Micro-grid active power distribution network reliability evaluation method and system considering flexible soft switch

Abstract

The invention discloses a micro-grid active power distribution network reliability evaluation method and system considering a flexible soft switch, and belongs to the technical field of power system power distribution network evaluation. The method specifically comprises the following steps: combining element states of a micro-grid active power distribution network of a sampling meter and a flexible soft switch and carrying out supply-demand matching to obtain a micro-grid state, generating a system state according to the micro-grid state, judging whether an island distributed power supply exists or not, and determining whether the island distributed power supply exists in combination with a flexible soft switch transfer condition; using a mixed integer linear programming model of the dynamic boundary microgrid or using a region division and minimum path search method to determine whether the power supply state of the load node is influenced by the fault; load classification is carried out, a load power supply state is obtained, a reliability index is solved, and the reliability evaluation of the micro-grid active power distribution network considering the flexible soft switch is realized. According to the invention, micro-grid and SOP cooperative control is realized, the utilization rate of the distributed power supply is improved, and the power supply reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system distribution network assessment, and more specifically, to a reliability assessment method and system for a microgrid active distribution network considering flexible soft switches. Even more specifically, it relates to a reliability assessment method for an active distribution network considering the access of flexible soft switches under the background that distributed power sources are mainly connected in the form of microgrids. Background Art

[0002] Traditional distribution networks are limited by complex and fixed regulation and control measures, and have very limited capacity to absorb strongly random and intermittent distributed energy. The research subjects of existing related technologies only consider active distribution networks with simple distributed power source access, and there is no research on the reliability assessment of active distribution networks with more complex microgrids in terms of structure, operation, and fault recovery after the access of soft switches.

[0003] Existing technical document 1 (CN113468705A) discloses a reliability analysis method for a distribution system with microgrids based on path description. Its disadvantages are that it only relies on the passive adjustment of the load - power source connection relationship caused by faults, resulting in low fault recovery efficiency. For example, but not limited to, the load transfer path is single and distributed power sources are not fully utilized; for the island area with microgrids, only static boundary division is adopted, and the power supply range cannot be dynamically adjusted, resulting in low utilization rate of distributed power sources. For example, but not limited to, the recovery rate of critical loads is limited and non - critical loads are redundantly powered. Summary of the Invention

[0004] To solve the deficiencies in the existing technology, the present invention proposes a reliability assessment method and system for a microgrid active distribution network considering flexible soft switches. Through the triggering of topology reconstruction by the transfer conditions of SOP (Soft Open Point, flexible soft switch), a dynamic power supply boundary is formed in coordination with the microgrid. When a fault occurs, SOP switches to control modes 5 - 8 in Table 1, increasing the transfer path, providing active power and voltage for the power - lost area, shortening the average recovery time of the power - lost area. Based on the dynamic - boundary microgrid model MILP (Mixed - Integer Linear Programming), with the goal of maximizing the total weighted recovered load, the power supply range of the microgrid is dynamically optimized to achieve the coordinated control of the microgrid and SOP, improve the utilization rate of distributed power sources, and enhance the power supply reliability. Under the background of widespread access of microgrids, a scientific quantification method is explored to realize the reliability assessment of the active distribution network considering the access of soft switches.

[0005] The present invention adopts the following technical solutions.

[0006] The first aspect of the present invention provides a reliability assessment method for a microgrid active distribution network considering flexible soft switches, including:

[0007] Construct a microgrid active distribution network considering the access of flexible soft switches, and sample the component state combinations of the microgrid active distribution network considering flexible soft switches within the set maximum simulation time;

[0008] Match the supply and demand of the component state combinations to obtain the output of distributed power sources in the corresponding microgrid and the numerical values of microgrid load demands;

[0009] Obtain the microgrid state based on the output of distributed power sources and the numerical values of microgrid load demands, and generate a system state according to the microgrid state;

[0010] Combine the system state to determine whether there are islanded distributed power sources. If there are islanded distributed power sources, combine the flexible soft switch transfer conditions and use the mixed-integer linear programming model of the dynamic boundary microgrid for fault correction to obtain the changes in the power supply states of each load point before and after fault correction; if there are no islanded distributed power sources, combine the flexible soft switch transfer conditions and use the regional division and minimum path search method to determine whether the power supply states of load nodes are affected by faults;

[0011] Classify the loads according to the changes in the power supply states of each load point before and after fault correction or the determination results of whether the power supply states of load nodes are affected by faults to obtain the load power supply states;

[0012] Obtain the load fault times and power outage times within the maximum simulation time according to the load power supply states, and solve the reliability indexes to realize the reliability assessment of the microgrid active distribution network considering flexible soft switches.

[0013] Preferably, the obtaining of the microgrid state according to the output of distributed power sources and the numerical values of microgrid load demands specifically includes:

[0014] Obtain the exchange power P at each common connection point PCC according to the distributed generation capacity and the microgrid load demand PCC , if P PCC is positive, the microgrid is in the power supply state; otherwise, the microgrid is in the load state, and the exchange power P at each common connection point PCC PCC is expressed by the following formula:

[0015] P PCC =P S -P L

[0016] In the formula,

[0017] P S represents the distributed generation capacity,

[0018] P L represents the microgrid load demand;

[0019] After obtaining the status of each microgrid, first add the point of common coupling (PCC) where the microgrid is located to the distribution network node list, and add the microgrid with power supply status to the power node list, which is marked as the microgrid power node; for the microgrid with load status, superimpose the load demand on the load demand of the PCC where it is located and mark it as the load node.

[0020] Preferably, in combination with the system status, determine whether there is an island distributed power source. If there is an island distributed power source, in combination with the transfer conditions of the flexible soft switch, use the mixed-integer linear programming model of the dynamic boundary microgrid for fault correction, which specifically includes:

[0021] When the fault causes the regional loss of the main power supply of the distribution network and the region contains microgrid power nodes, it is determined that there is an island distributed power source;

[0022] If there is an island distributed power source and the transfer conditions of the flexible soft switch are met, the flexible soft switch adds a transfer path, and then the fault status correction is performed by the mixed-integer linear programming of the dynamic boundary microgrid to obtain the change in the power supply status of each load point before and after the fault correction;

[0023] If the transfer conditions of the flexible soft switch are not met, directly perform the fault status correction by the mixed-integer linear programming of the dynamic boundary microgrid to obtain the change in the power supply status of each load point before and after the fault correction.

[0024] Preferably, for the mixed-integer linear programming of the dynamic boundary microgrid, the objective function of the mixed-integer linear programming of the dynamic boundary microgrid is constructed with the maximum weighted restored load total as the target, and is expressed by the following formula:

[0025]

[0026] In the formula,

[0027] s i represents the load switch status connected to the load node i, 0 means disconnected, and 1 means closed;

[0028] c ij represents the status of the line (i, j), 0 means disconnected, and 1 means closed;

[0029] v ik represents whether the node is within the power supply range;

[0030] γ ik represents the auxiliary binary decision variable, γ ik = s i ·v ik , γ ik ∈{0,1}, 1 means the node is within the power Within the power supply range and the load is restored with power supply;

[0031] 0 indicates that the load switch connected to load node i is open or node i is not within the power supply range;

[0032] and indicate the active and reactive power provided by power source k to load node i;

[0033] and indicate the voltage magnitude of load node i relative to power source k and its slack variable;

[0034] indicate the set of non-island nodes;

[0035] indicate the set of power source nodes;

[0036] W i indicates the weight of load node i;

[0037] p i indicates the active load of load node i;

[0038] β k indicates the generation cost discount factor of power source k, β k ∈[0,1], the closer it is to 0, the higher the generation cost;

[0039] For the mixed integer linear programming of the dynamic boundary microgrid, the constraint conditions include:

[0040] Node clustering constraints, microgrid connectivity constraints, branch-node constraints, microgrid load restoration constraints, microgrid operation constraints, and distribution system condition constraints.

[0041] Preferably, if there is no island distributed power source, in combination with the flexible soft-switch transfer conditions, the regional division and minimum path search method are used to determine whether the power supply status of the load node is affected by the fault, specifically including:

[0042] If there is no island distributed power source and the flexible soft-switch transfer conditions are met, the flexible soft-switch adds transfer paths, and the minimum paths from the regional power source to each load node within the region are solved by the regional division and minimum path search method. Whether the power supply status of the load node is affected by the fault is determined based on whether the load node is on the minimum path;

[0043] If the flexible soft-switch transfer conditions are not met, the minimum paths from the regional power source to each load node within the region are directly solved by the regional division and minimum path search method. Whether the power supply status of the load node is affected by the fault is determined based on whether the load node is on the minimum path.

[0044] Preferably, the method for solving the minimum path from the regional power source to each load node in the region based on region division and minimum path search method specifically includes:

[0045] Taking the circuit breaker as the boundary, the distribution network is divided into several regions to form an equivalent network model with regions as units. Based on the connection relationship between regions, with the main network as the power source and regions as the basic units, the minimum paths of each region are formed according to the minimum path search method. For each load node in the region, the minimum path from the regional power source to each load node in the region is obtained according to the minimum path search method.

[0046] Preferably, the method for determining whether the power supply status of a load node is affected by a fault based on whether the load node is on the minimum path specifically includes:

[0047] First, locate the region where the faulty component is located and determine whether the faulty region is on the minimum path;

[0048] If it is not on the minimum path, it is determined that the power supply status of the load nodes in the region is not affected; if it is on the minimum path, it is determined whether the fault occurs on the main feeder;

[0049] If the fault occurs on the main feeder, the load node experiences a power outage; if not, it is determined whether the faulty region is located in the currently determined region;

[0050] If the faulty region is located in the currently determined region, it is determined that the load nodes associated with the branch feeder where the faulty component is located are powered off, and the status of the remaining load nodes in the region is determined by the protection device of the faulty component. If not, the load status of the determined region is determined according to the protection device of the faulty component. If there is a fuse isolation, the load node is not powered off. Finally, the determination result of whether the power supply status of the load node is affected by the fault is obtained.

[0051] Preferably, the load classification is performed based on the change in the power supply status of each load point before and after fault correction or the determination result of whether the power supply status of the load node is affected by the fault. The load classification after the fault is performed based on the change in the power supply status of each load point before and after fault correction, specifically including:

[0052] The load node was originally in a powered state and remains in a powered state;

[0053] The load node was originally in a powered state, lost power due to the fault, and resumed power supply after fault isolation and continues to be in a powered state;

[0054] The load node was originally in a powered state, lost power due to the fault, and resumed power supply after switching the energy supply path and continues to be in a powered state;

[0055] The load node was originally in a powered state, lost power due to the fault and lost the energy supply path, and was converted to a power-off state;

[0056] The load node was originally in a power outage state and resumed power supply after the fault was repaired, converting to a power supply state;

[0057] The load node was originally in a power outage state and continues to be in a power outage state.

[0058] Preferably, the determination result of whether the power supply state of the load node is affected by the fault is used for load classification, specifically including:

[0059] The load node loses power due to the fault and resumes power supply after the fault is isolated, remaining in the power supply state. The power outage interruption time is set to the fault isolation time;

[0060] The load node loses power due to the fault and resumes power supply after the energy supply path is converted, remaining in the power supply state. The power outage interruption time is set to the transfer supply time;

[0061] The load node loses power due to the fault and loses the energy supply path, converting to a power outage state. The power outage interruption time is set to the duration of this system state;

[0062] The load node is not affected by the fault and maintains the power supply state. The power outage interruption time is set to zero.

[0063] The second aspect of the present invention provides a reliability evaluation system for a microgrid active distribution network considering flexible soft switches, which operates the reliability evaluation method for a microgrid active distribution network considering flexible soft switches described in the first aspect, specifically including:

[0064] Component state acquisition module: used to construct a microgrid active distribution network considering the access of flexible soft switches, and sample the component state combinations of the microgrid active distribution network considering flexible soft switches within the set maximum simulation time;

[0065] Supply-demand matching module: used to perform supply-demand matching on the component state combinations to obtain the output of distributed power sources in the corresponding microgrid and the numerical values of microgrid load demands;

[0066] Microgrid state acquisition module: used to obtain the microgrid state based on the output of distributed power sources and the numerical values of microgrid load demands, and generate a system state according to the microgrid state;

[0067] Fault impact determination module: used to determine whether there are island distributed power sources in combination with the system state. If there are island distributed power sources, in combination with the flexible soft switch transfer supply conditions, use the mixed-integer linear programming model of the dynamic boundary microgrid for fault correction to obtain the change in the power supply state of each load point before and after fault correction; if there are no island distributed power sources, in combination with the flexible soft switch transfer supply conditions, use the regional division and minimum path search method to determine whether the power supply state of the load node is affected by the fault;

[0068] Power supply status acquisition module: used to classify loads according to the change in the power supply status before and after fault correction at each load point or the determination result of whether the power supply status of the load node is affected by the fault, and obtain the load power supply status;

[0069] Reliability solution module: used to obtain the number of load faults and power outage time within the maximum simulation time according to the load power supply status, and solve the reliability index to realize the reliability assessment of the microgrid active distribution network considering flexible soft switches.

[0070] Compared with the prior art, the beneficial effects of the present invention at least include: The present invention fully considers the cooperation between the microgrid and switches in the active distribution network, comprehensively considers the two factors of fault impact and the formation of a dynamic boundary microgrid that affect the change of the connection relationship between loads and power sources, and is more in line with the actual operation status of the active distribution network. Through the SOP transfer condition (one end not losing power / one end losing power), the topology reconstruction is triggered, and a dynamic power supply boundary is formed in cooperation with the microgrid. When a fault occurs, the SOP switches to control modes 5-8 in Table 1, increasing the transfer path, providing active power and voltage for the power outage area, shortening the average recovery time of the power outage area. Based on the dynamic boundary microgrid model MILP with the goal of maximizing the total weighted restored load, the power supply range of the microgrid is dynamically optimized to realize the coordinated control of the microgrid and SOP, improve the utilization rate of distributed power sources, and improve power supply reliability. Brief Description of the Drawings

[0071] Figure 1 is the reliability assessment flowchart of the microgrid active distribution network considering the access of flexible soft switches;

[0072] Figure 2 is the state transition process of the power components of the present invention;

[0073] Figure 3 is the state time sequence diagram of all components of the present invention;

[0074] Figure 4 is the regional state analysis flowchart of the present invention. Detailed Embodiment

[0075] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0076] Under the background of the widespread access of microgrids to the distribution network, the present invention starts from the control mode of flexible soft switches and establishes a reliability evaluation model for active distribution networks with flexible soft switches connected. By studying the influence of flexible soft switches on the topological structure of the distribution network after a fault and the influence of their control modes on the reduction and transfer of power-off loads in the distribution network, a dynamic boundary microgrid mixed-integer linear programming model considering the access of flexible soft switches, regional division, and a minimum path search system state analysis method for active distribution networks with microgrids are constructed. By calculating relevant reliability evaluation indicators, a reliability evaluation method for active distribution networks with microgrids considering the access of flexible soft switches is realized.

[0077] As Figure 1 shown, Embodiment 1 of the present invention provides a reliability evaluation method for an active distribution network with a microgrid considering the access of flexible soft switches, including the following steps:

[0078] Step 1, set the maximum simulation time and sample the component states.

[0079] Step 1.1: Construct a component model for an active distribution network with a microgrid considering the access of flexible soft switches.

[0080] In a preferred but non-limiting embodiment of the present invention, a component model for an active distribution network with a microgrid considering the access of flexible soft switches is built, the maximum simulation time for reliability evaluation is set, and the duration of each state of each component is sampled within a given time span to obtain the time-sequential state transition process of each component in the whole simulation, and the component state combinations are combined. Among them, the component model for an active distribution network with a microgrid considering the access of flexible soft switches includes: a flexible soft switch reliability evaluation model and a power component reliability evaluation model.

[0081] Further preferably, constructing a component model for an active distribution network with a microgrid considering the access of flexible soft switches specifically includes:

[0082] Step 1.1.1, construct a flexible soft switch reliability evaluation model.

[0083] The topological structure of the flexible soft switch is realized by connecting two converters through a DC capacitor. Each converter adopts a converter steady-state model. The typical control modes of the soft switch are shown in Table 1, and different control modes are adopted in different operating states of the distribution network. In the normal operation of the distribution network, the soft switch adopts control modes 1-4, and the two converters respectively control the transmission power and the DC voltage stability. In the fault state, the control mode of the converter VSC on the power-off area side switches to U ac θ control mode to perform load voltage support and achieve uninterrupted power supply in the non-fault area.

[0084] Table 1 Control Modes of Flexible Soft Switches Based on B2B VSC

[0085]

[0086] The mathematical model of the flexible soft switch in the reliability evaluation model is as follows:

[0087] During normal operation, the soft switch adopts the power flow control mode, that is, control modes 1-4 in the table, and its mathematical model satisfies certain power flow constraint conditions.

[0088] Power constraint:

[0089]

[0090]

[0091]

[0092] In the formula,

[0093] is the active power of the converter i at both ends of the flexible soft switch;

[0094] P i SOP,loss is the active power loss of the converter i;

[0095] is the reactive power of the converter i at both ends of the flexible soft switch;

[0096] and are the upper and lower limits of the reactive power of the converter respectively, and the reactive power constraint of the converter means that it satisfies within the output power limit;

[0097] represents the active power loss coefficient of the converter i, and the active power constraint means that the output active power of the converter is balanced with the active power loss.

[0098] Capacity constraint:

[0099]

[0100] In the formula,

[0101] S i SOP is the capacity of the converter i of the flexible soft switch, and the output capacity of the converter should satisfy the capacity constraint.

[0102] When one end of the converters on both sides of the flexible soft switch is connected to the fault side and the other end is connected to the normal side, the soft switch adopts the fault recovery control mode, that is, 5-8 in the table. On the basis of satisfying the original power flow constraint conditions, its mathematical model plays a role in supporting the voltage of the fault side, and the voltage constraint is as follows:

[0103]

[0104] Wherein,

[0105] U i SOP is the voltage amplitude of the converter i on the fault side;

[0106] U set is the preset lower voltage limit of the converter on the fault side.

[0107] Step 1.1.2, construct a reliability evaluation model for power components.

[0108] Power components have obvious Markov properties. Subject to random system disturbances, they may switch from the normal operating state to the fault outage state and return to the normal operating state after repair. That is, as Figure 2 shown in the state change process of the repairable component.

[0109] Figure 2 where TTR represents the component fault repair time, and TTF represents the fault-free working time. Both follow an exponential distribution.

[0110]

[0111]

[0112] Wherein,

[0113] t TTR,k represents the time required for component k to recover from the fault state to the normal operating state in the current state sampling, that is, the fault repair time TTR;

[0114] t TTF,k represents the duration of component k from the normal operating state to the fault state, that is, TTF;

[0115] R k,i is a random number between 0 and 1 randomly generated by the computer for component k at the i-th sampling;

[0116] μ and λ are the reliability parameters of the component, the repair rate and the failure rate respectively. Thus, a power component model can be established. Through the above model, the state duration of the power component after sampling can be determined.

[0117] Step 1.2, set the maximum simulation time. Within the set maximum simulation time, complete the sampling of the duration of each state of each component in the active distribution network component model with flexible soft switches connected to the microgrid, and obtain the time-sequential state transition process of each component in the entire simulation, that is, obtain the component state combination.

[0118] In a preferred but non-limiting embodiment of the present invention, the repair time TTR or the trouble-free operation time TTF of the component is obtained, and the state of the component automatically changes at each sampling. For example, when the repair time arrives, the component changes from the faulty state to the normal state; when the trouble-free operation time arrives, the component changes from the normal state to the faulty state.

[0119] The mathematical expression for the duration of the component state can be represented by the following formula:

[0120]

[0121] In the formula,

[0122] D k,i is the duration of the state of component k after the i-th sampling;

[0123] x k,i is a binary variable representing the state of component k at the i-th sampling (the initial state of the component is normal). Denote x k,i = 0 as normal and x k,i = 1 as faulty;

[0124] λ k is the failure rate of component k;

[0125] r k is the average repair time of component k for a failure;

[0126] R k,i is a random number generated by the computer between 0 and 1 for component k at the i-th sampling;

[0127] N c is the number of system components;

[0128] N i,k is the number of samplings of component k.

[0129] After accumulation, the occurrence time T ik of the i-th state transition of component k can be obtained, which can be represented by the following formula:

[0130]

[0131] From this, the state transition process of each component within the simulation time can be obtained, which can be represented by the following formula:

[0132]

[0133] In the simulation, the initial state of the component is normal operation, i.e., x k,1 = 0. Draw the state time sequence diagram of all components, as shown in Figure 3 shown.

[0134] Figure 3 The dotted line represents the moment when the state transition of the component combination occurs. The moments when all state transitions of all components occur form the set T1. Define the function f min,s Find the s-th smallest element in the set. This function is used to find the moment when the state transition of the system occurs in chronological order. The chronological transfer process of the system state can be expressed by the following mathematical formula:

[0135] T1 = {T k,i | i = 1, 2,...., N i,k , k = 1, 2,..., N c} (11)

[0136]

[0137] Among them, there are N x system states in one simulation, X m is the m-th combined state of the system, and the duration d m of the m-th combined state can be expressed by the following formula:

[0138]

[0139] In the formula,

[0140] f min,m (T1) is the m-th smallest element in the set T1, and f min,m-1 (T1) is the (m - 1)-th smallest element in the set T1.

[0141] Step 2: Use the method of random sampling to match the supply and demand of the component state combinations, and obtain the corresponding output of the distributed power sources in the microgrid and the numerical values of the microgrid load demand.

[0142] In a preferred but non-limiting embodiment of the present invention, Step 2 specifically includes:

[0143] Use the method of random sampling to match the corresponding supply and demand scenarios for each simulated component combination state. Perform Min-Max normalization processing on the distributed power source data and load data in the microgrid; then discretize the value range of the normalized data, that is, equally divide the value range [0, 1] into 20 sub-intervals: Secondly, count the number of samples in each sub-interval in the historical data to obtain the cumulative probability distribution, and finally uniformly sample random numbers from the interval [0, 1], and match the random numbers with the cumulative probability to obtain the output of the distributed power sources and the load demand. Thus, the supply and demand matching of one system state is completed, and the corresponding output of the distributed power sources in the microgrid and the numerical values of the microgrid load demand are obtained.

[0144] The output demand of the distributed power sources in the microgrid, and the calculation of the output of a single distributed power source in the microgrid can be expressed by the following formula:

[0145]

[0146] Wherein,

[0147] represents the output demand of a single distributed power source in the microgrid;

[0148] p min and p max are respectively the minimum and maximum values of the distributed power source data;

[0149] p pu is the distributed power source data after Min-Max normalization processing;

[0150] is the rated capacity of a certain distributed power source in the microgrid.

[0151] The microgrid load demand and the single-node load demand of the microgrid can be expressed by the following formula:

[0152]

[0153] Wherein,

[0154] represents the load demand of a certain load node in the microgrid;

[0155] l min and l max are respectively the minimum and maximum values of the actual load data;

[0156] l mean is the average value of the actual load data and is the load data after Min-Max normalization processing;

[0157] is the average load of a certain load node in the microgrid.

[0158] Step 3: Obtain the microgrid status based on the output of the distributed power source and the microgrid load demand value, and generate the system status according to the microgrid status.

[0159] Step 3.1: Obtain the microgrid status based on the output of the distributed power source and the microgrid load demand value.

[0160] The operation modes of the microgrid in the distribution network are two types: island operation and grid-connected operation. When component failures occur in the main network of the distribution network and the power quality deteriorates, the microgrid can be separated from the main network by disconnecting the relevant switches and enter the island operation mode of self-sufficient power supply. Grid-connected operation means that the microgrid is connected to the distribution network during the operation of the distribution network and exchanges electric energy with the main network.

[0161] In the power flow exchange with the main grid, the microgrid has two states: load or power source, which is determined according to the supply-demand matching data of each microgrid, that is, according to the distributed generation capacity P S and the microgrid load demand P L , the exchange power P PCC at each point of common coupling (PCC) is obtained. According to the positive or negative of P PCC , the internal supply-demand relationship of the microgrid is judged: if P PCC is positive, it means that the microgrid is in the power source state; otherwise, the microgrid is in the load state. The exchange power P PCC at each point of common coupling (PCC) can be expressed by the following formula:

[0162] P PCC = P S - P L (16)

[0163] After confirming the state of each microgrid, first add the point of common coupling (PCC) where the microgrid is located to the distribution network node list: the microgrid in the power source state is added to the power source node list, and to distinguish it from the power supply mode of the main grid, it is marked as the microgrid power source node; for the microgrid in the load state, its demand is superimposed on the load demand of the point of common coupling (PCC) where it is located, marked as the load node, and the active distribution network containing the microgrid is simplified. Step 3.2, analyze the state of the distribution network.

[0164] According to the flexible soft-switch control mode, during the power supply restoration process, the flexible soft-switch itself cannot generate active power. It needs to be connected to a non-power-failure area at one end to obtain active power from the non-power-failure area and provide power and voltage for the power-failure area. That is, the transfer condition of the flexible soft-switch is that one end is connected to the non-power-failure area and the other end is connected to the power-failure area. Under the condition of sufficient power, it can supply power to the power-failure area after isolating the fault. Therefore, when there is a power-failure area at one end of the two connected ends, the soft-switch enters the fault mode, providing a transfer path for the load transfer of the power-failure area, thereby reducing the power outage time of the load in the power-failure area affected by the fault.

[0165] When analyzing the state of the distribution network, the flexible soft-switch judges whether it is connected to the non-power-failure area and the power-failure area according to the transfer condition of the flexible soft-switch. The flexible soft-switch is connected, changing the topological structure of the distribution network after fault isolation, and then performing fault state analysis and correction.

[0166] According to the established rules, determine the energy supply path and energy supply source before the state transformation of each load node, that is, each load node is preferentially powered by the main grid bus and then by the microgrid.

[0167] When the fault causes the area to lose the power supply of the distribution network main grid and there are microgrid power source nodes in the area, that is, there are island distributed power sources, the MILP (mixed integer linear programming) of the dynamic boundary microgrid model is used for fault state correction to perform load restoration and shedding.

[0168] A dynamic boundary microgrid refers to a microgrid powered by distributed power sources and with a flexible power supply range adjustable through switching devices. When the load loses the main grid power supply, a dynamic boundary microgrid is formed using flexible soft switches and the microgrid to maintain the power supply to critical loads.

[0169] The objective function of the MILP for the dynamic boundary microgrid model is to maximize the total weighted restored load:

[0170]

[0171] In the formula,

[0172] s i represents the state of the load switch connected to load node i, 0 means open, and 1 means closed;

[0173] c ij represents the state of line (i, j), 0 means open, and 1 means closed;

[0174] v ik represents whether node is within the power supply range of the power source ;

[0175] γ ik is an auxiliary binary decision variable, γ ik = s i ·v ik , γ ik ∈ {0, 1}, 1 means the node is within the power supply range of the power source and the load has its power restored;

[0176] 0 means the load switch connected to load node i is open or node i is not within the power supply range of the power source ;

[0177] and represent the active and reactive power provided by power source k to load node i;

[0178] and represent the voltage magnitude of load node i relative to power source k and its slack variable;

[0179] represents the set of non-island nodes;

[0180] represents the set of power source nodes;

[0181] W i represents the weight of load node i;

[0182] p iRepresents the active load of load node i;

[0183] β k Represents the generation cost discount factor of power source k, β k ∈[0,1], the closer it is to 0, the higher the generation cost.

[0184] The constraints are divided into node clustering constraints, microgrid connectivity constraints, branch-node constraints, microgrid load restoration constraints, microgrid operation constraints, and distribution system condition constraints.

[0185] Node clustering constraints:

[0186]

[0187] Microgrid connectivity constraints:

[0188]

[0189] In the formula,

[0190] θ k (i) represents the parent node of node i relative to power source k.

[0191] Branch-node constraints:

[0192]

[0193] In the formula,

[0194] ζ k (i,j) represents the child node of line (i,j) relative to microgrid k.

[0195] Microgrid load restoration constraints:

[0196]

[0197]

[0198]

[0199] Microgrid operation constraints:

[0200]

[0201] In the formula,

[0202] Represents the set of child nodes of node i relative to microgrid k,

[0203] If node i does not belong to microgrid k, then the injection power of this node into the microgrid (P i k and ) is zero, which is represented by the inequality:

[0204]

[0205] Wherein,

[0206] and represent the active and reactive power capacities of microgrid k.

[0207]

[0208] Wherein,

[0209] is the reference value of the voltage at the node where the power source is located;

[0210] r i and x i represent the resistance and reactance of line (i, j).

[0211] If node i belongs to microgrid k, then V i k should be less than Otherwise, V i k = 0. It is expressed as:

[0212]

[0213] Distribution system condition constraints:

[0214]

[0215]

[0216]

[0217]

[0218] Some switches in the distribution system are in the normally open state due to faults, and the lines where they are located are represented by the set L O ; some lines are in the normally closed state due to faults, and their lines are represented by the set L C ; similarly, the sets N O and N C represent the sets of load switches that are in the normally open and normally closed states due to faults.

[0219] By solving the mixed-integer linear programming of the dynamic boundary microgrid through a solver, the system state with island operation is corrected for the fault state, and the power-off load with higher priority is restored.

[0220] It should be noted that the existing technology only relies on the physical line interruption caused by faults to passively adjust the load-power connection relationship, resulting in low fault recovery efficiency (such as a single load transfer path and insufficient utilization of distributed power sources). For island areas containing microgrids, only static boundary division is adopted, and the power supply range cannot be dynamically adjusted, resulting in low utilization rate of distributed power sources (such as limited recovery rate of critical loads and redundant power supply for non-critical loads). In the present invention, the topological reconstruction is triggered by the SOP transfer condition (one end not powered off / one end powered off), and a dynamic power supply boundary is formed in cooperation with the microgrid. When a fault occurs, the SOP switches to control modes 5-8 in Table 1, increasing the transfer path, providing active power and voltage for the powered-off area, shortening the average recovery time of the powered-off area. Based on the dynamic boundary microgrid model MILP, with the goal of maximizing the total weighted recovered load, the power supply range of the microgrid is dynamically optimized, realizing the coordinated control of the microgrid and the SOP, improving the utilization rate of distributed power sources, and enhancing the power supply reliability.

[0221] When there is no microgrid in the island state in the region, based on the regional division and the minimum path search method ZPMPS, the fault consequences are determined. Taking the circuit breaker as the boundary, the distribution network is divided into several regions to form an equivalent network model with regions as units. According to the basic idea of the minimum path method, based on the connection relationship between regions, with the main grid as the power source and regions as the basic units, the minimum paths of each region are formed. For each load node in the region, the optimal path from the regional power source to each load node in the region is obtained according to the traditional minimum path method.

[0222] Analysis of regional status: Without considering power transfer, according to the change of component status, such as Figure 4 shown, the status determination of each region is carried out to obtain the determination of the influence of the component status change on the power supply status of the load node.

[0223] First, locate the region where the faulty component is located, and determine whether the faulty region is on the minimum path. If it is not on the minimum path, it is determined that the power supply status of the load nodes in the region is not affected. If it is on the minimum path, then determine whether the fault occurs on the main feeder. If so, the load nodes are powered off. If not, determine whether the faulty region is located in the currently determined region. If so, it is determined that the load nodes associated with the branch feeder where the faulty component is located are powered off, and the status of the remaining load nodes in the region is determined by the protection device of the faulty component. If not, the load status of the determined region is determined according to the protection device of the faulty component. If there is fuse isolation, the load nodes are not powered off. Finally, the power supply status of the load nodes is obtained.

[0224] Step 4: Statistically analyze the status and power outage time of each load point during the component fault in the active distribution network, and statistically analyze the reliability indexes of the load nodes and the distribution network.

[0225] In a preferred but non-limiting embodiment of the present invention, step 4 specifically includes:

[0226] Classify the loads, and use the MILP of the dynamic boundary microgrid model to correct the fault state. According to the change in the power supply state of each load point before and after fault correction, the loads after the fault are classified as follows:

[0227] 1) The load node was originally in the powered state, remains in the powered state, and does not lose power due to the fault.

[0228] 2) The load node was originally in the powered state, loses power due to the fault, and resumes power supply after fault isolation and continues to be in the powered state.

[0229] 3) The load node was originally in the powered state, loses power due to the fault, and resumes power supply after switching the energy supply path and continues to be in the powered state.

[0230] 4) The load node was originally in the powered state, loses power due to the fault and loses the energy supply path, and converts to the power-off state.

[0231] 5) The load node was originally in the power-off state, resumes power supply after fault repair, and converts to the powered state.

[0232] 6) The load node was originally in the power-off state and continues to be in the power-off state.

[0233] Based on the regional division and the minimum path search method ZPMPS, the load classification for determining the fault consequences is as follows:

[0234] 1) The load node loses power due to the fault, resumes power supply after fault isolation, and continues to be in the powered state. The power outage interruption time is the fault isolation time.

[0235] 2) The load node loses power due to the fault, resumes power supply after switching the energy supply path, and continues to be in the powered state. The power outage interruption time is the transfer power supply time.

[0236] 3) The load node loses power due to the fault, loses the energy supply path, and converts to the power-off state. The power outage interruption time is the duration of this system state.

[0237] 4) The load node is not affected by the fault, remains in the powered state, and the power outage interruption time is zero.

[0238] Determine the load status according to the load classification, count the number of load faults and the power outage time within the maximum simulation time. After MILP correction, re-determine the load status through load classification. Determining the number of load faults and the power outage time is carried out after determining the load status according to the load classification; after determining the load classification of this system state, further obtain each load status. The faulty load is counted as one fault, and the power outage time is the duration of this system state.

[0239] The annual average outage duration of the corresponding load is obtained by multiplying the number of load faults by the outage time, and then the system outage index, the expected energy not supplied (EENS), which is the load reliability index. The expected energy not supplied (EENS) is determined by the average load L a,i and the annual average outage duration U of the load i and can be obtained. The calculation formula is as follows:

[0240]

[0241] In the formula,

[0242] L a,i and U i are the average load and the annual average outage duration of the load, respectively.

[0243] Calculate the coefficient of variation CV of the expected energy not supplied, and determine whether it is less than 0.05. If the coefficient of variation CV is less than 0.05, the reliability index converges, and the simulation ends and is valid.

[0244] CV is defined as the ratio of the sample standard deviation to the sample mean of the expected energy not supplied (EENS). The calculation formula of CV is as follows:

[0245]

[0246] Among them,

[0247] σ EENS and υ EENS represent the standard deviation and the mean value of the expected energy not supplied (EENS), respectively.

[0248] Compared with the prior art, the beneficial effects of the present invention at least include: The present invention fully considers the cooperation between the microgrid and the switch in the active distribution network, comprehensively considers the two factors of fault impact and the formation of a dynamic boundary microgrid that affect the change of the connection relationship between the load and the power source, is more in line with the actual operation status of the active distribution network. Through the SOP transfer condition (one end not powered off / one end powered off), the topological reconstruction is triggered, and a dynamic power supply boundary is formed in cooperation with the microgrid. When a fault occurs, the SOP switches to control modes 5-8 in Table 1, increasing the transfer path, providing active power and voltage for the powered-off area, shortening the average recovery time of the powered-off area. Based on the dynamic boundary microgrid model MILP, with the goal of maximizing the total weighted restored load, the power supply range of the microgrid is dynamically optimized, realizing the coordinated control of the microgrid and the SOP, improving the utilization rate of distributed power sources, and enhancing the power supply reliability.

[0249] Embodiment 2 of the present invention provides a reliability evaluation system for a microgrid active distribution network considering flexible soft switches, which operates the reliability evaluation method for a microgrid active distribution network considering flexible soft switches described in Embodiment 1, including:

[0250] Component Status Acquisition Module: It is used to construct a microgrid active distribution network considering the access of flexible soft switches, and sample the component status combinations of the microgrid active distribution network considering flexible soft switches within the set maximum simulation time.

[0251] Supply-Demand Matching Module: It is used to perform supply-demand matching on the component status combinations to obtain the output of distributed power sources and the numerical values of microgrid load demands in the corresponding microgrid.

[0252] Microgrid Status Acquisition Module: It is used to obtain the microgrid status based on the output of distributed power sources and the numerical values of microgrid load demands, and generate the system status according to the microgrid status.

[0253] Fault Impact Judgment Module: It is used to determine whether there are islanded distributed power sources in combination with the system status. If there are islanded distributed power sources, in combination with the flexible soft switch transfer conditions, a mixed-integer linear programming model of the dynamic boundary microgrid is used for fault correction to obtain the changes in the power supply status of each load point before and after fault correction; if there are no islanded distributed power sources, in combination with the flexible soft switch transfer conditions, the regional division and minimum path search method are used to determine whether the power supply status of load nodes is affected by the fault.

[0254] Power Supply Status Acquisition Module: It is used to classify the loads based on the changes in the power supply status of each load point before and after fault correction or the judgment results of whether the power supply status of load nodes is affected by the fault, and obtain the load power supply status.

[0255] Reliability Solving Module: It is used to obtain the load fault times and power outage time within the maximum simulation time based on the load power supply status, and solve the reliability index to realize the reliability assessment of the microgrid active distribution network considering flexible soft switches.

[0256] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A reliability assessment method for active distribution network of microgrid taking flexible soft switches into account, characterized in that: Construct a microgrid active distribution network taking into account the flexible soft switch access, and sample the component state combination of the microgrid active distribution network taking into account the flexible soft switch within the set maximum simulation time; Match the supply and demand of the component status combination to obtain the output of the corresponding distributed power source in the microgrid and the microgrid load demand value; According to the output of distributed power sources and the microgrid load demand value, the microgrid state is obtained, and the system state is generated according to the microgrid state; Determine whether there is an isolated distributed power source based on the system status. If there is an isolated distributed power source, combined with the flexible soft switch transfer conditions, use the mixed integer linear programming model of the dynamic boundary microgrid to perform fault correction, and obtain the power supply status changes before and after the fault correction of each load point; if there is no isolated distributed power source, combined with the flexible soft switch transfer conditions, use regional division and minimum path search method to determine whether the power supply status of the load node is affected by the fault; According to the change of power supply status before and after the fault correction of each load point or the determination result of whether the power supply status of the load node is affected by the fault, the load is classified to obtain the load power supply status; According to the load power supply status, the number of load failures and power outage time within the maximum simulation time are obtained, and the reliability index is solved to realize the reliability evaluation of the microgrid active distribution network taking into account the flexible soft switch.

2. According to claim 1, a method for evaluating the reliability of a microgrid active distribution network taking into account flexible soft switches is characterized in that: The step of obtaining the microgrid state according to the output of the distributed power source and the microgrid load demand value specifically includes: According to the distributed generation capacity and microgrid load demand, the exchange power P at each public node PCC is obtained. PCC , if P PCC If positive, the microgrid is in power supply state; otherwise, the microgrid is in load state, and the power exchanged at each public node PCC is P Pcc , expressed as follows: P PCc =P S -P l In the formula, P S represents the distributed generation capacity, P L represents the microgrid load demand; After obtaining the status of each microgrid, first add the public node PCC where the microgrid is located to the distribution network node list, and add the microgrid in power supply status to the power supply node list and mark it as the microgrid power supply node; the microgrid in load status superimposes the load demand with the load demand of the public node PCC where it is located and marks it as a load node.

3. The reliability assessment method of a microgrid active distribution network taking flexible soft switches into account according to claim 1 is characterized in that: Combined with the system status, determine whether there is an isolated distributed power source. If there is an isolated distributed power source, combined with the flexible soft switch transfer conditions, the mixed integer linear programming model of the dynamic boundary microgrid is used for fault correction, including: When a fault causes the area to lose power supply from the main distribution network, and the area contains microgrid power supply nodes, it is determined that an island distributed power source exists; If there is an isolated distributed power source and the flexible soft switch transfer conditions are met, the flexible soft switch will increase the transfer path, and then the mixed integer linear programming of the dynamic boundary microgrid will be used to correct the fault state to obtain the power supply state changes before and after the fault correction of each load point; If the flexible soft switch power transfer conditions are not met, the fault state correction is directly performed by the mixed integer linear programming of the dynamic boundary microgrid to obtain the power supply state changes before and after the fault correction of each load point.

4. The reliability assessment method of a microgrid active distribution network taking flexible soft switches into account according to claim 3 is characterized in that: The mixed integer linear programming of the dynamic boundary microgrid is constructed with the objective of maximizing the total weighted restoration load as the goal, and is expressed as follows: In the formula, s i Indicates the state of the load switch connected to load node i, 0 means open, 1 means closed; c ij Indicates the state of line (i, j), 0 means open, 1 means closed; v ik Representation Node Is the power on? Within the power supply range; γ ik represents the auxiliary binary decision variable, γ ik =s i ·ν ik , γ ik ∈{0,1}, 1 represents a node In the power supply Within the power supply range and the load has resumed power supply; 0 means the load switch connected to load node i is disconnected or node i is not in power supply. Within the power supply range; and represents the active and reactive power provided by power source k to load node i; V i k and Represents the voltage magnitude of load node i relative to power source k and its relaxation variable; Represents a set of non-island nodes; Represents a collection of power nodes; W i represents the weight of load node i; p i represents the active load of load node i; β k represents the power generation cost discount coefficient of power source k, β k ∈[0,1], the closer it is to 0, the higher the power generation cost; The mixed integer linear programming of the dynamic boundary microgrid, the constraints include: Node clustering constraints, microgrid connectivity constraints, branch-node constraints, microgrid load recovery constraints, microgrid operation constraints and distribution system condition constraints.

5. The reliability assessment method of a microgrid active distribution network taking flexible soft switches into account according to claim 1 is characterized in that: If there is no isolated distributed power source, combined with the flexible soft switch transfer condition, the regional division and minimum path search method are used to determine whether the power supply status of the load node is affected by the fault, specifically including: If there is no isolated distributed power source and the flexible soft switch transfer conditions are met, the flexible soft switch adds a transfer path, and the minimum path from the regional power source to each load node in the region is solved by the regional division and minimum path search method. Whether the power supply status of the load node is affected by the fault is determined based on whether the load node is on the minimum path; If the flexible soft switch power transfer conditions are not met, the minimum path from the regional power supply to each load node in the region is directly solved based on regional division and minimum path search method, and whether the power supply status of the load node is affected by the fault is determined based on whether the load node is on the minimum path.

6. The reliability assessment method of a microgrid active distribution network taking flexible soft switches into account according to claim 5 is characterized in that: The method of solving the minimum path from the regional power source to each load node in the region based on regional division and minimum path search method specifically includes: The distribution network is divided into several areas with circuit breakers as boundaries to form an equivalent network model with areas as units. Based on the connection relationship between areas, the main grid is used as the power source, and the area is used as the basic unit. The minimum path of each area is formed according to the minimum path search method. For each load node in the area, the minimum path from the regional power source to each load node in the area is obtained according to the minimum path search method.

7. The reliability assessment method of a microgrid active distribution network taking flexible soft switches into account according to claim 1 is characterized in that: The determining whether the power supply state of the load node is affected by the fault according to whether the load node is on the minimum path specifically includes: First, locate the area where the faulty component is located and determine whether the faulty area is on the minimum path; If it is not on the minimum path, it is determined that the power supply status of the load nodes in the area is not affected; if it is on the minimum path, it is determined whether the fault occurs on the main feeder; If the fault occurs in the main feeder, the load node will experience a power outage. If not, it is determined whether the fault area is located in the current determination area. If the fault area is located in the current judgment area, the load node associated with the branch feeder where the faulty component is located is judged to be out of power, and the status of the remaining load nodes in the area is determined by the faulty component protection device. If not, the load status of the judgment area is determined according to the faulty component protection device. If there is fuse isolation, the load node will not be out of power, and finally a judgment result is obtained as to whether the power supply status of the load node is affected by the fault.

8. The reliability assessment method of a microgrid active distribution network taking flexible soft switches into account according to claim 1 is characterized in that: The load classification is performed based on the power supply status change before and after the fault correction of each load point or the determination result of whether the power supply status of the load node is affected by the fault, and the load classification after the fault is performed based on the power supply status change before and after the fault correction of each load point, specifically including: The load node was originally in a power supply state and remains in the power supply state; The load node was originally in a power supply state, but lost power due to a fault. After the fault was isolated, power was restored and the load node continued to be in a power supply state. The load node was originally in a power supply state, but lost power due to a fault. After the energy supply path was switched, power was restored and the load node continued to be in a power supply state. The load node was originally in a power supply state, but due to the fault, it lost power and lost the energy supply path, and converted to a power outage state; The load node was originally in a power outage state, and after the fault was repaired, the power supply was restored and converted to a power supply state; The load node was originally in a power outage state and continues to be in a power outage state.

9. The reliability assessment method of a microgrid active distribution network taking flexible soft switches into account according to claim 8 is characterized in that: The load classification is performed based on the result of determining whether the power supply status of the load node is affected by the fault, specifically including: The load node loses power due to a fault, and after the fault is isolated, power is restored and the load node continues to be powered. The power outage time is set to the fault isolation time. The load node loses power due to a fault, and after switching the energy supply path, the power supply is restored and the load node continues to be powered. The power outage interruption time is set to the transfer time. The load node loses power due to the fault, loses the energy supply path, and switches to the power outage state. The power outage interruption time is set to the duration of the system state. The load node is not affected by the fault, remains powered, and the power outage interruption time is set to zero.

10. A reliability assessment system for a microgrid active distribution network taking into account flexible soft switches, running a reliability assessment method for a microgrid active distribution network taking into account flexible soft switches as claimed in any one of claims 1 to 9, characterized in that: Component state acquisition module: used to construct a microgrid active distribution network taking into account the flexible soft switch access, and to sample the component state combination of the microgrid active distribution network taking into account the flexible soft switch within the set maximum simulation time; Supply and demand matching module: used to match the supply and demand of the component state combination to obtain the output of the corresponding distributed power source in the microgrid and the microgrid load demand value; Microgrid status acquisition module: used to obtain the microgrid status according to the output of distributed power sources and the microgrid load demand value, and generate the system status according to the microgrid status; Fault impact determination module: used to determine whether there is an isolated distributed power source in combination with the system status. If there is an isolated distributed power source, combined with the flexible soft switch transfer conditions, the mixed integer linear programming model of the dynamic boundary microgrid is used to perform fault correction to obtain the power supply status changes before and after the fault correction of each load point; if there is no isolated distributed power source, combined with the flexible soft switch transfer conditions, the regional division and minimum path search method are used to determine whether the power supply status of the load node is affected by the fault; Power supply status acquisition module: used to classify loads and acquire load power supply status according to the power supply status changes before and after fault correction of each load point or the determination result of whether the power supply status of the load node is affected by the fault; Reliability solution module: It is used to obtain the number of load failures and power outage time within the maximum simulation time according to the load power supply status, and solve the reliability index to realize the reliability evaluation of the microgrid active distribution network taking into account the flexible soft switch.

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