi-state probability model, record reliability indicators, analyze the faults of all years cyclically, and execute the functions of the data acquisition sub-module after analyzing all emergencies; if the nodes of the microgrid in the power system equivalent to distributed power sources converge to the standard value, or when there is a preset sample size in the historical simulation data such that the parameters of the microgrid converge, execute the functions of the test sub-module;

[0274] A test sub-module, which 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.

[0275] Each sub-module is mainly used to implement each step of the method embodiment, which will not be elaborated here.

[0276] It should be noted that according to the needs of implementation, each step / component described in this application can be split into more steps / components, or 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.

[0277] This embodiment further includes a processor, a communication interface, a memory, and a communication bus; wherein the processor, the communication interface, and the memory complete communication 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 the support of the microgrid for the distribution network.

[0278] This embodiment also provides a computer-readable storage medium, on which executable instructions are stored, and when the instructions are executed by the processor, the processor implements a reliability assessment method considering the support of the microgrid for the distribution network.

[0279] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0280] Moreover, the present application can 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 code.

[0281] This application is described with reference to the flowcharts 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 and the combination of processes in the flowchart can be implemented by computer program instructions.

[0282] These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a reliability evaluation system for a microgrid supporting a distribution network that realizes the functions specified in one process Figure 1 one process or multiple processes or blocks Figure 1 or multiple blocks.

[0283] These computer program instructions can also be stored in a computer-readable memory capable of guiding the computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in one process Figure 1 one process or multiple processes or blocks Figure 1 or multiple blocks.

[0284] These computer program instructions can also be loaded onto the computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for a reliability evaluation method for a microgrid supporting a distribution network that realizes the functions specified in one process Figure 1 one process or multiple processes or blocks Figure 1 or multiple blocks.

[0285] The above embodiments are only used to illustrate the design concept and features of the present invention, and the 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, all equivalent changes or modifications made according to the principles and design concepts 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.

Images